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SOCIAL CAPITAL EMERGENCE AND THE
CO-EVOLUTION OF ORGANIZATIONAL CAPABILITIES
CHRISTOPHER FREDETTE
A DISSERTATION SUBMITTED TO THE FACULTY OF GRADUATE STUDIES
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
GRADUATE PROGRAM IN ADMINISTRATION
YORK UNIVERSITY,
TORONTO, ONTARIO
AUGUST 2009
iv
ABSTRACT
This dissertation explores the relationship between social capital and an
organizational capability during the earliest phases of emergence. Using an
experimental methodology based on a virtual crisis simulation, this research examines
the influence of social capital emergence on the evolution of capability performance in
real time. Results illustrate the cross-sectional, autoregressive, and cross-lagged change
in social capital and capability performance over three measurement intervals,
suggesting the presence of a co-evolving relationship between the two constructs. This
dissertation contributes valuable insight to the management literature by examining the
micro-foundations of organizational capability emergence; demonstrating that the
social, relational, and structural context of work is central, especially in its ability to
shape collaborative practice and contribute to the collective ability to meet
organizational needs. This study demonstrates how the process of social capital
emergence occurs, and explains how it relates to the triggering of capability evolution.
As a result, this dissertation has generated greater insight into how organizational
capabilities grow and evolve, and how social capital contributes to these processes. By
better understanding the role that social capital networks play in the emergence and
evolution of organizational capabilities, we add some measure of control and
predictability to capability evolution allowing organizations to take action to encourage,
stabilize, or discourage capability change via specific intervention mechanisms, and
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provide an opportunity to maintain alignment between internal processes and
performance objectives.
vi
DEDICATION
Two roads diverged in a yellow wood, And sorry I could not travel both And be one traveler, long I stood
And looked down one as far as I could To where it bent in the undergrowth.
Then took the other, as just as fair,
And having perhaps the better claim, Because it was grassy and wanted wear;
Though as for that the passing there Had worn them really about the same.
And both that morning equally lay
In leaves no step had trodden black. Oh, I kept the first for another day!
Yet knowing how way leads on to way, I doubted if I should ever come back.
I shall be telling this with a sigh
Somewhere ages and ages hence: Two roads diverged in a wood, and I--
I took the one less traveled by, And that has made all the difference.
Robert Frost (1915), The Road Not Taken I dedicate this work and all of the effort it entailed to the people in my life that helped me find my path, held my hand along the journey, kept the rain off my shoulders, and gave me shelter when I needed it most. Each of you has touched me beyond words. I am not naïve enough to believe that this would have been possible without the love and support of my Nicole, whose constant companionship has given me strength through my darkest hours. For this I am forever grateful. My life has been shaped by our family, to them I owe a special debt for teaching me the value of work, the virtue of perseverance, and a love of learning. Along the way I have been fortunate to have been joined by wonderful travelling companions, but have also lost one or two. Grandpa, I miss you every day. I have watched as mentors became friends, and as friends became mentors. Christine Oliver, you are an inspiration to me and a model that I can only aspire to emulate in
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some small way. Ellen Auster, you have been an outstanding mentor and motivator, always pushing me to “go for it” even when I was unsure. Oana Branzei, you have shown me what commitment to the craft means, and how to achieve and over-achieve time and time again. While I may have moved more quickly had I traveled lighter, taking the trip without my classmates would have been unthinkable. You have all endured endless ribbing and prodding over the years and deserve at least this small note of thanks.
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TABLE OF CONTENTS
Chapter One: Introduction………………………………………………………………………………………… 1
Dissertation Research Overview………………………………………………………………………………. 2
Emergence, Evolution, and Co-Evolution…………………………………………………………………. 6
Review of Prior Research…………………………………………………………………………………………. 7
Purpose and Intent of this Dissertation………………………………………………………………….. 10
Dissertation Contributions and Practical Implications……………………………………………. 17
Organization of the Dissertation……………………………………………………………………………. 20
Chapter Two: Literature Review……………………………………………………………………………….22
Social Capital Literature Review: Conceptual, Theoretical, and Empirical………………. 22
The Constitution of Social Capital…………………………………………………………………….. 30
The Value of Social Capital……………………………………………………………………………….. 37
The Configuration of Social Capital – Does Configuration Matter? ..................... 40
Implications of Findings……………………………………………………………………………………. 44
Organizational Capabilities: An Organizational Theory Approach…………………………… 44
Origins of Capability Literature………………………………………………………………………… 46
Capabilities Research – Dynamic and Otherwise – in Organizations…………………. 51
Micro-Foundations of Capabilities Evolution……………………………………………………. 52
Implications of Findings……………………………………………………………………………………. 57
Conclusions of Social Capital and Organizational Capabilities Literature………………… 58
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Chapter Three: A Theory of Social Capital Emergence and Organizational Capability Evolution…………………………………………………………………………………………………. 59
Central Motivation for this Conceptual Argument…………………………………………………. 60
Social Capital Emergence and Organizational Capability Evolution………………………… 61
The Contribution of Social Capital to Organizational Capability Building and Change………………………………………………………………………………………… 66
Threat Identification: The Evolution of an Organizational Capability………………… 75
Longitudinal Change in the Social Capital–Capability Performance Relationship…………………………………………………………………………………………………….. 80
Summary of Central and Peripheral Arguments……………………………………………………… 86
Chapter Four: Research Design and Methodology…………………………………………………… 88
Sample Population, Characteristics, and Selection…………………………………………………. 89
Experimental Design and Methodology…………………………………………………………………. 92
Experimental Simulation Overview………………………………………………………………………… 94
Introduction, Informed Consent, and Pretest………………………………………………………… 96
Variable Measurement: Objective and Subjective Components…………………………….. 97
Social Capital: Structural, Cognitive, and Relational Measurements…………………. 98
Organizational Capability Performance and Evolution Measurements…………… 117
Methodological Limitations – Validity of Quantitative Methodology…………………… 127
Concluding Research Design Remarks………………………………………………………………….. 129
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Chapter Five: Results and Findings of Study…………………………………………………………… 131
Cross-Sectional Model Fitting Results…………………………………………………………………..132
Latent Growth Curve Model Fitting Results…………………………………………………………. 153
Longitudinal Cross-Lagged Regression Model Fitting Results……………………………….. 160
Summary of Research Findings……………………………………………………………………………. 167
Chapter Six: Research Contribution, Discussion, and Implications…………………………. 168
Contributions to the Organizational Capabilities Literature…………………………………. 174
Contributions to the Social Capital Literature………………………………………………………. 178
Limitations and Future Research Directions…………………………………………………………. 185
Chapter Seven: Research Conclusions……………………………………………………………………. 189
References…………………………………………………………………………………………………………….. 217
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LIST OF FIGURES
Figure 3-1: Social capital emergence and the co-evolution of organizational capabilities…………………………………………………………………………………………… 63
Figure 3-2: Multiplex relationship between social capital emergence and capability performance evolution…………………………………………………………………………. 65
Figure 4-1: Configuration of repeated measures experimental design……………………. 93
Figure 4-2: Social Capital Measurement Model……………………………………………………. 113
Figure 4-3: Capability Performance Measurement Model……………………………………..121
Figure 5-1: Structural Equation Model of Hypotheses with Comparative Cross-Sectional Relationships……………………………………………………………… 135 Figure 5-2: Latent Growth Curve Model (Curve of Factors Model)……………………….. 154
Figure 5-3: Average Growth Curves of Second Order Factors……………………………….. 159
Figure 5-4: Longitudinal Cross-Lagged Regression Model………………………………………161
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LIST OF TABLES
Table 2.1: Conceptual Milestones in Social Capital Research………………………………… 27
Table 2.2: Summary of Key Empirical Findings in Social Capital Research……………… 28
Table 2.3: Comparison of Social Capital Approaches in Organizational Studies………29
Table 2.4: Conceptual Milestones in Organizational Capabilities Research……………. 54
Table 2.5: Summary of Key Empirical Findings in Organizational Capabilities Research…………………………………………………….………………………………………… 55
Table 4.1: Age Distribution of Sample…………………………………………………………………… 91
Table 4.2: Educational Distribution of Sample………………………………………………………. 91
Table 4.3: Gender Distribution of Sample……………………………………………………………… 91
Table 4.4: Demographic Distribution of Sample……………………………………………………. 92
Table 4.5: Measurement Interval One – Means, Standard Deviations, and Zero-Order Correlation Coefficients……………………………………………………. 101 Table 4.6: Measurement Interval Two – Means, Standard Deviations, and Zero-Order Correlation Coefficients……………………………………………………. 102 Table 4.7: Measurement Interval Three – Means, Standard Deviations, and Zero-Order Correlation Coefficients……………………………………………………. 103 Table 4.8: Confirmatory Factor Analysis Results for Comparative Social Capital
Measurement Models………………………………………………………………………… 111 Table 4.9: Discriminant Validity Analysis of Comparative Social Capital Factor
Structures…………………………………………………………………………………………… 111 Table 4.10: Regression Weights for Social Capital Measurement Model………………..114
Table 4.11: Standardized Total Effects for Social Capital Measurement Model…….. 115
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Table 4.12: Summary of Model Fit Indices for Social Capital Measurement Model………………………………………………………………………….. 116
Table 4.13: Regression Weights for Capability Performance Measurement Model………………………………………………………………………….. 122
Table 4.14: Standardized Total Effects for Capability Performance Measurement Model………………………………………………………………………….. 123 Table 4.15: Summary of Model Fit Indices for Capability Performance Measurement Model………………………………………………………………………….. 124 Table 4.16: Confirmatory Factor Analysis Results for Comparative Capability
Performance Measurement Models…………………………………………………… 125 Table 4.17: Discriminant Validity Analysis of Comparative Capability Performance Factor Structures…………………………………………………………… 125
Table 5.1: Comparative Structural Equation Model Fit Summary………………………… 136
Table 5.2: Regression Weights for Structural Model (Measurement Interval One)………………………………………………………………. 136
Table 5.3: Covariance Estimates of First Order Indicators of Social Capital (Measurement Interval One)………………………………………………………………. 137
Table 5.4: Correlation Estimates for Social Capital Indicators
(Measurement Interval One)………………………………………………………………. 137
Table 5.5: Factor Score Weights for Structural Model (Measurement Interval One)………………………………………………………………. 137
Table 5.6: Standardized Total Effects for Structural Model (Measurement Interval One)………………………………………………………………. 138
Table 5.7: Structural Equation Model Fit Summary (Measurement Interval One)………………………………………………………………. 139
Table 5.8: Regression Weights for Structural Model (Measurement Interval Two)……………………………………………………………….141
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Table 5.9: Covariance Estimates of First Order Indicators of Social Capital (Measurement Interval Two)……………………………………………………………….142
Table 5.10: Correlation Estimates for Social Capital Indicators
(Measurement Interval Two)……………………………………………………………….142
Table 5.11: Factor Score Weights for Structural Model (Measurement Interval Two)……………………………………………………………….142
Table 5.12: Standardized Total Effects for Structural Model (Measurement Interval Two)……………………………………………………………….143
Table 5.13: Structural Equation Model Fit Summary (Measurement Interval Two)……………………………………………………………….144
Table 5.14: Regression Weights for Structural Model (Measurement Interval Three)……………………………………………………………. 145
Table 5.15: Covariance Estimates of First Order Indicators of Social Capital (Measurement Interval Three)……………………………………………………………. 146
Table 5.16: Correlation Estimates for Social Capital Indicators
(Measurement Interval Three)……………………………………………………………. 146
Table 5.17: Factor Score Weights for Structural Model (Measurement Interval Three)……………………………………………………………. 146
Table 5.18: Standardized Total Effects for Structural Model (Measurement Interval Three)……………………………………………………………. 147
Table 5.19: Structural Equation Model Fit Summary (Measurement Interval Three)……………………………………………………………. 148
Table 5.20: Comparative Results of Cross-Sectional Hypothesis Testing of the Emergence of Social Capital across Temporal Periods………………………… 149
Table 5.21: Comparative Fit Summary for Growth Curve Models…………………………. 158
Table 5.22: Estimates of Means for Growth Curve Models……………………………………. 158
Table 5.23: Estimates of Covariance Parameters for Growth Curve Models………….. 158
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Table 5.24: Regression Weights for Cross-Lagged Model………………………………………. 163
Table 5.25: Standardized Total Effects for Cross-Lagged Regression Model………….. 163
Table 5.26: Longitudinal Cross-Lagged Regression Model Fit Summary………………… 164
Table 5.27: Comparative Fit Summary for Cross-Lagged Regression Models…………. 165
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LIST OF APPENDICES
APPENDIX A: Threat Management Analyst Job Description……………………………………… 192
APPENDIX B: ELICIT Description……………………………………………………………………………….. 194
APPENDIX C: Sensitivity Analysis………………………………………………………………………………. 204
1
Chapter One: Introduction
The evolution of organizational resources, from both an analytical and an
empirical perspective, merits additional research... Like the evolution of
capabilities, the evolution of organizational resources is a key component of the
dynamic RBV. A more complete understanding of the joint evolution of resources
and capabilities also merits further research. Only then we can more fully
understand evolution and change of competitive advantage and disadvantage
of firms over time (Helfat & Peteraf, 2003: 1009).
Organizational capabilities have been characterized in a variety of ways, as
collections of transformative routines (Nelson & Winter, 1982; Winter, 2003), or as
socially complex processes (Collis, 1994), or even as dynamic emergent patterns of
activity (Eisenhardt & Martin, 2000), yet most scholars agree that they provide the
central means of coordinated collaborative task-performance in organizations today.
The research project described in this dissertation addresses an issue of fundamental
importance to understanding the effectiveness organizational capabilities, and therefore
the effectiveness of organizations of every type. This research examines the soft-social
underbelly of organizational capabilities by focusing on how underlying social resources
emerge and come to influence the evolution of these performance systems. Studying
how social resources, and in particular social capital that flows through underlying social
networks, support capability performance is a crucial missing link in the field’s collective
understanding; dynamically capturing the process of network emergence and capability
birth can offer an altogether novel contribution to a maturing field. At the core of this
2
dissertation lies one fundamental research question: ‘How does the emergence of social
capital influence the evolution of organizational capabilities?’ The solution, it will be
argued, resides with a tighter focus on the social aspects of collective performance
through the integration of social and relational factors within an organizational
capabilities framework, which until very recently has remained largely neglected.
Dissertation Research Overview
The substance of this dissertation focuses on examining the role that growth in
social capital has on influencing the effectiveness of organizational capabilities. The
capabilities an organization possesses shape its ability to get things done. Capabilities
are defined as the “collections of routines that, together with their implementing input
flows, confer upon an organization’s management a set of decision options for
producing significant outputs of a particular type” (Winter, 2003: 991), which at their
essence are “socially complex routines that determine the efficiency with which firms
physically transform inputs into outputs” (Collis, 1994: 145). Social capital in contrast,
defined herein as “the sum of the actual and potential resources embedded within,
available through, and derived from the network of relationships possessed by an
individual or social unit” which thus “comprises both the network and the assets that
may be mobilized through that network” (Nahapiet & Ghoshal, 1998: 243), has often
been endorsed as having potential value in capability building and use (Adner & Helfat,
2003; Helfat & Peteraf, 2003; Tsai, 2002). Notwithstanding this claim, comparatively
3
little research has been conducted with explicit focus on the building or evolution of
organizational capabilities (for exceptions see, Capaldo, 2007; Haas & Hansen, 2005;
Jones, Hesterly, Fladmoe-Lindquist, & Borgatti, 1998) and at present, none that I am
aware of have empirically investigated the implications of social capital emergence on
the evolution of organizational capabilities.
The overarching research question articulated in this study is, ‘How does the
emergence of social capital influence the evolution of organizational capabilities?’ In
later portions of this chapter this central question will be dissected first into its
component parts, and later into empirically testable hypotheses, each focused on
supporting or refuting the relationships among and between these concepts. This
research employs an experimental simulation approach set in the context of a practice-
based crisis simulation. The simulation was conducted with the Experimental Laboratory
for Investigating Collaboration, Information-Sharing, and Trust in organizations (ELICIT)
platform, using protocols consistent with those used to study real-world organization
members of Defense Research and Development Canada - Toronto, Collaborative
Performance and Learning Section. Justifying a course of research built around crisis
recognition and response is not difficult in today’s turbulent times. Moreover, the
growing influence of research that Karl Weick and others (Weick, 1988, 1993; Weick &
Roberts, 1993; Weick & Sutcliffe, 2001) have done studying the impact of crisis, threat,
and disaster situations on collective sensemaking and collaborative performance under
extreme pressure, makes this context a highly relevant and an ever more valuable one.
4
In this experimental simulation, the primary aim is to establish a collective ‘threat
identification’ which requires active and ongoing coordinated collaboration among the
simulation participants, clearly illustrating the emergence of an organizational
capability. In this context, it is threat identification then that functions as an
organizational capability and this project explores the relationship between social
capital and the performance of this capability in real-time under a variety of conditions.
The intent of this research project is threefold: first, to investigate how social capital
emerges; second, to isolate and examine whether and how social capital contributes to
the evolution of an organizational capability; and third, to explore how the social
capital-capability performance relationship evolves over time.
The aim here is to contribute valuable insight to the management literature by
examining the micro-foundations of organizational capability emergence; demonstrating
that the social, relational, and structural context of work is central, especially in its
ability to shape collaborative practice and contribute to the collective ability to meet
organizational needs. Focusing on capability change offers a unique contribution to the
understanding of organizational capabilities because it begins to question the causal
factors underlying the origins and emergence of organizational capabilities beyond the
study of positions, paths, and process evolution (Dierickx & Cool, 1989; Nelson &
Winter, 1982; Teece, Pisano, & Shuen, 1997). Opening the ‘black box’ of capability
emergence is an important first step in expanding our knowledge of dynamics of
organizational adaptation and evolution, and understanding how the micro-foundations
5
of organizational capabilities function is a necessary antecedent to further enquiry in
the line of study (Abell, Felin, & Foss, 2008; Felin & Foss, 2005, 2006). Concentrating on,
and testing, a series of relationships believed to hold promise, but whose implications
are largely unknown, may reduce the ambiguity surrounding the valence of social capital
in collective performance. While proponents in the social capital literature have
asserted the concept’s importance in individual performance outcomes such as advice-
seeking (Cross & Sproull, 2004), we know little about whether these arguments are
appropriable to collective settings or how differing configurations of social capital may
influence collaborative performance, a critical dimension of organizational capabilities
research.
The results of this research project give greater insight into how organizational
capabilities grow and evolve, and whether social capital contributes to these processes,
both of which are highly relevant to a practitioner audience interested in theory
application. Here, the added value to management practice stems from potential to
construct intervention mechanisms which would add some measure of control and
predictability to capability evolution. The ability to take action to encourage, stabilize, or
discourage capability change via specific intervention mechanisms, provides a powerful
opportunity to maintain alignment between internal processes and performance
objectives. By better understanding the role that social capital networks play in the
emergence of capabilities and their performance outcomes, we open the door to a
6
variety of intervention strategies amenable to the specific context the organization finds
itself in.
Emergence, Evolution, and Co-Evolution
Rare is the opportunity to examine the first instance of a collective phenomena;
this research affords one such opportunity. Throughout this document the terms
emergence, evolution, and co-evolution will be used to denote growth – more
accurately – temporal periods in which the pace and direction of growth change over
time. Discussion of these points will ensue in later portions of this dissertation; for now
we simply explain the meaning of emergence, evolution, and co-evolution in this
context.
Emergence refers to the earliest stage in the development of something, which
lays the basis for its subsequent development (Helfat & Peteraf, 2003). It reflects the
process of birth or building – from nothing to something – of a new process, outcome or
construct. Alternatively, evolution speaks to the generation of progressive change
resulting from variations in the dynamic processes that created our current state and
“from which a quite different future will emerge by those same dynamic processes”
(Nelson & Winter, 1982: 10). It too reflects the changing of a process, outcome, or
construct over time (March, 1994), but unlike emergence, change here is from one state
or quality to another (i.e. from something to something). Evolution does not presuppose
a particular rate or pace of change, nor whether change must be the product of random
7
endogenous variation or a deliberate response to exogenous stimuli; rather, these are
qualities of the process of evolution and may vary from circumstance to circumstance
(Nelson & Winter, 1982; Penrose, 1959). Co-evolution extends the idea of evolution;
and in practice “*t]he idea of coevolution means that concepts will evolve over time,
and in so doing, will impact other concepts. Thus, various scenarios could unfold
depending on how the joint venture moves from initial conditions to evolved
conditions” (Inkpen & Currall, 2004: 587). Co-evolution emphasizes the related change
among two or more processes, outcomes, or constructs where change (and possibly the
pace of change) occurs together over time. Investigating co-evolutionary influences
shed light on how direct interactions and feedback within social and performance
systems give rise to their dynamic behavior over time (Baum & Singh, 1994: 380). The
longitudinal design of this research project allows a unique examination of emergent,
evolutionary, and co-evolutionary change within and between social capital and
capability performance.
Review of Prior Research
In the literature focusing on the inter-workings of organizations and their
processes, there are perhaps no more pressingly relevant issues to understand than the
relationship between social interactions, collaborative practicing, and collective
performance (Feldman & Pentland, 2003; March, 1991; Weick, 1998; Winter, 2000).
Organizational capabilities (hereafter used interchangeably with capabilities) lie at the
8
intersection of these issues in many ways (Eisenhardt & Martin, 2000; Helfat & Peteraf,
2003; Winter, 2003). Capabilities – planned or emergent – develop from collective
effort; they involve “socially complex routines that determine the efficiency with which
firms physically transform inputs into outputs” (Collis, 1994: 145). They bring human,
social, and physical capital together through socio-structural interaction, and are applied
purposefully for a particular – although at times ambiguous – intent. The relationship
between capabilities and performance has been studied in a variety of forms divisible
primarily by context, capability content, or by varying levels of analysis. With respect to
context, studies have examined the implications of organizational capabilities on
performance across a variety of domains including: high technology hardware (Choo,
Linderman, & Schroeder, 2007; Hansen & Løvås, 2004; Rosenbloom, 2000; Tripsas &
Gavetti, 2000) and software developers (Ethiraj, Prashant, Krishnan, & Singh, 2005;
Hoopes & Postrel, 1999); medical, pharmaceutical and health care providers (Kor &
Mahoney, 2005; Pisano, 1994; Verona & Ravasi, 2003; Wooten & Crane, 2004); the
greater automotive industry (Dyer & Hatch, 2006); the petrochemical industry (Adner &
Helfat, 2003); and even business consulting firms (Haas & Hansen, 2005). Similarly,
others have chosen to focus on the capability content – performance relationship by
investigating alliance formation (Capaldo, 2007; Gulati, 1999; Reuer, Zollo, & Singh,
2002), strategic information asymmetries (Levinthal & Myatt, 1994; Miller, 2003),
knowledge creation, sharing and transfer (Cohen & Levinthal, 1990; Orlikowski, 2002;
Tsai, 2002), product development (Danneels, 2002; Leonard-Barton, 1992; Zander &
9
Kogut, 1995), process innovation (Leonard-Barton, 1992; Pisano, 1994) and governance
(Reuer et al., 2002). The literature has also added conceptual depth through the
examination of the performance impact of capability acquisition and deployment at
varied levels of analysis, from the study of intra-organizational groups (Hansen & Løvås,
2004; Wooten & Crane, 2004), projects (Choo et al., 2007; Lorenzoni & Lipparini, 1999;
Pisano, 1994), to strategic business units (Adner & Helfat, 2003; Lee, Lee, & Rho, 2002;
Miller, 2003; Tsai, 2002) and organizations (Levinthal & Myatt, 1994; Moliterno &
Wiersema, 2007; Montealegre, 2002; Teece, 2007) and even among inter-organizational
arrangements (Gulati, 1999; Zott, 2003). In combination, our improved understanding of
the importance of delineating context, content, and level of analysis has underscored
the benefits of explicating the composition and characteristics of capabilities which
contribute to performance on a situated and situationally-specific basis (Haas & Hansen,
2005). However, despite this breadth of understanding less is known about the socio-
relational factors that lie at the core of coordinated social interaction and collaborative
performance, the soft – often unrecognized – underbelly of capability development (for
notable exceptions see, Orlikowski, 2002; Wooten & Crane, 2004).
Although frequently acknowledged in conceptual thinking as well as in the
discussion of empirical results (for example, Blyler & Coff, 2003; Haas & Hansen, 2005;
Jones et al., 1998), explicit examination of the socio-relational micro-foundations of
capability emergence, development and deployment has been largely neglected (Adner
& Helfat, 2003; Helfat & Peteraf, 2003; Tsai, 2002). In spite of this neglect in the
10
capabilities domain, a growing movement in the antecedent literature focusing on
routine practicing and performativity has taken up the study of socio-relational and
socio-structural interactions in relation to performance (Feldman & Rafaeli, 2002;
Gersick & Hackman, 1990; Vera & Crossan, 2005; Visser, 2007), a micro-foundation in
the development and stabilization of organizational capabilities (Nelson & Winter, 1982;
Salvato, 2009). Here, there is increasing agreement that recognition of the social,
relational, and structural context of work is essential in order to understand the
patterns within networks of practice (Brown & Duguid, 2000; Lave & Wenger, 1991;
Wasko & Faraj, 2005), the stability and adaptation resulting from the practice of
routines (Denrell & March, 2001; Feldman & Pentland, 2003), and the service of
routines as resources for others (Feldman, 2004). Given the prominence of
collaborative-coordinated-collective action in the conceptual, theoretical and
operational understanding of organizational capabilities, it would appear that socio-
relational interactions are a necessary – if not sufficient – condition for capability
growth and change. Yet we know so little.
Purpose and Intent of this Dissertation
This dissertation aims to address a number of fundamental questions in the
contemporary organizational capabilities literature, by arguing that social capital is an
influential determinant in the emergence and development of organizational
capabilities. This proposition stems from the growing evidence endorsing the impact of
11
social capital on individual, group and organizational performance. While we do know a
great deal about the relationship between social capital and differing measures of
organizational performance, such as alliance, intra- and inter-organizational relationship
success (Batjargal & Liu, 2004; Galaskiewicz, Bielefeld, & Dowell, 2006; Gargiulo &
Benassi, 2000; Hansen, 1999; Maurer & Ebers, 2006; Tsai, 2000), knowledge creation,
sharing and transfer (Hansen, 2002; Levin & Cross, 2004; Tsai, 2002; Uzzi, 1997), gender
and diversity (Belliveau, 2005; James, 2000), team performance and viability (Balkundi &
Harrison, 2006; Leana & Pil, 2006; Reagans & Zuckerman, 2001; Shah, Dirks, & Chervany,
2006; Shaw, Duffy, Johnson, & Lockhart, 2005), product and process innovation (Oh,
Chung, & Labianca, 2004; Tsai & Ghoshal, 1998) and governance (Talmud & Izraeli, 1999;
Westphal & Stern, 2006), critical gaps remain. One such critical gap originates from the
organizational capabilities literature, in which an increasing number of authors implicate
social capital as a highly relevant, yet under-explored construct, suspected to offer a
significant contribution to our understanding of the relationship between organizational
capabilities and performance (Adner & Helfat, 2003; Blyler & Coff, 2003; Capaldo, 2007;
Haas & Hansen, 2005; Helfat & Peteraf, 2003; Tsai, 2002). If we are to advance our
collective thinking about organizational capabilities, it is necessary to begin to address
some of these micro-foundational gaps by determining not only whether social capital is
important, but if so, how it is important in the building and evolution of organizational
capabilities. More importantly we ought to examine the emergence of social capital and
its implications during the birth of organizational capabilities to better understand not
12
only the relationships among social capital, organizational capabilities and performance,
but also the interrelationship between social capital and capabilities over time.
Against this backdrop, this research addresses the nature and significance of the
relationship between social capital and organizational capabilities, and the implications
of this relationship for organizational performance over time. In the contemporary
evolutionary organization theory literature, social capital is an often mentioned and as
yet ambiguous, contributor to the creation, maintenance and revision of organization
capabilities (Adner & Helfat, 2003; Blyler & Coff, 2003; Capaldo, 2007; Haas & Hansen,
2005; Helfat & Peteraf, 2003; Tsai, 2002). We recognize that the value of routines and
capabilities – the former performed by the individual and the latter performed in
combination by groups and social collectives (Nelson & Winter, 1982) – rest
predominantly on their ability to shape, direct, and organize the behaviors of
organizational participants in a deliberate, consistent and predictable manner
(Danneels, 2002; Dosi, Nelson, & Winter, 2001; Winter, 2003), yet when it comes to
capabilities little is known about how they are formed or the involvement of social
capital in this formation.
Coming to grips with issues of emergence and evolution of social capital and
organizational capabilities is a much-needed first step in further advancing management
practice. Global explanations for the persistence of organizational capabilities and social
capital have been offered in the literature: current and future organizational capabilities
originate primarily as a result of an organization’s past (Dierickx & Cool, 1989; Nelson &
13
Winter, 1982; Teece et al., 1997); whereas social capital has been argued to largely
result from hereditary societal standing, socio-economic and cultural position, and
historic patterns of privilege and opportunity (Bourdieu, 1986; Coleman, 1990; Lin,
1999; Portes, 1998). In general it is easy to agree with, and difficult to argue against,
these global claims however they do little to recognize the idiosyncratic character of
organizational context and content desirability that a situated understanding affords.
The introduction of an evolutionary lifecycle model of organizational capabilities
(Helfat & Peteraf, 2003), a recent development in the evolutionary organization theory
literature which has yet to be supported or refuted with empirical evidence, holds three
important facets warranting discussion here. First, the authors delineate capabilities as
residing at the group or team level of analysis, but embedded in the social and structural
framework of the organization. In more recent research, this contextualized view of
organizational capabilities as embedded in the social and structural fabric of the
organization has been reinforced; capabilities are conceived as developed in the context
of resource allocation, but at their core, capabilities are seen to be “distinct behavioral
patterns, which are complex in nature involving both formal and informal processes”
(Schreyögg & Kliesch-Eberl, 2007: 914 their emphasis).
Second, the creation of new capabilities is attributed to either a process involving
the rejuvenation of established capabilities by ‘branching’ or evolving into new ones
through the reconfiguration of existing resources, routines and processes; or through a
process of emergence in which new capabilities arise from systematic patterns of
14
practice largely dependent on human, social and structural capital endowments (Helfat
& Peteraf, 2003), echoing the evolutionary theory’s emphasis on the importance of
‘position’ in shaping path dependence (Nelson & Winter, 1982; Teece & Pisano, 1994).
Although absent from the authors’ theoretical modeling, they emphasize the
importance of future studies examining the influence of social capital during the
emergence, development and maturity of organizational capabilities (Helfat & Peteraf,
2003). Similarly, Adner and Helfat (2003) more explicitly consider the potential of social
capital during the emergence of organizational capabilities. Finally, invoking a resource-
based argument in which social capital is presumed to be one of the many resources
whose value is appropriable by the organization, the authors argue that early social
capital allocation decisions impact the emergence of ‘dynamic managerial capabilities’
by facilitating or inhibiting both the acquisition of information and resources, and the
ability to exercise influence (Adner & Helfat, 2003). Thus, whether considered from the
perspective of intra-organizational workgroups, or at the interface between distinct
business units of an organization, the strength and content of the socio-relational
linkages – or social capital – matter in the creation and use of organizational capabilities.
This final point, that social capital may be a persuasive determinant in the lifecycle of
organizational capabilities, begins to consider each dimension of social capital –
structural connections, cognitive contribution, and relational linkages – in terms of its
ability to contribute unique yet complementary utility during the process of capability
building and change. The relationship between the emergence of social capital and the
15
evolution of organizational capabilities, and its impact on capability change, warrants
investigation. Therefore, this research poses the following questions:
How does social capital emerge in new organizational contexts?
How does social capital emergence influence the creation and growth of
organizational capabilities?
The raison d’être for organizing lies in the assumption that under some conditions,
organizations are more efficient mechanisms for the coordination of collective action
than would otherwise be available in the greater marketplace (Williamson, 1975). The
underlying notion that in some situations – particularly those characterized by
complexity, uncertainty, information asymmetry, or general casual ambiguity – there
are organizational benefits to taking coordinated collective action in the form of
collaborative performance inside the firm is important. It has been argued that the
contribution of collective action to effective performance outcomes is contingent: first,
on the internal architecture of the organizational design and its compatibility with the
desired organization strategy (Chandler, 1962); and second, on the ability of the
organization to marshal, align and deploy its resources, in support of its combined
integrated knowledge, competences and distinctive processes (Dosi et al., 2001; Grant,
1996; Nelson & Winter, 1982; Teece & Pisano, 1994; Teece et al., 1997). The latter
point, that the ability to align resources, routines, and processes – deployed in the form
16
of organizational capabilities – is a primary determinant in organizational success, is of
course important in terms of inter-organizational competition. However, it is equally
relevant in terms of intra-organizational performance.
The value, then, of organizational capabilities lies in their ability to generate
deliberate, consistent and predictable organizational performance (Danneels, 2002; Dosi
et al., 2001; Winter, 2003), and is thus outcome oriented (Haas & Hansen, 2005). But
not just outcome oriented: oriented to a context specific or ‘situated’ outcome in which
the value of a capability is demonstrated by the performance resulting from its
application in a specific situation (Dewey, 1938; Haas & Hansen, 2005; Pentland, 1992).
For example, the value of integrated knowledge is neither in the fact that it is shared
among group members, nor in the amount of knowledge integrated within the group,
but in its application to meet a specified end. Similarly, this approach to the valuation of
a relatively tacit domain such as knowledge integration is equally appropriate in the
valuation of social capital, a similarly tacit concept.
Just as others have taken a pragmatic perspective toward the evaluation of the
contributions flowing from organizational capabilities to a specific performance
objective, so too should we here take a similar approach with respect to the relationship
between social capital and the emergence of organizational capabilities (a performance
outcome in its own right). However, in contrast to the capabilities–performance linkage
which has been relatively well established (Dyer & Hatch, 2006; Kor & Mahoney, 2005;
Leonard-Barton, 1992; Makadok, 2001; Miller, 2003; Zott, 2003), substantially less is
17
understood about the relationship between the emergence of social capital and the
evolution of organizational capabilities1. Notable-others have called for an exploration
into the micro-foundations that underpin capabilities, stressing the need for future
research to examine the role of structural, cognitive and relational factors in capability
change (Dosi & Marengo, 2007; Gavetti, 2005; Winter, 2000; Zollo & Winter, 2002). For
these reasons it is important to question both the nature of the relationships between
social capital and capability performance, as well as the patterns of relationships among
these constructs over time. Thus, this research also examines the following questions:
What impact does social capital emergence have on the longitudinal evolution of
organizational capabilities?
Do social capital and organizational capabilities co-evolve over time?
The next section specifies begins to identify the precise hypotheses associated with the
theoretical model to be specified and tested herein.
Dissertation Contributions and Practical Implications
We have indicated that this dissertation’s fundamental research question is:
‘How does social capital emergence influence the evolution of organizational
1 Theoretical exceptions include: Blyler & Coff (2003) who discuss the relationship between social capital,
dynamic capabilities, and the generation and appropriation of resulting rents; and Gooderham (2007) who addresses management-initiatives to enhance knowledge transfer within Multi-National Corporations using a social capital framework.
18
capabilities?’ Providing a conclusive answer to this question requires that we address
four key areas, as noted in the previous section: how social capital emerges and
develops over time; how this emergence influences the building of organizational
capabilities; whether social capital impacts capability evolution or change over time;
and, whether social capital and organizational capabilities longitudinally co-evolve.
Addressing these fundamental issues is therefore the terrain of this research project. To
do so we take a situated performance perspective (Haas & Hansen, 2005), as it focuses
on a pragmatic practice-based investigation (Bourdieu, 1990; Dewey, 1938) taking us
closer to the phenomena as they exist in the specific research context, and reducing the
conceptual distance between occurrence, observation and understanding. The benefits
of this orientation lie: first, in the treatment of capability evolution as being of value for
its contribution to creating reliable collaborative performance outcomes; second, in the
recognition that the value of social capital stems from when, whether and how it
contributes to the fitness of organizational capabilities; and third, in determining the
implications of early performance on the evolution of organizational capabilities and the
growth of social capital.
Opening the ‘black box’ of capability emergence is an important first step in
expanding our knowledge of the dynamics of organizational adaptation and evolution.
Unlike previous research which has focused more on post hoc examinations of
capabilities (for example, Montealegre, 2002; Tripsas & Gavetti, 2000), this work focuses
on the preliminary stage of capability development to attempt to shed new light on how
19
capabilities initially emerge. Management theory and application can clearly be
enhanced by examining the micro-foundations of capability change. Studying patterns
of social capital emergence offers a unique contribution to the understanding of how
capabilities evolve because it begins to untangle the causal factors that drive capability
change from a socialized perspective rather than an historical one based on the study of
positions, paths, and processes. Proponents in the social capital literature have asserted
the concept’s importance in generating performance outcomes, but we know little
about whether the causality of these arguments is appropriate or how the pattern of
influence occurs.
Apart from their theoretical novelty, the results of this research project hold
relevance to a practitioner audience interested in capabilities building. Here, the added
value to management practice stems from the potential development of intervention
mechanisms which would add some measure of control and predictability to capability
emergence. The ability to take action to encourage, stabilize, or discourage the
emergence of organizational capabilities via specific intervention mechanisms, provides
a powerful opportunity to maintain alignment between internal processes and
performance objectives. Offering the fields of organizational design and strategic
management the opportunity to intervene meaningfully would clearly enhance future
practice by increasing the alternatives available to control performance variation (for
example: encouraging emergence during product or process innovation cycles;
controlling emergence during production start-up cycles; discouraging emergence
20
during routine production cycles), beyond their current repertoire. By better
understanding the role that social capital plays in the emergence of organizational
capabilities we may open the door to a variety of intervention strategies amenable to
the specific context in which a given organization resides.
In conclusion, linking these distinct fields of thought in a longitudinal framework
illustrating their combined performance is a strong contribution in its own right.
However, connecting the performance implications resulting from mutual emergence
and co-evolution of social capital and organizational capabilities makes a potentially
significant leap forward. Therefore, understanding how social capital emerges and
organizational capabilities evolve is a worthwhile endeavor at this time, as it offers
organization scientists and managers the opportunity to take action in a deliberate,
purposeful, and timely fashion to encourage capability change and enhance
performance.
Organization of the Dissertation
This research develops and tests arguments which illustrate and advance the
current state of knowledge reflected in the relevant literatures; the structure of the
remainder of the thesis is as follows. Chapter two provides a comprehensive review of
the most relevant findings and conclusions contained in the primary bodies of literature
addressing social capital and organizational capabilities. The conceptual and intellectual
heart of this study is located in chapter three. Here, discussion centers on three core
21
areas: construct and theory development; introduction and elaboration of a conceptual
model; and restatement of the model’s central arguments in the form of testable
hypotheses.
Research design and research methodology are addressed in the fourth chapter,
which contains a description of the research site and the method of study. In addition,
this chapter includes specifics regarding the sampling parameters, the mixed method
survey-experimental approach, as well as the operationalization and measurement of
each of the primary constructs under study. Chapter five details the analytic approach
used in this research, and examines the results of these analyses. Findings of this study,
including an examination and interpretation of the results – their implications and
limitations, are presented and contributions to the literature discussed in the sixth
chapter. The seventh and final chapter summarizes this research and provides an
overview of the contributions and future directions made possible by this project.
22
Chapter Two: Literature Review
The social capital and organizational capabilities literature considered relevant
for this research project is reviewed herein; first from a conceptual perspective, and
later with the introduction of empirical contributions. The materials originate from two
distinct intellectual traditions; social capital derived from developments in the field of
sociology, and organizational capabilities from the advent of evolutionary economic and
later resource-based theorizing. Maintaining consistency with the central research
question driving this dissertation – ‘How does the emergence of social capital influence
the evolution of organizational capabilities?’, the concept of social capital will first be
considered, followed by an illustration of the foundational work as well as more
contemporary developments in the organizational capabilities literature. Intersections
between the two streams of theory, while rare, afford the opportunity to reflect on
potential similarities between the concepts, and provide a foothold for further
theorizing in support of the third chapter. Given the already large, and constantly
expanding bodies of research associated with these two concepts, the aim here is not to
provide the reader with a general understanding of each field, but rather with a
comprehensive knowledge of areas which apply directly to this project.
Social Capital Literature Review: Conceptual, Theoretical, and Empirical
In general, social capital has been defined, discussed, and considered from a
plethora of perspectives, and with a multitude of intents (Adler & Kwon, 2002). Consider
23
Adler and Kwon’s (2002) review of the various usages of social capital, and how the
construct has been construed across multiple disciplines and levels of analysis. In the
broader social capital literature, terms such as cultural capital and relational capital have
entered the social capital lexicon (Bourdieu, 1986, 1990; Putnam, 1995), and distinctions
between the public versus private value of social capital have been considered (Burt,
1997; Coleman, 1988; Granovetter, 1985; Lin, 1999; Portes, 1998; Putnam, 1995; Uzzi,
1997). As a result, questions about the constitution of social capital are warranted in
understanding what is meant by an individual’s, a group’s, or an organization’s social
capital, as each have distinct and incommensurate facets (Ibarra, Kilduff, & Tsai, 2005)2.
Although the notion of social capital has been conceptualized and applied in
tremendously diverse ways both inside the management literature and outside, this
diversity of understanding calls for future research to employ greater specificity with
respect not only to terminology, but also to intent and level of analysis. Support for this
assertion is offered in the form of two literature summary tables: Table 2-1 captures key
conceptual milestones; while Table 2-2 illustrates the most relevant empirical findings
for this dissertation.
In the field of organizational studies, the primary distinctions in the social capital
literature stem from the issues of constitution, value, and configuration. Historically
these dimensions were represented by two distinct theoretical perspectives: one
2 Additionally, consider the treatment given at the individual level (Burt, 2000); at the group level (Oh,
Labianca & Chung, 2006); at the organizational level (Uzzi, 1997); or at the societal level (Onyx & Bullen, 2000), for example.
24
represented by the Structural Holes theory approach which emphasizes the private
value of social capital to the individual or focal actor from a structural perspective (Burt,
1997, 2000; Granovetter, 1985; Seibert, Kraimer, & Liden, 2001); the other associated
with James Coleman and the emergence of a theory of social connectivity (Coleman,
1986, 1988; Portes, 1998), presumes that social capital has a public value, nested within
the relational connections between individuals (Leana & Pil, 2006; Leana & Van Buren,
1999). Alternatively, an integrative perspective that bridges the private-public
dichotomy is emerging (Inkpen & Tsang, 2005; Nahapiet & Ghoshal, 1998; Tsai &
Ghoshal, 1998). This holistic perspective is the one that best informs our definition of
social capital as “the sum of the actual and potential resources embedded within,
available through, and derived from the network of relationships possessed by an
individual or social unit” which thus “comprises both the network and the assets that
may be mobilized through that network” (Nahapiet & Ghoshal, 1998: 243). Because this
definition considers social capital from a comprehensive perspective, the literature
introduced here will incorporate both orientations as well.
In Table 2-3 the essence of each conceptualization of social capital is illustrated
for comparative purposes, however, it is worth mentioning that variance among
scholars within each major division does exist. The central point illustrated in Table 2-3
lies in the distinctions between how social capital is constituted, valued and configured,
across each of the three perspectives. While each of these dimensions will be
considered in depth, it is worth noting a pair of critical points of agreement among the
25
three approaches. First, there is generally agreement recognizing the generative nature
of social capital as an enduring social resource (Adler & Kwon, 2002; Burt, 2000; Moran,
2005). In contrast to other types of resources whose values tend to diminish with use,
social capital is considered to be generative in that its value is presumed to increase
with constructive use (obviously decreasing in value to the degree that destructive
conflict arises among linked members). The more a given pattern of relationships is
relied upon, the more likely that trust, mutual respect, and social solidarity will develop
and persist between network members, as “most behavior is closely embedded in
networks of interpersonal relations” (Granovetter, 1985: 504)3. This point, that social
capital is a generative resource within organizations, is a critical attribute of social
capital and an incredibly important consideration in the study of the micro-foundations
of organizational capabilities within the context of organizations, a point to which we
return at the conclusion of the chapter.
A second point of agreement lies in universal treatment of the term
embeddedness. Although authors typically precede embeddedness with a qualifier, such
as structural, relational, or cognitive, until recently it was attributed only a vague
definition referring broadly to a process in which the network of social relations come to
pattern the exchanges among actors, such that it becomes increasingly difficult to
3 From a structural perspective, this argument holds in that as brokerage opportunities increase, the value
to the broker’s position would similarly increase due to an increased dependence on the broker by the alters (or information seekers). However, the value of information or knowledge – as distinct from position – may decrease as weaker ties (commonly associated with diverse information) become stronger (or more closely linked) due to an increasing frequency in the number of interactions.
26
separate the actions of the individual from the social context in which they occur
(Granovetter, 1985; Uzzi, 1997). Increasingly however, researchers including those
interested in social capital have recognized the need to qualify the nature of
embeddedness, and their interpretation of it, by situating both the actor and pattern of
social relations in a specific structural or institutional context (Baum & Dutton, 1996;
Moran, 2005; Oliver, 1996; Uzzi, 1996, 1997, 1999; Zuckin & DiMaggio, 1990). This shift,
by some, toward creating a comprehensive understanding of social capital, based on an
integrative perspective, lends support to the choice of definition used in this
dissertation, that social capital is “the sum of the actual and potential resources
embedded within, available through, and derived from the network of relationships
possessed by an individual or social unit” which thus “comprises both the network and
the assets that may be mobilized through that network” (Nahapiet & Ghoshal, 1998:
243). In this paper, the general term embeddedness is taken as referring to the
patterned nesting of an individual’s activities within the situated context of the group,
but which requires substantive qualification to be meaningful (Granovetter, 1985;
Moran, 2005)4. It is from this perspective, one that merges integrative and situated
approaches, that we consider the recent developments in the social capital literature.
4 Here, we take an approach consistent with that of Oliver (1996:164) in which embeddedness was
qualified as “institutional embeddedness” and contextualized as a firm level construct defining the pattern of activity between firms and their institutional context.
27
Au
tho
rsA
rtic
le T
itle
Lev
el o
f A
na
lysis
Ind
ep
en
de
nt
Vari
ab
les
De
pe
nd
en
t V
ari
ab
les
Majo
r F
ind
ing
s a
nd
Co
nc
lus
ion
s
Gra
no
vetter
(1985)
Eco
no
mic
action a
nd s
ocia
l str
uctu
re:
The p
rob
lem
of
em
beddedness
Indiv
idual; D
yad
Th
is p
ap
er
co
nce
rns the e
xte
nt
to w
hic
h e
co
nom
ic
actio
n is e
mb
ed
de
d in
str
uctu
res o
f so
cia
l
rela
tio
ns, in
mo
de
rn in
du
str
ial so
cie
ty. U
nder-
and
overs
ocia
lize
d a
cco
un
ts a
re p
ara
do
xic
ally
sim
ilar
in th
eir n
egle
ct
of
ong
oin
g s
tru
ctu
res o
f socia
l
rela
tio
ns. A
so
ph
istica
ted
acco
un
t o
f e
co
no
mic
actio
n m
ust co
nsid
er
its e
mb
ed
de
dn
ess in
such
str
uctu
res.
Bourd
ieu (
1986)
Form
s o
f C
apital
Indiv
idual; S
ocie
tal
Dis
cusse
s thre
e f
orm
s o
f capita
l: e
conom
ic,
so
cia
l, a
nd c
ultu
ral. W
here
so
cia
l ca
pita
l is
the
agg
reg
ate
of
the
actu
al o
r p
ote
ntia
l re
so
urc
es
wh
ich a
re lin
ke
d to p
osse
ssio
n o
f a
dura
ble
netw
ork
re
latio
nsh
ips w
hic
h p
rovid
e e
ach o
f its
me
mb
ers
with
th
e b
ackin
g o
f th
e c
olle
ctivity-
ow
ned
capita
l, a
"cre
de
ntia
l" w
hic
h e
ntitle
s t
hem
to c
redit, in
th
e v
ariou
s s
ense
s o
f th
e w
ord
.
Cole
man (
1988)
Socia
l ca
pita
l in
the c
reation o
f
hum
an c
apital
Indiv
idua
l; G
roup; C
om
mu
nity
Con
ce
pt
of
so
cia
l ca
pita
l is
in
tro
duce
d a
nd
illu
str
ate
d, its f
orm
s a
re d
escrib
ed
, th
e s
ocia
l
str
uctu
ral cond
itio
ns u
nd
er
wh
ich
it arises a
re
exa
min
ed
. T
he c
once
ptio
n o
f so
cia
l ca
pital a
s a
resourc
e f
or
actio
n in
tro
duces s
ocia
l str
uctu
re into
the r
atio
na
l a
ctio
n p
ara
dig
m. T
he r
ole
of
clo
su
re in
the s
ocia
l str
uctu
re in
fa
cili
tatin
g s
ocia
l capita
l is
describ
ed
.
Nahapie
t &
Ghoshal (1
998)
Socia
l capita
l, inte
llectu
al captial, a
nd
the o
rganiz
ational advanta
ge
Indiv
idua
l and G
roups w
ithin
Org
aniz
ations a
nd
Institu
tions
So
cia
l C
apita
lIn
telle
ctu
al C
apita
l
Th
e a
uth
ors
arg
ue:
so
cia
l ca
pita
l fa
cili
tate
s t
he
cre
ation o
f n
ew
in
telle
ctu
al ca
pita
l; o
rgan
izatio
ns,
as in
stitu
tio
nal se
ttin
gs, are
co
nd
uciv
e to
th
e
develo
pm
ent
of
hig
h le
ve
ls o
f so
cia
l ca
pital; a
nd it
is b
eca
use
of
mo
re d
en
se
so
cia
l ca
pita
l firm
s
have a
n a
dva
nta
ge
ove
r m
ark
ets
in
cre
ating a
nd
sh
aring in
telle
ctu
al ca
pita
l.
Adle
r &
Kw
on (
2002)
Socia
l capita
l: P
rospects
for
a n
ew
constr
uct
Indiv
idual
So
cia
l S
tru
ctu
re; M
otivation;
Op
po
rtu
nity;
Ab
ility
Va
lue C
reatio
n
Cla
rifie
s t
he s
ocia
l ca
pita
l a
nd
help
assess its
utilit
y fo
r org
an
iza
tio
na
l th
eory
. S
ynth
esiz
es
theo
retica
l re
se
arc
h u
nde
rta
ke
n in
va
rious
dis
cip
lines a
nd
de
ve
lop a
co
mm
on c
onceptu
al
fram
ew
ork
tha
t id
en
tifie
s the
so
urc
es,
be
ne
fits
,
risks, an
d c
ontin
ge
ncie
s o
f so
cia
l ca
pita
l.
Ibarr
a, K
ilduff
& T
sai (2
00
5)
Zoom
ing in a
nd o
ut: C
onnecting
indiv
iduals
and
co
llectivitie
s a
t th
e
frontiers
of
org
aniz
ational netw
ork
rese
arc
h
Indiv
idual; O
rgan
izatio
n
Th
e a
uth
ors
arg
ue f
or
zo
om
ing b
ack a
nd f
ort
h
betw
een
in
div
idua
l a
nd
co
llective le
ve
ls o
f
ana
lysis
, to
co
nsid
er
how
acto
rs m
ay
benefit
or
detr
act
fro
m the
co
llective
go
od
. T
he a
uth
ors
co
nsid
er
how
in
div
idua
l co
gn
itio
ns a
bo
ut
sh
ifting
netw
ork
co
nn
ectio
ns a
ffe
ct, a
nd a
re a
ffe
cte
d b
y,
larg
er
so
cia
l str
uctu
res.
Inkpen &
Tsang (
2005)
Socia
l capita
l, n
etw
ork
s,
and
kn
ow
ledge tra
nsfe
rO
rganiz
ation;
Inte
rorg
aniz
atio
nS
ocia
l C
apita
l; N
etw
ork
Typ
eK
now
ledg
e T
ransfe
r
Usin
g a
so
cia
l capita
l fr
am
ew
ork
, th
e a
uth
ors
identify
str
uctu
ral, c
ogn
itiv
e, a
nd
re
latio
na
l
dim
ensio
ns f
or
thre
e n
etw
ork
typ
es. T
hey
link
so
cia
l capita
l d
ime
nsio
ns t
o t
he
co
nd
itio
ns t
ha
t
facili
tate
know
ledg
e tra
nsfe
r. T
he a
uth
ors
pro
pose
a s
et o
f co
nd
itio
ns tha
t p
rom
ote
kn
ow
ledge
tra
nsfe
r fo
r th
e d
iffe
ren
t netw
ork
typ
es.
Oh, Labia
nca &
Chung (
2006)
A m
ultile
vel m
odel of
gro
up s
ocia
l
capital
Gro
up
s (
intr
a/in
ter)
Gro
up S
ocia
l C
apita
l C
ond
uits;
Gro
up's
Socia
l C
apita
l R
esourc
es
Gro
up
Pe
rfo
rma
nce
; In
div
idua
l
Gro
wth
; S
atisfa
ctio
n
Th
e a
uth
ors
intr
od
uce
th
e c
once
pt of
gro
up s
ocia
l
ca
pita
l--t
he s
et o
f re
so
urc
es m
ade a
vaila
ble
to a
gro
up th
rou
gh m
em
bers
' so
cia
l re
latio
nship
s
within
th
e s
ocia
l str
uctu
re o
f th
e g
roup
and in
the
bro
ader
form
al a
nd
in
form
al str
uctu
re o
f th
e
org
aniz
atio
n. T
hey
arg
ue tha
t g
rea
ter
gro
up s
ocia
l
ca
pita
l le
ad
s to g
rea
ter
gro
up e
ffe
ctive
ness a
nd
diffe
ren
t co
nd
uits thro
ug
h w
hic
h r
eso
urc
es f
low
.
Table 2.1 Conceptual milestones in social capital research
28
Table 2.2 Summary of key empirical findings in social capital research
Au
tho
rsA
rtic
le T
itle
Em
pir
ical S
ett
ing
Lev
el o
f A
naly
sis
Meth
od
of
An
aly
sis
Ind
ep
en
den
t V
ari
ab
les
Dep
en
den
t V
ari
ab
les
Majo
r F
ind
ing
s a
nd
Co
nclu
sio
ns
Burt
(1997)
The c
ontingent valu
e o
f socia
l
capital
Quantita
tive f
ield
surv
ey
of
two
sam
ple
s:
(1)
170 m
ale
senio
r
managers
(A
merican
ele
ctr
onic
s c
om
ponents
in
1989)
for
baselin
e c
om
para
tive
data
; (2
) re
analy
sis
of
a
pre
vio
us "
bankin
g"
data
set
deta
iled in B
urt
(1992)
Indiv
idual
Data
colle
cte
d u
sin
g f
ield
surv
ey
instr
um
ent and n
etw
ork
based n
am
e-g
enera
tor;
Analy
sis
based o
n L
ogit
Pro
babili
ty E
stim
ate
s
Num
ber
of
Manag
erial P
eers
;
Com
petitive P
ressure
s a
mong
Peers
; Legitim
acy o
f P
ositio
n
Valu
e o
f an indiv
idual's
str
uctu
rally
defined s
ocia
l
capital
Str
uctu
ral ecolo
gy o
f socia
l capital describes the
valu
e o
f socia
l capital to
indiv
iduals
is c
ontingent
on the n
um
ber
of
people
doin
g the s
am
e w
ork
.
Info
rmation a
nd c
ontr
ol benefits
of
bridgin
g t
he
str
uctu
ral hole
s t
hat constitu
te s
ocia
l capital are
especia
lly v
alu
able
to those w
ith f
ew
peers
,
because these m
anagers
do n
ot have the
legitim
acy p
rovid
ed b
y n
um
ero
us p
eople
doin
g t
he
sam
e type o
f w
ork
.
Uzzi (1
997)
Socia
l str
uctu
re a
nd
com
petition in inte
rfirm
netw
ork
s: T
he p
ara
dox o
f
em
beddedness
Eth
nogra
phic
stu
dy o
f 23 b
ett
er-
dre
ss f
irm
s in the N
ew
York
City
appare
l in
dustr
y.
Org
aniz
ation
Data
colle
ction a
nd a
naly
sis
consis
tent w
ith g
rou
nded
theo
ry a
ppro
ach
.
Socia
l S
tructu
ral A
nte
cedents
;
Tru
st; F
ine-g
rain
ed
Info
rmatio
n; and J
oin
t P
roble
m-
solv
ing A
rrangem
ents
De
gre
e o
f E
mbeddedness
(Over
or
Under
Em
bedded in
Ne
twork
of
Firm
s)
Identifies t
he c
om
ponents
of
em
bedded
rela
tionship
s a
nd the d
evic
es b
y w
hic
h
em
beddedness s
hapes o
rganiz
ational outc
om
es.
Fin
din
gs s
uggest
that em
beddedness is a
logic
of
exchange that
pro
mote
s e
conom
ies o
f tim
e,
inte
gra
tive a
gre
em
ents
, and c
om
ple
x a
dapta
tion.
Em
beddedhess c
an m
ake f
irm
s v
uln
era
ble
to
exogenous s
hocks o
r in
sula
te them
fro
m
info
rmation t
hat beyond their n
etw
ork
.
Tsai &
Ghoshal (1
998)
Socia
l capital and v
alu
e
cre
ation: T
he r
ole
of
intr
afirm
netw
ork
s
Managers
(3 p
er
unit)
from
one
multin
ational ele
ctr
onic
s
com
pany
with 1
5 d
istinct
busin
ess u
nits w
ere
surv
eye
d
usin
g a
questionaire
consis
tent w
ith a
fie
ld s
tudy
appro
ach.
Intr
a-o
rganiz
ation; S
trate
gic
Busin
ess U
nit (
SB
U)
Con
vers
iona
l of
rela
tiona
l data
into
dya
dic
fro
m u
sin
g M
ultip
le
Regre
ssio
n Q
uadra
tic
Assig
nm
ent P
rocedure
s
(MR
QA
P),
follo
we
d b
y data
analy
sis
usin
g L
ISR
EL 8
str
uctu
ral e
quation m
odelin
g.
Socia
l C
apital; R
esourc
e
Exchan
ge a
nd C
om
bin
ation
Valu
e C
reation
The a
uth
ors
exam
ine t
he r
ela
tionship
s a
mong the
str
uctu
ral, r
ela
tional, a
nd c
ognitiv
e d
imensio
ns o
f
socia
l capital and b
etw
een those d
imensio
ns a
nd
patt
ern
s o
f re
sourc
e e
xchange a
nd p
roduct
innovation.
The s
tructu
ral dim
ensio
n a
nd the
rela
tional dim
ensio
n,
were
sig
nific
antly r
ela
ted to
the e
xte
nt
of
inte
runit r
esourc
e e
xchange, w
hic
h
had a
sig
nific
ant eff
ect
on p
roduct
innovation.
Hansen (
1999)
The s
earc
h-t
ransfe
r pro
ble
m:
The r
ole
of
weak tie
s in s
haring
know
ledge a
cro
ss o
rganiz
ation
subunits
Uses n
etw
ork
stu
dy o
f 120
pro
duct develo
pm
ent pro
jects
undert
aken b
y 41 d
ivis
ions in a
larg
e e
lectr
onic
s c
om
pany.
Pro
ject (B
usin
ess U
nit le
vel)
Data
first an
aly
zed u
sin
g
UC
INE
T IV
, fo
llow
ed b
y
inte
gra
tio
n into
haza
rd r
ate
models
, w
hic
h w
ere
ana
lyzed
usin
g M
axim
um
Lik
elih
ood
Estim
atio
n in the T
DA
sta
tistical pro
gra
m.
Inte
runit T
ie W
eakness;
Nonco
difie
d K
now
ledge;
Dependent K
now
ledge
Pro
ject C
om
ple
tion T
ime
This
paper
com
bin
es t
he c
oncept
of
weak tie
s
from
socia
l netw
ork
researc
h a
nd the n
otion o
f
com
ple
x k
now
ledge t
o e
xpla
in the r
ole
of
weak
ties in s
haring k
now
ledge. F
indin
gs s
how
that
weak inte
runit t
ies h
elp
a p
roje
ct
team
searc
h f
or
usefu
l know
ledge in o
ther
subunits b
ut
impede the
transfe
r of
com
ple
x k
now
ledge, w
hic
h r
equire
str
ong tie
s b
etw
een the p
art
ies. H
avin
g w
eak
inte
runit tie
s s
peeds u
p p
roje
cts
when k
now
ledge
is n
ot com
ple
x b
ut
slo
ws t
hem
dow
n w
hen
know
ledge is h
ighly
com
ple
x.
Levin
& C
ross (
2004)
The s
trength
s o
f w
eak t
ies y
ou
can tru
st: T
he m
edia
ting r
ole
of
trust in
eff
ective k
now
ledge
transfe
r
Tw
o-p
hase q
uantita
tive f
ield
surv
ey
data
colle
ction f
rom
118
respondents
work
ing o
n
pro
jects
and in p
roje
ct
team
s in
thre
e o
rganiz
ations a
cro
ss
thre
e s
ecto
rs (
Am
erican
Pharm
aceutical; B
ritish
Bankin
g;
Canadia
n O
il and
Gas).
Indiv
idua
l; D
yadic
Ord
inary
Least S
quare
s
Regre
ssio
n o
f data
colle
cte
d
sole
ly f
rom
the p
resp
ective o
f
the k
now
ledg
e s
eeker
Tie
Str
ength
; C
om
pete
nce-
based T
rust; B
ene
vole
nce-
based T
rust; T
acitness o
f
Know
ledge
Re
ceip
t of
Usefu
l K
now
ledge
The a
uth
ors
pro
pose a
nd t
est a m
odel of
dya
dic
know
ledge e
xchange. T
he lin
k b
etw
een s
trong
ties a
nd r
eceip
t of
usefu
l know
ledge w
as
media
ted b
y c
om
pete
nce-
and b
enevole
nce-
based t
rust. C
ontr
olli
ng f
or
the tw
o tru
stw
ort
hin
ess
dim
ensio
ns,
the s
tructu
ral benefit
of
weak t
ies
em
erg
ed. F
indin
gs s
uggests
weak t
ies p
rovid
e
access t
o n
onre
dundant in
form
ation.
Com
pete
nce-
based t
rust w
as im
port
ant fo
r th
e r
eceip
t of
tacit
know
ledge.
Mora
n (
2005)
Str
uctu
ral vs. re
lational
em
beddedness: S
ocia
l capital
and m
anagerial perf
orm
ance
Based o
n a
sam
ple
of
120
pro
duct and s
ale
s m
anagers
in
a F
ort
une 1
00 p
harm
aceutical
firm
.
Indiv
idual
Da
ta a
naly
ze
d u
sin
g S
TA
TA
6.0
multiv
ariate
re
gre
ssio
n to
test hyp
oth
eses.
Socia
l C
apital
Manag
erial S
ale
s
Perf
orm
ance; M
anageria
l
Innovation P
erf
orm
ance
This
paper
exam
ines t
he im
pact of
socia
l capital
on m
anagerial perf
orm
ance. T
wo d
imensio
ns o
f
socia
l capital are
com
pare
d—
str
uctu
ral
em
beddedness a
nd r
ela
tional em
beddedness.
Evid
ence indic
ate
s b
oth
ele
ments
influence
managerial perf
orm
ance. S
tructu
ral pla
ys a
str
onger
role
in e
xpla
inin
g m
ore
routine, execution-
oriente
d tasks, w
here
as r
ela
tional pla
ys a
str
onger
role
in e
xpla
inin
g n
ew
, in
novation-o
riente
d tasks.
Balk
undi &
Harr
ison (
2006)
Tie
s, le
aders
, and tim
e in
team
s: S
trong infe
rence a
bout
netw
ork
str
uctu
re's
eff
ects
on
team
via
bili
ty a
nd p
erf
orm
ance
Meta
-analy
sis
conta
inin
g 3
7
stu
die
s w
ith 6
3 e
ffect siz
es
involv
ing 3
098 team
s.
Indiv
iduals
in G
roup
s; G
roup
Meta
-an
aly
sis
of
stu
die
s b
ase
d
on thre
e c
rite
ria: (1
) te
am
s o
f
adults in n
atu
ral w
ork
ing
environm
ent; (
2)
opera
tio
naliz
ed info
rmal
netw
ork
s u
sin
g s
ocia
l n
etw
ork
meth
odo
logy; (3
) o
utc
om
e
variab
le h
ad to b
e tea
m le
vel.
Eff
ect siz
es o
f: D
ensity-
Perf
orm
ance
Rela
tio
nship
;
Density-
Via
bili
ty R
ela
tionship
;
Tie
Conte
nt-
Team
Outc
om
e
Rela
tio
nship
; C
entr
alit
y-
Perf
orm
ance
Rela
tio
nship
;
Modera
tin
g E
ffects
of
Tim
e
Team
Task P
erf
orm
ance;
Team
Via
bili
ty
A m
eta
-analy
sis
of
team
s in n
atu
ral conte
xts
suggests
that te
am
s w
ith d
ensely
configure
d
inte
rpers
onal ties a
ttain
better
team
task
perf
orm
ance a
nd v
iabili
ty.
Team
s w
ith leaders
who a
re c
entr
al in
the t
eam
s’ in
tragro
up n
etw
ork
s
and team
s that are
centr
al in
their inte
rgro
up
netw
ork
tend to p
erf
orm
bett
er.
Team
s w
ith
densely
configure
d inte
rpers
onal ties a
ttain
better
team
task p
erf
orm
ance a
nd v
iabili
ty.
29
Table 2.3 Comparison of social capital approaches in organizational studies
Structural Holes Theory
Relational Network Theory
Integrated Social Capital Theory
Definition “Structural holes are thus an opportunity to broker the flow of information between people, and control the projects that bring people form opposite sides of the hole” (Burt, 2000: 353 emphasis in original).
“Features of social organization such as networks, norms, and social trust that facilitate coordination and cooperation for mutual benefit” (Putnam, 1995: 67).
“The sum of the actual and potential resources embedded within, available through, and derived from the network of relationships possessed by an individual or social unit” which thus “comprises both the network and the assets that may be mobilized through that network” (Nahapiet & Ghoshal, 1998: 243).
Emphasis Individual or focal actor as broker may benefit from relative position in a network, and linkage patterns among others.
System benefits attributable to a variably closed system of relations among nearby network actors.
Balances both individual and collective considerations, where brokerage and closure are equally relevant.
Level of Analysis
Generally individual or ego-centric, but can be applied at many levels based on researcher specification.
Whole network, but network may be conceptualized at many levels based on researcher specification.
Ego-centric or whole network are equally applicable at many levels based on researcher specification.
Constitution Emphasis on network structure and the structural embeddedness of among actors.
Emphasis on relational embeddedness and quality of relationships between actors.
Emphasis on structural, relational and cognitive embeddedness among actors.
Value Private value based on opportunity for focal actor to benefit from network position and brokerage of unique connections (tertius gaudens).
Public value based on mutuality of linkages creating shared access to information, influence, and social status.
Private and public value relevant, contingent on contextually-specific demands. Possible to consider from a variety of perspectives.
Configuration Network structure leads to unique patterns of association, creating network holes. Lack of structural equivalence allows actor to uniquely bridge gaps in network.
The content quality of linkages within the network structure creates closure, which results in bonding among actors in social network.
Integrates structural embeddedness as context, relational embeddedness as content of network, allowing consideration of both bridging and bonding activities.
30
The Constitution of Social Capital
“What constitutes social capital?” has been a hotly debated topic within
organizational studies (Adler & Kwon, 2002; Burt, 2000). Early writing addressing social
capital from a societal perspective suggested that it is a convertible currency accruing to
people with networks composed of strong multi-directional relationships which
developed over time, providing the basis for trust, cooperation, coordination and
collective action (Bourdieu, 1986). In the context of interpersonal networks, social
capital was seen as a credential; to be used in place of traditional forms of capital to
appropriate benefits among networks of relations and acquaintances (Bourdieu, 1986).
While removed from an organizational context, this early work continues to impact our
current conception of social capital, especially in contexts where organizations are seen
as communities or networks of practice (for a review of this field, consider Davenport &
Hall, 2002). Here, social capital is presumed to convey social status, demonstrate
reputation quality through membership or affiliation with high status others, and thus
provide benefits to the holder (Burt, 1992). Differences persist with respect to the scope
of what constitutes social capital, and how many dimensions should be considered
relevant (Adler & Kwon, 2002; Burt, 2000; Inkpen & Tsang, 2005; Nahapiet & Ghoshal,
1998); the integrative perspective assumed in this dissertation asserts that social capital
is constituted of structural, cognitive and relational embeddedness within and among
individuals and social units. Social capital is widely recognized as dualistic, reflecting the
quality of relationships among social collectives, while facilitating an actor’s actions and
31
their access to network resources (Wasko & Faraj, 2005). Recognizing that structural,
cognitive, and relational embeddedness contribute to the constitution of social capital is
a critical first step in understanding how social capital can come to influence the
emergence of organizational capabilities.
Addressing the Embeddedness of Relationships
Adopting a nuanced perspective that reflects not only the structure of
relationships but also the quality and content of the relationships within the structure
adds value by recognizing the importance of social interactions within organizations
(Antonacopoulou & Chiva, 2007; Feldman, 2004; Orlikowski, 2002; Orr, 1996). Some
would argue that social capital is largely a result of structural relationships among actors
(Burt, 1992, 1997); here however we assert that social capital is constituted of three
parts: structural embeddedness; cognitive embeddedness; and relational
embeddedness. In general, the unqualified concept of embeddedness is understood to
recognize the nesting of actors in collective patterns of social relations, set in a specific
organizational context (Baum & Dutton, 1996; Granovetter, 1985; Uzzi, 1997).
Structural embeddedness then, is concerned with the properties of social
systems and networks of relations as a whole (Granovetter, 1985), dealing particularly
with the impersonal configurations of linkages, or overall patterns of connections
between actors within a social network (Burt, 1992, 2000). The underlying assumption
here is that network structure captures the pattern of interactions between
32
organizational participants in the form of network ties, where network “ties serve as
conduits for the flow of interpersonal resources” (Balkundi & Harrison, 2006: 50). The
principal facets of structural embeddedness presume the presence of network ties
between actors (Wasserman & Faust, 1994), and concentrate on the morphology of
network patterns in terms of network density, network connectivity and linkages that
span levels of hierarchy (Tichy & Fombrun, 1979). Network density refers to the ratio of
network connections present in proportion to the potential number of possibilities
(Labianca & Brass, 2006; Marsden, 1990), and has been demonstrated to encourage
more frequent interaction, while simultaneously increasing information redundancy
among members of the network due to increasing interrelatedness (Balkundi &
Harrison, 2006; Labianca & Brass, 2006; Reagans & Zuckerman, 2001). Similarly, network
connectedness, which connotes “the extent to which members of the network are
linked to each other” (Tichy & Fombrun, 1979: 928), and network centrality – or the
extent to which the actor in a network is interconnected with other relationships in the
network (Raider & Krackhardt, 2002) – have been thought to improve access to valued
information and resources (Burt, 1997; Freeman, 1979; Hansen, 1999).
In addition, two important structural considerations that have recently received
considerable attention in the literature focus on issues of structural equivalency of
networks (Burt, 1997), and the potential for appropriable organization (Adler & Kwon,
2002; Coleman, 1988). Structural equivalence speaks to the extent to which network
members reside in similar functional or network positions (Brass, Galaskiewicz, Greve, &
33
Tsai, 2004), which can be seen to influence attitude formation and contagion among
equivalent members (Burt, 1992). As a result, structural equivalence has been shown to
increase confirmatory or redundant information benefits (Burt, 1997). The concept of
appropriable organization refers to the potential for network members to utilize
structural connections as transmission channels for a purpose for which they were not
intentionally created (Adler & Kwon, 2002; Coleman, 1988). Although theoretically
distinct, both cases have been implicated in increasing information transmission and
knowledge sharing among members and across networks (Balkundi, Barsness, &
Michael, 2009; Balkundi & Harrison, 2006; Burt, 1997).
The nature of cognitive embeddedness has largely gone unspecified in the social
capital literature. In fact, despite including a cognitive dimension in their tripartite
conception of social capital, Nahapiet and Ghoshal (1998) avoided using or defining the
term cognitive embeddedness; although they were clear in considering the implications
of structural and relational embeddedness. This may be the result of Zuckin and
DiMaggio’s (1990) definition of cognitive embeddedness as "the ways in which the
structured regularities of mental processes limit the exercise of economic reasoning".
Here, our intent is similarly focused on the patterned shaping of mental processes, with
a continued assumption of bounded rationality, but more so with respect to
situationally-specific context rather than on the exercise of economic reasoning. In this
context, cognitive embeddedness refers to the extent to which an individual is
entrenched in a collective cognitive schema, present between individuals in a social
34
network. The nature of cognitive embeddedness is akin to the idea of a collective mind
which is “manifest when individuals construct mutually shared fields … that emerges
during the interrelating of an activity system” (Weick & Roberts, 1993: 365); or the
notion of a collectively-held knowledge code which “affects the beliefs of individuals,
even while it is being affected by those beliefs” (March, 1991: 75). Invoking the term
habitus, Bourdieu (1990) recognized the presence of collective meaning systems or
shared patterns of interpretation shaping preferences, perceived opportunities, and
decisions, which mediates the relationship between structures and practices. To the
degree that individuals share a similar habitus, they could be presumed to see the same
world.
The application of social capital theory to organizational settings has tended to
focus more exclusively on the role of social resources in providing shared
representations and systems of meaning (Nahapiet & Ghoshal, 1998: citing Cicourel
1973); shared narratives within communities and networks of practice (Brown & Duguid,
2000; Orr, 1996; Wasko & Faraj, 2005); and the potential for routines to serve as
repertoires for collective practice or repositories of shared knowledge from trial-and-
error learning outcomes (Nelson & Winter, 1982; Winter, 2000). Understanding that the
collective knowledge embedded in social and organizational practices resides within
practice-based and tacit experiences enacted as collective action (Brown & Duguid,
2000), reflects the notion that “we can know more than we can tell” (Polanyi, 1966: 4
emphasis in original). Building on the social interaction perspective of situated learning,
35
organizational knowing is not a static embedded capability or stable disposition of
actors, but rather an ongoing social accomplishment, constituted and reconstituted as
actors engage the world in practice (Orlikowski, 2002: 257). But practice is not mindless;
it focuses on knowing rather than knowledge, and agency within the structure of
organization, cognitive embeddedness reflects knowledge that is held by individuals, but
is also expressed in regularities by members who cooperate in a social network (Kogut &
Zander, 1992). At one extreme, over-embeddedness takes on the qualities of groupthink
(Janis, 1982) or collective myopia in which informational and cognitive diversity are
quashed and collective schema are shared precisely (Branzei & Fredette, 2008); at the
other extreme, similarities are sparse with potential dysfunction resulting from
incommensurate languages, codes, or mutual understanding (Denrell & March, 2001;
Kogut & Zander, 1996; March, 1991; Snyder & Cummings, 1998). Cognitive
embeddedness then, is the extent to which a collective cognitive schema is present
between individuals in a social network.
Relational embeddedness describes the assets created and leveraged through
relationships, reflecting a behavioral rather than structural orientation (Nahapiet &
Ghoshal, 1998). Whereas a structural perspective emphasizes the interaction or
information advantages derived from network location or position, a relational view
highlights the assets embedded within each relationship, such as trust and
trustworthiness (Tsai & Ghoshal, 1998; Uzzi, 1996, 1997) or social solidarity (Adler &
Kwon, 2002; Sandefur & Laumann, 1998). Relational embeddedness is understood to
36
illustrate the quality of “personal relationships people have developed with each other
through a history of interactions” (Nahapiet & Ghoshal, 1998: 244).
Although factors such as identification and social status (Ibarra et al., 2005) are
certainly complementary to the our definition of relational embeddedness, the primary
facets that have been considered in the literature are: trust and trustworthiness
(Fukuyama, 1995; Putnam, 1995); social obligations and expectations (Burt, 1992;
Coleman, 1988; Granovetter, 1985); and collective norms and sanctions (Coleman, 1990;
Putnam, 1995), which collectively shape the quality of relationships within a network
community. Among these characteristics of the relational dimension of social capital,
trust and trustworthiness that develop over time through a history of interaction are
perhaps paramount, because they both constrain opportunistic behavior and reduce
monitoring costs (Granovetter, 1985; Tsai, 2000). At an inter-organizational level,
trustworthiness has been suggested to overcome forms of market inefficiency where it
“facilitated the exchange of resources and information that are crucial for high
performance but are difficult to value and transfer via market ties” (Uzzi, 1996: 678).
While the exact definition and operationalization of trust remains contested in the
literature, the general notion that trust deepens relationships by reducing risk and
uncertainty through familiarity, closeness, and a history of well-intentioned social
interactions, has demonstrated the powerful contribution of social capital’s relational
dimension (Ferrin, Dirks, & Shah, 2006; Leana & Pil, 2006; Levin & Cross, 2004; Moran,
2005; Nahapiet & Ghoshal, 1998; Tsai & Ghoshal, 1998; Uzzi, 1997).
37
While differences persist with respect to the scope of what constitutes social capital and
the substance of the relevant dimensions (Adler & Kwon, 2002; Burt, 2000; Inkpen &
Tsang, 2005; Nahapiet & Ghoshal, 1998), this research study, as noted in the
introduction, is based on an integrative perspective which asserts that social capital is
constituted of structural, cognitive and relational embeddedness within and among
individuals and social units. Doing so affords the opportunity to recognize all of the
‘actual and potential resources embedded within, available through, and derived from
the network of relationships possessed by an individual or social unit’ – a substantial
dimension of our definition of social capital. Recognizing that structural, cognitive, and
relational embeddedness each contributes to the constitution of social capital is a
necessary first step in understanding how social capital shapes the evolution of
organizational capabilities.
The Value of Social Capital
Social capital takes many forms, but most share two characteristics: first, that
social capital constitutes some aspect of a shared social structure; and second, that this
social structure facilitates action among the members within the social structure, that
would otherwise occur only at an additional cost if at all (Coleman, 1988). This notion of
individual action situated within an established social system is perhaps one of the
important contributions that social capital theorizing brings to organization studies
38
(Adler & Kwon, 2002; Burt, 1997; Inkpen & Tsang, 2005; Leana & Van Buren, 1999), as it
explicitly acknowledges the impact of an individual’s position, the social nature of work
processes and the structural boundaries present within any organizational context
(Brown & Duguid, 2000; Feldman & Pentland, 2003; Feldman & Rafaeli, 2002;
Orlikowski, 2002; Orr, 1996). However, the question of who benefits or to whom value
accrues is an ever-present consideration in the literature (Blyler & Coff, 2003; Burt,
1997; Coleman, 1988; Dhanaraj & Parkhe, 2006; Fleming & Waguespack, 2007; Lin,
1999; Portes, 1998; Shaw et al., 2005; Westphal & Stern, 2006; Xiao & Tsui, 2007).
Private, Public or Shared Good
While Bourdieu (1986: 249), speaking of the collective value of social capital
asserted that it provided members with “a ‘credential’ which entitles them to credit, in
the various senses of the word”, others have more limitedly focused on the ability to
extract benefits valued by the individual, in the form of personal and career
advancement (Balkundi & Harrison, 2006; Burt, 1997; Burt, Hogarth, & Michaud, 2000;
Westphal & Stern, 2006), or rent appropriation (Blyler & Coff, 2003) for example. The
issue here stems from the distinction between whether social capital is a privately held
commodity used for the benefit (or detriment) of the individual, or whether it is
construed as collectively held, residing in the linkages between – and patterns of social
interactions among – network members, with benefits accruing collectively (Burt, 2000;
Coleman, 1988; Lin, 1999; Portes, 1998; Uzzi, 1997). The distinction between whether
39
social capital contributes to public and private value creation (in the broad sense) is
largely a matter of perspective, where the level of analysis or choice of dependent
variable tends to shape the way in which value is construed5. Identifying the benefits
and risks of reliance on social capital in the forms of information, influence and social
solidarity, Adler & Kwon (2002: 35) note that “[a]lthough the mechanics of research are
simplified by restricting ourselves to a single level of analysis, the reality of organizations
is shaped by the constant interplay of the individual, group, business unit, corporate,
and inter-firm levels. Many of the phenomena we study as organizational researchers
involve both forms of social capital simultaneously.” The flexibility to recognize multiple
perspectives of value is a fundamental contribution of the integrative approach to social
capital, as it facilitates consideration of shared value, where the private and public value
of social capital are equally relevant, although contingent on contextually-specific
demands and circumstances (Adler & Kwon, 2002; Burt, 2000; Hansen, 1999, 2002;
Inkpen & Tsang, 2005; Maurer & Ebers, 2006; Nahapiet & Ghoshal, 1998; Tsai, 2000,
2002; Tsai & Ghoshal, 1998). This unified perspective is an essential dimension of an
integrative approach to understanding how social capital contributes to the emergence
of organizational capabilities.
5 In this regard consider the implications of a situation in which an individual with many weak ties brings
much needed and otherwise unavailable information to bear on a group project. Here we may be able to examine the individual benefits (private) accruing to status and career advancement, however it is equally likely that we may witness group learning (collective) as new knowledge is received and integrated, or even project-level outcomes (public) in terms of innovation success. All may be equally present, yet the focus on specific outcomes (or dependent variables) tends to censor the ways in which value is construed.
40
The Configuration of Social Capital – Does Configuration Matter?
A final point of differentiation in the organizational social capital literature
relates to the configuration or architecture of the social networks inhabited by
organization members. While two distinct theories proposing fundamentally divergent
conceptions of network architecture have taken hold – the first emphasizing structural
holes (Burt, 1992, 1997) in the network and the other emphasizing network closure
(Coleman, 1988; Portes, 1998) – there is growing recognition that these may be
complementary rather than incommensurable (Adler & Kwon, 2002; Burt, 2000;
Nahapiet & Ghoshal, 1998). Moreover, growing reconciliation of the complementarities
of the two has lead authors to consider the importance of a unified perspective such
that “closure provides social capital’s cohesiveness benefits within an organization or
community; structural holes in the focal actor’s external linkages provide cost-effective
resources for competitive action” (Adler & Kwon, 2002: 25), where the value of
configuration is contingent on the task and contextual environment confronting the
actor.
A contextually-specific or a situated approach, where the implications of social
capital are contingent on the complementarity among actors, network configuration,
and task environment is well suited to this research project because it facilitates the
incorporation of situation-specific considerations which add richness and realism to the
organizational context under study. In this regard, four primary considerations have
been demonstrated to characterize interactions in organizational contexts (Nahapiet &
41
Ghoshal, 1998). The first among these is the impact of time, where social capital
depends on stability and continuity of social structure (Bourdieu, 1986; Coleman, 1988).
Second, interdependence among actors is essential. Under conditions characterized by
mutual interdependence the importance of social capital is driven up, while erosion is
believed to occur as dependence declines (Coleman, 1990). A third factor is social
interaction, where social capital requires continued social interaction among tied-
partners or maintenance of relationships to maintain the quality of network ties. Here,
social interaction is a precondition for development and maintenance of dense, strong
social capital (Adler & Kwon, 2002; Bourdieu, 1986), regardless of whether ties are
considered predominantly instrumental (task oriented), expressive (socially supportive),
or comprehensive in nature (Oh et al., 2004). Closure – understood to mean the extent
to which network members’ contacts are themselves connected – a final characteristic
of formal organizations, has been suggested to improve the cognitive and relational
dimensions of social capital by bonding the members to one another with strong
relationship ties within densely configured networks (Adler & Kwon, 2002; Coleman,
1988; Oh et al., 2006). Within an organizational context closure is naturally occurring,
based primarily on membership within the socially constructed boundaries of the
organization (Kogut & Zander, 1996), but may also occur across organizational
boundaries based on structural embeddedness and common strong ties among actors
(Uzzi, 1997). Although distinct in nature, each of these four characteristics illustrates the
42
need to undertake research from a situated perspective, where fit between
organizational context and social capital configuration is emphasized.
Bridging Ties, Bonding Ties or Both
The utility of social capital is contingent on the fit between context factors and
its configuration within the organization. The ability to benefit from potential increases
in the efficiency of action, such as improved information transfer and diffusion through
weak ties (Burt, 1992), personal and career advancement (Burt et al., 2000;
Granovetter, 1985; Westphal & Stern, 2006), or instrumental advice-seeking (Cross &
Sproull, 2004; Nebus, 2006), are representative of the bridging configuration
perspective (Burt, 2000; Oh et al., 2006). In contrast, the ability to benefit from
increases in the effectiveness of action, such as through cooperative behaviors that
support coordinated or improvised learning (Vera & Crossan, 2005), innovation (Grant,
1996; Kogut & Zander, 1992), or rich information exchange (Hansen, 1999, 2002), is
deeply reflective of the bonding configuration perspective (Burt, 2000; Oh et al., 2006)6.
Given the divergence between the perspectives, in combination with the recognition
that both types of relationships are generally needed within organizations, it is not
surprising to see reconciliation of the two views developing in the field (Nahapiet &
Ghoshal, 1998; Oh et al., 2004; Oh et al., 2006). Some have even gone so far as to
6 Many in depth reviews of the construal of bonding (closure) and bridging (brokerage) relationships have
been constructed from a variety of perspectives. For a more thorough review consider any of these examples (Burt, 2000; Lin, 1999; Davenport & Snyder, 2005; Portes, 1998; Raider & Krackhardt, 2002).
43
endorse the value of “dual network” architectures in which weak external ties are
bridged with strongly bonded internal ties to support innovation capacity (Capaldo,
2007).
Similarly, Oh et al. have suggested that organizations could achieve an optimal
network configuration, where “[t]he optimal [configuration] profile would be a group
where there is moderate closure within the group, group’s formal and informal
leadership roles are either fulfilled by the same person or by closely connected
individuals, the formal leader has close connections to each of the group’s subgroups,
various group members have nonredundant external ties to a diverse range of other
groups in the organization, and members have ties to influential dominant coalition
members whom they can count on for political support as needed” (2006: 578).
Consistent with Oh et al. (2006), the assertion here is that integrating both the bridging
and bonding perspectives is necessary to properly capture the implications of structural,
cognitive, and relational embeddedness, reflecting the value of configuration as
contingent on the task and contextual environment confronting the actor. In contrast
however, we avoid asserting or endorsing an archetypal configuration, and instead
suggest that an optimal configuration of ties will result as a product of perfect fit
between organizational context and the social capital configuration7.
7 It would seem here that a claim of “optimal configuration profile” presumes a static environment, and a
retrospective point of view. Since these are neither present nor relevant to the underlying pragmatic or situated perspective in which we have already underscored the implications of time, interdependence, social interaction, and closure, this point of departure is explicitly recognized.
44
Implications of Findings
The literature reviewed in this section spans nearly 25 years, and covers a wide
variety of perspectives and orientations. Its relevance however, has been established by
demonstrating how this varied body of literature contributes to an integrated theory of
social capital in organizations, a small but growing body of research. Differences in
terms of the constitution, value, and configuration of social capital lead to the assertion
that an integrative approach affords an enlarged recognition of social capital within an
organizational context, as it explicitly recognizes the implications of both network
structure and relationship quality. Further, the inclusion of cognitive considerations
adds an important degree of richness overlooked by alternate approaches. In
conclusion, the combined implications of structural, cognitive, and relational
embeddedness capture an awareness of the potential variations in the complexity of
social interactions between individuals and within social systems. The notion that the
constitution, value, and configuration of social capital is nested within contextually
specific settings, demonstrates the importance of adopting a pragmatic or situated
perspective to better understand the implications of social capital emergence on the
evolution of organizational capabilities.
Organizational Capabilities: An Organizational Theory Approach
As noted earlier, an organizational capability is defined here as a “collections of
routines that, together with their implementing input flows, confer upon an
45
organization’s management a set of decision options for producing significant outputs of
a particular type” (Winter, 2003: 991), which at their essence are “socially complex
routines that determine the efficiency with which firms physically transform inputs into
outputs” (Collis, 1994: 145). The advent of organizational capabilities was born of the
evolutionary economic theory perspective first articulated by Nelson and Winter (1982).
Although early variations in how the concept of capabilities was defined and used
served to obscure the exact nature of the term – often using it interchangeably with
routines, skills and capacities for action at various levels of analysis (Amit & Schoemaker,
1993; Cohen et al., 1996; Nelson & Winter, 1982) – more contemporary work in the field
has focused on delineating and differentiating organizational capabilities from other
forms of skill, routine, or collective action (Becker, Lazaric, Nelson, & Winter, 2005; Dosi
et al., 2001).
The literature introduced in support of this dissertation focuses explicitly on the
micro-foundations of organizational capabilities, dynamic and otherwise; recognizing
that the notions of individual skills and organizational routines are complementary,
organizational capabilities are argued to be distinct in nature, scope and impact on
organization performance outcomes as captured in our definition. For the purpose of
this research, routines pertain exclusively to the individual level of analysis, while
capabilities are conceptualized at the level of the group or social collective (Helfat et al.,
2007; Helfat & Peteraf, 2003). Here, the organizational value of routines and capabilities
– the former performed by the individual and the latter performed in combination by
46
groups and social collectives (Nelson & Winter, 1982) – rests predominantly on their
ability to shape, direct, and organize the behaviors of organizational participants in a
deliberate, consistent and predictable manner (Danneels, 2002; Dosi et al., 2001;
Winter, 2003). The notion that theories dealing with the development of organizational
capabilities ought to be grounded in a micro-foundational understanding is a broad and
relatively new assertion (Dosi & Marengo, 2007; Salvato, 2003, 2009; Teece, 2007).
Here, the research question articulated in this dissertation, ‘How does the emergence of
social capital influence the evolution of organizational capabilities?’ seeks to sharpen
focus on the social and relational micro-foundation of organizational capabilities by
emphasizing the role of structural, cognitive, and relational resources in the evolution of
organizational capabilities. In order to address the issue of organizational capability
change, we introduce results from only the most relevant conceptual and empirical
investigations with the intent of illustrating both the current state of knowledge and the
need for further study in the field. We begin with the origins of organizational
capabilities, but move quickly to more contemporary themes.
Origins of Capability Literature
Early explanations for the existence of organizational capabilities – which
defined organizational capabilities as residing in “the collection of individual members’
repertoires” of routines “associated with the possession of particular collections of”
resources (Nelson & Winter, 1982: 103) – suggested that organizational capabilities are
47
heavily influenced by the past acquisition of resources and accumulation of practice
(Amit & Schoemaker, 1993). Authors adopting this point of view have asserted that
influential patterns of dependence evolve over organizational lifetimes in three primary
domains: organizational position, organizational paths, and organizational processes,
each of which is rooted in the decisions of its past (Dierickx & Cool, 1989; Nelson &
Winter, 1982; Teece et al., 1997). While organizational position has largely been
considered to reflect the combination of internal factors (asset or resource endowment)
and external environmental factors (specified market position), Teece et al. (1997: 518)
sharpened the distinction to focus on “the current specific endowments of technology,
intellectual property, complementary assets, customer base, and its external relations
with suppliers and complementors”. The position, then, that an organization has
assumed and the asset portfolio in which it has invested, constrain the range of
opportunities that it may viably pursue going forward (Helfat & Lieberman, 2002).
In contrast, dependence in terms of organizational paths and processes reflect
an internal consistency in the evolution of current practices, resulting from the
accumulation and reinforcement of past ones (Cohen & Levinthal, 1990; Makadok,
2001). Organizational paths, then, “refer to the strategic alternatives available to the
firm” where dependence is illustrated by “presence or absence of increasing returns and
attendant path dependencies” (Teece et al., 1997: 518). The notion of path dependence
acknowledges that the set of options available to the firm today are largely dependent
on the ‘capability trajectory’ established at prior points in time (Dierickx & Cool, 1989;
48
Helfat & Raubitschek, 2000; Teece & Pisano, 1994). From the perspective of change,
path dependence can be seen as a constraint imposed on the ability to rapidly alter
course or adapt to new developments in the environments (Cohen & Levinthal, 1990;
Eisenhardt & Martin, 2000; Leonard-Barton, 1992); however, path dependence may well
support organizational coherence, providing internal stability and incremental or
exploitative learning improvements over time (Karim & Mitchell, 2000; March, 1991;
Teece, Rumelt, Dosi, & Winter, 1994). Thus, while differences remain with respect to the
qualitative implications of path dependence, it is clear that current and future
capabilities “are imprinted by past decisions and their underlying patterns” (Schreyögg
& Kliesch-Eberl, 2007: 916). In contrast, organizational processes have been referred to
as reflecting the vague notion of “the way things are done in the firm” (Teece et al.,
1997: 518), where process dependence may result from the accumulation of experience
(Zollo & Winter, 2002), the investment in supporting coordination systems and
technology (Ethiraj et al., 2005; Montealegre, 2002; Schreyögg & Kliesch-Eberl, 2007), or
internal organizational inertia (Cohen & Levinthal, 1990; Leonard-Barton, 1992; Tripsas
& Gavetti, 2000). In combination, this triumvirate suggests that the evolutionary
trajectory of organizational capabilities is essentially constrained – although not
exclusively predetermined – by the decisions of the past, thereby emphasizing the
tendency for capabilities to persist and the importance of historical context, especially
with respect to lineage during organizational founding (Helfat & Lieberman, 2002;
Tripsas, 2009).
49
Here, the notion of organizational capabilities (as described earlier) is important
because it recognizes the specific content embodied in the concept, as well as the level
of analysis at which it operates. Addressing the level of analysis, capabilities have been
delineated as residing at collective levels, occurring among groups or teams, but
embedded in the social and structural framework of the unit, organization, or network
(Helfat et al., 2007; Helfat & Peteraf, 2003; Kogut & Zander, 1992; Nelson & Winter,
1982; Zollo & Winter, 2002). This contextualized view of organizational capabilities as
embedded in the social and structural fabric of the organization has been reinforced;
capabilities are conceived as developed in the context of resource allocation, but at
their core, capabilities are seen to be “distinct behavioral patterns, which are complex in
nature involving both formal and informal processes” (their emphasis Schreyögg &
Kliesch-Eberl, 2007: 914). The framing of capabilities as position, path, and process
dependent collections of resource and routine combinations embedded in complex
patterns of social interaction is powerful in understanding not only the essence of an
organizational capability at a point in time and why it persists, but also how its current
form was achieved, both much needed in moving theory development forward (Arend &
Bromiley, 2009; Helfat & Peteraf, 2009).
In the recently introduced capability lifecycle theory (Helfat & Peteraf, 2003), the
authors offer a nuanced addition to the perspective of dependence stemming from
organizational position, path, and processes. They attribute the creation of new
capabilities to either a process involving the rejuvenation of established capabilities by
50
‘branching’ or evolving into new ones through the reconfiguration of existing resources
and processes echoing the evolutionary theory’s emphasis on the importance of
‘position’ in shaping path dependence (Nelson & Winter, 1982; Teece & Pisano, 1994);
or alternatively, through a process of emergence in which new capabilities arise from
systematic patterns of practice largely dependent on human, social and structural
capital endowments (Helfat & Peteraf, 2003). While the theory has yet to be supported
or refuted with empirical evidence, the conceptual arguments underscore the
importance of examining the social resources which influence the micro-foundations of
organizational capabilities. In addition, they emphasize the importance of future studies
examining the influence of social capital during the emergence, development and
maturity of organizational capability evolution (Helfat & Peteraf, 2003).
Similarly, Adner and Helfat (2003) more explicitly consider the potential of social
capital during the building of organizational capabilities. Adopting the position that
social capital is but one of many resources allocated by the organization during the
founding of strategic business units, the authors argue that early social capital allocation
decisions impact the emergence of ‘dynamic managerial capabilities’ by facilitating or
inhibiting both the acquisition of information and resources, and the ability to exercise
influence (Adner & Helfat, 2003; Peteraf & Reed, 2007). Collectively, these arguments
suggest that the strength and content of the socio-relational factors, through their
collective influence on the micro-foundations of organizational capabilities, matter in
terms of capability evolution. However, despite recognizing that the strength and
51
content matter, relatively little is understood about the characteristics of the social
resources that lie at the core of coordinated social interaction and collaborative
performance, the micro-foundations of capability change.
Capabilities Research – Dynamic and Otherwise – in Organizations
The distinction between organizational capabilities and the growing movement
toward the study of dynamic capabilities is worth noting at this point (Arend & Bromiley,
2009; Helfat & Peteraf, 2009). Among a number of other similar variations, dynamic
capabilities have been defined in the literature as “the firm’s ability to integrate, build,
and reconfigure internal and external competences to address rapidly changing
environments” (Teece et al., 1997: 516). The key point of departure between the two
constructs centers on whether dynamic capabilities are simply a higher order form of
organizational capability (Collis, 1994; Danneels, 2002; Schreyögg & Kliesch-Eberl, 2007;
Winter, 2000, 2003), or a qualitatively distinct entity characterized as “[t]he firm’s
processes that use resources – specifically the processes to integrate, reconfigure, gain
and release resources – to match and even create market change. Dynamic capabilities
thus are the organizational and strategic routines by which firms achieve new resource
configurations as markets emerge, collide, split, evolve, and die” as others have
described (Eisenhardt & Martin, 2000: 1107). In recognition of the lack of a coherent
‘dynamic capabilities theory’ (Helfat et al., 2007; Helfat & Peteraf, 2003, 2009; Winter,
2003), we acknowledge that the nature of capability change and the dynamics involved
52
in that change remain hotly contested issues. This research project takes the
evolutionary perspective to examine the endogenous dynamics of change from within a
‘regular’ organizational capability rather than looking for change in the form of episodic
intervention (i.e. higher order capabilities that change operating capabilities). For the
purposes of this dissertation, it is enough to recognize the current inconsistency in the
literature and suggest that in terms of the evolution of newly emerged organizational
capabilities, little distinction between the two has been addressed in the literature. This
lack of differentiation with respect to the evolution of each form of capability is
reflected in the inclusion of literature summary tables (Table 2-4 and Table 2-5
emphasizing conceptual and empirical progress respectively) which illustrate the
introduction of dynamic capabilities as a progressive subcomponent of the greater
organizational capabilities field.
Micro-Foundations of Capabilities Evolution
Returning, for a minute, to the early works in which the relationship between
organizational capabilities and performance was portrayed as “based on developing,
carrying, and exchanging information through the firm’s human capital” (Amit &
Schoemaker, 1993: 35; Makadok, 2001), provides a well-founded rationale for
encouraging further investigation of the social micro-foundations which underpin
capability change. Similarly, when performance is seen to be reliant on coordination,
where “central to coordination is that individual members, knowing their jobs, correctly
53
interpret and respond” to circumstances in the environment (Nelson & Winter, 1982:
104), social micro-foundational factors are again at the root of organizational capability
evolution. Given the prominence of collaborative-coordinated-collective action in the
conceptual, theoretical and operational understanding of organizational capabilities,
would it not appear that socio-relational interactions are a necessary condition for
capability emergence?
In the portion of the literature focused on the inter-workings of organizations
and their processes, there are perhaps no more pressingly relevant issues to understand
than the relationship between social interactions, collaborative practicing, and collective
performance (Antonacopoulou & Chiva, 2007; Feldman & Pentland, 2003; Howard-
Grenville, 2005; March, 1991; Weick, 1998; Winter, 2000). Although frequently
acknowledged in conceptual thinking as well as in the discussion of empirical results (for
example Blyler & Coff, 2003; Capaldo, 2007; Gavetti, 2005; Gulati & Puranam, 2009;
Haas & Hansen, 2005; Jones et al., 1998), explicit examination of the social micro-
foundations of capability evolution has all-to-frequently been neglected (Adner &
Helfat, 2003; Dosi & Marengo, 2007; Helfat & Peteraf, 2003; Tsai, 2002). In spite of this
neglect, a growing movement in the routine practicing and performativity literature has
taken up the study of socio-relational and socio-structural interactions in relation to
performance (Feldman & Rafaeli, 2002; Gersick & Hackman, 1990; Pentland & Feldman,
2005; Vera & Crossan, 2005; Visser, 2007), a micro-foundation in the development and
stabilization of organizational capabilities (Gavetti, 2005; Nelson & Winter, 1982).
54
Au
tho
rsA
rtic
le T
itle
Lev
el o
f A
naly
sis
Ind
ep
en
den
t V
ari
ab
les
Dep
en
den
t V
ari
ab
les
Majo
r F
ind
ing
s a
nd
Co
nclu
sio
ns
Nels
on &
Win
ter
(1982)
An e
volu
tionary
theory
of
econom
ic
change
Indiv
idual; G
roup; O
rganiz
ation
Sem
inal w
ork
in the f
ield
of
org
aniz
ational
evolu
tionary
theory
. E
sta
blis
hes the f
orm
ative
definitio
ns o
f skill
s, ro
utines, re
pert
oires, and
org
aniz
ational capabili
ties.
Kogut &
Zander
(1992)
Know
ledge o
f th
e f
irm
, com
bin
ative
capabili
ties, and the r
eplic
ation o
f
know
ledge
Indiv
idual; O
rganiz
ation
Org
aniz
ational K
now
ledge;
Com
bin
ative C
apabili
ties
Org
aniz
ing a
nd T
echnolo
gic
al
Opport
unitie
s
This
art
icle
arg
ues that w
hat firm
s d
o b
etter
than
mark
ets
is the s
haring a
nd tra
nsfe
r of
the
know
ledge o
f in
div
iduals
and g
roups w
ithin
an
org
aniz
ation. T
his
know
ledge c
onsis
ts o
f
info
rmation a
nd o
f know
-how
. C
entr
al to
our
arg
um
ent is
that know
ledge is h
eld
by
indiv
iduals
,
but als
o e
xpre
ssed in r
egula
rities b
y w
hic
h
mem
bers
coopera
te in a
com
munity.
Because
new
ways
of
coopera
ting c
annot be e
asily
acquired, gro
wth
occurs
by
build
ing o
n the s
ocia
l
rela
tionship
s that exis
t in
a f
irm
.
Am
it &
Schoem
aker
(1993)
Str
ate
gic
assets
and o
rganiz
ational
rent
Org
aniz
ation
Resourc
es; C
apabili
ties; S
trate
gic
Assets
; and S
trate
gy
Industr
y F
acto
rsR
ents
due to S
trate
gic
Assets
Vie
ws the f
irm
as a
bundle
of
resourc
es a
nd
capabili
ties, and e
xam
ines c
onditio
ns that
contr
ibute
to s
usta
inable
econom
ic r
ents
. F
irm
s
diffe
r in
resourc
es a
nd c
apabili
ties they
contr
ol.
Rent ste
ms f
rom
im
perf
ect and d
iscre
tionary
decis
ions to d
evelo
p a
nd d
eplo
y sele
cte
d
resourc
es a
nd c
apabili
ties.
Teece &
Pis
ano (
1994)
The d
ynam
ic c
apabili
ties o
f firm
s: A
n
intr
oduction
Indiv
idual; O
rganiz
ation;
Inte
rorg
aniz
ational
Em
phasiz
es the k
ey
role
of
managem
ent in
appro
priate
ly a
dapting, in
tegra
ting a
nd r
e-
configuring inte
rnal and e
xte
rnal org
aniz
ational
skill
s, re
sourc
es a
nd f
unctional com
pete
ncie
s
tow
ard
the c
hangin
g e
nvironm
ent. A
pra
gm
atic
appro
ach to a
pro
cess-b
ased p
ers
pective o
f
learn
ing, change a
nd s
tagnation in term
s o
f
com
petitive a
dvanta
ge.
Teece, P
isano &
Shuen (
1997)
Dynam
ic c
apabili
ties a
nd s
trate
gic
managem
ent
Org
aniz
ation
Org
aniz
ational P
ositio
n;
Org
aniz
ational P
ath
s; O
rganiz
ational
Pro
cesses
Dynam
ic C
apabili
ties
The c
om
petitive a
dvanta
ge o
f firm
s is r
ests
on
dis
tinctive p
rocesses o
r w
ays
of
coord
inating a
nd
com
bin
ing, shaped b
y th
e f
irm
's s
pecific
asset
positio
ns, and the e
volu
tion p
ath
(s)
it h
as a
dopte
d
or
inherite
d. T
he f
ram
ew
ork
suggests
that w
ealth
cre
ation in r
egim
es o
f ra
pid
technolo
gic
al change
depends o
n h
onin
g inte
rnal te
chnolo
gic
al,
org
aniz
ational, a
nd m
anagerial pro
cesses insid
e
the f
irm
.
Eis
enhard
t &
Mart
in (
2000)
Dynam
ic c
apabili
ties: W
hat are
they?
Org
aniz
ation
Dynam
ic c
apabili
ties a
re a
set of
specific
and
identifiable
pro
cesses. T
hey
are
idio
syn
cra
tic in
their d
eta
ils a
nd p
ath
dependent in
their
em
erg
ence. M
ore
hom
ogeneous, fu
ngib
le,
equifin
al, a
nd s
ubstitu
table
than is u
sually
assum
ed. In
modera
tely
dyn
am
ic m
ark
ets
, th
ey
are
deta
iled, analy
tic, sta
ble
pro
cesses w
ith
pre
dic
table
outc
om
es. In
hig
h-v
elo
city
mark
ets
,
they
are
sim
ple
, hig
hly
experiential and f
ragile
pro
cesses w
ith u
npre
dic
table
outc
om
es.
Helfat &
Pete
raf
(2003)
The d
ynam
ic r
esourc
e-b
ased v
iew
:
Capabili
ty lifecyc
les
Gro
up; O
rganiz
ation
Intr
oduces the c
oncept of
the c
apabili
ty lifecyc
le,
whic
h a
rtic
ula
tes g
enera
l pattern
s a
nd p
ath
s in the
evolu
tion o
f org
aniz
ational capabili
ties o
ver
tim
e.
Incorp
ora
tes f
oundin
g, develo
pm
ent, a
nd m
atu
rity
of
capabili
ties to h
elp
expla
in s
ourc
es o
f
hete
rogeneity
in c
apabili
ties. In
clu
des ‘bra
nchin
g’
of
an o
rigin
al capabili
ty into
severa
l possib
le
altere
d f
orm
s.
Win
ter
(2003)
Unders
tandin
g d
ynam
ic c
apabili
ties
Indiv
idual; O
rganiz
ation
Definin
g o
rdin
ary
or
zero
-level capabili
ties a
s
those that perm
it a
firm
to m
ake a
liv
ing in the
short
term
, one c
an d
efine d
ynam
ic c
apabili
ties a
s
those that opera
te to e
xte
nd, m
odify
or
cre
ate
ord
inary
capabili
ties. T
he s
ubsta
nce o
f capabili
ties
involv
es p
attern
ing o
f activity,
and c
ostly
investm
ents
are
typ
ically
required to c
reate
and
susta
in s
uch p
attern
ing.
Table 2.4 Conceptual milestones in the organization capabilities research
55
Au
tho
rsA
rtic
le T
itle
Em
pir
ical S
ett
ing
Lev
el o
f A
naly
sis
Ind
ep
en
den
t V
ari
ab
les
Dep
en
den
t V
ari
ab
les
Majo
r F
ind
ing
s a
nd
Co
nclu
sio
ns
Cohen &
Levin
thal (1
990)
Absorp
tive c
apacity:
A n
ew
pers
pective o
n learn
ing a
nd
innovation
Cro
ss-s
ectional field
surv
ey
data
colle
cte
d in the A
merican
manufa
ctu
ring s
ecto
r fr
om
R&
D lab m
anagers
and the
Federa
l T
rade C
om
mis
sio
n's
Lin
e o
f B
usin
ess P
rogra
m.
Org
aniz
ation
Technolo
gic
al O
pport
unity
Appro
priabili
ty; D
em
and
Conditio
ns
Researc
h a
nd D
evelo
pm
ent
Inte
nsity
A f
irm
's a
bsorp
tive c
apacity
is a
function o
f th
e
firm
's level of
prior
rela
ted k
now
ledge. A
uth
ors
arg
ue that th
e d
evelo
pm
ent of
absorp
tive
capacity,
and innovative p
erf
orm
ance a
re h
isto
ry-
or
path
-dependent. T
he a
uth
ors
form
ula
te a
nd
test a m
odel of
firm
investm
ent in
R&
D, in
whic
h
R&
D c
ontr
ibute
s to a
firm
's a
bsorp
tive c
apacity.
Leonard
-Bart
on (
1992)
Core
capabili
ties a
nd c
ore
rigid
itie
s: A
para
dox in
managin
g n
ew
pro
duct
develo
pm
ent
Tw
enty
case s
tudie
s o
f new
pro
duct and p
rocess
develo
pm
ent pro
jects
in f
ive
firm
s p
rovid
e illu
str
ative d
ata
.
Pro
ject
Core
Capabili
ties; C
ore
Rig
iditie
s; F
it o
f K
now
ledge S
et
Chara
cte
ristics
Ease o
f C
hange; M
anagerial
Response
This
paper
exam
ines the n
atu
re o
f th
e c
ore
capabili
ties o
f a f
irm
, fo
cusin
g o
n their inte
raction
with n
ew
pro
duct and p
rocess d
evelo
pm
ent
pro
jects
. C
ore
capabili
ties a
re tre
ate
d a
s c
luste
rs
of
dis
tinct te
chnic
al sys
tem
s, skill
s, and
managerial sys
tem
s, but core
capabili
ties h
ave a
dow
n s
ide that in
hib
its innovation, here
calle
d c
ore
rigid
itie
s.
Pis
ano (
1994)
Know
ledge, in
tegra
tion, and
the locus o
f le
arn
ing: A
n
em
piric
al analy
sis
of
pro
cess
develo
pm
ent
This
paper
uses d
ata
on 2
3
pro
cess d
evelo
pm
ent pro
jects
in p
harm
aceuticals
to e
xplo
re
the b
roader
issue o
f how
org
aniz
ations c
reate
,
imple
ment, a
nd r
eplic
ate
new
routines.
Pro
ject
Researc
h P
erc
enta
ge; P
ilot
Develo
pm
ent P
erc
enta
ge; P
ilot
Lead T
ime to P
roduction;
Inte
gra
ted O
rganiz
ational
Str
uctu
re
Ela
psed L
ead T
ime
The f
ram
ew
ork
suggests
that w
here
scie
ntific
know
ledge is s
uff
icie
ntly
str
ong, eff
ective learn
ing
may
take the f
orm
of
'learn
ing-b
efo
re-d
oin
g'.
The
data
indic
ate
that in
an e
nvironm
ent chara
cte
rized
by
deep theore
tical and p
ractical know
ledge o
f th
e
pro
cess technolo
gy-
more
em
phasis
on 'l
earn
ing-
befo
re-d
oin
g' i
s a
ssocia
ted w
ith m
ore
rapid
develo
pm
ent.
Hoopes &
Postr
el (1
999)
Share
d k
now
ledge, "g
litches",
and p
roduct develo
pm
ent
know
ledge
Longitudin
al qualit
ative d
ata
, in
the f
orm
of
arc
hiv
al and
inte
rvie
w d
ata
, w
as c
olle
cte
d
from
a tota
l of
44 m
em
bers
from
pro
gra
mm
ing a
nd
mark
eting d
epart
ments
of
a
hig
h tech f
irm
over
the c
ours
e
of
a tw
o y
ear
period.
Pro
ject
The a
uth
ors
pro
pose that corr
ela
tion r
esults f
rom
inte
gra
tion leadin
g to p
attern
s o
f share
d
know
ledge a
mong f
irm
mem
bers
, w
ith the s
hare
d
know
ledge c
onstitu
ting a
resourc
e u
nderlyi
ng
pro
duct develo
pm
ent capabili
ty. D
efine the g
litch
as a
costly
err
or
possib
le o
nly
because k
now
ledge
was n
ot share
d, and m
easure
influence o
f glit
ches
on p
erf
orm
ance.
Lore
nzoni &
Lip
parini (1
999)
The levera
gin
g o
f in
terf
irm
rela
tionship
s a
s a
dis
tinctive
org
aniz
ational capabili
ty: A
longitudin
al stu
dy
Longitudin
al stu
dy
of
the
str
uctu
re o
f th
ree lead f
irm
-
netw
ork
rela
tionship
s a
t tw
o
poin
ts in tim
e, in
the p
ackin
g
machin
e industr
y.
Pro
ject (P
roduct)
The c
apabili
ty to inte
ract w
ith o
ther
com
panie
s -
a
rela
tional capabili
ty -
accele
rate
s the lead f
irm
s
know
ledge a
ccess a
nd tra
nsfe
r w
ith r
ele
vant
eff
ects
on c
om
pany
gro
wth
and innovativeness.
The s
tudy
pro
vid
es e
vid
ence that in
terf
irm
netw
ork
s c
an b
e s
haped a
nd d
elib
era
tely
desig
ned. T
he a
bili
ty to inte
gra
te k
now
ledge
resid
ing b
oth
insid
e a
nd o
uts
ide the f
irm
s
boundaries e
merg
es a
s a
dis
tinctive
org
aniz
ational capabili
ty.
Mill
er
(2003)
An a
sym
metr
y-b
ased v
iew
of
advanta
ge: T
ow
ard
s a
n
attain
able
susta
inabili
ty
Qualit
ative c
ase s
tudy
data
com
posed o
f in
terv
iew
s a
nd
docum
ent analy
sis
conducte
d
for
22 f
irm
s o
r in
dependent
pro
fit cente
rs w
ere
conducte
d
over
an 1
8 m
onth
period.
Org
aniz
ation (
SB
U)
Asym
metr
ies; R
esourc
es;
Capabili
ties; C
ore
Capabili
ties;
Org
aniz
ation D
esig
n; C
apabili
ty
Configura
tion
Susta
inable
Com
petitive
Advanta
ge
This
stu
dy
show
s h
ow
som
e f
irm
s w
ere
able
to
captu
re r
ents
by
build
ing o
n a
sym
metr
ies.
Asym
metr
ies a
re typ
ically
skill
s, pro
cesses, or
‘assets
’ a f
irm
's c
om
petito
rs d
o n
ot and c
annot
copy
at a c
ost th
at aff
ord
s e
conom
ic r
ents
. B
y
dis
covering a
nd r
econceptu
aliz
ing these
asym
metr
ies, m
any
firm
s w
ere
able
to turn
asym
metr
ies into
susta
inable
capabili
ties.
Gavetti (2
005)
Cognitio
n a
nd h
iera
rchy:
Reth
inkin
g the
mic
rofo
undations o
f
capabili
ties' d
evelo
pm
ent
Com
pute
r sim
ula
tion o
f an
agent based m
ode o
f searc
h,
based o
n c
ognitio
n a
nd
heirarc
hic
al positio
n.
Org
aniz
ation
Regim
es o
f C
orp
ora
te
Influence; C
ognitio
ns o
n
Fitness L
andscapes;
Tra
nsla
ting C
ognitio
n into
Behavio
r
Perf
orm
ance o
f R
egim
e a
cro
ss
Landscapes
This
art
icle
identifies g
aps in m
icro
foundations o
f
capabili
ties r
esearc
h. T
his
art
icle
off
ers
thre
e
contr
ibutions: it d
elin
eate
s the tra
its o
f a
mic
rofo
undational str
uctu
re f
or
researc
h o
n
capabili
ties; it h
ighlig
hts
negle
cte
d c
ausal
mechanis
ms that contr
ibute
to u
nders
tandin
g h
ow
capabili
ties d
evelo
p; and s
how
s that th
e a
ccura
cy
of
the r
epre
senta
tions c
hoosen m
ight vary
accord
ing to location in the o
rganiz
ational
hie
rarc
hy.
Haas &
Hansen (
2005)
When u
sin
g k
now
ledge c
an
hurt
perf
orm
ance: T
he v
alu
e o
f
org
aniz
ational capabili
ties in a
managem
ent consultin
g
com
pany
Quantita
tive f
ield
surv
ey
of
182
pro
ject pro
posals
assem
ble
d
by
sale
s team
s in a
busin
ess
consultin
g o
rganiz
ation.
Pro
ject
Am
ount of
Codifie
d K
now
ledge
Obta
ined -
Not U
sed; A
mount
of
Pers
onal K
now
ledge
Obta
ined -
Not U
sed; T
eam
's
Level of
Task E
xperience;
Num
ber
of
Com
petito
rs
Contr
act W
on
The a
uth
ors
develo
p a
situate
d p
erf
orm
ance v
iew
that hold
s that th
e v
alu
e o
f obta
inin
g a
nd u
sin
g
know
ledge w
ithin
a f
irm
depends o
n the task
situation. R
esults s
uggest th
at com
petitive
perf
orm
ance d
epends n
ot on h
ow
much f
irm
s
know
but on h
ow
they
use w
hat th
ey
know
.
Table 2.5 Summary of key empirical findings in organizational capabilities research
56
Here, there is increasing agreement that integration of the social, relational, and
structural context of work is essential in order to understand the patterns within
communities and networks of practice (Brown & Duguid, 2000; Lave & Wenger, 1991;
Orlikowski, 2000, 2002), the stability and adaptation resulting from the practice of
routines (Denrell & March, 2001; Feldman & Pentland, 2003), and the service of
routines and capabilities as resources for others (Feldman, 2004; Orr, 1996; Salvato,
2003).
A practice-based perspective contributes to our understanding of the micro-
foundations of capability emergence because the ongoing performance of routines
accomplishes two things: first, performance is self-informing in that it elicits various
responses from the surrounding social community which shapes future performances
(Argyris, 2004; Becker, 2004; Espedal, 2006; Feldman & Rafaeli, 2002; Weick & Roberts,
1993; Weick & Sutcliffe, 2006; Weick, Sutcliffe, & Obstfeld, 2005); and second,
performance conveys information and understanding from the performer to the
collective, thereby serving as an important channel for sharing tacit knowledge and
personal insight with others (Brown & Duguid, 2000; Dyer & Hatch, 2006; Feldman,
2003; Maitlis, 2005; Orr, 1996; Rouleau, 2005). In both of these situations, both the tacit
and explicit feedback received by the performer is influential in shaping the way in
which the performer understands the performance, and in shaping how future
performances are enacted (Hoopes & Postrel, 1999; Weick, 1996; Winter, 2000).
Although others have considered the impact of defensive routines in shielding
57
established social practices from the shaping effects of feedback (Argyris, 1990, 2004), it
remains absent from the construal of social interactions, their dynamics, and their
implications during capability change.
Implications of Findings
The definition of organizational capabilities adopted in this research is well
supported in the literature. This choice reflects the position that at their core,
organizational capabilities are underpinned on a micro-foundational base of socio-
structural, socio-cognitive, and socio-relational factors, yet remain an organization’s
central means of goal accomplishment through coordinated, collaborative task-
performance. This suggests an emerging convergence between the way in which social
capital is constituted, valued, and configured, and the socially complex practicing
enacted by individuals working in groups, which underpin the social micro-foundations
of organizational capability evolution. This final point, that social capital may be a
persuasive determinant in the lifecycle of organizational capabilities, begins to consider
each dimension of social capital – structural, cognitive, and relational embeddedness –
in terms of its ability to contribute unique yet complementary utility during the process
of capability change.
58
Conclusions of Social Capital and Organizational Capabilities Literature
In chapter two, the social capital and organizational capabilities literature
considered relevant for this dissertation have been reviewed. Although the two bodies
of literature originate from two distinct intellectual traditions – social capital derived
from developments in the field of sociology, and organizational capabilities from the
advent of evolutionary economic theorizing – a number of important complementarities
have been illustrated. Consistent with the central research question driving this
dissertation, ‘How does the emergence of social capital influence the evolution of
organizational capabilities?’, we have sought to illustrate the performance implications
derived from structural, cognitive, and relational embeddedness, while exposing the
micro-foundations of organizational capabilities as explicitly socially complex in nature.
Here, what little research there is available has been offered in support of this
dissertation’s working assertion that the evolution of organizational capabilities is highly
dependent on the emergence and growth of social capital. Social capital affords
organizations a valuable resource, which by its nature is both socially dynamic and
generative, two characteristics which are proposed to be influential contributors to the
evolution of organizational capabilities. The intersection of these two streams of theory
affords the opportunity to reflect on potential similarities and provide a foothold for
further theorizing developed in the third chapter.
59
Chapter Three: A Theory of Social Capital Emergence and Organizational Capability Evolution
This chapter begins by introducing and explaining the theoretical model that
guides this dissertation research. The purpose here is to illustrate how social capital
emerges and to demonstrate its influence on the evolution of organizational
capabilities, and to address the motivation behind examining the hypothesized
relationships. In the interest of brevity, throughout the introductory discussion of the
theoretical model, generalized propositions will be offered to articulate the essence of
the relationships between constructs. The purpose, here, is to succinctly underscore the
conceptual rationale for the relationships expressed in the model as well as their
importance to this field of study.
The second major component of this chapter addresses the substance of each of
the principal concepts, their individual and collective contribution to the theoretical
argument hypothesized in this study, and the central and peripheral arguments which
result. This portion of chapter three begins with the elaboration of the conceptual and
operational definitions of each construct. Here, supporting arguments are offered to
explain the relevance of each of the choices made with regard to the conceptual and
operational definitions selected for this dissertation. The results lead to a further
delineation of the generalized propositions, creating a series of empirically testable
hypotheses, which form the theoretical basis for the remainder of this research project.
60
Building on the literary foundation developed in the previous chapter, this
component of the dissertation emphasizes the development and articulation of new
organizational theory, and the intended contributions which result from its application.
To summarize, in this dissertation, I argue that the dependence stemming from
organization position, path, and process, articulated in the organizational capability
literature offers, at best, a partial explanation for capability change. Organizational
capabilities evolve throughout their lifecycle, as well, from the interaction of socio-
structural, socio-cognitive, and socio-relational factors which influence the socially
complex micro-practices among group members, which lie at the core of organizational
capabilities. Variations in the emerging patterns of social capital, construed as
structural, cognitive and relational embeddedness, shape the micro-foundations of
organizational capability evolution.
Central Motivation for this Conceptual Argument
This dissertation is motivated by the desire to investigate the micro-foundations
of organizational capability change; to examine the implications of social capital
emergence in this process; and, to determine whether and how the evolution of
organizational capabilities impacts performance. Providing an improved understanding
of how organizational capabilities evolve is much needed among academics and
management practitioners alike. However, the explicit consideration of social capital in
the process provides a unique, yet essential, glimpse into the socio-relational core of
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capability change because it recognizes the novel and idiosyncratic ‘resource’ value of
social capital (Adler & Kwon, 2002; Burt, 2000; Moran, 2005). While each of these fields
– social capital and organizational capabilities – warrant independent study in their own
right, investigated in tandem they present the opportunity to make a significant
contribution to our understanding of organizational performance. Their study may allow
us to not only describe how the process of emergence occurs, but also to explain what
triggers evolution and why change occurs – each fundamental in making a contribution
to organization and management theory (Whetten, 1989).
A Theory of Social Capital Emergence and Organizational Capability Evolution
Consistent with the overarching research question in this dissertation, ‘How does
the emergence of social capital influence the evolution of organizational capabilities?’
we assert that the growth of social capital plays an influential role in shaping the
performance of capabilities as they occur, but also in their future trajectories. Central to
this argument is the proposition that organizational capabilities emerge, evolve, and
change from the interaction of socio-structural, socio-cognitive, and socio-relational
factors which influence the complex social micro-practicing among group members at
the core of organizational capabilities (Helfat & Peteraf, 2003; Peteraf & Maritan, 2007;
Salvato, 2009). Developed and deployed by members of organizational networks, social
capital and variations in its structural, cognitive and relational embeddedness, shape the
micro-foundations of organizational capability evolution (Helfat & Peteraf, 2003; Teece,
62
2007). Previously defined elsewhere, social capital is understood to mean “the sum of
the actual and potential resources embedded within, available through, and derived
from the network of relationships possessed by an individual or social unit” which thus
“comprises both the network and the assets that may be mobilized through that
network” (Nahapiet & Ghoshal, 1998: 243); embeddedness is taken to refer to the
patterned nesting of an individual’s activities within the situated context of the group
(Granovetter, 1985; Moran, 2005). In this dissertation, structural embeddedness is
concerned with the properties of social systems and networks of relations as a whole
(Granovetter, 1985; Moran, 2005), dealing particularly with the impersonal
configurations of linkages, or overall patterns of connections between actors within a
social network (Burt, 1992, 2000). Structural embeddedness, then, reflects the pattern
of connections between and among members (i.e. the configuration) of a network, but
not the content-quality exchanged between linked members. For example, two people
may be structurally embedded within the same work group (they may even have
structurally equivalent configurations) and therefore have common ties to other group
members, but the characteristics of these structural connections (in terms of strength or
proximity) are distinguishable from the quality of the relationships among linked
members (one person may be well liked, while the other is not) despite both being
similarly structurally embedded. In contrast, cognitive embeddedness is the extent to
which a collective cognitive schema is present between individuals in a social network;
and relational embeddedness is understood to mean the “personal relationships people
63
have developed with each other through a history of interactions” (Nahapiet & Ghoshal,
1998: 244). The theoretical framework illustrated in Figure 3-1 captures the pattern of
relations between social capital and capability performance suggesting a causal
relationship among the concepts over time.
Figure 3-1: Social capital emergence and the co-evolution of organizational capabilities
The emergence of organizational capabilities represents the earliest phase of
capability development, in which a group, team or social collective come together and
take action resulting in the creation of a new organizational capability (Helfat & Peteraf,
2003). The evolution of an organizational capability reflects its growth and change over
time (Inkpen & Currall, 2004; March, 1994; Nelson & Winter, 1982). Based on the
proposed relationships among each of the constructs illustrated in Figure 3-1, four
specific research questions guide the discussion from conceptual argument to one
appropriately specified for empirical testing.
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Central Research Question: How does the emergence of social capital influence the
evolution of organizational capabilities?
Research Question 1: How does social capital emerge?
Research Question 2: Does social capital emergence improve capability performance?
Research Question 3: What impact does social capital emergence have on the
evolution of organizational capabilities?
Research Question 4: Do social capital and capability performance co-evolve over
time?
In moving from theoretic to operational understanding, these research questions
will be relied upon to maintain a consistent focus on the relationships of interest, as
each of the underlying focal constructs are examined and discussed. Figure 3-2 puts
each of these research questions in context, reflecting both the pattern of causality in
the social capital—capability performance relationship as well as the process through
which this relationship is argued to develop over time.
65
Figure 3-2: Multiplex relationship between social capital emergence and capability performance evolution
66
The Contribution of Social Capital to Organizational Capability Building and Change
An integrative perspective of social capital, defined as “the sum of the actual and
potential resources embedded within, available through, and derived from the network
of relationships possessed by an individual or social unit” which thus “comprises both
the network and the assets that may be mobilized through that network” (Nahapiet &
Ghoshal, 1998: 243), well-captures the scope and influence of this construct. From this
description, we understand social capital to be a socially embedded resource, based
foremost on the configuration and content-quality of the relationships among members
of a social collective linked by virtue of shared network connections or ties (Adler &
Kwon, 2002; Bourdieu, 1986; Coleman, 1988). Delineating social capital as a resource
embedded within, and derived from, the social relationships between interconnected
network members, is powerful; it brings to light the extractable contributions that
reside – and often remain unrecognized – in the social domain, that are appropriable to
other functions in the organization and which contribute to the performance of
desirable outcomes (Capaldo, 2007; Dyer & Hatch, 2006; Hansen, 2002; Uzzi, 1997;
Verona & Ravasi, 2003). This definition also recognizes social capital as a
multidimensional construct, in this case composed of structural, cognitive, and
relational dimensions (Inkpen & Tsang, 2005; Nahapiet & Ghoshal, 1998; Tsai & Ghoshal,
1998), as opposed to a one-dimensional construct based on any one factor (Burt, 1992,
1997; Seibert et al., 2001). Based on this conceptual foundation, where social capital is
understood to be a resource, socially embedded within the structural, cognitive, and
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relational connections of network members, situated in a particular organizational
context, but appropriable to other situations. Viewing structural embeddedness as the
context for communication, where cognitive and relational embeddedness are reflected
as transmitted content within the structures of the network, provides a comprehensive
consideration of both bridging and bonding activities. This contextually-sensitized
approach, where the implications of social capital are contingent on the
complementarity between actors, network configuration, and task environment is well
suited to this research project because it facilitates the incorporation of situation-
specific considerations which add richness and realism to the organizational context
under study.
Embeddedness within the Structural Dimension of Social Capital
In this dissertation, embeddedness has been taken as referring to the patterned
nesting of an individual’s activities within the situated context of the group
(Granovetter, 1985; Moran, 2005). Increasingly however, researchers have recognized
the need to qualify the nature of embeddedness, and their interpretation of it, by
situating both the actor and pattern of social relations in a specific structural or
institutional context (Moran, 2005; Oliver, 1997; Uzzi, 1996, 1997, 1999; Zuckin &
DiMaggio, 1990). Structural embeddedness is concerned with the impersonal
configuration of linkages, or overall patterns of connections between actors within a
social network (Burt, 1992, 2000; Granovetter, 1985; Moran, 2005). The underlying
68
assumption here is that network structure captures the pattern of actual and potential
interactions between organizational participants in the form of network ties, where
network “ties serve as conduits for the flow of interpersonal resources” (Balkundi &
Harrison, 2006: 50). The principle facets of structural embeddedness presume the
presence of network ties between actors (Wasserman & Faust, 1994), and concentrate
on the morphology of network patterns in terms of network density, network
connectivity and linkages that span levels of hierarchy (Tichy & Fombrun, 1979).
Network density refers to the ratio of network connections present in proportion to the
potential number of possibilities (Labianca & Brass, 2006; Marsden, 1990), and has been
demonstrated to encourage more frequent interaction, while simultaneously increasing
information redundancy among members of the network due to increasing
interrelatedness (Balkundi & Harrison, 2006; Labianca & Brass, 2006; Reagans &
Zuckerman, 2001). Similarly, network connectedness, which connotes “the extent to
which members of the network are linked to each other” (Tichy & Fombrun, 1979: 928),
and network centrality – or the extent to which the actor in a network is interconnected
with other relationships in the network (Raider & Krackhardt, 2002) – have been shown
to improve access to valued information and resources (Burt, 1997; Freeman, 1979;
Hansen, 1999). A further structural consideration that has also received considerable
attention in the literature focuses on issues of structural equivalency of networks (Burt,
1997). Structural equivalence speaks to the extent to which network members reside in
similar functional or network positions (Brass et al., 2004), and can be seen to influence
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attitude formation and contagion among equivalent members (Burt, 1992), while
increasing confirmatory or redundant information benefits (Burt, 1997). Although
theoretically distinct from other constructs such as group cohesion or network
connectedness, structural equivalence has been implicated in increasing information
transmission and knowledge sharing among members and across networks (Balkundi &
Harrison, 2006; Burt, 1997).
Therefore, for the purposes of this dissertation social capital is presumed to
enhance the potential emergence of organizational capabilities to the degree that
network structures support access to varied and diverse sources of information, as well
as the transmission and integration of this information among organizational
participants. In this case, each organization member should appear both deeply
connected and structurally equivalent to the other members inside the organization,
thus members should be well-embedded structurally throughout the group network. As
a result:
H1a: Increasing structural embeddedness enhances the emergence of social capital.
Embeddedness within the Cognitive dimension of Social Capital
The nature of cognitive embeddedness, despite having gone unspecified in much
of the organizational social capital literature, reflects the extent to which a collective
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cognitive schema is present between individuals in a social network8. The nature of
cognitive embeddedness is akin to the idea of a collective mind which is “manifest when
individuals construct mutually shared fields … that emerges during the interrelating of
an activity system” (Weick & Roberts, 1993: 365); or the notion of a collectively-held
knowledge code which “affects the beliefs of individuals, even while it is being affected
by those beliefs” (March, 1991: 75). These examples emphasize the patterned shaping
of processes that occur during task-performances by individuals and groups situated in
specific contexts (Grant, 1996), recognizing the prevalence of bounded rationality and
the implications of the boundaries themselves as situated in a specific environment
(Haas & Hansen, 2005).
Applying the cognitive dimension of social capital theory to organizational
settings, research has predominantly focused on the role of resources providing shared
representations and systems of meaning (Nahapiet & Ghoshal, 1998); shared narratives
within networks of practice (Brown & Duguid, 2000; Orr, 1996; Wasko & Faraj, 2005);
and the potential for routines to serve as repertoires for collective practice or
repositories of shared knowledge from trial-and-error learning outcomes (Nelson &
Winter, 1982; Winter, 2000). Understanding that the collective knowledge embedded in
social and organizational practices resides within practice-based and tacit experiences
enacted as collective action (Brown & Duguid, 2000), reflects the notion that “we can
8 The absence of the term “cognitive embeddedness” was discussed extensively during the review of the
social capital literature, however, as was in noted there, Zuckin and DiMaggio (1990) do offer a definition that is consistent with the intent of the term as used here.
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know more than we can tell” (emphasis in original Polanyi, 1966: 4). The issue for
capability emergence, then, is one of understanding and absorption rather than just the
communication of declarative or procedural knowledge (Kogut & Zander, 1992, 1996;
Montealegre, 2002; Zollo & Winter, 2002). Here, the notion of absorptive capacity
within the group and by the individual is a particularly relevant contributor to the micro-
foundations of organizational capabilities that influence emergence (Cohen & Levinthal,
1990; Gavetti, 2005; Lorenzoni & Lipparini, 1999). Organizational knowing is not a static
embedded capability or stable disposition of actors, but rather an ongoing social
accomplishment, constituted and reconstituted as actors engage the world in practice
(Orlikowski, 2002). Because practice is not mindless and focuses on knowing rather than
knowledge, and agency within the structure of organization, cognitive embeddedness
reflects knowledge that is held by individuals, but is also expressed in regularities by
members who cooperate in a social network (Dyer & Hatch, 2006; Ethiraj et al., 2005;
Kogut & Zander, 1992). An individual’s ability to recognize novelty or deviation during
the performance of some function, to meaningfully communicate this insight to others,
where there is collective absorption of this insight among group members, is a critical
aspect of the contribution of social capital’s cognitive dimension to the building of
capabilities (Cohen & Levinthal, 1990; Gavetti, 2005; Hoopes & Postrel, 1999; Lorenzoni
& Lipparini, 1999; Tripsas & Gavetti, 2000). Cognitive embeddedness then, is the extent
to which a collective cognitive schema is present between individuals in a social
network.
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At one extreme, over-embeddedness takes on the qualities of groupthink (Janis,
1982) or collective myopia in which informational and cognitive diversity are quashed
and collective schema are shared precisely (Branzei & Fredette, 2008); at the other
extreme, similarities are sparse with potential dysfunction resulting from
incommensurate languages, codes, or mutual understanding (Denrell & March, 2001;
March, 1991; Snyder & Cummings, 1998). In the earliest phases of social capital
emergence over-embeddedness is an unlikely occurrence; instead the emergence of
social capital is presumed to enhance capability performance to the degree that
cognitive schemas support recognition and integration of varied and diverse sources of
information, as well as the absorption and distribution of this information to each
required organizational participant. Building the capacity of organization members to
assimilate, exchange, and combine information from their own sources, as well as those
of the other members inside the organization, requires members to become cognitively
embedded in the network of the group. This argument leads to the conclusion that:
H1b: The greater the cognitive embeddedness, the more likely the emergence of social
capital.
Embeddedness within the Relational Dimension of Social Capital
The concept of relational embeddedness describes the assets created and
leveraged through relationships, reflecting a behavioral rather than structural
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orientation (Nahapiet & Ghoshal, 1998). Whereas a structural perspective emphasizes
the interaction or information advantages derived from network location or position, a
relational view highlights the assets embedded within each relationship, such as trust
and trustworthiness (Ferrin et al., 2006; Levin & Cross, 2004; Tsai & Ghoshal, 1998; Uzzi,
1996, 1997), diversity and overlapping identities (Ibarra, 1993; James, 2000; Lin, 1999;
Reagans & Zuckerman, 2001; Xiao & Tsui, 2007), or social solidarity (Adler & Kwon,
2002; Sandefur & Laumann, 1998). Relational embeddedness is understood to illustrate
the “personal relationships people have developed with each other through a history of
interactions” (Nahapiet & Ghoshal, 1998: 244), and has been demonstrated to affect
performance (Moran, 2005). Some have endorsed the importance of relationships in
facilitating interaction with others in ways that accelerate knowledge access and
transfer across units, where the ability to integrate knowledge residing both inside and
outside the firm’s boundaries emerges as a distinctive capability (Lorenzoni & Lipparini,
1999). For example, using a case study approach, Wooten and Crane (2004) illustrated
the performance implications of strong and valued relationships based on compassion,
virtuous actions, and honorable behavior, as contributing to organizational capabilities,
by supporting functional task-performance in difficult and highly emotional
circumstances within the healthcare industry. Similarly, relational embeddedness, or the
quality of social relationships based on relational closeness and trust, has been
demonstrated to better explain innovation performance than structural embeddedness,
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where “under increasing uncertainty performance is particularly enhanced by relational
embeddedness” (Moran, 2005: 1147).
Therefore, for the purposes of this dissertation social capital is enhanced to the
degree that relational connections within the network foster trust and trustworthiness
among organization members and support relational closeness. To the degree that an
organization member exhibits trust and trustworthiness, and relational closeness for
those they interact with, members are said to be relationally embedded within the
group network. From this perspective, it is hypothesized that:
H1c: The greater the relational embeddedness, the more likely the emergence of social
capital.
In its earliest phase of development social capital emerges from the
accumulation of structural, cognitive and relational connections among members
embedded in a social network. Levels of embeddedness will undoubtedly emerge
unevenly among members, with growth of social capital occurring over a history of
subsequent interactions at strengths contingent on each member’s level of
embeddedness in the network. Whether driven foremost by structural embeddedness
as in the case of first interactions or by the building of trust over time, the implications
for performance will be the same: the emergence of social capital will enhance
capability performance over time.
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H2: Social capital emergence will improve capability performance as demonstrated
by increasing levels of capability accuracy, capability speed and capability
quality.
Threat Identification: The Evolution of an Organizational Capability
Capability emergence represents the earliest phase of development for
organizational capabilities within the capability lifecycle model, in which a group, team
or social collective comes together and takes action resulting in the creation of a new
organizational capability (Helfat & Peteraf, 2003). During this phase, we would expect
social capital to be very influential in coordinating effective action. Having argued that
embeddedness enhances the emergence of social capital, we now consider the
implications for capability performance and change. Here, we begin to address whether
and how social capital influences the evolution of organizational capabilities.
This dissertation has suggested that organizational capabilities emerge from the
social interactions among structural, cognitive, and relational dimensions which
influence the socially complex micro-practices of network members. Variation in social
capital developed and deployed by organizational members across the network shape
the micro-foundations of organizational capability performance and change. In this
research context, we are particularly interested in investigating the emergence of one
powerful organizational capability: that of threat identification. Here, ‘threat
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identification’ is defined as the ability to develop an awareness of an impending event
whose incidence would be acknowledged and categorized as a threat. For purposes of
this study, a threat refers to an “environmental event that has impending negative or
harmful consequences” for the organization (Staw, Sandelands, & Dutton, 1981: 502).
More precisely in this case, a threat refers to an impending terrorist attack within the
context of a crisis simulation, where threat recognition requires that group members
must determine and consolidate four specific dimensions of information about the
imminent crisis. Group members must determine the specific source responsible for the
attack, the location of the attack, the date and time at which the attack will occur, and
type of target on which the attack will be carried out. Effective performance of this
capacity, it is hypothesized here, requires that members establish and maintain shared
awareness and mutual understanding of the crisis situation through a process of
collaboration throughout the social network.
Based on this definition, it is argued that threat identification falls well within the
scope of an organizational capability because it not only requires the use of
organizational resources in the performance of collections of activities where
performance requires socially complex collaborative coordination (Collis, 1994; Winter,
2003), but comes extremely close to meeting the criteria of a higher order – or dynamic
– capability in this context; where dynamic capabilities are understood to refer to “the
firm’s ability to integrate, build, and reconfigure internal and external competences to
address rapidly changing environments” (Teece et al., 1997: 516). In both cases, the
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ability to develop and deploy organizational capabilities (dynamic and otherwise) has
been shown to contribute significantly to organization performance, such as the impact
of dynamic managerial capabilities on business performance (Adner & Helfat, 2003); the
recognition of innovation asymmetries creating sustained competitive advantage
(Miller, 2003); or the deployment of resource-picking and capability-building
competencies proposed to contribute to rent creation (Makadok, 2001)
Given our capability of threat identification, for our purposes capability
performance reflects the capacity to identify four qualities of an impending crisis in a
timely and accurate manner. The accuracy, speed and quality of the identification are
paramount; time, in the form of capability speed, which is defined as the duration
“between the first reference to a deliberate action, and the time in which a
commitment to action is made” (Eisenhardt, 1989: 549; Judge & Miller, 1991). Capability
accuracy is delineated as the fit-quality between the outcomes of group task-
performance and the aspiration level of the organizationally desirable performance
outcomes or goals (Dooley & Fryxell, 1999). In this dissertation this dimension has the
advantage of being very straight forward nature because the identification of a threat
(and each of its subcomponents) may be objectively known. However, in other contexts
the degree of environmental complexity, uncertainty and the pace of exogenous change
may well introduce subjectivity or require retrospective evaluation (Brown &
Eisenhardt, 1997; Dooley & Fryxell, 1999; Eisenhardt, 1989; Eisenhardt & Martin, 2000).
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Capability quality reflects a perceptual belief about the quality of the
identification assessment, or an indicator of the calibration of the individual to the crisis.
Previous studies have been conflicted in their approach to dissent: one view suggests
dissent may improve the quality of decision outcomes (Eisenhardt, 1989); or, an
alternative perspective suggests that dissent lowers capability quality due to the
introduction of political maneuvering (Eisenhardt & Bourgeois, 1988). In either case,
accurate quality assessments are an important factor in terms of capability
performance, because they may interact with capability accuracy and capability speed
(Dooley & Fryxell, 1999).
Therefore to summarize, capability performance is defined as a multifaceted
concept, reflecting the degree to which performance corresponds to the demands and
intent of the situation (Helfat et al., 2007). Thus, threat identification is an important
organizational capability in this context that contributes to organization performance to
the degree that it provides accurate, timely and high-quality opportunities for action.
The fitness of this threat identification capability relies on its capacity to satisfy the
needs that it fulfills, all of which are a function of capability speed, capability accuracy
and capability quality. In the case of earliest capability enactments, fit performance
outcomes and the emerging patterns of activity on which those early successes are
based are proposed to take root, forming the basis for future capability performance as
they set the trajectory of the future (Helfat & Peteraf, 2003). It is argued in this
dissertation that a network’s capacity to develop an awareness of an impending event
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necessary to acknowledge and qualify the incident as a threat depends significantly
upon the degree to which members of the group share information, collaborate and
maintain awareness. Thus, in the earliest instances of capability building, social capital
will impact performance by creating a necessary infrastructure for social resources to
flow through.
H3: Social capital emergence will improve capability performance over time.
Later, social capital will shape the trajectory of capability performance by
enhancing the likelihood that early capability performance increases acquisition by
group members and further development. Helfat and Peteraf (2003: 1002) suggest that,
“In pursuing its initial alternatives, a team may elect to imitate a capability that exists in
another organization or the team may develop a capability from scratch. Both cases
require organizational learning, since the team has not performed the activity before.
More generally, capability development entails improvement over time in carrying out
the activity as a team.” At the earliest phases of capability building, it is very likely that
patterns formed during early task-performances will be very influential on the
acquisition, development and deployment of future capabilities.
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Longitudinal Change in the Social Capital–Capability Performance Relationship
As Helfat and Peteraf (2003) suggest, learning plays an integral role in the
evolution of capability lifecycles. Zollo and Winter (2002) note the importance of
knowledge sharing patterns in capability performance; others have long endorsed the
impact of learning curve effects on process improvements (Argote, 1999). Social capital
is built on a history of interactions (Adler & Kwon, 2002; Burt, 2000); knowing that
history we can suggest that once it has emerged, social capital will continue to grow and
develop among network members. We would expect that the constitution of social
capital may change over time, especially as we compare the earliest phases of
emergence to later stages of social capital development and use. While no study that I
am aware of has formally investigated the emergence of social capital or change in its
structure over time, there is good reason to believe that the importance of structural,
cognitive and relational embeddedness would vary as relationships develop and change
over time. For example, at the earliest phases of emergence structural linkages with a
network may be relatively more powerful contributors to performance than other
components such as relational linkages, which may be more contingent on developing
trust through patterns of sharing and reciprocity. Despite variation in the strength with
which each dimension of embeddedness will impact social capital, in aggregate it is
argued that social capital will grow over time.
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H4: Social capital will increase with network use, growing over time through
interaction and changes in the power of structural, cognitive and relational
embeddedness components.
The history of interactions on which social capital depends has an impact on
future performance too (Helfat & Peteraf, 2003). Emerging patterns of social capital
shape the social structure of current and future capability performance, imprinting
organizational capabilities with a shadow of the past. This may be for better or worse;
providing a trajectory for future success by instilling process stability (Feldman &
Pentland, 2003; Miller, 2003), or a source of rigidity and inertia (Gilbert, 2005; Leonard-
Barton, 1992). Because we focus our attention on a single capability – threat
identification – and its evolution over time, it is argued that social capital will provide a
beneficial social resource that contributes to performance in the future. However, if we
were instead to focus on radical transitions among capabilities that represent
discontinuous change we would have greater cause to question the inertial liabilities
resulting from network dependencies, or at a minimum to examine the
commensurabilities between the underlying social structures of each organizational
capability (Kogut & Zander, 1996; Perlow, Gittell, & Katz, 2004; Perry-Smith & Shalley,
2003); for example the need for network closure and internal consistency versus the
need for network reach and external diversity (Oh et al., 2004; Oh et al., 2006). Given
the focus of this research and our emphasis on the relationship between the emergence
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of social capital and the evolution of a single organizational capability over time, it is
proposed that:
H5: The emergence of social capital in previous periods will enhance the evolution of
capability performance in subsequent periods.
Whereas we have previously argued that historical dependencies provide at best
a partial explanation for capability change, we include the effect of process
dependencies in our theorizing by considering the period-to-period relationship
between levels of capability performance. We predict that capability performance
evolution will be impacted by the processes of the past, with performance gains of the
future resulting from patterns of success in the past. Dependence of previous processes
reflects an internal consistency in the evolution of current practices, resulting from the
accumulation and reinforcement of past ones (Cohen & Levinthal, 1990; Makadok,
2001). Established patterns of activity constitute the present value of prior learning-by-
doing (Nelson & Winter, 1982; Teece et al., 1997), providing firms a set of options
available today that are largely dependent on the ‘capability trajectory’ established in
prior periods (Dierickx & Cool, 1989; Helfat & Raubitschek, 2000; Teece & Pisano, 1994).
From the perspective of capability change, path dependence can be seen as a constraint
imposed on the ability to rapidly alter course or adapt to new developments in the
environments (Cohen & Levinthal, 1990; Eisenhardt & Martin, 2000; Leonard-Barton,
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1992); however, historical dependencies may also support organizational coherence,
providing internal stability and incremental or exploitative learning improvements over
time (Karim & Mitchell, 2000; March, 1991; Teece et al., 1994). Thus, while differences
remain with respect to the qualitative implications of process dependence, it is clear
that the evolution of capabilities “are imprinted by past decisions and their underlying
patterns” (Schreyögg & Kliesch-Eberl, 2007: 916). “The way things are done in the firm”
(Teece et al., 1997: 518) results from the accumulation of experience among its
members (Zollo & Winter, 2002), the investment in supporting coordination systems
and technology (Ethiraj et al., 2005; Montealegre, 2002; Schreyögg & Kliesch-Eberl,
2007), and internal organizational inertia (Cohen & Levinthal, 1990; Leonard-Barton,
1992; Tripsas & Gavetti, 2000). In combination, this triumvirate suggests that the
evolutionary trajectory of organizational capabilities is essentially constrained –
although not exclusively predetermined – by the decisions of the past, thereby
emphasizing the tendency for capabilities to persist and the importance of historical
context, especially with respect to lineage during capability founding (Helfat &
Lieberman, 2002; Tripsas, 2009). For these reasons it is argued that:
H6: Capability performance of the past will contribute positively to improvements in
capability performance in subsequent periods.
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Finally, a question of co-evolution remains. Having argued that the emergence of social
capital influences the evolution of organizational capabilities, we turn our attention to
the possibility of co-evolution among these constructs. In this instance co-evolution
would be illustrated by correlated rates of growth among social capital and capability
performance.
We assert that the social capital–capability performance relationship in the
earliest phases of capability building, where the emergence of social networks are found
to have provided some initial level of performance, influences the strength and rate of
change in this relationship in subsequent periods. This claim is based on the notion that
social resources support and imprint the accumulation of expertise and reinforce
learning-by-doing as the learning occurs (Argote, 1999; Espedal, 2006; Feldman, 2003;
Howard-Grenville, 2005; Nelson & Winter, 1982; Orlikowski, 2002; Reagans, Argote, &
Brooks, 2005; Staw & Ross, 1978; Zollo & Winter, 2002). Experience gained in the early
stages of a capability’s lifecycle is highly informative for future performance: successful
performances preserve the patterns of structural, cognitive and relational
embeddedness which generated the performance, whereas early failures, in contrast,
reinforce the need for change and discount the patterns of social capital that resulted in
poorly fit performances. The future evolution of a capability, then, results from a
combination of both the emergence of social capital as well as the quality of capability
performance in prior periods. Once acquired, a capability’s evolution is set on a
trajectory whereby future learning and practicing required to advance development
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may be much more dependent (Teece & Pisano, 1994; Teece et al., 1997; Winter, 2003;
Zollo & Winter, 2002) and focused on the refinement or exploitation of processes which
are known to result in legitimate success (March, 1991; Winter, 2000), marking a
transitional moment in the lifecycle of an organizational capability, from emergence to
development (Helfat & Peteraf, 2003). Social capital supports this trajectory; we argue
that social capital will vary in time with capability change, with rates of change mutually
co-evolving over time. To this end, we assert that:
H7: Social capital and capability performance will co-evolve over time such that
changes in rates of change in social capital will correlate to changes in rates of
change in capability performance.
While the emergence of social capital is argued to drive capability performance
within each time period, capability evolution and the co-evolution of the social capital—
capability performance relationship requires mutual adjustment, such that the stability
or variation in social resources and collective processes occur together over time.
Contributing to the literature by developing an understanding of how social networks
dynamically evolve around organizational capabilities and teasing apart the social
aspects of endogenous capability change are both important aims of this study, which
will address significant gaps in our understanding of the social micro-foundations of
capability performance and change.
86
Summary of Central and Peripheral Arguments
Building on the literary foundation developed in the previous chapter, this
chapter has emphasized the development and articulation of new organizational theory,
and the intended contributions which results from its exploration. To summarize, the
primary arguments asserted in this dissertation are that the emergence of social capital
has a significant influence on the performance of organizational capabilities, and that
the relationship between the emergence of social capital and the performance of
organizational capabilities is fundamental to capability evolution over time. In this
chapter the dissertation proposes that previous arguments regarding dependence based
on organization position, path, and process, articulated in the organizational capability
literature provide only a partial explanation of capability change. We have suggested
that organizational capabilities also evolve from variations in social capital developed
and deployed by network members. The interaction of structural, cognitive and
relational embeddedness is important because they influence the socially complex
micro-practices among group members which lie at the core of capability performance
and change.
Emphasizing an examination of the evolution of a single organizational
capability, threat identification, focusing on its performance and change over time helps
in part to overcome the tendencies in the capability literature to either fuse the
relationships between performance and outcomes together because they are hard to
87
distinguish or define; or to fail to recognize the importance of context in determining
the relevance of the performance-outcome relationship (Haas & Hansen, 2005: 19). This
research addresses both and lays out an agenda for a comprehensive study of two well
studied concepts: social capital and organizational capabilities. In what follows the
methodology and findings of this project will be discussed; these results examine each
of the relationships argued for in this chapter and provide insight into the relationship
between social capital and organizational capabilities from both cross-sectional and
longitudinal perspectives. Our findings provide answers to each of the four research
questions introduced earlier, and offer guidance in answering the question of whether
the emergence of social capital influences the evolution of organizational capabilities
more generally.
88
Chapter Four: Research Design and Methodology
In the last chapter, a theoretical framework was developed to explain the
substance of social capital and organizational capabilities, as well as the pattern of
relationships between these constructs, concluding with a series of empirically testable
hypotheses to investigate both cross-sectional and longitudinal patterns of change. In
studying these relationships we propose to employ an experimental design, based on a
practice-based crisis simulation performed collectively in real-time. The simulation relies
on the Experimental Laboratory for Investigating Collaboration, Information-Sharing,
and Trust in organizations (ELICIT) simulation platform, using protocols consistent with
those used to study real-world organization members of Defense Research and
Development Canada - Toronto, Collaborative Performance and Learning Section.
Justifying this choice of research setting is not difficult in today’s turbulent times.
Moreover, the growing influence of research that Karl Weick and others (Weick, 1993;
Weick & Roberts, 1993; Weick & Sutcliffe, 2001) have done studying the impact of crisis,
threat, and disaster situations on collective sensemaking and collaborative performance
under extreme pressure, makes this context a highly relevant and an ever more valuable
one9.
9 Moreover, Leadership Quarterly recently (August, 2007) placed a call for research to explore “extreme
conditions” citing: “organizational scientists are beginning to call for research on “extreme” contexts rather than average situations, and panels and symposiums on dangerous contexts were conducted at the most recent Gallup Leadership Institute Summit, SIOP, and served as the central theme at the last biannual Global Leadership Conference at West Point. As noted by McKelvey (call for papers for 2008 Org Science Winter Conference), “managers don’t really need the advice of organization science scholars when faced with ‘average’ situations. It is when they confront extreme events, emergent outcomes,
89
This chapter offers a systematic approach to testing the hypotheses and
examines one specific research design which can viably focus on supporting or refuting
the core dimensions of the relationships among these constructs over time. The topics
discussed in this chapter address issues of research design and methodology, focusing
on issues central to the empirical examination of our theoretical model, including:
experimental design; sampling approach and sample characteristics; operational
definitions of dependent and independent variables; and construct measurement.
In this experimental simulation, the primary aim is to identify a collective threat
which requires active and ongoing coordinated collaboration among the simulation
participants, clearly illustrating the substance of an organizational capability. This
project explores the relationships between social capital and organizational capability
performance in real-time and under a variety of conditions. The aim is: first, to examine
whether and how social capital emerges during collaborative performance; second, to
isolate and examine whether and how social capital contributes to the performance of a
capability; and third, to determine the impact of social capital on the evolution of
organizational capabilities over time.
Sample Population, Characteristics, and Selection
For the purpose of effectively conducting this experiment, a convenience
sampling approach was selected because it was appropriate for early exploration of
irregularities, or crises that managers should find it useful to learn from organization scientists.” There is a need for study in this context, and an immediate applicability to management in all organizational types.
90
causal relationships (Singleton & Straits, 1999). The sample for this study consisted of
131 students enrolled in graduate (n = 120) and undergraduate (n = 11) studies at York
University. Demographic distribution relating to sample age, educational attainment,
gender and ethno-cultural diversity are presented in Tables 4.1 – 4.4, and provide
further elaboration of participant details. These characteristics are largely consistent
with hiring criteria for individuals currently working in those service roles most
commonly associated with emergency preparedness and crisis response in Canada (see
appendix A for a sample job description used in hiring), and provide some
representative similarity with requirements of real-world operators in this field. The
intent of the sampling design is to support data collection, which aims to investigate a
main causal effect between social capital and organizational capability performance.
Non-probability (non-random) convenience samples are often used in conjunction with
experimental designs based on the researcher’s desire to explore effects and discover
patterns among variables. Given limitations on time and resources, this sampling
method was not only appropriate for the needs of this project, but also consistent with
the selected experimental research design (Pedhazur & Schmelkin, 1991).
Our sample of 131 participants provided a reasonable sample size to allow data
analysis using a variety of means; first addressing socio-metric measures with UCINET
and later applying structural equation modeling and growth curve modeling techniques
(MacCallum, Browne, & Sugawara, 1996).
91
Table 4.1
Age Distribution of Sample
Variables N
Valid Missing Mean Median Mode
Indicate age in years: 125.00 6.00 29.65 28.00 27.00
Table 4.2
Educational Distribution of Sample
Indicate the level of your program of study: Frequency Percent
Valid
Percent
Cumulative
Percent
Valid Undergraduate 11 8.4 8.7 8.7
Fulltime Masters 62 47.3 48.8 57.5
Parttime Masters 9 6.9 7.1 64.6
Doctoral 45 34.4 35.4 100.0
Total 127 96.9 100.0
Missing Non-response 4 3.1
Total 131 100.0
Table 4.3
Gender Distribution of Sample
Indicate gender: Frequency Percent
Valid
Percent
Cumulative
Percent
Valid Male 38 29.0 29.9 29.9
Female 88 67.2 69.3 99.2
Alternate - please specify 1 .8 .8 100.0
Total 127 96.9 100.0
Missing Non-response 4 3.1
Total 131 100.0
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Experimental Design and Methodology
The explanation of the proposed experiment will be introduced in sections,
based on the linear sequential progression in which they would occur. As a result,
discussion will begin with preparation and recruitment, followed by the experiment
Table 4.4
Demographic Distribution of Sample
Indicate which of the following you
most self-identify as: Frequency Percent
Valid
Percent
Cumulative
Percent
Valid Aboriginal (First Nations, Native
Canadian, North American Indian,
Metis or Inuit)
1 .8 .8 .8
Arab 3 2.3 2.4 3.2
Black 9 6.9 7.2 10.4
Chinese 19 14.5 15.2 25.6
Filipino 3 2.3 2.4 28.0
Korean 2 1.5 1.6 29.6
Latin American 5 3.8 4.0 33.6
South Asian (East Indian, Pakistani,
Punjabi, Sri Lankan, etc.) 10 7.6 8.0 41.6
South East Asian (Vietnamese,
Cambodian, Malaysian, Laotian, etc.) 1 .8 .8 42.4
West Asian (Iranian, Afghan, etc.) 6 4.6 4.8 47.2
White 46 35.1 36.8 84.0
Other - please specify 20 15.3 16.0 100.0
Total 125 95.4 100.0
Missing Non-response 6 4.6
Total 131 100.0
93
itself, concluding with the subject debriefing session. The general design of the
experiment proposed in this dissertation follows a ‘pre-experiment briefing
simulation one measurement simulation two measurement simulation three
measurement and debriefing’ approach (illustrated in Figure 4-1). The specific
aspects of the experimental design of the simulation have been included in the form of
an appendix (Appendix B), which was created by the simulation software developers as
a means of demonstrating the versatility and robustness of the system. The information
in this appendix provides the reader with a general overview of the underlying
experimental treatment or simulation in which the organization members will
participate, and illustrates the dynamics of player-simulation across all conditions. Here,
our interest is in using the platform as a means to test relationships of interest and as
such, we focus on the unique approaches and attributes specific to this research project,
rather than reiterating well-documented details of the simulation platform (outlined in
Appendix B; see also Ruddy, 2007).
Figure 4-1: Configuration of repeated measures experimental design
94
Unlike many social network studies, which rely exclusively on subjective self-
reports (such as name generators or information-quality recollection) in either a cross-
sectional (Bowler & Brass, 2006; Totterdell, Wall, Holman, Diamond, & Epitropaki, 2004:
Diamond & Epitropaki, 2004) or longitudinal format (Venkataramani & Dalal, 2007), the
data collected in this dissertation are composed of subjective self-reported measures as
well as objective structural, interaction, and performance measures captured
dynamically as they occur in real time. Measurements taken in this research project are
largely based on measures demonstrated to be well-established (reliable and valid) in
the management literature, and where necessary these have been adapted, modified or
altered to suit the specific context in which the experiment takes place (specific details
are outlined below in each variable’s respective measurement section).
Experimental Simulation Overview
The ELICIT simulation, which was originally intended to examine the implications
of organization structure on collaborative performance, is an entirely computer-based
activity performed in real-time by participants who interact through an entirely virtual
medium, consistent with many distributed networks found in the field today. The
simulation revolves around the need to detect and acknowledge an impending crisis
situation (i.e. threat) in which the participants must collectively interact to identify four
specific types of information (who is responsible; when will the attack occur; where will
95
the attack occur; what the target is). As the simulation begins, the group of participants
is provided with an overview of their organization’s design, illustrating both the
configuration of the structure (hierarchical or network-centric) as well as the relative
position of their role in the structure. In this study, only the network-centric
configuration was used. As outlined in Appendix B, participants are provided with
factoids (pieces of information) at scheduled intervals throughout the simulation;
however, the value of each factoid is variable, ranging in degree from highly relevant to
insignificant. No one participant or single factoid provides enough information to
conclusively resolve any of the four dimensions of the impending crisis, thus only
through information sharing and collaboration is the group collectively able to
accurately identify the threat. By the conclusion of the simulation, participants have
identified all four aspects of the impending crisis.
After each round of the simulation, self-report measurements are taken of the
participants’ perceptions of social capital (cognitive and relational), and their
assessment of the quality of their threat identification. Once data have been collected
from each participant, the performance results of the previous simulation are revealed,
providing performance feedback detailing whether or not each facet of the threat was
correctly identified. This process is repeated as outlined in Figure 4-1 until the third
round is completed, at which time participants conclude with a final series of measures
and a debriefing session.
96
Introduction, Informed Consent and Pretest
In advance of the experimental sessions, it was necessary to satisfy the ethical
standards and human participant requirements held at York University, which required
the collection of informed consent forms from each participant in advance of their
participation, ensuring that they were aware of the risks involved in this experiment.
Upon completion each participant was randomly assigned a role and pseudonym in the
simulation, and viewed an instructional video which instructed them not to divulge any
information that would offer others the ability to link their real identity to their role and
pseudonym.
It is important to note that participants are randomly assigned pseudonyms and
are instructed not to disclose their true identity throughout the simulation. In previous
reviews of the social capital literature, two prominent alternatives to the “social capital
hypothesis” have emerged – selectivity bias and social homophily (Lin, 1999; Mouw,
2006). In both instances the benefits of social capital are proposed to result from
underlying influences based on demographic differences and social similarity biases. We
address these biases with both a design-oriented correction (random assignment of
pseudonyms) and a statistically-oriented one as well (measurement of demographic
dimensions). Given the wealth of research supporting the potential effects of social
homophily in confounding the implications of social capital, our experimental protocols
were particularly relevant in proactively curbing the possible development of social
homophily. Demographic data were collected to further decrease the probability of
97
spurious results. Research respondents were asked to provide personal demographic
data (age, gender, ethnicity and ethnic origin, first language, program information), and
pseudonym data (name, presumed gender). These data were then coded and
introduced into preliminary statistical modeling in order to control for potentially
confounding results.
Following the collection of pretest measures, participants were provided with an
instruction set and prompted to watch introductory video footage detailing the specifics
of the simulation, the instructions for participation, and the organizationally desired
outcome. While this video segment provides an adequate explanation for the activities
to follow in a standardized format, participants were afforded a collective opportunity
to question any facet of the simulation’s functionality to ensure that participants had a
general understanding of how to proceed. At this point in the first simulation session, a
‘practice round’, commences. The practice round serves as a baseline or reference point
against which to compare future objective measures (such as the structure of
interpersonal interaction patterns, or the duration to completion), and also as an
opportunity to provide immediate performance feedback in advance of the second
simulation trial.
Variable Measurement: Objective and Subjective Components
This section of the dissertation focuses on the operational definitions of each
construct outlined in the theoretical model illustrated in chapter three (Figure 3-2).
98
More specifically, this section explores the objective and subjective measurement of
each variable operationalized in this dissertation. We begin with the measures of social
capital, following which we address the measurement of our organizational capability,
threat identification.
Social Capital – Structural, Cognitive, and Relational Embeddedness Measurements
For the purposes of this dissertation social capital is hypothesized to
enhance the performance and evolution of organizational capabilities to the degree that
social networks emerge to support access to varied and diverse sources of information,
as well as the transmission and integration of this information among participants. An
integrative perspective of social capital, then, views structural embeddedness as the
context for communication, where cognitive and relational embeddedness are reflected
as transmitted content within the structures of the network, and provides a
comprehensive consideration of both bridging and bonding activities.
In this section we discuss the measurement of each dimension of social
capital, explaining how each is understood in operational terms. Consistent with much
of the network-based investigation of social capital, where possible we employ a socio-
metric measurement approach in which each respondent is requested to assess every
other network participant. As a result, in many cases we rely on single-item measures to
capture variables or their specific dimensions (Doreian, Batagelj, & Ferligoj, 2005;
Marsden, 1990, 2005; Scott, 2000; Wasserman & Faust, 1994); in many cases it would
99
be infeasible and overly cumbersome to have each respondent complete an identical set
of questions for every other network member, essentially completing (N [N-1]) sets of
responses. Others have noted that this is increasingly the norm in social network
research, due to the scale on which network studies are based (Ferrin et al., 2006: citing
Burt & Knez, 1996; Labianca, Brass & Gray, 1998; and Shah, 1998).
Although single-item questions have become standard practice in social network
research, we recognize the desirability and value of multi-item measures, and employ
them where suitable and complementary, to enhance potential triangulation in our
modeling. Collectively, our measures for each facet of social capital generate an “N x N”
matrix that includes the entire network of practice, providing a whole network
perspective in that we capture subjective and objective data for every participant,
leaving no holes. This said, because this study examines the earliest phases of social
capital emergence one might expect low or omitted subject responses in some areas of
structural, cognitive and relational embeddedness. To address these gaps and ensure a
conservative measure of embeddedness in each area we score missing or omitted
scores as zero before calculating the in-bound and out-bound means for each measure.
While UCINET does have several ways of dealing with missing data (Borgatti, Everett, &
Freeman, 2002), preliminary investigation revealed that means became highly inflated
for relatively peripheral participants without the inclusion of zero-scores; we therefore
used a zero score indicating that an individual had not transacted with one or more
network members (i.e. 0 = I do not recall this person) and omitted diagonal or self-
100
scores from our analysis as has been done elsewhere in similar studies (Borgatti & Cross,
2003: 437). Tables 4.5 – 4.7 report the means, standard deviations, and correlation
coefficients among the study variables in each simulation round.
101
Table 4.5
Measurement Interval One – Means, Standard Deviations, and Zero-Order Correlation Coefficients
M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1. Individual Information Posting Activity 4.70 3.20
2. Individual Information Sharing Activity 0.27 0.56 .094
3. A1T - Was relied on for knowledge or
information 2.53 0.77 .552
** .295
**
4. A2T - Was provided with knowledge and
information 2.21 0.54 .245
** .167 .458
**
5. A3T - Provided valuable information to
help 2.42 0.91 .477
** .258
** .779
** .525
**
6. A4T - Displayed awareness and
understanding of the threat 2.28 0.77 .406
** .331
** .748
** .521
** .880
**
7. A5T - Displayed competence during
interactions 2.35 0.81 .367
** .309
** .668
** .619
** .831
** .857
**
8. A6T - Relationally close in working 1.78 0.57 .335**
.347**
.647**
.534**
.743**
.772**
.732**
9. A7A - The identification decision was a
high quality decision 4.54 1.63 .000 -.090 .038 -.113 .018 -.015 .011 .129
10. A7B - The identification decision helps
your organization achieve its objectives 4.30 1.74 .040 -.079 .031 -.026 .030 .026 .038 .115 .784
**
11. A7C - The team members put a great
deal of effort into making this identification 4.09 1.67 .042 -.086 -.085 -.093 -.021 -.030 -.032 .028 .563
** .561
**
12. Natural Logarithm of Elapsed Time 4.75 1.85 .026 .051 .002 -.033 .036 .098 .089 .028 .242**
.147 .075
13. Identification Dimension – “Who” 0.54 0.50 -.028 -.042 -.039 -.052 -.018 -.031 -.018 .063 .476** .394** .338** .416**
14. Identification Dimension – “What” 0.59 0.49 -.215* -.069 -.165 -.084 -.137 -.107 -.100 -.010 .341** .251** .255** .423** .351**
15. Identification Dimension – “Where” 0.40 0.49 -.006 .042 -.035 -.053 -.079 -.062 -.058 -.037 .347** .332** .367** .333** .414** .343**
16. Identification Dimension – “When” 0.18 0.38 -.007 -.116 -.173* -.188* -.134 -.131 -.142 -.058 .253** .220* .132 .147 .183* .346** .110
* Correlation is significant at the 0.05 level (2-tailed); ** Correlation is significant at the 0.01 level (2-tailed)
102
Table 4.6
Measurement Interval Two – Means, Standard Deviations, and Zero-Order Correlation Coefficients
M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1. Individual Information Posting Activity 4.64 3.37
2. Individual Information Sharing Activity 0.27 0.55 .062
3. B1T - Was relied on for knowledge or
information 2.91 0.96 .438
** .269
**
4. B2T - Was provided with knowledge and
information 2.50 0.79 .250
** .278
** .633
**
5. B3T - Provided valuable information to
help 2.81 1.02 .397
** .213
* .880
** .617
**
6. B4T - Displayed awareness and
understanding of the threat 2.68 0.85 .297
** .273
** .810
** .628
** .871
**
7. B5T - Displayed competence during
interactions 2.70 0.90 .317
** .354
** .756
** .622
** .776
** .871
**
8. B6T - Relationally close in working 2.13 0.79 .298**
.453**
.691**
.545**
.700**
.795**
.856**
9. B7A - The identification decision was a
high quality decision 5.33 1.65 -.064 .061 .240
** .176
* .229
** .184
* .092 .061
10. B7B - The identification decision helps
your organization achieve its objectives 5.06 1.81 -.025 .067 .227
** .223
* .206
* .159 .077 .057 .850
**
11. B7C - The team members put a great
deal of effort into making this identification 4.95 1.60 .056 -.010 .125 .168 .119 .120 .042 .050 .567
** .600
**
12. Natural Logarithm of Elapsed Time 5.50 1.61 -.055 -.158 .173* .080 .166 .098 .043 .006 .219
* .171 .225
**
13. Identification Dimension – “Who” 0.66 0.47 -.062 -.002 .201* .186* .186* .149 .076 -.002 .512** .499** .315** .387**
14. Identification Dimension – “What” 0.69 0.46 -.204* -.062 .163 .076 .155 .128 .040 .027 .441** .353** .200* .397** .511**
15. Identification Dimension – “Where” 0.48 0.50 -.157 -.022 .230** .195* .216* .157 .064 .097 .624** .560** .383** .299** .458** .539**
16. Identification Dimension – “When” 0.56 0.50 -.100 -.110 .116 .210* .105 .096 .047 .001 .212* .285** .178* .297** .310** .410** .366**
* Correlation is significant at the 0.05 level (2-tailed); ** Correlation is significant at the 0.01 level (2-tailed)
103
Table 4.7
Measurement Interval Three – Means, Standard Deviations, and Zero-Order Correlation Coefficients
M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1. Individual Information Posting Activity 4.76 2.81
2. Individual Information Sharing Activity 0.30 0.67 .271**
3. C1T - Was relied on for knowledge or
information 2.77 0.97 .428
** .346
**
4. C2T - Was provided with knowledge and
information 2.30 0.76 .226
** .403
** .761
**
5. C3T - Provided valuable information to
help 2.79 0.94 .399
** .270
** .938
** .709
**
6. C4T - Displayed awareness and
understanding of the threat 2.71 0.81 .412
** .367
** .848
** .776
** .856
**
7. C5T - Displayed competence during
interactions 2.62 0.89 .261
** .369
** .863
** .811
** .864
** .892
**
8. C6T - Relationally close in working 2.16 0.77 .318**
.456**
.783**
.789**
.762**
.863**
.849**
9. C7A - The identification decision was a
high quality decision 4.18 1.77 -.044 -.142 .097 -.122 .134 -.060 .074 -.087
10. C7B - The identification decision helps
your organization achieve its objectives 4.02 1.78 -.024 -.184
* .082 -.085 .128 -.059 .061 -.046 .854
**
11. C7C - The team members put a great
deal of effort into making this identification 4.43 1.72 .082 -.027 .208
* .105 .217
* .110 .170 .108 .438
** .535
**
12. Natural Logarithm of Elapsed Time 5.46 1.31 -.002 .043 .136 .092 .160 .109 .097 .040 -.018 .027 .017
13. Identification Dimension – “Who” 0.48 0.50 -.066 -.032 .053 -.064 .059 .030 .044 .032 .291** .363** .093 .141
14. Identification Dimension – “What” 0.02 0.12 .033 -.056 -.030 -.075 -.051 -.070 .035 -.063 -.048 -.036 -.140 -.003 .005
15. Identification Dimension – “Where” 0.46 0.50 .127 .057 .140 .005 .147 .114 .102 .035 .314** .364** .239** .201** .403** .010
16. Identification Dimension – “When” 0.36 0.48 .035 .011 .130 -.026 .126 .042 .085 -.046 .364** .425** .166 .107 .427** .037 .303**
* Correlation is significant at the 0.05 level (2-tailed); ** Correlation is significant at the 0.01 level (2-tailed)
104
Structural Embeddedness Measurement
A structural approach to social capital measurement presumes that network
structures support access to varied and diverse sources of information, and facilitate the
transfer of information between members of the organization. In this regard, structural
embeddedness is concerned with the properties of social systems and networks of
relations as a whole (Granovetter, 1985), dealing particularly with the impersonal
configurations of linkages, or overall patterns of connections between actors within a
social network (Burt, 1992, 2000). Here, each organization member should appear both
deeply connected and structurally equivalent to the other members inside the
organization, thus members should be well-embedded structurally throughout the
network. In order to assess the degree to which network members are structurally
embedded within the organizational network we take two distinct, yet complementary,
measurements: the out-degree mean of individual posting activities (defined below),
and the out-degree mean of individual sharing activities. Both indicators reflect the
directional flow of information between network members and are objectively captured
within the parameters of the simulation thereby requiring no participant recall; data are
derived from simulation log reports, converted into socio-metric matrices from which
means are calculated using UCINET 6.0 (Borgatti et al., 2002). While others commonly
rely on recollection or self-report name-generators for data collection, in this research
structural embeddedness is based on objective network data captured within the
framework of the crisis simulation. While these data must be coded into an analyzable
105
format, the measure derived from an individual calculation for each simulation
participant based on actual or known (observed) data. Posting is a one-to-many action
allowing individual to distribute factoids to all members of the network simultaneously
by posting them to a public web-forum, whereas sharing is a one-to-one activity in
which an email is distributed to a single party.
Once calculated, the outbound means of posting and sharing ties merged into a
single factor for each round of the simulation, providing three separate indicators of
structural embeddedness. We chose to measure and rely upon the outbound scores
because they reflect each individual’s tie creation activities. While inbound ties might
also provide a measure of network connectivity, or “the extent to which members of the
network are linked to each other” (Tichy & Fombrun, 1979: 928), in this study we were
more focused on how social capital emerges than in patterns of network morphology
(Wasserman & Faust, 1994). As a result the analysis emphasizes the role of structural
embeddedness as providing conduits for the flow of cognitive and relational resources
such as improvements in access to valued information and resources (Burt, 1997;
Freeman, 1979; Hansen, 1999).
Cognitive Embeddedness Measurement
Social capital is presumed to enhance the potential emergence of organizational
capabilities to the degree that cognitive schemas support recognition, absorption and
integration of varied and diverse sources of information. In addition, the absorption and
106
integration of this information should be apparent among each of the organizational
participants. As a result, each organization member should be able to assimilate,
exchange, and combine information from their own sources, as well as those of the
other members inside the organization, thus members should be well-embedded
cognitively throughout the group network. To measure the degree to which a shared or
collective cognitive schema was present among respondents, we employed three
directed self-report questions scored using seven-point scales.
“A1T” Relied on the person listed below for knowledge or information in this
round of the simulation. (1 = Not at All; 7 = To a Great Extent)
“A3T” The person listed below provide you with valuable information to help
identify the threat in this round. (1 = Strongly Disagree; 7 = Strongly
Agree)
“A4T” The person listed below displayed awareness and understanding of the
threat in this round of play. (1 = Strongly Disagree; 7 = Strongly Agree)
This set of measures captures the presence of cognitive social capital in the form
of mutually aligned schema. For each item we tabulate the column mean using UCINET
6.0, resulting in a measure for Xi based on Σ = Xj responses; this provides each
participant with an item score that is rated by the responses of every other network
member (i.e. network’s perception of participant i on the basis of item X). Items scores
107
were then evaluated for reliability in each simulation round independently (round one α
= .922; round two α = .944; round three α = .955). Results suggest that these socio-
metric scales provide a reliable measure of the degree to which individuals believe that
networked-others share an understanding of the impending crisis. Taken together these
items provide the capacity to assess cognitive embeddedness across the network and
give greater insight into the specific patterning of social capital emergence among
members of a network.
Relational Embeddedness Measurement
For the purposes of this dissertation social capital was presumed to enhance the
performance and evolution of organizational capabilities to the degree that relational
connections within the network foster trust, trustworthiness and relational closeness
among members of the networks of practice. To measure relational embeddedness, or
the degree to which an organization member exhibits trust and trustworthiness, and
relational closeness for those they interact with, we adapted the methodological
approach recently taken by Moran (2005). Taken together his measures of relational
trust and relational closeness complement our research context to the degree that only
semantic modifications are necessary to his original design. Relational trust was also
measured using three socio-metric items, where respondents were asked to rate their
degree of agreement or disagreement with the following items for each member of the
network.
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“A2T” Provided the person listed below with knowledge and information during
this round of the simulation. (1 = Not at All; 7 = To a Great Extent)
“A5T” The person listed below displayed competence during our interactions in
this round of the simulation. (1 = Strongly Disagree; 7 = Strongly Agree)
In each case, ratings were assessed on a seven point scale, and demonstrated a
reasonably acceptable reliability (α = 0.68) in previous studies. For relational closeness
we asked participants to complete a single item socio-metric measure for each of the
other network members, ranging from “1 = Distant/Arm’s Length” to “7 = Very Close”.
“A6T” How close do you feel your working relationship is with the person listed
below in this round of play?
Similar to the approach taken with the cognitive embeddedness items, column
means were tabulated using UCINET 6.0 to generate items scores for respondents based
on the ratings of other network members. Here, reliability statistics for the proposed
measurement of relational embeddedness were calculated separately for each round of
play (round one α = .823; round two α = .862; round three α = .928), suggesting a
greater than adequate reliability in all three measurement intervals (Carmines & Zeller,
1979).
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Social Capital Measurement Models
Here we present the measurement model for social capital and its underlying
factor structure, illustrating the contribution of structural, cognitive and relational
embeddedness to social capital and the appropriateness of modeling social capital as a
second order latent factor. We present the measurement models using data collected in
the first measurement interval, rather than presenting all of the measurement models
across time for the sake of brevity and ease of understanding (and since they are
illustrated in later structural model analyses). Only the first measurement model is
illustrated in this chapter to avoid confusion, however, factor structures in all three
rounds have been examined for convergent and discriminant validity to ensure that the
models presented provide a statistically significant improvement over other variants.
The procedure followed to test for convergent and discriminant validity in
justifying the construction of social capital as a second order latent factor is consistent
with those identified elsewhere (Balasubramanian, Konana, & Menon, 2003; Bollen &
Lennox, 1991; Byrne, 2001a; Edwards, 2001; Jarvis, MacKenzie, & Podsakoff, 2003;
Mathieu & Farr, 1991). Previous theoretical and empirical research has consistently
made the case for a three dimensional social capital construct (for example, Inkpen &
Tsang, 2005; Leana & Pil, 2006; Leana & Van Buren, 1999; Nahapiet & Ghoshal, 1998;
Tsai, 2000; Tsai & Ghoshal, 1998), and our preliminary results illustrated in Tables 4.8
and 4.9 support this conclusion. Table 4.8 presents the findings of our confirmatory
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factor analysis (CFA) for social capital; these results suggest that modeling social capital
as a second order latent factor provides a statistically significant improvement in fit
when compared to other measurement models (Δχ2/df = 4.572, p < 0.05 using
comparable models). Allowing two error terms to covary across the structural and
cognitive embeddedness factors further improved overall model fit (constrained model
χ2/df = 2.445, CFI = .963, RSMEA = .105 [p = .011+; freely estimated model χ2/df = 1.681,
CFI = .984, RSMEA = .072 [p = .192]), further supporting the presence of a second order
latent factor (Byrne, 2001b; Edwards, 2001).
Table 4.9 presents the results of our discriminant analyses which test whether
first order factors are statistically distinct (Balasubramanian et al., 2003; Venkatraman,
1989). To establish discriminant validity, we first begin with an assumption that first
order factors are unrelated and therefore do not covary, we then allow covariance
among all three factors and sequentially constrain the relationship between each to
unity, suggesting that constrained pairs of factors are indistinguishable
(Balasubramanian et al., 2003). Relative to the independence model all constrained
models provided improved model fit although these were identified as inadmissible
solutions due to non-positive definite covariance matrices indicating the presence of
one or more negative variance estimates imposed during constraint (Arbuckle, 2007;
Byrne, 2001b).
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Table 4.8
Confirmatory Factor Analysis Results for Comparative Social Capital Measurement Models
Measurement Model Structure χ2 df p χ2/df CFI GFI AGFI RMSEA
Independent First Order Factors 288.290 21 .000 13.728 .727 .533 .642 .313 (p = .000)
Covarying First Order Factors 44.522 17 .000 2.619 .924 .839 .963 .112 (p = .008)
Common Social Capital Factor 51.032 20 .000 2.552 .958 .909 .836 .109 (p = .006)
Latent Second Order Factor 46.460 19 .000 2.445 .963 .918 .846 .105 (p = .011)
Latent Second Order Factor* 30.262 18 .035 1.681 .984 .948 .896 .072 (p = .192)
* Estimates covariance of error term 1 and 3
Table 4.9
Discriminant Validity Analysis of Comparative Social Capital Factor Structures
Factor Structure Models χ2 df p χ2/df CFI GFI AGFI RMSEA
Independent First Order Factors 288.290 21 .000 13.728 .727 .533 .642 .313 (p = .000)
Constrained Covariance of Structural - Cognitive - Relational Factors*
81.848 20 .000 4.092 .917 .885 .792 .154 (p = .000)
Constrained Covariance of Cognitive - Relational Factors*
71.058 18 .000 3.948 .929 .886 .773 .151 (p = .000)
Constrained Covariance of Structural - Relational Factors*
44.571 18 .000 2.476 .964 .924 .848 .107 (p = .012)
Constrained Covariance of Structural - Cognitive Factors*
44.532 18 .000 2.474 .965 .924 .847 .106 (p = .012)
Unconstrained First Order Factors
44.522 17 .000 2.619 .924 .839 .963 .112 (p = .008)
* Solution inadmissible due to non-positive definite covariance matrix among first order factors
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Comparing admissible solutions between the independence model and the
unconstrained covariance model our findings demonstrate significant improvement in
model fit when factor covariance is allowed to freely estimate (independent factors
χ2/df = 13.728, CFI = .727, RSMEA = .313 *p = .000+; freely estimated covariance χ2/df =
2.619, CFI = .924, RSMEA = .112 [p = .008]), although inferior to the fit social capital
modeled as a second order latent factor (χ2/df = 1.681, CFI = .984, RSMEA = .072 [p =
.192]). Combined with the findings presented in previous CFA, these results imply that
the factors are related but statistically distinct.
The second order factor model for social capital in round one is presented in
Figure 4-2. Tables 4.10 – 4.12 provide an overview of the model regression weights,
standardized total effects, and model-fit indices, demonstrating the contribution that
each item makes to their respective first order factors of structural, cognitive and
relational embeddedness. This model illustrates the appropriateness of these first order
factors in identifying social capital. In the next chapter, factor scores are computed and
used to construct structural models in which social capital is modeled as a formative
latent second order factor resulting from structural, cognitive and relational
embeddedness.
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Figure 4-2: Social Capital Measurement Model
Social
Capital
Structural
Embeddedness
Share
Post1
Cognitive
Embeddedness
A2T
A1T 1
A3T
A4T
Relational
EmbeddednessA5T
A6T
1
1
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Table 4.10
Regression Weights for Social Capital Measurement Model
Estimate Standardized Estimate S.E. C.R. P
Structural Embeddedness <--- Social Capital 1.989 1.000 .366 5.438 ***
Cognitive Embeddedness <--- Social Capital .848 1.000 .070 12.192 ***
Relational Embeddedness <--- Social Capital 1.000 .968
Share <--- Structural Embeddedness .129 .332 .040 3.221 .001
Post <--- Structural Embeddedness 1.000 .451
A1T <--- Cognitive Embeddedness 1.000 .799
A3T <--- Cognitive Embeddedness 1.378 .932 .106 13.056 ***
A4T <--- Cognitive Embeddedness 1.193 .948 .089 13.370 ***
A5T <--- Relational Embeddedness 1.000 .921
A6T <--- Relational Embeddedness .627 .821 .047 13.244 ***
A2T <--- Relational Embeddedness .451 .620 .055 8.223 ***
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Table 4.11
Standardized Total Effects for Social Capital Measurement Model
Social Capital Relational Embeddedness Cognitive Embeddedness Structural Embeddedness
Relational Embeddedness .968 .000 .000 .000
Cognitive Embeddedness 1.000 .000 .000 .000
Structural Embeddedness 1.000 .000 .000 .000
A6T .794 .821 .000 .000
A5T .891 .921 .000 .000
A2T .600 .620 .000 .000
A4T .948 .000 .948 .000
A3T .932 .000 .932 .000
A1T .799 .000 .799 .000
Post .451 .000 .000 .451
Share .332 .000 .000 .332
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Table 4.12 Summary of Model Fit Indices for Social Capital Measurement Model
CMIN
Model NPAR CMIN DF P CMIN/DF
Default model 18 30.262 18 .035 1.681
Saturated model 36 .000 0
Independence model 8 775.547 28 .000 27.698
RMR, GFI
Model RMR GFI AGFI PGFI
Default model .039 .948 .896 .474
Saturated model .000 1.000
Independence model .490 .311 .114 .242
Baseline Comparisons
Model NFI
Delta1 RFI
rho1 IFI
Delta2 TLI
rho2 CFI
Default model .961 .939 .984 .974 .984
Saturated model 1.000
1.000
1.000
Independence model .000 .000 .000 .000 .000
RMSEA
Model RMSEA LO 90 HI 90 PCLOSE
Default model .072 .019 .116 .192
Independence model .453 .426 .481 .000
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Organizational Capability Performance and Evolution
Threat identification in this context represents an organizational capability; in
our discussion of capability performance, we focus extensively on the capacity to
generate a high quality, accurate and timely identification of an impending threat. Here,
threat identification speaks to the network capacity to identify and acknowledge four
facets of an impending threat accurately, to make an assessment about the quality of
this identification, and to do so in as little time as possible. Threat identification is
portrayed as a multidimensional construct, and is characterized by coordinated
collaborative performance within social networks, although like other interactive
processes we realize that capability performance may occur and evolve despite varying
degrees of internal dissent, intra-group fractures or coalitional conflict (Jehn, 1995;
Jehn, Northcraft, & Neale, 1999; Levine & Moreland, 1990; Levine, Resnick, & Higgins,
1993). Building an operational understanding of capability performance in this context
relies on three constructs: capability accuracy, capability quality, and capability speed.
The measurement of each construct is discussed in sequence in the following
paragraphs.
Capability Accuracy Measurement
Capability accuracy reflects the capacity to come up with the correct answer in
each of four threat dimensions; it is an indicator of fit-quality between the performance
outcomes of the participants and desired or aspirational performance goals (Dooley &
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Fryxell, 1999). In this dissertation capability accuracy is measured from objective data
capturing the correctness of each threat dimension identified by the participant,
compared against the objective information known to be correct (i.e. how many
dimensions in the identification were correct). As a result, each dimension on which
accuracy is scored is a binary measure (0 = incorrect; 1 = correct), providing a standard
that may be objectively compared across groups and which reflects an assessment of
absolute performance.
Capability Quality Measurement
The capability quality dimension of capability performance reflects a level of
confidence with or divergence from the threat identification made by a participant.
Capability quality provides an assessment of the degree to which the best possible
identification has been made (Dooley & Fryxell, 1999), which is important in terms of
capability performance because it is suggestive of subject calibration to each simulation
scenario (Blais, Thompson, & Baranski, 2005; Keren, 1991; Liberman & Tversky, 1993).
In keeping with this operational definition, capability quality is measured using three
items adapted from the work of Dooley and colleagues (2000) to suit this research
context.
“A7A” The identification decision was a high quality decision.
“A7B” The identification decision helps your organization achieve its objectives.
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“A7C” The team members put a great deal of effort into making this
identification decision successful.
Here, each item is measured in each round of the simulation using a seven-point Likert
scale in which respondents were asked to rate their own level of agreement with each
item, ranging from “1 = Strongly Disagree” to “7 = Strongly Agree”. Missing data for
these items was limited to no more than five instances in any given measurement
interval, and a series-mean replacement technique was used to estimate the missing
values. Scale reliability was assessed by calculating Cronbach’s alpha for each round of
the simulation separately (round one α = .840; round two α = .861; round three α =
.825); the results suggest reasonable reliability across all three measurement intervals
(Carmines & Zeller, 1979).
Capability Speed Measurement
The capacity to capture capability performance in real-time under relatively
stable conditions is a rare opportunity; we include a measure of time in our construction
of capability performance to take full advantage of this opportunity. Capability speed is
derived from a measure of elapsed time captured in real-time. It reflects the duration
“between the first reference to a deliberate action, and the time in which a
commitment to action is made” (Eisenhardt, 1989; Judge & Miller, 1991: 455). In this
research context, then, capability speed is measured as the duration from the beginning
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of the simulation until the time at which all four dimensions of the threat identification
are complete or the simulation times-out at 25 minutes. Initial measurements of
duration are captured in seconds with duration reflecting slow identification (i.e. 25
minutes = 1500 seconds which illustrates a high value corresponding to a poor
performance). Consistent with Judge and Miller’s (1991) treatment of time, we too
reverse score the elapsed time to make a threat identification to provide an intuitive
metric for capability speed. In addition, given the magnitude of the values we are
working with relative to all of our other measures (binary and seven point scales), we
calculate the natural logarithm for each reverse-scored measure of elapsed time
providing a more manageable set of values.
Capability Performance Measurement Models
We repeat the procedure used to assess the factor structure of our social capital
construct to demonstrate the appropriateness and contribution of treatment of
capability performance as a second order latent factor composed of capability accuracy,
capability speed and capability quality. Again, we present only the measurement model
based on data from the first round of the simulation; measurement models for rounds
two and three appear embedded in the structural models evaluated in the next chapter.
Figure 4-3 illustrates our measurement model of Capability Performance as a reflective
second order latent factor; regression weights and standardized total effects for this are
presented in Tables 4.13 and 4.14, with model-fit indices provided in Table 4.15.
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Figure 4-3: Capability Performance Measurement Model
Capability
Performance
Capability
Accuracy
ID WHO
ID WHAT
ID WHERE
ID WHEN
Capability
Quality
A7A
A7B
A7C
Capability
Speed
1
1
1
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Table 4.13
Regression Weights for Capability Performance Measurement Model
Estimate Standardized Estimate S.E. C.R. P
Capability Accuracy <--- Capability Performance 1.000 .779
Capability Quality <--- Capability Performance 4.022 .717 .880 4.569 ***
Capability Accuracy <--- Capability Speed .115 .627 .020 5.894 ***
Capability Quality <--- Capability Speed .178 .222 .073 2.436 .015
ID WHO <--- Capability Accuracy 1.000 .679
ID WHAT <--- Capability Accuracy .852 .586 .153 5.571 ***
ID WHERE <--- Capability Accuracy .818 .564 .152 5.394 ***
ID WHEN <--- Capability Accuracy .366 .325 .112 3.256 .001
A7A <--- Capability Quality 1.000 .910
A7B <--- Capability Quality 1.008 .858 .090 11.178 ***
A7C <--- Capability Quality .722 .640 .091 7.917 ***
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Table 4.14
Standardized Total Effects for Capability Performance Measurement Model
Capability Performance Capability Speed Capability Quality Capability Accuracy
Capability Quality .717 .222 .000 .000
Capability Accuracy .779 .627 .000 .000
A7C .459 .142 .640 .000
A7B .616 .190 .858 .000
A7A .653 .202 .910 .000
ID WHEN .253 .204 .000 .325
ID WHERE .439 .354 .000 .564
ID WHAT .456 .367 .000 .586
ID WHO .529 .426 .000 .679
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Table 4.15 Summary of Model Fit Indices for Capability Performance Measurement Model
CMIN
Model NPAR CMIN DF P CMIN/DF
Default model 18 23.351 18 .177 1.297
Saturated model 36 .000 0
Independence model 8 346.420 28 .000 12.372
RMR, GFI
Model RMR GFI AGFI PGFI
Default model .054 .958 .916 .479
Saturated model .000 1.000
Independence model .574 .524 .388 .408
Baseline Comparisons
Model NFI
Delta1 RFI
rho1 IFI
Delta2 TLI
rho2 CFI
Default model .933 .895 .984 .974 .983
Saturated model 1.000
1.000
1.000
Independence model .000 .000 .000 .000 .000
RMSEA
Model RMSEA LO 90 HI 90 PCLOSE
Default model .048 .000 .097 .484
Independence model .296 .268 .324 .000
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Table 4.16
Confirmatory Factor Analysis Results for Comparative Capability Performance Measurement Models
Measurement Model Structure χ2 df p χ2/df CFI GFI AGFI RMSEA
Independent First Order Factors
106.721 21 .000 5.082 .731 .843 .731 .177 (p = .000)
Covarying First Order Factors 23.351 18 .177 1.297 .983 .958 .916 .048 (p = .484)
Latent Second Order Factor* 23.351 18 .177 1.297 .983 .958 .916 .048 (p = .484)
* Hybrid model in which Capability Accuracy and Quality mediate the influence of Capability Speed on Capability Performance illustrated in figure 4.3
Table 4.17
Discriminant Validity Analysis of Comparative Capability Performance Factor Structures
Factor Structure Models χ2 df p χ2/df CFI GFI AGFI RMSEA
Independent First Order Factors
106.721 21 .000 5.082 .731 .843 .731 .177 (p = .000)
Constrained Covariance of Accuracy - Speed - Quality Factors
73.046 21 .000 3.478 .837 .889 .811 .138 (p = .000)
Constrained Covariance of Accuracy - Quality Factors
52.523 19 .000 2.764 .895 .917 .843 .116 (p = .003)
Constrained Covariance of Accuracy - Speed Factors
46.589 19 .000 2.452 .913 .926 .860 .106 (p = .011)
Constrained Covariance of Speed - Quality Factors
25.324 19 .150 1.333 .980 .954 .912 .051 (p = .452)
Unconstrained First Order Factors
23.351* 18 .177 1.297 .983 .958 .916 .048 (p = .484)
* Represents a relatively insignificant improvement in model fit using χ2 statistic (Δχ
2/df = 1.973, p = 0.1601)
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Consistent with the approach taken above, we rely on confirmatory factor
analysis and the results of covariance analysis to determine the convergent and
discriminant validity of our second order construct. Results of our analysis are presented
in Tables 4.16 and 4.17. Interestingly the confirmatory factor analysis reveals no
statistically significant difference in model fit between the first order factor model in
which factors are allowed to freely covary and the second order latent construction of
capability performance. Both models provide very robust support for our modeling
(χ2/df = 1.297, CFI = .983, RSMEA = .048 [p = .484]), representing a better than
reasonable approximation of data (Arbuckle, 2007; Byrne, 2001a, 2001b). Discriminant
validity analysis reveals that the model best fitting our data is consistent with the results
of our CFA, with significantly inferior model fit resulting from the constraint of
covariance among all factors except the quality-speed relationship. This finding may
result from two sources: first, due to the nature of the simulation, where interactions
center on a crisis scenario, time is known to be limited and therefore a highly salient
indicator of success; second, this finding may result from the use of first round data
where respondents may lack other indicators in making their performance quality
assessment. In both cases participants may perceive quicker identification rates as
having superior quality relative to slower threat identification in the absence of other
feedback. This may be one reason why constraining the speed-quality relationship does
not significantly alter the χ2 statistic (Δχ2/df = 1.973, p = 0.1601). Chi-square statistics
aside, we feel more than justified in using our measurement model of Capability
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Performance (Figure 4-3) based on the pattern of relations identified by the factor
scores as well as the overall model fit indices presented in Table 4.15. Our data support
the modeling of Capability Performance as a reflective second order latent construct
taking into account the pattern of relationships in which Capability Speed is seen to be a
formative indicator of Capability Quality and Capability Accuracy. The results of the CFA
and discriminant validity testing imply that the factors are clearly related, yet remain
statistically distinct.
Methodological Limitations – Validity of Quantitative Methodology
In this final section of the fourth chapter, we consider the implications of the
research design described in this dissertation paying particular attention to the
limitations of our approach. The two primary dimensions we consider here are based on
the ability to demonstrate causation or internal validity, and the ability to generalize
from our findings thereby demonstrating the external validity of our results. Here, these
are examined consecutively.
Internal Validity
In general, the internal validity derived from properly conducted experiments is
excellent and can be enhanced by using more rigorous longitudinal designs and
incorporating pretesttreatmentmeasurement procedures (Pedhazur & Schmelkin,
1991; Singleton & Straits, 1999; Stangor, 1998). In addition, by emphasizing the
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importance of an appropriate sampling approach and focusing on the structure or script
of experiment, it is possible to create an immersive activity, thereby overcoming many
of the weaknesses stemming from artificiality discussed above. Recognizing the
underlying strengths of experimental methods in demonstrating causation, the method
and approach illustrated above includes a number of components intended to address
the most prominent threats to internal validity (Pedhazur & Schmelkin, 1991; Singleton
& Straits, 1999). Here, we addressed internal validity using approaches based in
research design and statistical methods, including those derived from: history (random
assignment), maturation and experimental mortality (multiple sessions conducted in a
single day), testing-effects and instrumentation (where data were derived from a
combination of objective and subjective measures), and subject awareness effects (well-
developed simulation scripts).
External Validity
External validity and the generalizability of results are typically considered
limited in a single incident (non-replicated) experiment, but can be enhanced by future
replication or cross-validation of the study and by varying the content of sample
composition (i.e. using probability sampling) (Pedhazur & Schmelkin, 1991; Singleton &
Straits, 1999; Stangor, 1998). To this end, provisions in this experiment have attempted
to address some of the limitations associated with threats to external validity. Decisions
with respect to reactions and interaction-effects due to testing sensitivity were
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considered, thus avoiding priming effects of pre-test measures. In addition, we sampled
research participants whose age and educational background were relatively consistent
with real-world organizations in which the crisis simulation and underlying experimental
method were particularly relevant. Here, we randomized the participant identities using
pseudonyms and measured demographic differences to reduce potential confounding
effects that would encourage the interaction of selection biases with experimental
variables. Based on the choice of research platform, the selection of participants, and
the construction of the crisis simulation, the arrangement of this experiment was
intended to minimize the reactive effects of artificiality, thereby enhancing the external
validity of our findings to the best of our ability.
Concluding Research Design Remarks
In this chapter we outlined what we believe to be a systematic approach to
testing the relationships hypothesized in this dissertation, based on a research design
that would viably focus on modeling the core cross-sectional and longitudinal
relationships in our theoretical model. Our focus emphasized both the generalities and
the specifics of issues central to the empirical examination of our theoretical model,
including: experimental design; sampling approach and sample characteristics; and
operational definitions and measurement models of our variables. As noted earlier, in
this experimental design, we explored the relationships between social capital
emergence and organizational capability performance in real-time and under a variety
130
of simulated conditions, on the basis of a practice-based crisis simulation examined in
real-time. Given that the central research thrust of this dissertation was to unpack how
the emergence of social capital influences the evolution of organizational capabilities,
the study’s research design has provided what we believe to be an effective, relevant,
and viable platform for investigating the ascribed relationships among these constructs
both cross-sectionally and over time.
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Chapter Five: Results and Findings of Study
Building on the results of our measurement modeling illustrated in chapter four,
this chapter contains a series of structural equation models which test our theoretical
arguments laid out in chapter three and illustrated in Figures 3-1, 3-2, and 3-3. More
specifically, we test the relationships of focal interest in this dissertation by investigating
how the emergence of social capital influences the evolution of organizational
capabilities. We begin by examining the patterns of relationships and model fit of three
cross-sectional structural equation models which reflect our repeated measures
research design. Although the factor structure of each model is identical across all three
measurement intervals, we evaluate each model independently without allowing any
temporal effects to judge comparative model fit among all three models. This cross-
sectional evaluation allows an investigation of the static relationships among structural,
cognitive, and relational embeddedness, social capital and capability performance at
three points in time.
Next, we take steps to evaluate the longitudinal effects of social capital on
capability performance and change. To do so we employ a growth curve modeling
approach developed to examine the correlation and covariance growth among latent
factors over time. This modeling technique makes possible the determination of rates of
growth for each of our two latent factors, social capital and capability performance, and
aids in determining whether these rates of change are correlated. Finally, we introduce
a longitudinal cross-lagged structural model that incorporates the results of all three
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rounds of our simulation, giving us the opportunity to examine the pattern of change
within the social capital – capability performance relationship taking into account cross-
sectional, latent growth, and cross-lagged effects in a single model. Only the results of
these analyses are presented in this chapter; for a review of the measurement models,
correlation tables or sample demographics, please refer to the details presented in the
previous chapter.
Cross-Sectional Model Fitting Results
Figure 5-110 illustrates the cross-sectional structural model reflecting the
conceptual arguments presented in chapter three. This single structural model is used to
investigate the structure of data collected in each round of the simulation, and provides
a standard of comparison across all three time periods to assess the overall pattern of
relationships among each of the first order and second order constructs as well as any
potential change in these patterns measured at distinct intervals. As noted in chapter
four, factor scores using maximum likelihood estimation have been calculated for
structural, cognitive and relational embeddedness based on item loadings and
reliabilities reported on earlier; these first order factors are now used in the estimation
of our structural model with social capital illustrated as a latent formative second order
factor. This formative construction requires a constraint on the social capital residual
error variance estimate (σ = 0.00) in order to fully identify our model. We undertook to
10
Structural models illustrated in this chapter omit item error terms and measurement residual terms for clarity and simplicity, although these have been included and accounted for in the modeling of each.
133
investigate the consequences of this constraint and provide results of a sensitivity
analysis in which the social capital residual error variance estimate was relaxed to a
more reasonable degree (σ = 0.45) and we reassessed model fit. We found no significant
change in model fit and little difference in the pattern of relationships among our
variables although the magnitude of the social capital – capability performance
relationship increased substantially (results reported separately in Appendix C). Based
on the conclusions of our sensitivity analysis, the results presented in this chapter will
be based on the more stringent standard (residual variance constrained, σ = 0.00) as it
ensures a consistent and conservative estimator in our cross-sectional modeling.
Assessments of the overall fit of our cross-sectional structural models were
established using several fit indices including chi-square test (χ2), goodness of fit indices
(GFI), confirmatory fit indices (CFI), and root mean square error of approximation
(RMSEA). These fit indices provide an estimation of the degree to which the data fit or
support our structural models in each of the measurement intervals; Table 5.1 presents
a comparative table of model fitting results in each period. For clarity, others have
suggested that the “chi-square test provides a measure of the inappropriateness of a
model if the model is not truly representative of the observed data” (Tsai & Ghoshal,
1998: 472), whereas GFI indicates the correspondence between observed and
hypothesized covariances, and CFI compares our proposed model to the null – saturated
and independence – models (Arbuckle, 2007).
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Overall fit between the structural model and data captured in the first round of
the simulation suggested a strong correspondence (χ2 = 43.962, df = 38, p = .234; GFI =
.943; CFI = .986; RMSEA = .035 [p = .699]). These results indicate that our model reflects
a statistically significant approximation for the data as each of the indices represents a
better than acceptable model fit (Arbuckle, 2007). The regression weights for each
relationship illustrated in Figure 5-1 are provided in Table 5.2, as are the standardized
estimates and significance levels. Covariance and correlation estimates for the
relationships among structural, cognitive and relational embeddedness are outlined in
Tables 5.3 and 5.4. Factor score weights and the results of the standardized total effects
of each factor are contained in Tables 5.5 and 5.6 respectively; a summary of the model
fit indices associated with the data collected in round one are presented in Table 5.7 for
the reader to compare our results with those of the saturated and independence
models.
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Figure 5-1: Structural Equation Model of Hypotheses with Comparative Cross-Sectional Relationships
t1: β = - .492 t2: β = - .554 t3: β = - .391
t1: β = - .246
t2: β = 1.555
t3: β = 2.118
t1: β = 1.136
t2: β = - .756
t3: β = - 1.619
t1: β = .462
t2: β = .354
t3: β = .289
t1: β = .163
t2: β = .903
t3: β = .981
t1: β = .617
t2: β = .746
t3: β = .658
t1: β = .229
t2: β = .145
t3: β = - .006
t1: β = .123
t2: β = .429
t3: β = .193
Capability
Performance
Capability
Accuracy
ID WHO
ID WHAT
ID WHERE
ID WHEN
Capability
Quality
A7A
A7B
A7C
Capability
Speed
1
1
1
Cognitive
Embeddedness
Structural
Embeddedness
Relational
Embeddedness
Social
Capital
1
136
Table 5.1
Comparative Structural Equation Model Fit Summary
Model χ2 df P χ2
/df GFI AGFI CFI RMSEA PCLOSE
Structural Model – Round One 43.962 38 .234 1.157 .943 .900 .986 .035 .699
Structural Model – Round Two 58.408 39 .024 1.498 .928 .878 .970 .062 .261
Structural Model – Round Three 38.391 40 .543 .960 .951 .920 1.000 .000 .909
Table 5.2
Regression Weights for Structural Model (Measurement Interval One)
Estimate Standardized Estimate S.E. C.R. P
Social Capital <--- Structural Embeddedness -.300 -.492 .147 -2.040 .041
Social Capital <--- Cognitive Embeddedness -.246 -.392 .157 -1.563 .118
Social Capital <--- Relational Embeddedness .786 1.136 .166 4.719 ***
Capability Performance <--- Social Capital 1.000 .462
Capability Accuracy <--- Capability Performance .163 .617 .048 3.433 ***
Capability Quality <--- Capability Performance 1.000 .868
Capability Accuracy <--- Capability Speed .123 .651 .019 6.433 ***
Capability Quality <--- Capability Speed .229 .278 .068 3.373 ***
ID WHO <--- Capability Accuracy 1.000 .689
ID WHAT <--- Capability Accuracy .854 .598 .147 5.810 ***
ID WHERE <--- Capability Accuracy .811 .570 .146 5.570 ***
ID WHEN <--- Capability Accuracy .370 .337 .108 3.411 ***
A7A <--- Capability Quality 1.000 .921
A7B <--- Capability Quality .992 .857 .084 11.881 ***
A7C <--- Capability Quality .702 .635 .088 7.993 ***
137
Table 5.3
Covariance Estimates of First Order Indicators of Social Capital (Measurement Interval One)
Estimate S.E. C.R. P
Cognitive Embeddedness <--> Relational Embeddedness .414 .083 4.979 ***
Cognitive Embeddedness <--> Structural Embeddedness .513 .096 5.348 ***
Structural Embeddedness <--> Relational Embeddedness .360 .083 4.329 ***
Table 5.4
Correlation Estimates of Social Capital Indicators (Measurement Interval One)
Estimate
Cognitive Embeddedness <--> Relational Embeddedness .485
Cognitive Embeddedness <--> Structural Embeddedness .531
Structural Embeddedness <--> Relational Embeddedness .410
Table 5.5
Factor Score Weights for Structural Model (Measurement Interval One)
Relational
Embeddedness Structural
Embeddedness Cognitive
Embeddedness Capability
Speed A7C A7B A7A
ID WHEN
ID WHERE
ID WHAT
ID WHO
Social Capital .786 -.300 -.246 .000 .000 .000 .000 .000 .000 .000 .000
Capability Performance
.221 -.085 -.069 -.229 .063 .183 .368 .127 .218 .239 .333
Capability Quality
.087 -.033 -.027 .000 .089 .259 .521 .050 .086 .094 .131
Capability Accuracy
.026 -.010 -.008 .061 .007 .021 .043 .063 .108 .118 .165
138
Table 5.6
Standardized Total Effects for Structural Model (Measurement Interval One)
Relational
Embeddedness Structural
Embeddedness Cognitive
Embeddedness Capability
Performance Capability
Speed Capability
Quality Capability Accuracy
Social Capital 1.136 -.492 -.392 .000 .000 .000 .000
Capability Performance
.525 -.227 -.181 .000 .000 .000 .000
Capability Quality
.456 -.197 -.157 .868 .278 .000 .000
Capability Accuracy
.324 -.140 -.112 .617 .651 .000 .000
A7C .289 -.125 -.100 .551 .176 .635 .000
A7B .391 -.169 -.135 .744 .238 .857 .000
A7A .420 -.182 -.145 .799 .256 .921 .000
ID WHEN .109 -.047 -.038 .208 .219 .000 .337
ID WHERE .185 -.080 -.064 .352 .371 .000 .570
ID WHAT .194 -.084 -.067 .369 .389 .000 .598
ID WHO .223 -.097 -.077 .425 .448 .000 .689
139
Table 5.7 Structural Equation Model Fit Summary (Measurement Interval One) CMIN
Model NPAR CMIN DF P CMIN/DF Default model 28 43.962 38 .234 1.157 Saturated model 66 .000 0
Independence model 11 473.344 55 .000 8.606 RMR, GFI
Model RMR GFI AGFI PGFI Default model .074 .943 .900 .543 Saturated model .000 1.000
Independence model .442 .544 .452 .453 Baseline Comparisons
Model NFI
Delta1 RFI
rho1 IFI
Delta2 TLI
rho2 CFI
Default model .907 .866 .986 .979 .986 Saturated model 1.000
1.000
1.000
Independence model .000 .000 .000 .000 .000 RMSEA
Model RMSEA LO 90 HI 90 PCLOSE Default model .035 .000 .073 .699 Independence model .242 .222 .262 .000
140
Model fit for data collected in the second measurement interval of the
simulation suggests that our modeling provides a more than adequate representation of
the data (χ2 = 58.408, df = 39, p = .024; GFI = .928; CFI = .970; RMSEA = .062 [p = .261]),
however it is comparatively less fitting than in the first. Measurement modeling in this
round is identical to the previous round as is typical of a repeated measures design; our
cross-sectional models are considered independently and do not incorporate any effects
resulting from previous measurement or interaction, which provides a strict test of the
relationships at each point in time. Regression weights, standardized estimates and
significance levels resulting from the modeling of relationships in the second round data
are illustrated in Table 5.8. Covariance and correlation estimates generated by the
structural, cognitive and relational embeddedness factors are provided in Tables 5.9 and
5.10. Factor and standardized total effects scores are summarized in Tables 5.11 and
5.12. Table 5.13 provides a complete summary of the model fit indices for the second
measurement interval, allowing the reader to compare our default model to the
saturated and independence models.
141
Table 5.8
Regression Weights for Structural Model (Measurement Interval Two)
Estimate Standardized Estimate S.E. C.R. P
Social Capital <--- Structural Embeddedness -.223 -.554 .123 -1.809 .070
Social Capital <--- Cognitive Embeddedness .636 1.555 .200 3.183 .001
Social Capital <--- Relational Embeddedness -.307 -.756 .195 -1.573 .116
Capability Performance <--- Social Capital 1.000 .354
Capability Accuracy <--- Capability Performance .249 .903 .039 6.323 ***
Capability Quality <--- Capability Performance 1.000 .746
Capability Accuracy <--- Capability Speed .084 .429 .019 4.391 ***
Capability Quality <--- Capability Speed .137 .145 .083 1.658 .097
ID WHO <--- Capability Accuracy 1.000 .673
ID WHAT <--- Capability Accuracy .990 .684 .152 6.516 ***
ID WHERE <--- Capability Accuracy 1.166 .745 .168 6.947 ***
ID WHEN <--- Capability Accuracy .727 .461 .158 4.612 ***
A7A <--- Capability Quality 1.000 .932
A7B <--- Capability Quality 1.074 .911 .074 14.540 ***
A7C <--- Capability Quality .647 .619 .080 8.051 ***
142
Table 5.9
Covariance Estimates of First Order Indicators of Social Capital (Measurement Interval Two)
Estimate S.E. C.R. P
Cognitive Embeddedness <--> Relational Embeddedness .791 .109 7.237 ***
Cognitive Embeddedness <--> Structural Embeddedness .430 .093 4.604 ***
Structural Embeddedness <--> Relational Embeddedness .462 .095 4.859 ***
Table 5.10
Correlation Estimates of Social Capital Indicators (Measurement Interval Two)
Estimate
Cognitive Embeddedness <--> Relational Embeddedness .821
Cognitive Embeddedness <--> Structural Embeddedness .441
Structural Embeddedness <--> Relational Embeddedness .471
Table 5.11
Factor Score Weights for Structural Model (Measurement Interval Two)
Relational
Embeddedness Structural
Embeddedness Cognitive
Embeddedness Capability
Speed A7C A7B A7A
ID WHEN
ID WHERE
ID WHAT
ID WHO
Social Capital -.307 -.223 .636 .000 .000 .000 .000 .000 .000 .000 .000
Capability Performance
-.072 -.052 .149 -.212 .018 .085 .124 .249 .716 .595 .556
Capability Quality
-.012 -.009 .024 -.012 .069 .327 .478 .041 .117 .097 .091
Capability Accuracy
-.018 -.013 .037 .031 .004 .021 .031 .062 .179 .149 .139
143
Table 5.12
Standardized Total Effects for Structural Model (Measurement Interval Two)
Relational
Embeddedness Structural
Embeddedness Cognitive
Embeddedness Capability
Performance Capability
Speed Capability
Quality Capability Accuracy
Social Capital -.756 -.554 1.555 .000 .000 .000 .000
Capability Performance
-.268 -.196 .551 .000 .000 .000 .000
Capability Quality
-.200 -.146 .411 .746 .145 .000 .000
Capability Accuracy
-.242 -.177 .497 .903 .429 .000 .000
A7C -.124 -.091 .254 .462 .090 .619 .000
A7B -.182 -.133 .374 .680 .132 .911 .000
A7A -.186 -.136 .383 .696 .135 .932 .000
ID WHEN -.111 -.082 .229 .416 .198 .000 .461
ID WHERE -.180 -.132 .370 .673 .319 .000 .745
ID WHAT -.165 -.121 .340 .618 .293 .000 .684
ID WHO -.163 -.119 .335 .608 .289 .000 .673
144
Table 5.13 Structural Equation Model Fit Summary (Measurement Interval Two) CMIN
Model NPAR CMIN DF P CMIN/DF Default model 27 58.408 39 .024 1.498 Saturated model 66 .000 0
Independence model 11 692.108 55 .000 12.584 RMR, GFI
Model RMR GFI AGFI PGFI Default model .084 .928 .878 .548 Saturated model .000 1.000
Independence model .486 .453 .344 .378 Baseline Comparisons
Model NFI
Delta1 RFI
rho1 IFI
Delta2 TLI
rho2 CFI
Default model .916 .881 .970 .957 .970 Saturated model 1.000
1.000
1.000
Independence model .000 .000 .000 .000 .000 RMSEA
Model RMSEA LO 90 HI 90 PCLOSE Default model .062 .023 .093 .261 Independence model .299 .279 .319 .000
145
Table 5.14
Regression Weights for Structural Model (Measurement Interval Three)
Estimate Standardized Estimate S.E. C.R. P
Social Capital <--- Structural Embeddedness -.112 -.391 .115 -.977 .329
Social Capital <--- Cognitive Embeddedness .618 2.118 .235 2.636 .008
Social Capital <--- Relational Embeddedness -.481 -1.619 .232 -2.079 .038
Capability Performance <--- Social Capital 1.000 .289
Capability Accuracy <--- Capability Performance .305 .981 .064 4.747 ***
Capability Quality <--- Capability Performance 1.000 .658
Capability Accuracy <--- Capability Speed .046 .193 .026 1.783 .075
Capability Quality <--- Capability Speed -.007 -.006 .100 -.073 .942
ID WHO <--- Capability Accuracy 1.000 .619
ID WHAT <--- Capability Accuracy -.002 -.004 .041 -.039 .969
ID WHERE <--- Capability Accuracy .917 .569 .198 4.638 ***
ID WHEN <--- Capability Accuracy .969 .625 .198 4.896 ***
A7A <--- Capability Quality 1.000 .854
A7B <--- Capability Quality 1.173 1.000 .063 18.726 ***
A7C <--- Capability Quality .607 .535 .090 6.743 ***
146
Table 5.15
Covariance Estimates of First Order Indicators of Social Capital (Measurement Interval Three)
Estimate S.E. C.R. P
Cognitive Embeddedness <--> Relational Embeddedness .836 .111 7.551 ***
Cognitive Embeddedness <--> Structural Embeddedness .454 .095 4.803 ***
Structural Embeddedness <--> Relational Embeddedness .432 .092 4.682 ***
Table 5.16
Correlation Estimates of Social Capital Indicators (Measurement Interval Three)
Estimate
Cognitive Embeddedness <--> Relational Embeddedness .884
Cognitive Embeddedness <--> Structural Embeddedness .464
Structural Embeddedness <--> Relational Embeddedness .450
Table 5.17
Factor Score Weights for Structural Model (Measurement Interval Three)
Relational
Embeddedness Structural
Embeddedness Cognitive
Embeddedness Capability
Speed A7C A7B A7A
ID WHEN
ID WHERE
ID WHAT
ID WHO
Social Capital -.481 -.112 .618 .000 .000 .000 .000 .000 .000 .000 .000
Capability Performance
-.149 -.035 .191 -.070 .000 .184 .000 .592 .467 -.009 .557
Capability Quality
.000 .000 .000 .000 .000 .853 .000 .000 .000 .000 .000
Capability Accuracy
-.045 -.011 .058 .024 .000 .056 .000 .180 .142 -.003 .170
147
Table 5.18
Standardized Total Effects for Structural Model (Measurement Interval Three)
Relational
Embeddedness Structural
Embeddedness Cognitive
Embeddedness Capability
Performance Capability
Speed Capability
Quality Capability Accuracy
Social Capital -1.619 -.391 2.118 .000 .000 .000 .000
Capability Performance
-.467 -.113 .611 .000 .000 .000 .000
Capability Quality
-.307 -.074 .402 .658 -.006 .000 .000
Capability Accuracy
-.458 -.111 .600 .981 .193 .000 .000
A7C -.165 -.040 .215 .352 -.003 .535 .000
A7B -.307 -.074 .402 .658 -.006 1.000 .000
A7A -.263 -.063 .344 .562 -.005 .854 .000
ID WHEN -.286 -.069 .375 .613 .121 .000 .625
ID WHERE -.261 -.063 .341 .558 .110 .000 .569
ID WHAT .002 .000 -.002 -.004 -.001 .000 -.004
ID WHO -.284 -.069 .371 .608 .119 .000 .619
148
Table 5.19 Structural Equation Model Fit Summary (Measurement Interval Three) CMIN
Model NPAR CMIN DF P CMIN/DF Default model 26 38.391 40 .543 .960 Saturated model 66 .000 0
Independence model 11 584.295 55 .000 10.624 RMR, GFI
Model RMR GFI AGFI PGFI Default model .066 .951 .920 .577 Saturated model .000 1.000
Independence model .455 .579 .494 .482 Baseline Comparisons
Model NFI
Delta1 RFI
rho1 IFI
Delta2 TLI
rho2 CFI
Default model .934 .910 1.003 1.004 1.000 Saturated model 1.000
1.000
1.000
Independence model .000 .000 .000 .000 .000 RMSEA
Model RMSEA LO 90 HI 90 PCLOSE Default model .000 .000 .057 .909 Independence model .272 .252 .292 .000
149
Table 5.20 Comparative Results of Cross-Sectional Hypotheses Testing of the Emergence of Social Capital across Temporal Periods*
Time Interval
Measurement Variable
Hypothesis 1a Hypothesis 1b Hypothesis 1c Covariance Correlation
β M
(SE) p β
M (SE)
p β M
(SE) p β
M (SE)
p Estimate
t1
DV: Social Capital
Structural -.49 (.147) .041
Cognitive -.39 (.157) .118
Relational 1.14 (.166) .000
Structural Cognitive .51 (.096) .000 .531
Structural Relational .36 (.083) .000 .410
Cognitive Relational .41 (.083) .000 .485
t2
DV: Social Capital
Structural -.54 (.123) .070
Cognitive 1.56 (3.18) .001
Relational -.76 (-1.57) .116
Structural Cognitive .43 (.093) .000 .441
Structural Relational .46 (.095) .000 .471
Cognitive Relational .79 (.109) .000 .821
t3
DV: Social Capital
Structural -.39 (.115) .329
Cognitive 2.12 (.235) .008
Relational -1.62 (.232) .038
Structural Cognitive .45 (.095) .000 .464
Structural Relational .43 (.092) .000 .450
Cognitive Relational .84 (.111) .000 .884
* All data reported as standardized estimates.
150
Repeating the procedures outlined in the previous two paragraphs, we present
the model fit indices generated by the third round data (χ2 = 38.391, df = 40, p = .543;
GFI = .951; CFI = 1.000; RMSEA = .000 [p = .909]). These results suggest our modeling
provides a very strong approximation of the structure of the data (Arbuckle, 2007),
moreover the chi-square statistic supports a significant improvement in model fit in
comparison to round one (Δ χ2/df = 2.786, p = .0616) and round two (Δ χ2/df = 20.017, p
= .000) models. Regression weights for each relationship illustrated in Figure 5-1 are
provided in Table 5.14, as are the standardized estimates and significance levels.
Covariance and correlation estimates for the relationships among structural, cognitive
and relational embeddedness are outlined in Tables 5.15 and 5.16. Factor score weights
and the results of the standardized total effects of each factor are contained in Table
5.17 and Table 5.18 respectively, and the model fit indices associated with the data
collected in this final round are summarized in Table 5.19 for easy comparison with the
model fit of the saturated and independence alternatives.
Stepping back from comparisons of overall model fit, we can begin to notice the
implications of our proposed models and the changing composition of the latent second
order factors on which our hypotheses are focused. Table 5.20 provides a comparative
assessment of the patterns of social capital emergence among each of the three cross-
sectional structural models including the regression weights, covariance scores and
correlation estimates. From these results we get a sense of the evolving nature of social
capital during emergence. Fluctuating regression weights among the factors and
151
changing levels of significance suggest that the structure and constitution of social
capital is fluidly unfolding across each measurement interval. Additionally, while earlier
confirmatory factor analysis demonstrated the independence of the three first order
factors, it is clear that structural, cognitive and relational embeddedness are strongly
interrelated as the covariance of pairs of factors are highly significant when modeled
formatively. Our analysis suggests that the emergence of social capital is contingent on
structural, cognitive and relational embeddedness, although not as predictably as the
literature might suggest.
Hypothesis 1a argued that increasing structural embeddedness would enhance
the emergence of social capital, however rather than supporting this proposition our
results consistently indicate the opposite. Here we report a statistically significant
finding suggesting that the direct effect of increasing structural embeddedness actually
decreases the overall level of social capital (β1 = -.49, p < .05; β2 = -.54, p < .10; β3 = -.39,
p = .329 [non-significant finding]). Cognitive embeddedness in contrast grows in
magnitude and significance in relation to the emergence of social capital as predicted
(β1 = -.39, p = .118 [non-significant finding+; β2 = 1.56, p < .001; β3 = 2.12, p < .008). The
modeling of hypothesis 1b is consistent with our theorizing and supported by analysis.
The prediction that relational embeddedness would positively contribute to the
emergence of social capital holds true for the first measurement interval (β1 = 1.14, p <
.000), however in intervals two and three its impact on social capital is increasingly
negative (β2 = -.76, p = .116 [non-significant finding+; β3 = -1.62, p < .05). The magnitude,
152
direction, and significance of covariance among each component of embeddedness
suggests that while not contributing to social capital per say, all three are mutually
influential.
Hypothesis 2 predicted that the emergence of social capital would enhance
capability performance as demonstrated by increasing levels of capability accuracy,
capability speed and capability quality, and is independently supported in each of the
three cross-sectional models (β1 = .46; β2 = .35; β3 = .29). Taken together these results
provide preliminary support for hypothesis 3 although the diminishing impact of social
capital on capability performance at each subsequent measurement interval demands
further explanation and calls into question the viability of this support. Based on the
latent structure of social capital and capability performance in combination with the
perplexing nature of our preliminary results, we shift our attention from the analysis of
independent cross-sectional structural models to incorporate longitudinal effects of
growth and change into our modeling. Our preliminary results demonstrate the
emergence of social capital and its impact on organizational capability performance. In
what follows we examine the temporal influences on each latent second order factor to
determine whether and how each is changing over time and whether this change is co-
evolutionary. This further analysis relies on methods of latent growth curve modeling
and longitudinal cross-lagged regression modeling (Ferrer & McArdle, 2003), which are
presented in the remainder of this chapter.
153
Latent Growth Curve Model Fitting Results
Latent growth curve modeling is an ideal technique for assessing the linearity of
longitudinal change commonly associated with repeated measurement research designs
(Duncan & Duncan, 2004). Illustrated in Figure 5-2 is a latent growth curve model often
referred to as a ‘curve or factors’ or ‘associative latent growth curve’ model (Duncan &
Duncan, 2004), which uniquely combines two latent growth curve models and estimates
the covariance and correlation among the means of each slope to search for
relationships among the curves over time (McArdle, 2007). The mean estimate of each
slope provides an approximation of the rate of growth for each latent factor over time;
with regression weights for slopes and intercepts constrained appropriately, each
growth curve estimates the degree to which the data fit a linear trajectory of change
(Acock & Li, 1999; Ferrer & McArdle, 2003; Li & Acock, 1999).
We test the assumption of linear growth in both factors by constraining each of
the intercept regression paths to one and constraining each of the slope regression
paths according to their measurement interval (t1 = 0; t2 = 1; t3 = 2). The appropriate
coding of slope and intercept regression path is a much debated topic, however to test
growth linearity we fix the slope parameters in linear sequence of observation making
our first observation period path equal to zero ensuring the ability to accurately
interpret the intercept of each factor (Biesanz, Deeb-Sossa, Papadakis, Bollen, & Curran,
2004). Notice in Figure 5-2 that all of the error terms are freely estimated parameters in
this model but are assumed to be constant over time reflecting a consistent
154
measurement error across each measurement interval. Relaxing or altering these
parameter estimates alters the assumption of linear growth as is presented in Table
5.21 which contains the comparative fitness of three models.
Figure 5-2: Latent Growth Curve Model (Curve of Factors Model)
ICEPT1 SLOPE1
0
Social Capital (t1)
10
0
Social Capital (t2)
1 1
0
Social Capital (t3)
1
2
0, var_a
er1
1
0, var_a
er2
1
0, var_a
er3
1
ICEPT2 SLOPE2
0
Capability
Performance (t3)
0
Capability
Performance (t2)
0
Capability
Performance (t1)
0, var_b
er6
0, var_b
er5
0, var_b
er4
21 11 0
111
1
155
To test each growth curve we combine the items that generated our first order
factors to create composite measures for social capital and capability performance for
each measurement interval. Examining Table 5.21 we see that model one reflects a fully
constrained model providing a strict test of the linearity of growth in each factor (t1 = 0;
t2 = 1; t3 = 2), whereas model two allows the slope regression path for the third
measurement interval (t1 = 0; t2 = 1; t3 = x) associated with capability performance to
freely estimate, and model three allows the slope regression paths for both factors to
freely estimate in the third measurement interval (t1 = 0; t2 = 1; t3 = x). Here, we report
the NFI or normed fit index, which provides a comparable substitute for the GFI in
situations requiring the estimation of means and intercepts. Model fit indices for model
one (χ2 = 76.346, df = 14, p = .000; NFI = .729; CFI = .766; RMSEA = .185 [p = .000]),
model two (χ2 = 33.140, df = 13, p = .002; NFI = .882; CFI = .924; RMSEA = .109 [p =
.020+), and model three (χ2 = 18.754, df = 12, p = .095; NFI = .933; CFI = .975; RMSEA =
.066 [p = .290]), demonstrate that an assumption of linear growth provides a poor fit for
the data. Moreover if we isolate and remove the constraint on the capability
performance parameter estimate (t3), model fit substantially improves when comparing
models one and two (Δ χ2/df = 43.206, p = .000). While none of the models generate an
exact fit to the data, model three, the least constrained of the models does provide a
very good approximation, suggesting at least preliminarily that growth in the two factors
is occurring although not linearly over our measurement intervals. Figure 5-3 illustrates
156
the average growth curve of each factor, and provides an illustration of change among
some of the component pieces of each factor.
Tables 5.22 and 5.23 present estimates of the slope, intercept, and covariance
parameters for each of our three models allowing cross-model comparison of our
results, however, our interpretational emphasis will focus on the third growth curve
model which provided a superior fit relative to the others. Investigating first the
estimates of the model slope means (Table 5.22), we find that both social capital (M =
2.050, p < .001) and capability performance (M = 2.167, p < .001) are highly significant,
suggesting positive growth trajectories for both constructs (Ferrer & McArdle, 2003). In
addition, Table 5.23 demonstrates that the intercept of each factor is also highly
significant, although covariance among slopes and intercepts is not significant
(intercept1 slope1, p = .750; intercept2 slope2, p = .225), suggesting that the
rate of growth in either factor is independent of its initial strength. While we have
demonstrated that rates of growth in both social capital and capability performance are
highly significant and occurring simultaneously across the measurement intervals, the
lack of significant covariance among the slopes of each factor (β = -.124, p = .920)
implies that this growth is unrelated.
Returning to our hypotheses, we find social capital growing over time as
indicated by the trajectory and significant of the social capital mean slope (M = 2.050, p
< .001), supporting hypothesis 4. Hypothesis 5 argued for the impact of past social
capital building on future capability performance; while we do provide evidence of
157
capability change over time supporting hypothesis 6 (slope2: M = 2.167, p < .001), we
have yet to establish an historical link between current capability performance and
social capital in previous periods. As a result hypothesis 5 fails to receive support, with
the caveat that this preliminary assertion will be reconsidered within the framework of
the longitudinal cross-lagged regression modeling shortly. Our claim of co-evolution,
based on the notion that rate of change in social capital will correlate with rate of
change in capability performance, rests on the test of significant covariance among
slope1 and slope2 (β = -.124, p = .920), a conclusion that our data does not support.
Therefore in the strictest sense, we fail to corroborate our co-evolution premise argued
in hypothesis 7, although we will address this point too with cross-lagged regression
modeling.
158
Table 5.21
Comparative Fit Summary for Growth Curve Models
Model χ2 df P χ2/df NFI CFI RMSEA PCLOSE
Model One – Fully Constrained 76.346 14 .000 5.453 .729 .766 .185 .000
Model Two – Capability Performance t3 Parameter Unconstrained
33.140 13 .002 2.549 .882 .924 .109 .020
Model Two – Social Capital t3 and Capability Performance t3 Parameters Unconstrained
18.754 12 .095 1.563 .933 .975 .066 .290
Table 5.22
Estimates of Means for Growth Curve Models
Model One Model Two Model Three
Mean
Estimate S.E. C.R. P
Mean Estimate
S.E. C.R. P Mean
Estimate S.E. C.R. P
Intercept1 18.919 .541 34.943 *** 18.919 .541 34.943 *** 18.528 .540 34.316 ***
Slope1 .939 .225 4.163 *** .939 .225 4.170 *** 2.050 .431 4.755 ***
Intercept2 11.481 .297 38.644 *** 10.880 .272 39.970 *** 10.851 .273 39.735 ***
Slope2 .106 .164 .645 .519 2.136 .329 6.489 *** 2.167 .332 6.538 ***
Table 5.23
Estimates of Covariance Parameters for Growth Curve Models
Model One Model Two Model Three
Estimate S.E. C.R. P Estimate S.E. C.R. P Estimate S.E. C.R. P
Intercept1 Slope1 .787 1.601 .492 .623 .877 1.594 .550 .582 .922 2.895 .318 .750
Intercept2 Slope2 .695 .936 .743 .457 1.251 .868 1.441 .150 1.091 .899 1.214 .225
Slope1 Slope2 .068 .286 .239 .811 -.597 .684 -.872 .383 -.124 1.236 -.100 .920
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Figure 5-3: Average Growth Curves of Second Order Factors
1 2 3
Social Capital Trend 18.531 20.635 20.408
Capability Performance Trend 6.021 7.513 5.522
Capability Accuracy Trend 1.710 2.397 1.313
Capability Quality Trend 4.317 5.116 4.216
Elapsed Time 4.746 5.503 5.456
Post Activity Trend 4.702 4.641 4.763
Share Activity Trend 0.266 0.271 0.300
0.000
5.000
10.000
15.000
20.000
25.000
Me
an V
alu
e o
f Fa
cto
rs C
om
po
site
Sco
res
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Longitudinal Cross-Lagged Regression Model Fitting Results
Thus far we have assessed the cross-sectional model fit for each measurement
interval and have investigated the longitudinal growth effects of social capital and
capability performance across the measurement intervals. This portion of our analyses
draws on a cross-lagged longitudinal regression technique, allowing us to model cross-
sectional or ‘within interval’ effects, growth or autoregressive effects, and cross-lagged
effects among social capital and capability performance simultaneously (Ferrer &
McArdle, 2003; McArdle, 2007). Modeling cross-lagged effects allows the examination
of the effects of a variable measured at a previous time on the results of another
variable in a subsequent period. Figure 5-4 illustrates all three patterns of relationships:
cross-sectional relations represented by horizontal regression paths from social capital
to capability performance; autoregressive relations represented by vertical regression
paths among social capital and capability performance across periods (t1, t2, t3); and,
cross-lagged relations illustrated as diagonal regression paths from social capital in prior
periods to capability performance in subsequent intervals (t1t2, t2t3).
Using second order factor scores derived from maximum likelihood estimates of
our first order factors our modeling yields an excellent representation of the data (χ2 =
2.030, df = 5, p = .845; GFI = .995; CFI = 1.000; RMSEA = .000 [p = .915]). Regression
weights of the relationships in this model are illustrated in Figure 5-4 and summarized in
Table 5.24; standardized estimates of total effects and model fit indices are provided in
Tables 5.25 and 5.26 respectively. Estimates of the regression weights and total effects
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demonstrate the impact of each of the three types of relationships described above,
while model fit scores support our model as being a strong approximation for the data.
To further examine the effects of cross-sectional, autoregressive, and cross-lagged
relations in our modeling, we sequentially constrain each pattern of relationships and
assess changes in the overall fitness of the model as a whole. We begin by constraining
to equivalent the cross-sectional paths in the first and second intervals (model one),
followed by the autoregressive paths first for social capital (model two) and later for
capability performance (model three) in both the first and second intervals, and finally
Figure 5-4: Longitudinal Cross-Lagged Regression Model (Standardized Estimates)
Social Capital (t1)Capability
Performance (t1)
Social Capital (t2)Capability
Performance (t2)
Social Capital (t3)Capability
Performance (t3)
.01
Chi Square = 2.030; df = 5; p = .845
GFI = .995; AGFI = .978; CFI = 1.000
RMSEA = .000; P-close = .915
.42
.28.31
.60
.57
-.01
-.27
.16
.29
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we conclude with the cross-lagged paths between social capital and capability
performance (model four). The results of our testing are presented in Table 5.27, and
while all but one of the models is reasonably fit, the hypothesized model generates a
statistically superior fit (Δ χ2/df = 2.902, p < .10) relative to model four (χ2 = 4.932, df = 6,
p = .553; GFI = .987; CFI = 1.000; RMSEA = .000 [p = .724]), and only minor non-
significant improvements over model one (Δ χ2/df = 1,552, p = .2128) and three (Δ χ2/df
= 1.605, p = .2050).
These findings have important ramifications for the preliminary conclusions of
two of our hypotheses. Hypothesis 5 argued for the future impact of social capital on
capability performance, asserting that the emergence of social capital in previous
periods will enhance the evolution of capability performance in subsequent periods. We
find preliminary support for this hypothesis by examining the improvement in the chi-
square model fit static between our hypothesized model (Δ χ2/df = 2.902, p < .10) and
comparatively constrained fourth model (Table 5.27). Reviewing the estimated
regression weights for the cross-lagged paths in our hypothesized model (Table 5.24),
we find that a lack of significance in the first cross-lag path (Social Capital (t1)
Capability Performance (t2) β = .015, p = .897) and a negative result in the second (Social
Capital (t2) Capability Performance (t3) β = -.284, p < .05). From these mixed findings
we can conclude that social capital does have a cross-lagged effect on capability
performance, but that this effect may actually hinder rather than help capabilities
evolve over time.
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Table 5.24
Regression Weights for Cross-Lagged Model
Estimate Standardized Estimate S.E. C.R. P
Social Capital (t2) <--- Social Capital (t1) .658 .598 .077 8.503 ***
Capability Performance (t1) <--- Social Capital (t1) -.009 -.008 .101 -.091 .928
Capability Performance (t2) <--- Social Capital (t1) .015 .013 .113 .129 .897
Capability Performance (t2) <--- Capability Performance (t1) .422 .420 .079 5.375 ***
Social Capital (t3) <--- Social Capital (t1) .352 .315 .073 4.823 ***
Social Capital (t3) <--- Social Capital (t2) .582 .573 .066 8.783 ***
Capability Performance (t2) <--- Social Capital (t2) .171 .162 .102 1.664 .096
Capability Performance (t3) <--- Capability Performance (t2) .277 .278 .083 3.316 ***
Capability Performance (t3) <--- Social Capital (t2) -.284 -.272 .134 -2.121 .034
Capability Performance (t3) <--- Social Capital (t3) .302 .294 .131 2.301 .021
Table 5.25
Standardized Total Effects for Cross-Lagged Regression Model
Social Capital
(t1) Social Capital
(t2) Capability Performance
(t1) Social Capital
(t3) Capability Performance
(t2)
Social Capital (t2) .598 .000 .000 .000 .000
Capability Performance (t1) -.008 .000 .000 .000 .000
Social Capital (t3) .658 .573 .000 .000 .000
Capability Performance (t2) .106 .162 .420 .000 .000
Capability Performance (t3) .060 -.059 .117 .294 .278
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Table 5.26 Longitudinal Cross-Lagged Regression Model Fit Summary CMIN
Model NPAR CMIN DF P CMIN/DF Default model 16 2.030 5 .845 .406 Saturated model 21 .000 0
Independence model 6 237.971 15 .000 15.865 RMR, GFI
Model RMR GFI AGFI PGFI Default model .022 .995 .978 .237 Saturated model .000 1.000
Independence model .256 .636 .491 .454 Baseline Comparisons
Model NFI
Delta1 RFI
rho1 IFI
Delta2 TLI
rho2 CFI
Default model .991 .974 1.013 1.040 1.000 Saturated model 1.000
1.000
1.000
Independence model .000 .000 .000 .000 .000 RMSEA
Model RMSEA LO 90 HI 90 PCLOSE Default model .000 .000 .069 .915 Independence model .338 .301 .377 .000
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Table 5.27
Comparative Fit Summary for Cross-Lagged Regression Models*
Model χ2 df P χ2/df GFI CFI RMSEA PCLOSE
Hypothesized Model – Fully Unconstrained 2.030 5 .845 .406 .995 1.000 .000 .915
Model One – Constrained Cross-sectional Parameters in t1 and t2
3.582 6 .733 .597 .991 1.000 .000 .853
Model Two – Constrained Autoregressive Parameters for Social Capital in t1
2, t1
3, t2
3
10.196 7 .178 1.457 .977 .986 .059 .362
Model Three – Constrained Autoregressive Parameters for Capability Performance in t1
2
and t2
3 3.635 6 .726 .606 .991 1.000 .000 .848
Model Four – Constrained Cross-lagged parameters for Social Capital to Capability Performance in t1
2 and t2
3
4.932 6 .553 .822 .987 1.000 .000 .724
Model Five – Fully Constrained Model in all Intervals
95.656 13 .000 7.358 .791 .629 .221 .000
* In this instance constraining parameters refers to the imposing two or more paths to estimate freely but as equivalent to each other (i.e. identical relationships)
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The test of covariance among the slopes of factors in our latent growth curve
modeling failed to provide support for our coevolution hypothesis, and in the strict
sense of coevolution this is an accurate conclusion to draw from our findings. However,
the results of our cross-lagged regression modeling do suggest that more than cross-
sectional effects are at play in the relationship between social capital and capability
performance; the presence of longitudinal cross-lagged and autoregressive effects also
contribute to future performance and change. For social capital, each autoregressive
path presents highly significant growth across measurement intervals (Social Capital
(t1
2) β = .658, p < .001; Social Capital (t2
3) β = .582, p < .001; Social Capital (t1
3) β =
.352, p < .001) suggesting the presence of emergence and a trajectory of growth
although at diminishing rates. The autoregressive relationships of capability
performance are also following a similar path (Capability Performance (t1
2) β = .422, p
< .001; Capability Performance (t1
2) β = .277, p < .001). Taken together, the evidence
we have provided of cross-sectional, longitudinal growth and cross-lagged effects of the
social capital-capability performance relationship provide a compelling case in support
of co-evolutionary change. In practice we have two distinct constructs changing
significantly in relation to each other over time, which at least minimally meets the spirit
of coevolution, however, based on our findings we are tempted to concede that our
results do not strictly meet the definition of coevolution as laid out in the seventh
hypothesis. We return to this point in depth in our discussion of this research project.
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Summary of Research Findings
In this chapter we have applied three distinct modeling techniques to determine
whether support could be established for each of our hypotheses relating the
relationship between social capital and capability performance over time. In general,
our results support the idea that the emergence of social capital is influential in
generating capability performance, although not monotonically so as our individual
results reveal. In addition our findings demonstrate that social capital and organizational
capability performance are growing and evolving over time as revealed in the modeling
of latent growth and cross-lagged effects. In the following chapter, we discuss the
implications of our findings for theory and practice, and consider what these findings
mean for scholars in the field currently examining social capital, organizational
capabilities, and the relationship between the two.
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Chapter Six: Research Contribution, Discussion, and Implications
This study began with a question: how does the emergence of social capital
influence the evolution of organizational capabilities? Focusing on the relationships
among structural, cognitive and relational embeddedness, and how these influence the
growth of social capital from the earliest phases of emergence, we have shown how
social capital influences organizational capability performance. Our findings support the
proposition that social resources are influential in generating and sustaining
coordinated collective performance. These findings suggest that the three dimensions of
embeddedness do not arise and enhance social capital monotonically, instead they are
highly interrelated and interdependent, which suggests that building social capital is a
fitful process relying on combinations of interpersonal connection, awareness and
agreement which may over time develop into social cohesion, collective mindsets and
deep trust. Social capital consistently demonstrated its potential to contribute to
capability performance, increasing the rate at which capability performance occurred as
well as the quality of performance over time. The emergence of social capital it seems
does meaningfully enhance collaborative performance, both in the present and in the
future.
Capabilities can evolve quickly over time, and social capital does have a role in
shaping this change. Our analyses also illustrate the power of endogenous change in
each subsequent period of investigation and provides evidence of the capacity for
capabilities to change themselves (Helfat & Peteraf, 2003). Experience no doubt
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accumulates with each consecutive attempt to identify the source of an impending
threat (Zollo & Winter, 2002), yet the significant role of social capital builds over time as
embeddedness increasingly facilitates the sharing of knowledge and experience. Our
results bear this out, demonstrating both the cross-sectional and longitudinal impact of
social capital on capability performance and change. In sum our findings illustrate how
social capital and capability performance dynamically emerge and evolve from their first
instantiation. This final point, that social capital appears to be a persuasive determinant
in the lifecycle of organizational capabilities, begins to consider each dimension of social
capital – structural connections, cognitive contribution, and relational linkages – in
terms of its ability to contribute unique yet complementary utility during the process of
capability building and change. The relationship between the emergence of social
capital and the evolution of organizational capabilities, and its impact on longer term
capability change, warrants further investigation.
Winter and Zollo suggest that “*t+he literature does not contain any attempt at a
straightforward answer to the question of how routines – much less dynamic
capabilities – are generated and evolve” (2002: 341). Others have credited strategic
interventions with driving capability change (Teece et al., 1997): episodic interventions
punctuate operating routines allowing for the reconfiguration of capability micro-
foundations and bringing about improved fitness (Helfat et al., 2007). Endogenous
change and capability evolution have only recently received due consideration (Helfat &
Peteraf, 2003), propelled by a search for the constituent parts of organizational
170
capabilities – the micro-foundations and micro-processes that compose the content of
organizational capabilities (Felin & Foss, 2005; Salvato, 2009; Teece, 2007). This
dissertation is among the first that we are aware of to examine the emergence and
evolution of capability performance in a single study, and to empirically investigate
social micro-foundational sources of endogenous capability change. The presumption
that a capability hasn’t occurred until reliable performance has been established has
posed an obstacle to the study of capability emergence (Helfat & Peteraf, 2003: 999),
because it has limited the exploration of capabilities to periods of ‘reliable’
performance. This study however takes us to the performance period just preceding
‘reliability’, where ad hoc performances are evolving toward convergence and reliability.
The experimental methodology employed in this research captures the ‘co-emergence’
of social capital and capability performance with our first interval measurements, and
illustrates ‘co-evolution’ with each consecutive interval, as the two constructs
dynamically change over time.
How do social networks emerge and grow? And how does their growth impact
capability performance? This research examines these two issues, focusing on the
emergence, growth, and dynamic change of social capital and organizational capabilities
among individuals working collectively in real time. Social capital emerges and grows
from repeated interpersonal interactions within networks of practice; based on a
growing history of successful interactions, cognitive convergence and relational
closeness grow among network members. For organizational capabilities, social
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resources are explicated as both a constituent and catalyst of capability change: guiding
and supporting effortful collaboration as capabilities first unfold; later responsive to
subsequent revision, refinement, and reification which systematically embeds network
architectures into collective memory. Social capital significantly enhances capability
performance by improving the quality of performance and speeding the pace at which
performance occurs. Our research reveals that social capital constituted of structural,
cognitive, and relational embeddedness is a significant micro-foundation of capability
performance and change. The dominant “conceptual analyses of the process by which
capabilities change over time have often relied on the idea that dynamic capabilities
must act upon other (operational) capabilities in order to change them” (Helfat &
Peteraf, 2003: 1004), and only recently have we begun to challenge this dominant
perspective with endogenously dynamized views of capability change.
Examining the evolution of a single organizational capability, threat
identification, and focusing on its performance and change over time, helps in part to
overcome the tendencies in the capability literature to either fuse the relationships
between performance and outcomes together because they are hard to distinguish or
define, or else to fail to recognize the importance of context in determining the
relevance of the performance-outcome relationship (Haas & Hansen, 2005: 19). In this
study we have not only done both, we have laid out an agenda for a comprehensive
study of two well studied concepts: social capital and organizational capabilities.
Applying a new methodology, the findings of this project have generated new insight
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into the relationship between social capital and organizational capabilities from both a
cross-sectional and a longitudinal perspective. Our findings provide answers and offer
guidance in answering questions about how the emergence of social capital influences
the evolution of organizational capabilities more generally.
While researchers and managers continue to wrestle with the twin issues of
network emergence and endogenous capability change11, in this project we have
investigated the origins, mechanisms, and implications of social capital emergence on
future patterns of performance. We have also investigated whether and how emerging
social resources imprint organizational capability trajectories during their co-emergence.
Our capacity to investigate the emergence and evolution of these constructs is largely
due to the unique experimental methodology that we have applied in this research.
Neither the social capital nor the organizational capability literatures have a tradition of
employing experimental methodologies; they have instead relied upon a variety of
other qualitative and quantitative approaches to examine their subject matter. As a
consequence for the field, many questions about how these constructs emerge and
evolve – independently or jointly – have remained unasked and unanswered. In broad
strokes, this study begins to unravel the relationship between social resources and
collective processes by arguing that capabilities can and do endogenously emerge and
evolve with social capital. The emergence of social capital provides organizational
11
For example, consider Organization Science’s two recent calls for additional attention to these topics: Ahuja and colleagues (eds.) special issue “The Genesis and Dynamics of Networks”; and, Lewin & Burton’s (co-chairs) call to focus on co-evolutionary and endogenous capability change in the upcoming OSWC-XI.
173
capabilities with much needed social infrastructure, but also embeds patterns of
network dependence into the capability, preconditioning the capability’s future
trajectory by systematically supporting its future growth and stability.
Can capabilities change themselves, and if so how? Some indicate that we should
look to the situated social context for cues (Collis, 1994; Haas & Hansen, 2005), and that
social resources present in capability founding systematically pattern future processes
“by preconditioning the emergence of a capability” (Helfat & Peteraf, 2003: 1001). Until
now, this proposition has remained a largely untested assumption, receiving tacit
endorsement based primarily on retrospective accounts (Gulati, 1999; McEvily &
Marcus, 2005; Reagans & Zuckerman, 2001). The social complexity that underpins
collective processes results from combinations of uniquely experienced individuals
collaborating individually and in concert, which at founding provides an “initial source of
heterogeneity among capabilities” (Helfat & Peteraf, 2003: 1001). The social capital
developed during the early phases of capability development subsequently patterns
future legacies (as memories, truces, or aspirations, for example, Nelson & Winter,
1982), further preconditioning capability performance trajectories as our results
suggest. But our findings also support the role of performance feedback, or more
accurately feed-forward, in generating an endogenous impact on capability change.
The feed-forward effects of the past, both autoregressive and cross-lagged,
shape the evolution and co-evolution of social capital and capability performance. Both
are changing over time, and the pattern of relations resulting from our modeling
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reinforces the social complexity hypothesized by others (Collis, 1994; Eisenhardt &
Martin, 2000). Our findings suggest that co-evolution between the constructs is more
complex than expected; the rates of social capital and capability performance growth
are not equal over time, nor is either occurring in a linear path as our modeling
illustrates. Only when we include both the individual growth curve effects and the cross-
lagged effects simultaneously do we begin to get a clearer sense of the foreshadowing
of social capital on capability performance. This suggests that the co-evolution of social
capital and organizational capabilities relies upon mutual adjustment, such that the
stability or variation of network and processes occur together over time in response to
capability fitness.
Contributions to the Organizational Capabilities Literature
This study is one of only a few to empirically examine the role of social resources
in the development and change of organizational capabilities, and among the first to
investigate the impact of social micro-foundations on capability performance.
Unpacking the ‘black box’ of capability building, performance and change is an often
called for but seldom accomplished pursuit (Abell et al., 2008; Felin & Foss, 2005, 2006;
Helfat & Peteraf, 2003). Our study brings the individual to the foreground of capability
performance, highlighting the importance of social capital in creating positive
performance in the present and shaping patterns of performance in the future. These
findings demonstrate the significance of embracing a socialized perspective in the study
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of capability performance, as well as the consequences of omitting social micro-
foundations from future studies of capability change. Opening the ‘black box’ of
capability change is an important first step in expanding our knowledge of the dynamics
of organizational adaptation and evolution. Unlike previous research which has focused
on post hoc examinations of capabilities (for example, Montealegre, 2002; Tripsas &
Gavetti, 2000), this work examined the preliminary stages of capability development in
real time to shed new light on how capabilities first emerge. Management theory and
application can certainly be enhanced by examining the micro-foundations of capability
change (Abell et al., 2008; Teece, 2007). Studying patterns of social capital emergence
offers a unique contribution to the understanding of how capabilities evolve because it
begins to untangle the causal factors that drive capability change from a socialized
perspective rather than an historical one based on the study of positions, paths, and
processes. Proponents in the social capital literature have asserted the concept’s
importance in generating performance outcomes, but until now little about whether the
causality of these arguments was appropriate or how the patterns of influence occurred
was known.
A pressing question in the resource-based theory literature revolves around the
origins of organizational capabilities, as Zollo and Winter (2002: 341) outline: “*t+o our
knowledge at least, the literature does not contain any attempt at a straightforward
answer to the question of how routines – much less dynamic capabilities – are
generated and evolve.” We do not claim to resolve the question fully, but we do add
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two missing pieces to the answer. First, the intent of this research project was to
explicitly examine how capabilities develop and evolve over time beginning with the first
collective attempts at performance. As a result, our data capture both the initial
collective efforts to generate performance as well as the changing trajectory of
performance over time, and provide a picture of this process. Our results illustrate the
path of progress along which capability performance evolved, and demonstrate the role
of individuals in affecting the capability micro-foundations that make this happen.
Others have highlighted the “need to explain the individual-level origins, or micro-
foundations of collective structures as they arise from individual action and interaction,
while extant work seems to take organization, and structure more generally for
granted” (Felin & Foss, 2006: 255); a contribution of this project is that it actually
investigates and explains (rather than simply declaring the need to explain) the origins
of an individual-level micro-foundation of capability performance, and demonstrates its
impact on capability change over time.
A second contribution to our understanding of the origins and evolution of
organizational capabilities lies in the treatment of capability performance as inherently
social. While links between organizational capabilities and other facets of organization
are gaining prominence in the resource-based view literature, we still know very little
about the social constitution of capabilities, let alone how social aspects impact
capability emergence or change over time. This dissertation demonstrates that the
social context of work matters in its ability to shape collaborative practice, and we have
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generated a better understanding of the social network-collective process relationship.
Our results illustrate that the capacity to build a new capability is contingent on the
socially complex coordination of routines, resources and people, and that social capital
is central to effectively coordinating performance. In our case social capital infuses
capabilities with access to varied and diverse sources of information, the capacity to
absorb and integrate information within the network of practice, and relational
connections that foster trust and trustworthiness among organization members. These
social resources shape the performance of our organizational capability ‘threat
identification’ directly, and impact the trajectory of its performance over time as well.
Successful performance in our context requires that network members communicate,
share information and maintain a shared situational awareness through a process of
collaboration and functional cooperation; social capital serves as the infrastructure
through which these social resources flow. These results suggest that the interactive
context of capability development may be an important element in explaining
capabilities and their effectiveness as dynamic entities.
The issue of capability change remains somewhat contested in the literature (for
examples of varying perspectives refer to, Eisenhardt & Martin, 2000; Teece et al., 1997;
Winter, 2003), however Helfat and Peteraf (2003: 1004) remind us “that capability
building and change do not require dynamic capabilities”, the capacity to change resides
within capabilities themselves. Capabilities have the capacity to change themselves by
altering the composition and organization of their micro-foundational parts. This
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dissertation gives credence to prior claims of endogenous capability change by
demonstrating how practice-based and process-based learning longitudinally impact
performance (Helfat & Peteraf, 2003; Zollo & Winter, 2002). By teasing apart the social
aspects of endogenous capability change and illuminating an important social micro-
foundation of organizational capabilities, this study has advanced our thinking about
how emergence and evolution occur. While previous managerial intervention strategies
have focused on the advantages of the capacity to rapidly update organizational
processes, we caution that an unintended consequence of such updating may be to
disrupt existing social capital networks. Rather than treating social aspects of capability
performance and change as an exogenous accessory, our findings illustrate that social
resources provide a significant contribution to the emergence and evolution of
organizational capabilities. Social capital and social networks more broadly warrant
greater inclusion in the study of capability micro-foundations as they have been shown
to provide the social infrastructure that stabilizes the variability of performance while
allowing progressive evolution over time.
Contributions to the Social Capital Literature
This study has incorporated a relatively rare approach to the study of social
capital, as we are aware of no other published investigations of longitudinal
experimental simulations exploring the emergence of social capital, or the inclusion of
real-time whole-network data collection. As noted earlier, little empirical work has been
179
done to examine how social capital emerges and evolves within an organizational
context. While previous work has suggested that many forms of social capital have
developed through a history of repeated interaction, through mutual dependence and
reliance, and through social similarities that differentiate one group from another
(Bourdieu, 1986; Coleman, 1988; Portes, 1998; Sandefur & Laumann, 1998), studies to
date have left the examination of social capital emergence relatively untested. Our
approach captures the patterns of emergence in their earliest phases of development,
as social capital is building and solidifying into the structured network architectures
which are often the focus of organizational analysis (Adler & Kwon, 2002; Burt, 2000;
Leana & Pil, 2006; Oh et al., 2004; Tsai & Ghoshal, 1998). As a result, our findings are
somewhat unique among our colleagues in the field and offer a novel perspective to the
social capital literature.
In our study, the emergence of social capital is uneven over time; the
contribution of structural, cognitive and relational embeddedness are neither equal in
magnitude nor in direction as social capital grows; nor is this longitudinal growth linear.
Previous research in the field of management and organizational studies has largely
examined social capital at individual and small group levels of analyses, relying on the
interpretation of cross-sectional data collection for insight. Using multiple measurement
intervals, our research speaks to the changing constitution of social capital over time:
first relying on the structural connections to support cognitive and relational
embeddedness, with later instances illustrating the waning importance of structural
180
embeddedness and the growing contribution of cognitive embeddedness to both
relational embeddedness and capability performance. Building social capital it seems, is
highly context-sensitive and its influence changes over time as evidenced by a shift in
the values of embeddedness over time, demonstrating a change in the constitution of
social capital to better reflect the performance requirements of the situation. This
finding (in combination with consideration of the methodological and operational
choices of other researchers) may be the reason that the constitution of social capital
may look quite different from study to study, and yet has been shown to reliably
enhance a variety of performance outcomes across organizational settings.
We have demonstrated that the social capital–capability performance
relationship in the earliest phases of capability building, where the emergence of social
networks are found to have provided some initial level of performance, influences the
strength and rate of change in this relationship in subsequent periods. This finding
reinforces the notion that social resources support and imprint the accumulation of
expertise and reinforce learning-by-doing as the learning occurs (Argote, 1999; Espedal,
2006; Feldman, 2003; Howard-Grenville, 2005; Nelson & Winter, 1982; Orlikowski, 2002;
Reagans et al., 2005; Staw & Ross, 1978; Zollo & Winter, 2002). Experience gained in the
early stages of a capability’s lifecycle is highly informative for future performance:
successful performances preserve the patterns of structural, cognitive and relational
embeddedness which generated the performance, whereas less fit performance, in
contrast, reinforces the need for change in the patterns of social capital that led to the
181
performance. The future evolution of a capability, then, results from a combination of
both the emergence of social capital as well as the quality of capability performance in
prior periods. Once acquired, a capability’s evolution is set on a trajectory whereby
future learning and practicing required to advance development may be much more
dependent and focused on the refinement or exploitation of processes which are known
to result in legitimate success (March, 1991; Teece & Pisano, 1994; Winter, 2000). Social
capital appears to support the evolutionary trajectory of organizational capabilities, and
we show that social capital varies in time with capability change, where rates of change
mutually co-evolve over time.
While the emergence of social capital appears to drive capability performance
within each time period, capability evolution and the co-evolution of the social capital—
capability performance relationship requires mutual adjustment, such that the stability
or variation in social resources and collective processes occur together over time. This
research project contributes to the literature by developing an understanding of how
social networks dynamically evolve around organizational capabilities and by teasing
apart the social aspects of endogenous capability change. Our results address significant
gaps in our understanding, first by showing how networks of practice develop and grow,
and second by demonstrating how network embeddedness contributes to the social
micro-foundations of capability performance and change. However, our results also
raise some important questions about how social capital may come to precondition
future organizational capability emergence and evolution, and whether social capital
182
that has developed in one context can be successfully grafted on to other situations,
processes or purposes.
In organization studies social capital has been demonstrated to enhance
individual, collective, and organizational performance across a variety of contexts (Adler
& Kwon, 2002; Inkpen & Tsang, 2005; Leana & Pil, 2006; Nahapiet & Ghoshal, 1998); its
benefit stems from its utility in doing two things: bridging access to distant resources
(broadly construed both in terms of tangible resources as well as the knowledge,
information, and practices of others), and bonding people into coordinated localized
communities (i.e. generating social norms, shared tacit understanding, obligations and
reciprocity). Social capital, and the configuration of ties from which it is constituted, is
particularly important to developing capabilities because ties serve as conduits for the
flow of interpersonal resources (Balkundi & Harrison, 2006), and the patterns of
embeddedness on which social capital is based come to predictably amplify or attenuate
the coordination of people, routines, and resources.
Social capital can be characterized by bridging and bonding pressures; they pull
in opposite directions and with unique effects, making these tensions an important
consideration in capability preconditioning. Others have considered the implications of
bonding (i.e. centripetal) and bridging (i.e. centrifugal) pressures on collective decision-
making and performance (Alba, 1973; Sheremata, 2000), suggesting that varying levels
of intra-group tie density, interpersonal relatedness, and convergence of focus among
individuals, greatly impact the coordination of people, resources and routines within
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groups (Balkundi & Harrison, 2006; Labianca & Brass, 2006; Reagans & Zuckerman,
2001). Awareness and absorption of core and peripheral knowledge are particularly
important for shaping the micro-foundational activities that influence capability
emergence (Cohen & Levinthal, 1990; Gavetti, 2005), however it is also necessary to
direct some attention outward while attending to and coordinating the actions of others
inside the group to maintain reliable performance (Cohen, March, & Olsen, 1972; Cyert
& March, 1963). The dilemma posed by bridging and bonding tensions brings to the
foreground the dilemma of under- or over-embeddedness, and highlights the need to
balance external variety with internal coherence in order to maintain process
coordination while simultaneously pursuing stability and change (Bettis & Wong, 2003;
Branzei & Fredette, 2008).
The degree to which social capital preconditions the capability micro-
foundations that provide reliable coordinated performance and shape the trajectory of
capability change is relatively unknown. To our knowledge this topic has yet to be
empirically investigated, however, like other forms of dependence social aspects no
doubt play a powerful and largely underestimated role in shaping the actions of others
(Nolan, Schultz, Cialdini, Goldstein, & Griskevicius, 2008). Helfat and Peteraf suggested
“social capital and external ties that individual team members bring with them may
constitute important endowments of the founding team” and that “the endowments
present at founding set the stage for further capability development by preconditioning
the emergence of a capability” (2003: 1001). Our research captures some of the effect
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that they predicted, illustrating the potential preconditioning influence of social capital
on future capability performance, but only begins to unpack the consequences of the
systematic patterning of endogenous capability renewal. This is an area clearly
warranting future consideration as the long term effects of social capital may well prove
pervasive in their influence on process stability and change.
We have argued that the constitution of social capital is highly context-sensitive,
in that the value associated with specific patterns of relationships as well as the content
of these relationships (i.e. the structural, cognitive and relational composition of social
capital) is dependent on the localized environment in which they are built (Bourdieu,
1986, 1990). Our results illustrate the changing constitution of social capital, as can be
seen in the growing strength of relationships among each dimension of embeddedness
and their combined implications for performance. In organizations where structures and
processes are subject to revision and realignment established patterns of social capital
may present a powerful challenge to change. Disruptions to the established and
entrenched flow of social resources resulting from the introduction of new processes
may well prove costly, and could become a significant source of rigidity if left unchecked
during change initiatives. Whether social capital built around one capability can be
effectively applied to another is an important area for future research. While the
literature appears to implicitly support the idea that it is possible (for an exception see
Labianca & Brass, 2006), empirical examination would offer greater insight into the
hazards of network dependence in organizations. As Capron and Mitchell (2009)
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observe in their study of capability gaps and social friction, firms that develop
capabilities that fit with their existing internal social context are likely to be more
effective in obtaining new capabilities.
Limitations and Future Research Directions
Despite the contributions of this research project outlined above, our results do
not come without a series of caveats. Our findings are based on an experimental design
constructed around an artificial crisis context, both of which are known to pose a risk to
external validity (Pedhazur & Schmelkin, 1991; Singleton & Straits, 1999). While the
longitudinal research design and capacity to collect objective data in real-time are
valuable attributes of the simulation platform that afford a unique opportunity to study
the growth of social capital and capability performance over time, our reliance on this
method has consequences for the generalizability of our results as discussed in chapter
four. This could be improved in several ways in future studies. Replicating our results
would enhance the robustness of our findings and conclusions, as would extending our
study to include alternative methods. The current sample used in this project could be
broadened to include threat analysts or experts to determine how task specific
expertise interacts with social capital. Qualitative field work has generated a series of
outstanding contributions in related areas of study that examined routines (Feldman,
2000, 2004; Rerup & Feldman, 2009) and capability micro-foundations (Gavetti, 2005;
Salvato, 2003, 2009). This approach would complement our findings by clarifying how
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participants understand the relationship between social capital, performance and
context, and whether building social capital was a byproduct of interaction or based on
deliberate action to appropriate personal value (Blyler & Coff, 2003). The study of these
issues is in its infancy, and there is a need for further field work to examine the
relationship between social capital and capability change.
We have demonstrated how three dimensions of embeddedness contribute to
social capital and performance in our context, and shown differences in their relative
contribution over time. Future studies could consider how the relative contribution of
aspects of social capital impacts the exploitation or emergence of new capabilities in
other environments. Social capital’s capacity to provide a coordinating infrastructure
that allows a steady flow of information or the creation of trust may vary across settings
and may even prove context dependent. Unpacking how social capital is constituted,
valued, and configured in other environments may help to clarify the conditions under
which social capital leads to more efficient and effective performance, such as in the
economizing of routine transaction costs or coping with non-routine events.
This study has benefited from focusing on the examination of one capability,
allowing us to document emergence and change. The literature, however, discusses
many different types of capabilities that abound in organizations from alliance building
and acquisition capabilities to new product and process development capabilities.
Investigating how social capital manifests in these situations and how its effects vary by
the nature of the capability would further enrich our understanding of the relationships
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discussed in this dissertation. For example, researchers could examine whether the
effects of social infrastructure vary depending on the tacitness of the capability, its
social complexity, or novelty. This type of approach may clarify the conditions under
which social capital might be an impediment to capability emergence rather than an
adaptive or facilitative factor in capability change.
Our results have implications for the learning literature, and suggest the need for
a return to the study of social resources and contexts in shaping organizational learning
and innovation. We would encourage further exploration of how the social context of
capabilities shapes learning and change. Collective processes like organizational
capabilities rely on attention (Rerup, forthcoming; Weick & Sutcliffe, 2006), engagement
and coordination (Branzei & Fredette, 2008; Levinthal & Rerup, 2006), and collaborative
interrelating (Weick & Roberts, 1993) to function effectively. The effects of social capital
in contrasting social contexts may vary significantly based on the nature of the
capability. Contrasting how social capital facilitates the use of an existing stock of
capabilities versus the acquisition of new capabilities would provide valuable insight to
the research community. Understanding the conditions under which existing stocks of
social capital help or hinder the performance of new and established capabilities would
assist organizations struggling to keep pace with changing environmental demands.
The methodological approach employed in this research has allowed us to
examine longitudinal growth between social capital and capability emergence across a
series of measurement intervals. We believe that taking a systematic approach to
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testing the relationships hypothesized in this dissertation, one based on a research
design that viably focused on modeling both the core cross-sectional and longitudinal
relationships in our theoretical model, offers new insights into the dynamics of
capability development. Our protocols were consistent with those used to study real-
world organization members of Defense Research and Development Canada - Toronto,
Collaborative Performance and Learning Section. Given that the central research thrust
of this dissertation was to unpack how the emergence of social capital influences the
evolution of organizational capabilities, the study’s research design has provided what
we believe to be an effective, relevant, and viable platform for investigating how social
relations affect the emergence of capabilities.
The explicit consideration of social capital in the process of capability evolution
provides a unique, yet essential, glimpse into the socio-relational core of capability
change because it recognizes the novel and idiosyncratic resource value of social capital
(Adler & Kwon, 2002; Burt, 2000; Moran, 2005). While each of these fields – social
capital and organizational capabilities – warrant independent study in their own right,
investigated in tandem they present the opportunity to make a significant contribution
to our understanding of organizational performance. Their study has allowed us to not
only describe the process of how emergence occurs, but also to explain what triggers
evolution and why change occurs – each fundamental in making a contribution to
organization and management theory.
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Chapter Seven: Research Conclusions
Our investigation of social capital and organizational capabilities in combination
significantly contributes to our understanding of organizational performance and
change. This study has allowed us to demonstrate how the process of social capital
emergence occurs, and to explain how it relates to the triggering of capability evolution.
As a result, this research project has generated greater insight into how organizational
capabilities grow and evolve, and how social capital contributes to these processes. By
better understanding the role that social capital networks play in the emergence and
evolution of organizational capabilities, we open the door to a variety of intervention
strategies amenable to the specific context in which the organization finds itself. Adding
some measure of control and predictability to capability evolution is important because
it may allow organizations to take action to encourage, stabilize, or discourage capability
change via specific intervention mechanisms, and provide an opportunity to maintain
alignment between internal processes and performance objectives.
The aim of this dissertation was to contribute insight to the management
literature by examining the micro-foundations of organizational capability emergence;
demonstrating that the social, relational, and structural context of work matters,
especially in its ability to shape collaborative practice and contribute to the collective
ability to meet organizational needs. Focusing on capability change offers a unique
contribution to the understanding of organizational capabilities because it begins to
question the causal factors underlying the origins and emergence of organizational
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capabilities beyond the study of positions, paths, and process evolution (Dierickx & Cool,
1989; Nelson & Winter, 1982; Teece et al., 1997). Our attempt at opening the ‘black box’
of capability emergence is an important first step in expanding our knowledge of
dynamics of organizational adaptation and evolution; understanding how the social
micro-foundations of organizational capabilities function is a necessary antecedent to
further enquiry in the line of study (Felin & Foss, 2005). This study has reduced some of
the ambiguity surrounding the valence of social capital in collective performance. While
proponents in the social capital literature have asserted the concept’s importance in
individual performance outcomes such as advice-seeking (Cross & Sproull, 2004), we
know very little about whether these arguments are appropriable to collective settings
or how differing configurations of social capital influence collaborative performance.
The results of our study provide some insight in this regard. Advancing our collective
thinking about organizational capabilities will require further investigation of the
remaining gaps in our understanding of capability micro-foundations – of which there
are many. By determining not only whether social capital is important, but how it is
important in the building and evolution of organizational capabilities this dissertation
has made strides in this effort. This dissertation has proposed that previous arguments
regarding dependence based on organization position, path, and process, articulated in
the organizational capability literature provide only a partial explanation of capability
change. We have suggested that organizational capabilities also evolve from variations
in social capital developed and deployed by network members.
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The interaction of structural, cognitive and relational embeddedness is
important because it influences the socially complex micro-practices among group
members that lie at the core of capability performance and change, shaping the social
micro-foundations of organizational capability evolution. Linking these distinct fields of
thought in a longitudinal framework illustrating their combined performance is a
significant contribution in its own right. However, connecting the performance
implications resulting from mutual emergence and co-evolution of social capital and
organizational capabilities constitutes a potentially important step forward. Therefore,
understanding how social capital emerges and organizational capabilities evolve is a
worthwhile endeavor at this time, as it offers organization scientists and managers the
opportunity to take action in a deliberate, purposeful, and timely fashion to encourage
capability change and enhance organizational performance.
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APPENDIX A: Threat Management Analyst Job Description Reference Number: 08-CSIS-07-028 Closing Date: 2009-12-31 Job Summary The Canadian Security Intelligence Service (CSIS) is seeking motivated and responsible individuals to serve as Analysts for the Threat Management Centre (TMC). Candidates must possess excellent analytical and research skills, excellent interpersonal and communication skills as well as possess the ability to work under pressure reliably and autonomously within the TMC team. Candidates must also possess good knowledge of current affairs, of international geography as well as of national and international media (print, broadcast, electronic, Internet). The TMC operates on a 24 hours a day, 7 days a week basis. Analysts are required to work shifts and could at times be expected to work alone on shifts. The functions may involve the following:
ensure a continuous and reliable alert service and point of contact to CSIS for employees and others;
coordinate effective review and analysis of various information and to coordinate an effective response pertinent to a special event and/or incident for CSIS;
offer a high quality service to CSIS employees, to our partners in the intelligence community as well as to the public in both official languages.
Education Undergraduate degree and two (2) years related experience or a three (3) year Community College Diploma and three (3) years related experience. Any higher level of education could be recognized as related experience. Experience Candidates must possess a minimum of two (2) years experience in research and analysis, possess a minimum of two (2) years experience in writing reports and/or briefs and must also possess experience in providing service to the public. A written test will be administered. Only the top ranking candidates will be considered.
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Who Can Apply: Canadian citizens residing in Canada for the last ten (10)
consecutive years. Security Requirements Candidates must have no criminal record, be drug free for the last twelve (12) months and be able to obtain a Top Secret security clearance. This process involves a security interview, a background investigation that includes credit and financial verifications as well as a polygraph examination. Language Requirements: Bilingual imperative (C/B/C) Salary Range: $59,540 to $72,460 per year. Salary is commensurate with
qualifications/experience. Location: CSIS National Headquarters, Ottawa, Ontario
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APPENDIX B: ELICIT Description12
Background: In “Power to the Edge”, Alberts and Hayes (2003) argue that missions designed with superior shared awareness, trust and self-synchronization will perform with greater speed, precision, effectiveness, and agility than missions conducted under traditional hierarchical command structures. They further argue that this is achieved by placing decision rights at the “edge of the organization,” close to the points of consequence. As part of its network-centric warfare initiative, the Command and Control Research Program (CCRP) is engaged in developing and testing principles of organization that significantly revise traditional command and control practices, transferring power and decision rights to the edge of the organization. In order to test these assertions, CCRP needs to frame testable hypotheses about the relative effectiveness of edge organizations in comparison to other methods of organization through a series of real-world experiments. In order for CCRP to undertake such experiments, the following capabilities are needed: Replicable and valid measures of shared awareness, self-synchrony and trust.
Ideally, such metrics need to be derived from observed behaviors in organizational settings.
Non-intrusive instrumentation that can be used to capture real-time behavioral
metrics about different types of organizational interactions. Automated tools and techniques that can accommodate testing for the different
factors that might affect “edge performance” with respect to shared awareness, trust, and self-synchrony.
Privacy controls and methods of apparatus deployment and administration that do
not themselves entail significant organizational changes or overhead. Another major requirement for conducting such experiments is that they compare the relative effectiveness of edge organization to traditional command and control
12
Appendix B contains an abridged version of the complete experimental design overview offered in Ruddy (2007), which is consistent with the documentation found elsewhere Parity Communications, 2006).
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principles in missions that either mimic real world missions or are themselves actual missions. By creating and evolving the ELICIT software platform, tools and procedures, and by conducting ELICIT experiments we seek to test the validity of these propositions in a controlled environment and within a controlled task domain. The objective of the experiment design is to conduct a series of online experiments to compare the relative efficiency and effectiveness of traditional command and control (C2) vs. self-organizing, peer-based edge (E) organizational forms in performing tasks that require decision making and collaboration. Meeting the Core Requirements: The major efforts in this project included the design, development and testing of the software platform on which to run the experiments; the design and execution of the experiment task (including supporting materials); and data analysis. This project addressed the following specific objectives: Develop system-based behavioral measures of “shared awareness.” By controlling
for the distribution of content and its visibility, and capturing in time logs when different subjects have shared awareness, the project was designed to compare shared awareness among subjects and its impact upon the successful completion of a mission.
Develop system-based behavioral measures of “trust” by monitoring subject
interactions in terms of reciprocity, responsiveness, number of interactions, and willingness to share content. Such trust measures can be used as predictors of mission effectiveness and timeliness.
Develop subject-based indicators of “self-synchronization” based upon the
effectiveness of trust and shared awareness in reducing decision cycles. Approach: The ELICIT software was developed and iteratively refined using live subjects. The experiments are controlled-hypothesis testing experiments. The experiment task is to identify the who, what, where and when of an adversary attack based on simple information facts (called “factoids”) that become known to a team. The independent variable is whether a team is organized using traditional command and control hierarchy or using edge organization principles.
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Experiment Software Platform: The ELICIT software is a downloadable software application that is installed on each subject’s laptop. It was built on top of the open source Higgins Trust Framework software developed as part of the SocialPhysics project affiliated with the Berkman Center for Internet and Society at Harvard Law School. The software allows CCRP and other experimenters to precisely model specific C2 processes, as well as edge organization processes, and to fully instrument all interactions. The ELICIT software platform includes a measurement capability built over a messaging infrastructure. Unlike existing software messaging and analysis technology, this open source software was uniquely designed to enable shared awareness, trust building and self-synchronization. The ELICIT platform was designed to be configurable to support both initial and follow-on experiments. The software, which is built on the Eclipse Rich Client Platform (RCP), offers modular, plug-in based design. Thus it is relatively easy to modify the software to support different experiment features or to add further communications mechanisms to determine their impact on team efficiency. Experiment Design – Purpose: The objective is to conduct a series of online experiments to compare the relative efficiency and effectiveness of command and control (C2) organizational structure with a networked, peer-based edge (E) organization in performing tasks that require decision making and collaboration. One of the key propositions of edge organization is that unknown parties, when given a shared awareness, will collaborate and self-synchronize their behaviors to achieve common goals. It is also argued that by placing decision rights at the “edge of the organization,” close to the points of conflict and consequence, more efficient and effective decisions can be made than when decision rights are concentrated and controlled through a hierarchical command and control structure. (Alberts and Hayes, 2003) The experiment challenge is to provide an online test bed where the efficiency and efficacy of command and control and edge organizational models can be compared for tasks that mimic real world conditions and challenges. By isolating several of the key structural and interactive factors that characterize command and control and edge organizations, a series of experiments and measures can be constructed to compare their relative effectiveness and efficiencies for an identical set of tasks.
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The controlled hypothesis testing experiments were designed to further understanding of the advantages of edge organization that are being conducted as part of much larger efforts in investigating the applicability of edge organization. Approach: The approach taken was to design an online multi-subject task that is sufficiently rich that it mimics issues in real-world conditions. At the same time it needed to be simple enough to make it possible to control for multiple structural and interactive parameters that differentiate between command and control-based and edge-based organizational models. The experiment challenge was to construct an experiment task that had the following characteristics: Highly relevant to real-world situations Of current interest to the DoD community Of interest and engaging to experiment subjects Multi-user, with each subject treated equivalently Abstract enough that it can be fully modeled Capable of being constructed in several versions (to support practice round and
multiple rounds of experiments with the same subject group.) Short enough so that both the practice round and actual round can be conducted in
a reasonable amount of time Difficult enough that it is not trivial to accomplish regardless of the organization
structure used Comprehensive in its range of difficulties, so that the effects of organizational
structure can be seen Flexible, so it can be adapted to new settings by changing names, tables, etc.
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By developing the ELICIT software platform, we were able to precisely model specific C2 and edge organization processes, and instrument all interactions. By abstracting complex tasks to simple interactions we were able to control for variations in subject capability and skill, and for the inherent variability in live situations that have confounded the data in other experiments. This approach allows the experiments to focus on fundamental issues of organizational design and to isolate factors that can be used to improve efficiency and effectiveness. The modular software can be modified to test additional approaches in future experiments. In the experiment, subjects are randomly assigned to two groups: “Edge” (E) and “Command and Control” (C2). Each group is to identify the who, what, where and when of an adversary attack by combining and sharing a set of information factoids that are distributed among the subjects. There are four kinds of factoids corresponding to the four kinds of information required (who, what, where and when). Like pieces of a puzzle, each contains a piece of information, but each alone is insufficient. In the experiments conducted to date, all factoids were factually correct; no incorrect information was used. Since subjects have only partial information, they must collaborate and exchange information with other subjects in their group in order to complete the task. All interactions between subjects occur through a software application resident on each subject’s computer. In either group, any subject can communicate with any other subject, although all communication occurs only as mediated by the software application. The experiment software monitors the progress of the information gathering task and declares the trial over when each individual has identified the who, what, where and when – or when the experiment times out. The group of subjects (E or C2) that completes the task first is declared the “winner.” In order to minimize any side effects from variations in previous knowledge of subject matter or the ability to absorb subject information, the information in the task is highly abstracted. The experiment is fully instrumented by the ELICIT software. It records the time and particulars of every action by each subject. Organization of the C2 Community: In the C2 organization, there are four teams of four members each plus an overall cross-team coordinator (designated in the chart as E5). The four teams are organized along
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the lines of a traditional hierarchical command structure, each with a leader. The following shows the hierarchical relationship between the overall coordinator (E5), the four leaders (A4, B4, C4 and D4) and their subordinates: Team A is from country A. Teams B, C and D are from countries B, C and D respectively. The four teams (A, B, C and D) each have a functional specialization: Team A is focused on who, team B on what, team C on where, and team D on when. The overall coordinator coordinates information among the team leaders across team boundaries. Organization of the E Community: In the E community the subjects are organized along edge principles. Unlike the C2 organization, there is no hierarchical decomposition nor is there specialization by functional area. Decision rights are decentralized: subjects decide for themselves what aspect of the task to work on, and in some situations they can choose other subjects to work with. Control is achieved entirely through the shared awareness provided by
universal access to information on shared information systems. NOTE: The above diagram is an organization chart; it is not a description of the possible communication flows. For example, subjects are able to share information to any other subject, not just along the lines shown above. Details of Experiment Design: This section provides additional details of the experiment design. Subjects:
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Subjects are randomly assigned to two 17-member groups, C2 and E. The subjects are asked to perform the task using a software application that has been pre-loaded onto their computers. All experiment communications occur between anonymous identities, so previous relationships between subjects are irrelevant. The subjects need not be physically in the same location, as all of their interactions are mediated through the software application. Subjects see and use a simple screen that contains: A message queue (looks like an email inbox) that displays messages from the
moderator as well as factoids (which look like one-line email messages) A multi-tabbed information display area that displays information about other
organizational members as well as simplified Web site-like lists to which subjects can post factoids they have received
A set of actions (menu items) the subject can take. The important actions are: (i)
Sharing a factoid with another subject; (ii) Posting a factoid to one of the Web site-like lists; (iii) Pulling to see what is on a Web site-like list; and (iv) Identifying one or more aspects of the adversary attack (when the subject thinks they know some or all of this information).
By tightly constraining the forms of communication, the experiment controls for variations in communications styles among the subjects. Task Objective: The subjects are given the objective to solve the puzzle of the location, time, target and group responsible for the adversary attack. Instructions are tailored to fit either the E organization (designated the “A” group) and its roles or the C2 organization (designated the “B” group). All subjects are instructed that they are free to work on any aspect of the task. Subject Roles: The experiment software automatically provides a URL with group-specific instructions to each subject at the start of an experiment trial set. Factoids:
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During each round of the experiment, the application delivers four “factoids” to the inbox of each subject. Group members use these factoids to help identify the adversary attack. Two factoids arrive at the beginning of the experiment round. An additional factoid is distributed five minutes later and one last factoid five minutes after that. All of the information necessary to identify the adversary attack has been distributed to the subject group within the first 10 minutes after the start of an experiment round. There are four types of factoids that represent information about the anticipated attack: Who factoids – the likely actors What factoids – the target Where factoids – the country When factoids – the month, day and time. Initial Factoid Distribution: There are 68 factoids, including 4 expertise factoids. To model a hierarchy (C2) organization, in which team leaders traditionally have more expertise than their team members, each of the four team leaders is given an “expertise” factoid that represents pre-attained knowledge. In the E case, these expertise factoids are disbursed within the community at random. Some of the 68 factoids are more important than others. The more important ones are considered key factoids. Expertise factoids are special key factoids. The remaining non-key factoids are distributed among the subjects so as to ensure that no subject receives more than 1 key factoid. The base factoid sets are available at http://www.dodccrp.org/html3/elicit.html. This phased factoid distribution is designed so that the task can’t be solved until the last distribution is made. Distribution of factoids is controlled and specified in factoid set tables. Four complete factoid sets where created. Factoid set 4 is the easiest. Factoids sets 2 and 3 are very similar. For the initial experiments, all live subject tests were performed using factoid set 4-17 for the practice round and 1-17 for the actual round.
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In subsequent trials, factoid sets and or players can be changed. As the factoids are stored in tables, it is possible to create additional factoid sets. The Navel Post Graduate School in Monterey has already created and used derivative ELICIT factoid sets. Experiment Protocol: A full experiment trial consists of 34 subjects who are randomly assigned to either the Command and Control (C2) or Edge [E] team. The experiment consists of four phases. An introduction phase that includes: An overview of the experiment’s agenda, which is delivered via PowerPoint so that it
can be adjusted for any logistical specifics. An eight-minute subject pre-experiment briefing video, which is delivered via
Windows Media Player. A practice round (round 1), in which the subjects have the opportunity to use the
software with a sample scenario. This round is designed to run for 20 minutes. A second round, which uses a different scenario. This round is designed to run for up to an hour. A wrap-up, which includes: A Web-based survey, which requires 20 minutes to complete A two-minute subject post-briefing video, which is delivered via Windows Media
Player Discussion of the experiment. Total elapsed time for the four phases of the experiment is approximately 3 ½ hours. Materials necessary to conduct the experiment – including pre-briefing videos, instructions for the experiment moderator, etc. – are available for download at the project’s companion Web site http://www.dodccrp.org/html3/elicit.html. Data Collection Procedures and Analysis Approach: The principal means of data collection for the experiments are:
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Automated data collection that is integrated with the software used for the experiment
A Web-based post-experiment survey that is automatically administered by the test
software at the conclusion of an experiment trial Notes made by the subjects Observations/notes by the moderators and proctors. Task Difficulty: The goal had been to make a task that took a reasonable amount of time (e.g., one hour) and required inter-subject interactions. Additionally, the objective was to avoid making the purely cognitive aspects so difficult that the intelligence and information-managing aspects of the task would become the predominant criterion for success. Our intent was to measure organizational effectiveness, not individual IQ. While it is possible to discuss task difficulty in the abstract (i.e., presuming complete knowledge of all factoids) in practice we found that even after a full hour (50 minutes after all factoids have been distributed), full diffusion of all factoids did not occur. For example, in the June 22 Edge trial, two important factoids were not posted. As a consequence, many subjects never received all the information necessary to correctly identify all aspects of the adversary attack. At the conclusion of the pre-tests with live subjects, subjects were very interested in discussing the experience, conditions under which their performance could be improved and how organization impacts performance. Several persons in the discussion felt that the experience of participating in the experiment and subsequent discussions would be useful as part of an education process on organizational design and dynamics as it brings abstract issues to life.
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APPENDIX C: Sensitivity Analysis
This appendix illustrates the results of sensitivity analysis in which the variance of social capital’s residual error term was relaxed from 0.00 to 0.45. The purpose of this procedure was to determine the robustness of our preliminary findings, and to determine whether varying the level of variance associated with social capital would substantially alter model fit and patterns of relationships contained within each cross-sectional structural model. Our results suggest that constraining the variance equal to zero provides a more conservative standard against which to test our hypotheses. Relaxing the variance left the model fit indices unchanged suggesting that our interpretation of the modeling results with variance equal to zero were appropriate and durable. Results presented in this appendix are derived from raw AMOS 16.0 output, and have only been edited to ensure the inclusion of comparable content.
Capability
Performance
Capability
Accuracy
Capability
Quality
Capability
Speed
1
Cognitive
Embeddedness
Structural
Embeddedness
Relational
Embeddedness
Social
Capital
.45
res1-1
1
1
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Analysis Summary Maximum Likelihood Estimates – Measurement Interval One
Result (Default model)
Minimum was achieved Chi-square = 43.962 Degrees of freedom = 38 Probability level = .234
Regression Weights: (Group number 1 - Default model)
Estimate
Standardized Estimate
S.E. C.R. P
Social Capital <--- Structural Embeddedness -.300 -.330 .147 -2.040 .041 Social Capital <--- Cognitive Embeddedness -.246 -.263 .157 -1.563 .118 Social Capital <--- Relational Embeddedness .786 .763 .166 4.719 *** Capability Performance <--- Social Capital 1.000 .688
Capability Accuracy <--- Capability Performance .163 .617 .048 3.433 *** Capability Quality <--- Capability Performance 1.000 .868
Capability Accuracy <--- Capability Speed .123 .651 .019 6.433 *** Capability Quality <--- Capability Speed .229 .278 .068 3.373 *** ID WHO <--- Capability Accuracy 1.000 .689
ID WHAT <--- Capability Accuracy .854 .598 .147 5.810 *** ID WHERE <--- Capability Accuracy .811 .570 .146 5.570 *** ID WHEN <--- Capability Accuracy .370 .337 .108 3.411 ***
A7A <--- Capability Quality 1.000 .921
A7B <--- Capability Quality .992 .857 .084 11.881 *** A7C <--- Capability Quality .702 .635 .088 7.993 ***
Covariances: (Group number 1 - Default model)
206
Estimate S.E. C.R. P
Cognitive Embeddedness <--> Relational Embeddedness .414 .083 4.979 *** Cognitive Embeddedness <--> Structural Embeddedness .513 .096 5.348 *** Structural Embeddedness <--> Relational Embeddedness .360 .083 4.329 ***
Correlations: (Group number 1 - Default model)
Estimate
Cognitive Embeddedness <--> Relational Embeddedness .485 Cognitive Embeddedness <--> Structural Embeddedness .531 Structural Embeddedness <--> Relational Embeddedness .410
Factor Score Weights (Group number 1 - Default model)
Relational
Embeddedness Structural
Embeddedness Cognitive
Embeddedness Capability
Speed A7C A7B A7A
ID WHEN
ID WHERE
ID WHAT
ID WHO
Social Capital .599 -.229 -.187 -.076 .021 .061 .122 .042 .072 .079 .110
Capability Performance
.221 -.085 -.069 -.229 .063 .183 .368 .127 .218 .239 .333
Capability Quality
.087 -.033 -.027 .000 .089 .259 .521 .050 .086 .094 .131
Capability Accuracy
.026 -.010 -.008 .061 .007 .021 .043 .063 .108 .118 .165
207
Standardized Total Effects (Group number 1 - Default model)
Relational
Embeddedness Structural
Embeddedness Cognitive
Embeddedness Capability
Performance Capability
Speed Capability
Quality Capability Accuracy
Social Capital .763 -.330 -.263 .000 .000 .000 .000 Capability Performance
.525 -.227 -.181 .000 .000 .000 .000
Capability Quality
.456 -.197 -.157 .868 .278 .000 .000
Capability Accuracy
.324 -.140 -.112 .617 .651 .000 .000
A7C .289 -.125 -.100 .551 .176 .635 .000 A7B .391 -.169 -.135 .744 .238 .857 .000 A7A .420 -.182 -.145 .799 .256 .921 .000 ID WHEN .109 -.047 -.038 .208 .219 .000 .337
ID WHERE .185 -.080 -.064 .352 .371 .000 .570 ID WHAT .194 -.084 -.067 .369 .389 .000 .598 ID WHO .223 -.097 -.077 .425 .448 .000 .689
208
Model Fit Summary
CMIN
Model NPAR CMIN DF P CMIN/DF Default model 28 43.962 38 .234 1.157 Saturated model 66 .000 0
Independence model 11 473.344 55 .000 8.606
RMR, GFI
Model RMR GFI AGFI PGFI Default model .074 .943 .900 .543 Saturated model .000 1.000
Independence model .442 .544 .452 .453
Baseline Comparisons
Model NFI
Delta1 RFI
rho1 IFI
Delta2 TLI
rho2 CFI
Default model .907 .866 .986 .979 .986 Saturated model 1.000
1.000
1.000
Independence model .000 .000 .000 .000 .000
RMSEA
Model RMSEA LO 90 HI 90 PCLOSE Default model .035 .000 .073 .699 Independence model .242 .222 .262 .000
209
Analysis Summary Maximum Likelihood Estimates – Measurement Interval Two
Result (Default model)
Minimum was achieved Chi-square = 58.408 Degrees of freedom = 39 Probability level = .024
Regression Weights: (Group number 1 - Default model)
Estimate Standardized Estimate S.E. C.R. P
Social Capital <--- Structural Embeddedness -.223 -.284 .123 -1.809 .070 Social Capital <--- Cognitive Embeddedness .636 .797 .200 3.183 .001 Social Capital <--- Relational Embeddedness -.307 -.387 .195 -1.573 .116 Capability Performance <--- Social Capital 1.000 .691
Capability Accuracy <--- Capability Performance .249 .903 .039 6.323 *** Capability Quality <--- Capability Performance 1.000 .746
Capability Accuracy <--- Capability Speed .084 .429 .019 4.391 *** Capability Quality <--- Capability Speed .137 .145 .083 1.658 .097 ID WHO <--- Capability Accuracy 1.000 .673
ID WHAT <--- Capability Accuracy .990 .684 .152 6.516 *** ID WHERE <--- Capability Accuracy 1.166 .745 .168 6.947 *** ID WHEN <--- Capability Accuracy .727 .461 .158 4.612 *** B7A <--- Capability Quality 1.000 .932
B7B <--- Capability Quality 1.074 .911 .074 14.540 *** B7C <--- Capability Quality .647 .619 .080 8.051 ***
210
Covariances: (Group number 1 - Default model)
Estimate S.E. C.R. P
Cognitive Embeddedness <--> Relational Embeddedness .791 .109 7.237 *** Cognitive Embeddedness <--> Structural Embeddedness .430 .093 4.604 *** Structural Embeddedness <--> Relational Embeddedness .462 .095 4.859 ***
Correlations: (Group number 1 - Default model)
Estimate
Cognitive Embeddedness <--> Relational Embeddedness .821 Cognitive Embeddedness <--> Structural Embeddedness .441 Structural Embeddedness <--> Relational Embeddedness .471
Factor Score Weights (Group number 1 - Default model)
Relational
Embeddedness Structural
Embeddedness Cognitive
Embeddedness Capability
Speed B7C B7B B7A
ID WHEN
ID WHERE
ID WHAT
ID WHO
Social Capital -.212 -.154 .440 -.085 .007 .034 .050 .100 .289 .240 .224
Capability Performance
-.072 -.052 .149 -.212 .018 .085 .124 .249 .716 .595 .556
Capability Quality
-.012 -.009 .024 -.012 .069 .327 .478 .041 .117 .097 .091
Capability Accuracy
-.018 -.013 .037 .031 .004 .021 .031 .062 .179 .149 .139
211
Standardized Total Effects (Group number 1 - Default model)
Relational
Embeddedness Structural
Embeddedness Cognitive
Embeddedness Capability
Performance Capability
Speed Capability
Quality Capability Accuracy
Social Capital -.387 -.284 .797 .000 .000 .000 .000 Capability Performance
-.268 -.196 .551 .000 .000 .000 .000
Capability Quality
-.200 -.146 .411 .746 .145 .000 .000
Capability Accuracy
-.242 -.177 .497 .903 .429 .000 .000
B7C -.124 -.091 .254 .462 .090 .619 .000 B7B -.182 -.133 .374 .680 .132 .911 .000 B7A -.186 -.136 .383 .696 .135 .932 .000 ID WHEN -.111 -.082 .229 .416 .198 .000 .461
ID WHERE -.180 -.132 .370 .673 .319 .000 .745 ID WHAT -.165 -.121 .340 .618 .293 .000 .684 ID WHO -.163 -.119 .335 .608 .289 .000 .673
212
Model Fit Summary
CMIN
Model NPAR CMIN DF P CMIN/DF Default model 27 58.408 39 .024 1.498 Saturated model 66 .000 0
Independence model 11 692.108 55 .000 12.584
RMR, GFI
Model RMR GFI AGFI PGFI Default model .084 .928 .878 .548 Saturated model .000 1.000
Independence model .486 .453 .344 .378
Baseline Comparisons
Model NFI
Delta1 RFI
rho1 IFI
Delta2 TLI
rho2 CFI
Default model .916 .881 .970 .957 .970 Saturated model 1.000
1.000
1.000
Independence model .000 .000 .000 .000 .000
RMSEA
Model RMSEA LO 90 HI 90 PCLOSE Default model .062 .023 .093 .261 Independence model .299 .279 .319 .000
213
Analysis Summary Maximum Likelihood Estimates – Measurement Interval Three
Result (Default model)
Minimum was achieved Chi-square = 38.391 Degrees of freedom = 40 Probability level = .543
Regression Weights: (Group number 1 - Default model)
Estimate Standardized Estimate S.E. C.R. P
Social Capital <--- Structural Embeddedness -.112 -.154 .115 -.977 .329 Social Capital <--- Cognitive Embeddedness .618 .832 .235 2.636 .008 Social Capital <--- Relational Embeddedness -.481 -.636 .232 -2.079 .038 Capability Performance <--- Social Capital 1.000 .735
Capability Accuracy <--- Capability Performance .305 .981 .064 4.747 *** Capability Quality <--- Capability Performance 1.000 .658
Capability Accuracy <--- Capability Speed .046 .193 .026 1.783 .075 Capability Quality <--- Capability Speed -.007 -.006 .100 -.073 .942 ID WHO <--- Capability Accuracy 1.000 .619
ID WHAT <--- Capability Accuracy -.002 -.004 .041 -.039 .969 ID WHERE <--- Capability Accuracy .917 .569 .198 4.638 *** ID WHEN <--- Capability Accuracy .969 .625 .198 4.896 *** C7A <--- Capability Quality 1.000 .854
C7B <--- Capability Quality 1.173 1.000 .063 18.726 *** C7C <--- Capability Quality .607 .535 .090 6.743 ***
214
Covariances: (Group number 1 - Default model)
Estimate S.E. C.R. P
Cognitive Embeddedness <--> Relational Embeddedness .836 .111 7.551 *** Cognitive Embeddedness <--> Structural Embeddedness .454 .095 4.803 *** Structural Embeddedness <--> Relational Embeddedness .432 .092 4.682 ***
Correlations: (Group number 1 - Default model)
Estimate
Cognitive Embeddedness <--> Relational Embeddedness .884 Cognitive Embeddedness <--> Structural Embeddedness .464 Structural Embeddedness <--> Relational Embeddedness .450
Factor Score Weights (Group number 1 - Default model)
Relational
Embeddedness Structural
Embeddedness Cognitive
Embeddedness Capability
Speed C7C C7B C7A
ID WHEN
ID WHERE
ID WHAT
ID WHO
Social Capital -.316 -.074 .405 -.035 .000 .092 .000 .295 .233 -.005 .277
Capability Performance
-.149 -.035 .191 -.070 .000 .184 .000 .592 .467 -.009 .557
Capability Quality
.000 .000 .000 .000 .000 .853 .000 .000 .000 .000 .000
Capability Accuracy
-.045 -.011 .058 .024 .000 .056 .000 .180 .142 -.003 .170
215
Standardized Total Effects (Group number 1 - Default model)
Relational
Embeddedness Structural
Embeddedness Cognitive
Embeddedness Capability
Performance Capability
Speed Capability
Quality Capability Accuracy
Social Capital -.636 -.154 .832 .000 .000 .000 .000 Capability Performance
-.467 -.113 .611 .000 .000 .000 .000
Capability Quality
-.307 -.074 .402 .658 -.006 .000 .000
Capability Accuracy
-.458 -.111 .600 .981 .193 .000 .000
C7C -.165 -.040 .215 .352 -.003 .535 .000 C7B -.307 -.074 .402 .658 -.006 1.000 .000 C7A -.263 -.063 .344 .562 -.005 .854 .000 ID WHEN -.286 -.069 .375 .613 .121 .000 .625
ID WHERE -.261 -.063 .341 .558 .110 .000 .569 ID WHAT .002 .000 -.002 -.004 -.001 .000 -.004 ID WHO -.284 -.069 .371 .608 .119 .000 .619
216
Model Fit Summary
CMIN
Model NPAR CMIN DF P CMIN/DF Default model 26 38.391 40 .543 .960 Saturated model 66 .000 0
Independence model 11 584.295 55 .000 10.624
RMR, GFI
Model RMR GFI AGFI PGFI Default model .066 .951 .920 .577 Saturated model .000 1.000
Independence model .455 .579 .494 .482
Baseline Comparisons
Model NFI
Delta1 RFI
rho1 IFI
Delta2 TLI
rho2 CFI
Default model .934 .910 1.003 1.004 1.000 Saturated model 1.000
1.000
1.000
Independence model .000 .000 .000 .000 .000
RMSEA
Model RMSEA LO 90 HI 90 PCLOSE Default model .000 .000 .057 .909 Independence model .272 .252 .292 .000
217
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