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Community structure as collective identity construction and resource search
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Community structure as collective identity construction and resource search
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Content
COMMUNITY STRUCTURE AS
COLLECTIVE IDENTITY CONSTRUCTION AND RESOURCE SEARCH
by
Lin Chai
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BUSINESS ADMINISTRATION)
December 2009
Copyright 2009 Lin Chai
ii
Acknowledgements
I am greatly indebted to my dissertation Chair, Nandini Rajagopalan, for giving
me relentless support and freedom to explore new intellectual avenues. She is
always a source of light and inspiration. This dissertation is dedicated to her for
supporting me throughout the process.
I am also greatly indebted to Peter Monge for allowing me to connect with his
rich repository of network knowledge. This tie has sparked my intellectual
curiosity in network research and continues to illuminate me. This dissertation is
dedicated to him for giving me the seed to explore and grow. This dissertation
would not exist without the knowledge being transmitted from his tie.
I am thankful to Mark Kennedy for giving me valuable feedback on my previous
work that laid the foundation for my dissertation. His own intellectual thinking
is always inspirational.
iii
Table of Contents
Acknowledgements ii
List of Tables iv
List of Figures v
Abstract vi
Chapter 1: Introduction 1
Chapter 2: Collective Identity Construction and Community Structure 8
Chapter 3: Resource Search and Community Structure 38
Chapter 4: Overview of the Contemporary American Film Industry 58
Chapter 5: Research Method 64
Chapter 6: Discussion and Conclusion 95
References 107
Appendices 130
A: Ideal Types of Community Structure 130
B: Film Production Companies in 1985 131
C: Film Production Companies in 2005 133
D: Genres 137
E: Model Parameter Description 138
iv
List of Tables
Table 1: Film Production Patterns 72
Table 2: Summary of Company Ties 73
Table 3: 1985 Parameter Estimates of Higher-Order ERGM 74
Collaboration Network
Table 4: 2005 Parameter Estimates of Higher-Order ERGM 79
Collaborative Network
Table 5: Parameter Estimates of the Longitudinal ERGM in 81
Film Production Networks
Table 6: Summary of Hypothesis Testing Results 82
Table 7: 1985 Summary of Goodness of Fit Statistics (t-values) 93
Table 8: 2005 Summary of Goodness of Fit Statistics (t-values) 94
Table B-1: Film Production Companies in 1985 131
Table C-1: Film Production Companies in 2005 133
Table D-1: Genres 137
v
List of Figures
Figure 1a: 1985 Affiliation Matrix – Cultural Identity 84
Figure 1b: 2005 Affiliation Matrix – Cultural Identity 85
Figure 2a: 1985 Affiliation Matrix – Professional Identity 86
Figure 2b: 2005 Affiliation Matrix – Professional Identity 87
Figure 3a: 1985 Affiliation Matrix –Reputation Resources 89
Figure 3b: 2005 Affiliation Matrix – Reputation Resources 89
Figure 4a: 1985 Affiliation Matrix – Experience Resources 90
Figure 4b: 2005 Affiliation Matrix – Experience Resources 91
Figure A-1: Ideal Types of Community Structure 130
Figure E-1: Model Parameter Description 138
vi
Abstract
Drawing from organizational ecology, network, and economic sociology
theories, our study investigates how the mechanisms of collective identity
construction and resource sharing contribute to the formation of various
structural tendencies at the community level. Here, community is defined as an
aggregation of the network of inter-organizational ties. Community also consists
of various organizational forms connected by commensalistic and symbiotic
relations. These interdependencies are manifested in a number of interaction
patterns reflecting the macrostructure of the community. Using the U. S. film
industry as our empirical context, we analyze collaboration networks among film
producers at two points in time - 1985 and 2005 – to examine these interaction
patterns. Our findings suggest that both collective identity construction and
research sharing mechanisms explain decentralized, polycentric structural
tendencies of the film producer community. In addition, generalists that are
highly diversified tend not to collaborate with each other. They also are less
likely to collaborate with specialists within a narrow technological space.
Collaboration is most likely to occur among producers that are moderately
diversified and have greater technological distance from each other. In addition,
we found that producers differing in cultural identity are not precluded from
collaboration with each other, which is especially true for member organizations
from the dominant population. High-reputation producers tend to attract more
vii
collaborative partners, but they tend to cooperate only with other high-
reputation partners. Just as status-based competition is localized (Podolny, 1993;
1994), status-based cooperation is also localized to the extent that producers tend
to interact with those who are in similar status categories. Community structure
exhibits such tendencies and becomes polycentric and clustered around different
status categories. Our findings resonate with Fligstein’s (2001) political-cultural
approach to the market.
1
Chapter 1: Introduction
An open-systems approach to organizations recognizes that the goals and actions
of an organization are both enabled and constrained by the external
environment, which is largely comprised of other organizations. Organizations,
then, are fundamentally interdependent. This key insight has invited new lines of
inquiry to understand how organizational interdependence is constructed and
the generative power it produces. A large body of scholarly work has examined
the manifestations of organizational interdependence in the context of social
networks and strategic alliances. This stream of research takes an individual
perspective, and largely focuses on how a focal organization’s “immediate”
environment – its partnership and ego network – impact its performance. While
knowledge of this interface is undeniably substantive, this approach misses the
opportunity to illuminate the macro structure of collective action (Salancik, 1995).
The complex relationships among organizations can significantly influence the
mobilization of collective efforts to support or oppose various policy initiatives.
Taking collectivity into consideration, organization theorists have begun to
understand how interdependence among different organizational forms and
populations can affect the evolutionary process of community (Astley &
Fombrun, 1983; Ruef, 2000). A community can be conceptualized as “… sets of
relations between organizational forms or as places where organizations are
2
located in resource space or in geography” (Freeman & Audia, 2006: 145).
Populations of organizations develop commensalistic and symbiotic
relationships with each other and engage in activities that bind them into
organizational communities (Aldrich, 1999; Hannan & Freeman, 1989; Hawley,
1950). This line of research explores the evolution of community structure and
examines how population interdependence engenders a “collective self-
sufficiency” that, in turn, affects the persistence and stability of the community
(for a review, see Baum & Rao, 2004; Baum and Shipilov, 2006). The effects of
commensalistic and symbiotic mechanisms are often inferred from analyzing the
effect of one population density on the vital dynamics of another rather than by
studying them directly (Lomi, 1995, with exception of Powell, et al., 2005 and
Audia and Rider, 2006). This indirect method can be strengthened by examining
network relations among organizational members of populations and
community niches (Audia & Rider, 2006; DiMaggio, 1994).
Still missing, however, is a formal account of community structure properties,
and the understanding of how micro social processes of interaction between
organizations that reside in the same or different populations affect the
formation of community structure (Baldassarri & Diani, 2007). Laumann,
Galaskiewicz and Marsden (1978) define community structure as the aggregate
network of inter-organizational linkages. Showing that inter-organizational
3
structural properties have consequences has been the subject of considerable
research since Galaskiewicz examined the idea in 1976. An important step
forward is to study the relation between individual organization linkages and
system-level effects (Laumann, et al., 1978).
The motivation of my study follows this line of reasoning. I propose that
community structure properties and configurations can be explained by micro-
processes of inter-organizational interactions. Community structure often
exhibits two contravening tendencies. On the one hand, community structure
tends to become hierarchical when a few central organizations attract many ties
that connect to multiple domains of peripheral actors. One the other hand,
community structure tends to move towards fragmentation with polycentric
clusters when firms within a cluster are densely connected but the clusters are
loosely connected (see Appendix A, adapted from Baldassarri & Diani, 2007).
My study argues that collective identity construction and resource sharing are
two key micro social processes that determine the nature of inter-organizational
linkages, which, in turn, explain community structure formation tendencies. The
collective identity construction process induces organizations with similar
categorical memberships to forge associations and engage in identity-based
exchange behaviors. Thus, the identity construction process goes beyond
4
economic considerations and maintains solidarity of the identity category
(Baldassarri & Diani, 2007; Laumann, et al., 1978; Ruef, 2000). I predict that the
identity construction process produces a strong community structure tendency
towards exhibiting a polycentric structure with professional identity-based
linkages and a hierarchical structure with cultural identity-based linkages. The
resource sharing process allows organizations to expand their resource space
through exchanging similar or complimentary resources with other
organizations. I propose that this process shapes the contour of community
structure towards a triangulated, polycentric form.
I test this set of predictions in the context of the contemporary American cinema
industry. Collaboration is the dominant governance form for producing films.
The contemporary American cinema industry consists of two distinct
organizational forms or populations: independent film companies (“indies”) and
studios. Studios focus on producing blockbuster event films, while indies often
strive to preserve their entrepreneurial artistic freedom and produce small-
medium budget films reflecting the human conditions of modern societies. In
spite of the studios’ dominant market power, they nevertheless must compete for
audience attention. Over the years, the film industry has evolved into separate
yet related functional units and ideological components appearing on the
American cinema landscape. Thus, the industry represents a natural empirical
5
setting to study formal community structure properties and the associated micro
social processes underlying them.
My study applies the PNet social network analytical tool to analyze community
dynamics in the film industry. PNet utilizes a multi-theoretical, multiple-level
framework (MTML) and adopts the p* analytical strategy (Monge & Contractor,
2003). PNet is capable of handling new specifications for exponential random
graph models developed by Snijders, Pattison, Robins and Handcock (2006).
Building on Monte Carlo maximum likelihood estimation techniques, the new
specifications incorporate higher-order network transitivity statistics into a
parameter estimation process to obtain a stronger convergence between
structural effects of simulated graphs and those of observed network graphs.
The findings of my study shed light on the creation of organizational fields. An
organizational field is composed of a community of organizations that engages
in common activities and is subject to similar reputational and regulatory
pressures (DiMaggio & Powell, 1991; Powell, et al., 2005). Examining how micro-
level dynamics engender certain community structure configurations is central to
understanding how fields evolve (Hedstrom, 2005). Collective action and social
integration can be created in an organizational field if we gain insights into the
opportunity structure of a community and the ways in which inter-
6
organizational networks can be activated or mobilized for collective action
(Diani, 2003; Mellucci, 1996; Zald & McCarthy, 1987).
My study is among the first attempts to examine the entire collaboration network
in an industry production market. The findings make a number of significant
contributions to organization theory. They help to explain the micro processes
underlying inter-organizational collaboration, and show how they contribute to
community structure. The results suggest that the market is a stratified political
economy in which power, status category, and knowledge influence interactions
among organizations. This highlights the need to study the sociology of markets
and to treat these factors as essential to market exchange. My study contributes
to organizational ecology theory by providing a more fine-grained explanation
for the concept of collective identity and showing how it affects community
structure. This helps to meld niche width theory and the theory of organizational
forms into a more coherent and comprehensive model. It provides insights that
compliment density dependence theory. My study also adds to theory about
alliance formation. The results go beyond traditional firm-level explanations of
alliance formation to provide a more macro understanding of alliance dynamics.
Finally, this study provides a concrete exemplar for how the PNet social network
analytical tool can be applied to the study of community dynamics in
7
organization theory. It enables us to bring agency back into community studies
by linking agent attributes to network structures.
The following pages are organized into five chapters. Chapter two delineates the
effect of collective identity construction on community structural tendencies.
Chapter three examines how community structural tendencies are enabled by
resource sharing mechanisms. Chapter four presents the empirical context –
contemporary American Cinema – under which the study is undertaken.
Chapter five lays out the research method for examining the hypothesized effects
and presents the study results. Chapter six includes discussion of the results and
conclusions.
8
Chapter 2: Collective Identity Construction and Community
Structure
The concept of collective identity has evoked a great deal of scholarly interest
from multiple social science domains. Psychologists use the term to describe the
meanings of multiple roles a person plays in contemporary societies and to
uncover how identification to a collectivity or social category occurs (Calhoun,
1994; Tajfel, 1982). Sociologists and political scientists largely view collective
identity as means and ends of social movements as well as forces for generating
social change (Snow & Oliver, 1995). While approached from different analytical
angles, collective identity is generally defined as "… agreed upon definition of
membership, boundaries, and activities for the group" (Johnston, et al.,
1994:15).
In recent years, organizational scholars have brought the concept of collective
identity into the conversation to highlight the importance of shared identity in
shaping cooperation and competition among organizations (for a review, see
Ingram & Yue, 2008). In particular, Clemens (1997) suggests that organizational
forms can be sources of shared identity. Organization ecologists have used the
concept of collective identity to codify organizational forms (Hannan, et al., 2007;
Hsu & Hannan, 2005; Polos, et al., 2002). Collective identity specifies a set of social
codes or rules, which not only characterize the attributes of a corresponding
9
organizational form but also the governance of actions by providing “taken-for-
granted templates for structuring activities” (Aldrich & Ruef, 2005: 6). Collective
identity forms an evaluative basis for a market audience to judge whether the
strategic choices organizations make are in congruence with the expectations
defined by their collective identity. Thus, the categorical differences implied by
collective identity have imperative consequences to the extent that deviation or
violation of their code of conduct – collective identity – may result in market
sanctions and put firms in disadvantaged positions (Carroll & Swaminathan, 2000;
Phillips & Zuckerman, 2001; Zuckerman, 1999; Zuckerman, et al., 2003;
Zuckerman & Kim, 2003). Empirical research largely verifies the sanction effect of
collective identity (Dobrev, et al., 2001; Hsu, 2006a; Phillips & Zuckerman, 2001;
Zuckerman, 1999).
While collective identity creates distinctions among organizational forms, some
focal characteristics of the form may overlap with that of other forms (Carroll &
Ratner, 1996). For instance, mass-producing macro breweries, microbreweries,
and brewpubs are three distinctive organizational forms. The common attribute
they share is being a brewer; they have similar beer brewing and craft
production technology (Carroll & Swaminathan, 2000). New emerging
organizational forms rely on cultivating social, political and cognitive legitimacy
(Aldrich & Ruef, 2005; DiMaggio & Powell 1983; Meyer & Rowan, 1977; Scott
10
1995). Obtaining approval of the form’s focal characteristics is crucial to
legitimacy building. This requires the new form’s focal characteristics to have
some bearing on conventional norms, industry logics, or methods of production
so the market audience can drawn connections with the frames they are familiar
with for making judgments (Rao, et al., 2003). The distinctions that new
organizational forms intend to forge on industry structure can only be achieved
when their attributes can be directly compared and connected to the elements in
other existing forms. Thus, the collective identity defining an organizational
form has two components: a cultural one corresponding to the culture codes of a
specific organizational form that sharpen its boundary, and a general one
representing common attributes shared with other forms that diffuses its
boundaries. This distinction extends the notion of restrictive and general
identities that Dobrev, Kim and Hannan (2006) put forth. For the purpose of
discussion, we can label them as “cultural identity” and “professional identity.”
Organizational forms are the building blocks of community (for a review see
Ingram & Simons, 2000; McKendrick, et al., 2003; Ruef, 2000, 2004). Collective
identity construction is important for the development of organizational forms,
and the categorical boundaries it engenders are a pre-requisite for segregation
(Polos, et al., 2002; Zuckerman, 1999). In this section, we discuss the micro
11
processes and mechanisms for constructing collective identity along with their
impact on community structure as a whole. Using the collaboration network as a
context, we argue that similar organizations tend to cooperate with each other to
define their collective identity while excluding their distanced counterparts.
While this process is applicable to both components of collective identity,
community structure displays two contravening tendencies – hierarchical and
centralized versus decentralized and fragmented – contingent on the type of
identity-based cooperation. Drawing on literature from social identity theory,
social movement theory and organizational ecology theory, I illuminate how the
community structure at the macro level is affected by identity-based micro
processes and mechanisms.
Cultural Identity and Community Structure
Cultural identity specifies the culture codes of an organizational form and
activates powerful distinctions along social and culture dimensions (Baron 2004;
Polos, et al., 2002; McKendrick, et al., 2003; Rao, Monin & Durand, 2003). For
instance, “The distinction between craft and industrial unions had a great deal to
do with the demographic differences among workers represented by each type”
(Baron, 2004:11). In some cases, cultural identity rises as a form of opposition to a
dominant order. The rise of micro-brewers (Carroll & Swaminathan, 2000), low-
12
power FM radio stations (Greve, Posner & Rao, 2006) and right-wing newspapers
in Red Vienna (Barnett & Woywode, 2004) illuminates this powerful response to
dominant organizational forms or populations. Cultural identity also reflects the
ideological differences underlying various organizational forms (Ingram &
Simons, 2000; Simons & Ingram, 2003).
Cultural identity sharpens inter-population boundaries through two
mechanisms. One is related to its imperative ability to impose sanctions on
organizations that violate the values and expectations associated with an identity
category (Dobrev, et al., 2001; Hsu, 2006b; Meyer & Scott, 1983; Phillips &
Zuckerman, 2001; Podolny, 1993; Polos, Hannan, & Carroll, 2002; Zuckerman,
1999). Carroll and Swaminathan (2000) showed that the authentic status of craft
brewers adopting small scale operation and using traditional brewing techniques
has attracted mimic behavior from contract breweries. However, these breweries
failed to mobilize support from consumers because they violated the culture
code of their existing form and caused confusion in the market. Consequently,
they had a lower founding rate than microbrewers and brewpubs. In the film
industry, movies classified as major films tend to perform well in mainstream
markets while receiving little attention and recognition in the art-house market
(Zuckerman & Kim, 2003). Through analyzing audience reception of U.S.-
produced feature films during 2000–2003, Hsu (2006a) explained that audiences’
13
perceptions of a film’s fit with targeted genres increased the chance of success for
more focused films but reduced the appeal of films targeted at multiple genres.
Thus, conformity to categorical imperatives is necessary for firms to gain positive
evaluations from the market (Phillips & Zuckerman, 2001; Zuckerman, 1999).
A second mechanism through which cultural identity sharpens inter-population
boundaries is it creates a basis for social cohesion that strengthens the stance of a
particular organizational form’s position in the market. It also allows firms to
engage in collective action and institute institutional change. Studying the battle
between independent and chain stores in the regulatory environment of retailing,
Ingram and Rao (2004) showed that the homogenous and cohesive collectiveness
displayed by independents gave them more influence over tax laws than chain
stores.
Freeman and Audia (2006) show patterns of interdependence based on ideology
or social identity in which the glue holding communities together is common,
professed, cultural identity (p.148). On the one hand, similarity fosters
mutualistic interdependence among members of the same population (Audia &
Rider, 2006; Haveman & Nonemaker, 2000; Hawley, 1986). For example, Ingram
and Simons (2000) studied organizational communities by examining
interdependence in the ideologies that organizations pursue. They showed that
14
similarity in ideologies has mutualistic effects on organizations. Examining
Israeli worker cooperatives, they found that increases in the density of two
organizational populations sharing the same socialist ideology (kibbutz
organizations and credit cooperatives) were accompanied by declines in the
failure rate of the organizations. Ruef (2000) studied the organizational
community of U.S. health care organizations by focusing on relations of
interdependence among organizational identities. He found that identity
interdependence generates mutuality effects of organizations with similar
identities. Similar identities, according to Ruef, generate legitimacy and resources
spillovers (Barnett & Carroll, 1987; Hannan & Freeman, 1987). On the other hand,
when threatened by outsiders, population members respond collectively to
protect their shared identity space (Barnett & Woywode, 2004; Brewer & Kramer,
1986; Ingram & Rao, 2004; Pozner & Rao, 2006).
The way cultural identity creates in-group and out-group distinctions can be
explained by social identity theory (Hogg & Terry, 2000). It posits that social
actors cannot effectively resolve their identity problems or change their social
situations through individual action and mobility, but must act collectively in
terms of shared group membership. Defining social identity as a collective self
that is perceived by the group, social identity theory explains how motivations to
create and sustain a positive collective identity interact with specific intergroup
15
status differences to shape the identification process (Tajfel, 1982; Tajfel &
Turner, 1979, Turner, et al., 1994). The key contrasting elements of different
groups engender positive in-group distinctions and increase collective action
(Bornstein, Erev, & Rosen, 1990; Hogg & Terry, 2000; Tajfel & Turner, 1986).
In particular, two key social cognitive processes affect how collective identities
are enacted: self-verification and self -categorization. Self-verification suggests
that identities produce behaviors expressing the identities (Burke & Reitzes,
1981). Social actors use identity as a reference to compare self-relevant meanings
and values in the interactive situation. When actors’ identities are being
confirmed or verified, they will behave in ways that meet the identity standard,
which consists of the meanings and prescribed norms embodied in a particular
social category.
When an actor’s identity is repeatedly verified in interaction with others
increased trust and commitment occur. These in turn lead to relational cohesion,
giving the perception that the interacting verification unit is separate and
distinct. Such cohesion feeds back into the process to enhance commitment
(Burke & Stets, 1999). Trust and commitment can act in a virtuous circle, with
each reinforcing the other. The result is increased cooperation in the social unit.
16
The self-categorization process highlights those aspects of experience that are
subjectively meaningful to actors and suggests that collective identities form an
evaluative basis for sharpening inter-group boundaries by producing group-
distinctive stereotypes and assigning actors to a contextually relevant category
(Oakes, et al., 1994; Turner, 1985; Turner, 1991; Turner, et al., 1987). Self-
categorization operates through the way that cognitive contrast and assimilation
affect perceptions of social categories and groups, and ultimately the self. For
example, Smith and Henry (1996) have shown that when social categorizations
are salient, the in-group becomes psychologically merged with or linked to the
self. In particular, self-categorization reveals the nature of intergroup relations in
terms of the relative status of each group and the legitimacy and stability of the
status relationship (Abrams & Hogg, 1990; Brewer & Brown, 1998; Ellemers, et
al., 1999; Giles & Johnson, 1987; Hogg & Abrams, 1993). Ingram and McEvily
(2007) illustrate the self-categorization process in an analysis of cooperation
among food cooperatives. Entrance of a new competitor, national chain Whole
Foods, triggered cooperatives’ collective identity recognition of shared interests.
Whole Foods targeted at some of the same customers as the food cooperatives,
but its for-profit status represented a distinct departure from the ideology that
cooperatives share. Such distinctions of organizational form or ideology are
likely bases that divide competitors into in-groups and out-groups, because they
17
allow the engineers of cooperation to make resonant claims that “we are alike but
they are different from us” (Ingram & Yue, 2008: 292).
Based on the dual processes of self-verification and self-categorization, social
identity theory proposes that social structure depends on the functioning of
identities. This view is congruent with the homophily principle developed in
sociology: similarity breeds connection (for a review, see McPherson, 1983;
McPherson, 2004; McPherson, Smith-Lovin & Cook, 2001). The pervasive fact of
homophily implies that the formation of network ties obeys certain fundamental
dynamics embedded in social categories. Social actors construct network ties
based on the similar characteristics or social categories they share with one
another. While the similarities could derive from geographic propinquity,
families, organizations, and isomorphic positions in social systems, collective
identity plays an important role in creating homophilous relation forms.
Thus, network ties not only transmit information but also identity (Podolny &
Baron, 1997). When actors interact in a situation in which they have difficulty
verifying identities, existing ties are easily broken and structures tend to dissolve
(Burke & Stets, 1999). The implication is that organizations forge collaborative
ties based on the principle of similarity in identity. When the similarity is
recognized and shared among a group of organizations, they tend to increase
18
their interaction with each other and strengthen their identification to the shared
identity to produce positive in-group imagery. When dissimilarity is present,
organizations tend to forgo the opportunity to interact with each other with a
resultant enhancement of inter-group distinctions. Cultural identity, representing
the most restrictive identity category, follows the same verification and
categorization logic. Cultural identity sharpens inter-organizational boundaries
such that organizations tend to collaborate with others sharing the same identity,
while distancing themselves from those in other cultural identity categories.
Community structure at large reflects this identification process. We predict:
H1: Organizational collaboration tends to occur among organizations that share a similar
cultural identity.
In the previous discussion, we took collective identity as prior and argued that
market producers verify their identification with the collective by engaging in
economic collaborative and exchange activities with those possessing similar
attributes. Community structure, in this sense, represents a structural
manifestation of this tendency. Now, we extend this argument and situate the
identity-enabled categorization and homophily process in the broader context of
the evolving nature of organizational forms. In particular, two processes are
relevant. First, a new organizational form emerges through the construction of a
distinctive collective identity, the utility of which results in either a unique
19
functional or aesthetic appeal, or both, to the market. Second, the new form
infuses a set of values into the community and changes its “index of ceremonial
dominance” (Bush, 1987). The institutional change introduced by the new form
may rest on its ability to mobilize existing structures and to create distinctions
without causing significant disruption to the existing social order. The alignment
of the identity construction process with the macro-order of market structure
entails institutionalization of a new identity to the taken-for-granted category
and requires identity strategy to align with the prevailing interests (Friedman &
McAdam, 1992).
Collective identity is the cultural code that prescribes a set of values, normative
rules and an acceptable range of actions for a particular organizational form. The
emergence and growth trajectory of an organizational form is ultimately
contingent on the collective effort of encompassing organizations to successfully
construct and legitimate, defend and diffuse the form’s core identity.
Organization theorists applying this cultural frame view the construction of new
organizational forms as a political project involving collective action (Rao, et al., 2000:
240, original emphasis). This perspective has created a theoretical linkage
between social movement theory and the emergence and development of
organizational forms. Using the social movement framework, for example,
organization theorists have began to uncover the role of collective action in the
20
rise of new organizational forms, growth of new market niches, and diffusion of
business practices (Carroll & Swaminathan, 2000; Lounsbury, et al, 2003; Rao,
1998; Rao, et al., 2003; Ruef, 2000; Sine & Lee, 2009; Sine, et al., 2005;
Swaminathan & Wade, 2001; Weber, et al., 2008). In an empirical application,
Carroll and Swaminathan (2000) examined the emergence of new specialty
microbreweries and brewpubs. Participants of the microbrewery movement—
microbreweries and brewpubs—collectively drew their distinctions from
mainstream producers on the quality of their products, methods of production
and ingredients, and ideology towards consumers. Deploying identity-based
strategies, they were able to successfully create a specialty beer segment that
excludes major brewers and contract brewers.
Collective action rests at the core of social movement theory. A social movement
embodies collective effort to solve problems, construct a new identity category
and produce social change. Social movements are often initiated and largely
influenced by institutional entrepreneurs who actively develop movement
agenda, mobilize resources, and explore political structures and opportunities
(Delacroix & Rao, 1994; McAdam, et al., 1996). In studying the diffusion of Total
Quality Management (TQM) programs, Cole (1999) showed that TQM first
emerged in the form of a critique by entrepreneurs W. Edwards Deming, Joseph
Juran and Philip Crosby of the conventional control paradigm. TQM only
21
diffused widely after the powerful collective endeavor among seven central
activist organizations framed its intent and established infrastructures and
network support for disseminating TQM’s standards and methods. The role of
central social movement organizations or institutional entrepreneurs is crucial to
understand identity construction and emergence of a new organizational form
(Dacin, Goldstein & Scott, 2002; Suddaby & Greenwood, 2005). Socially central
organizations negotiate identities with a pre-existing order through framing an
alternative industry logic, communicating distinctions, and mobilizing political
structures.
The general consensus among social movement scholars is that collective identity
is the foundation of collective action (Coleman & Deutsch, 1995). Collective
identity plays a critical role in mobilizing and sustaining participation (Polletta &
Jasper, 2001). Social actors deciding to participate “… neither rely primarily on a
quasi-quantitative calculating of costs and benefits, as in the rational choice
approach to politics, nor on altruists impulses … . Rather, identity construction
points to the qualitative concerns and the desire activists have instantiated in
their actions and lives” (Teske, 1997: 121; Cited in Polletta & Jasper, 2001).
Connecting collective identity to self-oriented rational action, Friedman &
McAdam (1992) argue that becoming a “prized social identity” motivated students
to link to social activists in the 1960s. Participation was a rational bid to gain the
22
benefits that accrue to those who share a collective identity. This form of self-
directed politics, Lichterman (1996) argues, nurtures rather than curbs civic
engagement; it enables social change because individual social actors bring a
“politicized self” into their interactions with other social entities and make choices
that are consistent with the movement’s activist identity. Thus, collective identity
offers an alternative account for selective incentives to participate and relaxes the
free-rider problems of collective action (Polletta & Jasper, 2001).
To preserve the values, goals and rules of behavior embedded in a collective
cultural identity category, the collective identity of a new organizational form is
continuously constructed through the recruitment of new organizations (Carley,
1991; Rao, et al., 2003). The recruitment process, as we discussed above, may not
be as purposeful as seen in political social movement situations where activists
deliberately set up a movement agenda and develop propaganda to instill
movement beliefs in the target audience. In contemporary industrial society, the
recruitment process for constructing and preserving a collective identity is more
self-directed. This raises the question of whether there is any discernable
organizing structure at the collective level to direct or channel the exchange
and diffusion of identity beliefs and ensure that the value of collective
consciousness is infused into the prevailing social order?
23
Structural and network movement theorists offer an answer to this
question (Diani & McAdam 2003; Fernandez & McAdam, 1988; Fernandez &
Weinberg, 1997). Approaching movements as networks, they have modeled
the underlying process of how initially localized collective action motivates
emulation and spawns broader conventions (McAdam, 2003; Oliver &
Myers, 2003; Tilly & Wood, 2003). One view is that inter-organizational
networks operate in a micro-mobilization context (Osa, 2003). Curtis and
Zurcher (1973) contend that social movements are really multi-organizational
fields or networks of organizations that create esprit de corps among participants.
Through the creation of ties, network actors are able to generate collective
structures in the absence of initial supportive institutional infrastructures
(Minkoff, 1997).
Studying Italian environmentalism in the 1980s, Diani (2003) observed that
activist organizations occupy central and brokerage positions in the
network and act as catalysts for increased public awareness of the
movement’s identity. In a similar vein, Rosenthal, et al (1985) showed the
influence of three network-central organizations in the 19
th
century women’s
rights movement, indicated by their 792 joint ties to 500 organizations out of
1,015 organizations under study. Such a pattern persists throughout the network.
After removing the three central organizations from the network, the authors
24
observe five distinctive clusters connecting 36 organizations remaining in the
network. Among them, three peaks emerge in the cluster configuration of strong
ties and are represented by the three organizations that ranked third, forth and
fifth in centrality. While these findings are consistent with an institutional
entrepreneurship argument, not all social movements result in this hierarchical
pattern (Diani, 2003; Lowe & Goyder, 1983). Other researchers suggest that a
segmented, decentralized network structure is a powerful form of social
movement organization because it allows actors to penetrate into a variety of
social niches and promote innovation (Gerlach, 2001; Laumann, Galaskiewicz, &
Marsden, 1978).
Culturally-framed institution theorists argue that the contextual element of
an organizational field influences how a social movement is organized
(Rao, et al., 2000). When a field is fragmented by a variety of organizational
forms, the emergence of a new organizational form imposes little threat to
the existing order and therefore encounters minimal opposition because
power is distributed among subfields. Social movements within fragmented
fields are typically consensual (McCarthy & Wolfson, 1992), meaning that
movement structure is polycentric rather than hierarchical, as mobilization effort
is placed on gaining acceptance and agreement from multiple subfields rather
than from an elite group. In a hierarchical institutional field where power is
25
centralized within a dominant population, the cognitive and normative shift
introduced by a new organization form challenges the conventional industry
logic and causes disruption to the existing order (Fourcade-Gourinchas & Healy,
2007; Melucci, 1989; Rao, Monin, & Durand, 2003). The construction and
diffusion of a new collective identity, thus, relies on a mobilization schema to
mark a distinction while bringing truce to contested logics (Rao & Giorgi, 2006).
Movement structure reflects this schema and takes the form of centralized and
hierarchical structure.
Hierarchical movement structure is enabled through three core processes. First,
institutional entrepreneurs adopt an inclusion strategy to gain support
from the elite organizations by engaging them in the identity construction
process. Friedman and McAdam (1992) suggest “… successful movements
usually do not create attractive collective identities from scratch; rather
they redefine existing roles within established organizations as the basis of
an emerging activist identity” (p162). McAdam (1982) contends that the
rapid growth of the civil rights movement was largely due to its ability to
connect with existing Christian or other church members that played a
salient role in the black community. Collaborative ties not only transmit
information but can also transfer identity (Podolny & Benjamin, 1996).
26
The second core process enabling a hierarchical movement structure is identity
categorization at the micro-level by similar organizations which produces a
scale shift, which is “… a change in the number and level of coordinated
contentious actions leading to broader contention involving a wider range
of actors and bridging their claims and identities” (McAdam, et al., 2001:
331). The third core process occurs when organizations try to blend in by
mimicking the behaviors, rules, and procedures of more powerful organizations
in their environment (DiMaggio & Powell, 1983). Such efforts constitute a broad
support base for successful mobilization (Rosenthal, et al, 1985).
In a detailed ethnographic study of the nouvelle French cuisine movement,
Rao, Monin, and Durand (2003) illustrate how movement structure successfully
transitions from classical cuisine to nouvelle cuisine within the organizational
field. They show that the transition from classical to nouvelle cuisine was first
initiated by elite French chefs who held prestigious positions in the professional
society of French chefs. Chefs paid attention to the number of defectors, as well
as to reputational gains accruing to them, to make sense of the new role identity
implied by nouvelle cuisine. Thus, identity-discrepant cues create appeal for the
new logic, and identity movement celebrates differences from the dominant
code.
27
Mapping identity construction onto the network level, I argue that
community structure in a hierarchical field reflects the social movement
patterns discussed above. The core region of the community consists of a
few dominant organizations that are well connected to other organizations
sharing a similar cultural identity as well as to some of the key players
residing in different identity categories. Such dominant organizations are
centralized because they have numerous affiliations, and they tend to
behave like “activists” in promoting their identity category and recruiting
organizations whose self-identity resonates with the collective. This process
creates a “popularity effect” in which dominant organizations attract more
collaborative ties, leaving the less dominant with sparse ties in the
peripheral regions of the community. This popularity effect also engenders
a scale shift when new organizations increasingly collaborate with more
connected preexisting ones and imitate the actions of successful “activists.”
Consequently, a community tends to display a hierarchical structure with a
few dominant organizations assuming the highest degree of connectedness,
and this degree of connectedness diminishes gradually when moving
toward the less dominant. Thus, I argue that identity construction serves as
collaborative governance at the community level and produces a
centralized and hierarchical community structure. We predict:
28
H2: The community structure exhibits strong tendencies towards a hierarchical
structure, with a few central organizations with numerous ties occupying a core region
and less connected organizations inhabiting a peripheral region, when organizations
collaborate with others to construct a new culture identity.
Professional Identity and Community Structure
Unlike cultural identity, an organization’s professional identity develops around
the technological space in which it produces and sells. Firms residing in the same
technological space share a common professional identity. Professional identity is
not restrictive in the way that cultural identity draws sharp boundaries between
cultures. Professional identity segregates a community by its ability to facilitate
the formulation of exchange relations among producers who share similar
technological space while excluding others. Thus, professional identity segregates
a community into different specialized spaces where membership is based on the
technological core.
In a study that uses professional identity as a foundation, Baum, et al. (2000)
examined patenting and alliances in the Canadian biotechnology industry and
sorted producers into categories such as diagnostics, agriculture, and
biotechnology. The authors describe competitive and mutualistic relations
29
between firms within and across technological segments, thereby structuring
their study on the concept of professional identity.
Organizational ecologists have long dissected market producers into generalist
and specialist categories based on technological and resource spaces (Carroll &
Hannan, 2000). Generalists tend to occupy the center of a market and have
greatest appeal to mass consumers. Specialists survive on the peripheral region
of the market and concentrate on exploiting a narrow range of customers.
Powered by well-established accumulated resources, generalists strive for
market domination and often compete on economies of scale with other
generalists for market domination. This strategy can result in a higher quantity of
product being produced and delivered to a diverse consumer base while product
price remains the same or even decreases. As competition among generalists
intensifies, the market tends to become increasingly concentrated with only a few
players. Concentration not only frees up resources in the peripheral region of the
market but also renders unexploited technological niches possible for specialists
to explore. Without engaging in direct competition with generalists, specialists
are able to take advantage of this “sweet negligence” and focus on developing
products that tailor to consumers with unusual tastes. Such focus leads to the
fundamental prediction of resources-partitioning theory that market
concentration increases the failure rate of generalists and lowers the failure rate
30
for specialists (Carroll 1985). Empirical studies of newspaper organization
failures (Carroll, 1985, 1987), founding of specialty American breweries (Carroll
& Swaminathan, 2000), and founding of rural cooperative banks in Italy
(Freeman & Lomi, 1994) offer support to this theory.
Resource partitioning dynamics suggest that the professional identity of market
producers can be categorized along the continuum of generalists and specialists.
On the one hand, the most distinctive generalists produce diverse products and
target the mass market. Such firms tend to be large in size and possess the
greatest amount of slack resources for managing their product or service
portfolio. On the other end of spectrum, the most particular specialists focus on a
narrow market niche and produce highly specialized products or services. These
specialists are often young and small and constrained by limited resources.
Facing high market uncertainties, entrepreneurs of this specialist group of firms
strive to construct the social and cognitive legitimacy of their technological niche.
This specialist categorization reflects the differentials in the width of the
technological niche and resource space that firms occupy within a given
industry.
Resource partitioning theory also suggests that generalist organizations often
assume dominant and powerful roles in the market and reside in a central
31
position of the market status hierarchy. This perspective further implies that
market competition is localized and bounded inwardly by identity type, with
generalist competing with generalist and specialist with specialist.
With competition being localized, generalists and specialists can engage in
cooperative relationships with each other and tap into each other’s niche space
for greater economic advantages that may not be available outside a cooperative
arrangement. The economic advantages jointly derived from collaborative ties
can enhance market position against competitors in their identity space (Baum &
Oliver, 1991; Eisenhardt & Schoonhoven, 1996; Hagedoorn, 1993).
Specialist firms have both advantages and disadvantages. On the one hand, they
are often entrepreneurial and innovation-driven. Their relative small size and flat
organizational structure facilitates informal communication among employees
and allows nimble movements in face of environmental change. Hannan and
Freeman (1977) suggest that specialists out-compete generalists in fine-grained
environments containing a large magnitude of variations and frequent
fluctuations. Specialists’ nimbleness and innovativeness attract ties from
generalists facing structural inertia and under tight institutional scrutiny and
control.
32
On the other hand, specialists often face a high degree of uncertainty in their
social and economic environment, with challenging obstacles to their
developmental processes (Hannan & Freeman, 1977; Stinchcombe, 1965). Such
high uncertainty is manifested in liability of newness (Hannan & Freeman, 1984;
Stinchcombe, 1965) and liability of smallness (Hannan & Freeman, 1984), which
complicate specialists’ market navigation process, create obstacles for their
growth, and endanger their life chances (Aldrich & Auster, 1986). However,
forging ties with large, well-established generalist firms enables specialists to
gain legitimacy, especially in face of the instability of a nascent market, and can
signal to the market the underlying quality of their produce or service (Aldrich &
Fiol, 1994; Christensen & Raynor, 2003; Davis, 1991; Gould, 2002; Hannan &
Freeman, 1984; Podolny, 1993; Powell & DiMaggio, 1991; Suchman, 1995; White,
1981). The cooperative dynamics between generalists and specialists are perhaps
most evident in the biotechnology industry, in which specialists and generalists
forge symbiotic ties to commercialize new technologies developed by specialists
(Baum, Calabrese & Silverman, 2000; Podolny, 1994; Podolny and Stuart, 1995;
Stuart, et al., 1999).
However, these forms of collaboration at the community level seem to be
exceptions rather than the norm governing the interaction patterns of generalists
and specialists and regulating economic exchange activities between them within
33
the same industry. Two main reasons explain why cross-status collaboration at
the community level is rare. First, in order for this form of arrangement to occur,
specialist firms need to be at the forefront of innovation. Few specialists satisfy
this condition and therefore exist as viable collaboration candidates. Second,
findings from studying alliance relationships between generalist and specialist
high-technology firms suggest that these partnerships are not only elusive, but
also often damaging to the specialist companies. Alvarez and Barney (2001)
examined small high-technology firms and found that 80 percent of the 128
entrepreneurs they interviewed felt exploited by their larger alliance partners.
This phenomenon, the authors observed, is more acute when the only benefit a
specialist is able to bring into the alliance is a new technology. Large firms often
reap most of the market potential of this new technology, leaving small
specialists little opportunity to prosper. Thus, the tendency to forge collaborative
ties is more likely to stay localized, such that generalists tend to collaborate with
generalists and specialists with specialists. On the basis of the foregoing, I
predict:
H3: Organizational collaboration tends to occur among organizations sharing a similar
professional identity.
Collaboration occurs more readily with less effort in a common technological
community that is routinized (Powell, et al, 1996). Organizational researchers
34
have long viewed industry as an agglomeration of diverse strategic groups (e.g.,
Hoskisson, Hitt, Wan, & Yiu, 1999; Peteraf & Shanley, 1997). Strategic group
refers to a “… meaningful collection of firms or substructure within an industry”
(Peteraf & Shanley, 1997: 165). Firms within a strategic group can be identified
based on similarities in scale of production, quality of product or service, target
consumer base and strategy in use. Common mobility barriers sharpen strategic
group boundaries and explain differentials in firm actions and performance
(Caves & Porter, 1977). Gulati, Nohria and Zaheer (2000) proposed that network
relations can act as mobility barriers impeding the movement of firms across
strategic groups. Firms that are in the same clique or are structurally equivalent
may behave similarly and enjoy similar returns. For instance, in studying the
global automobile industry, Nohria and Garcia-Pont (1991) found that the
industry consisted of three main “strategic blocks,” which are cliques of firms
forging alliances with each other while excluding others in the same industry.
The strategic blocks were formed around the big three U.S. automobile
producers, GM, Ford and Chrysler, forging alliances with major Japanese and
Korean automakers. Shared membership of each block led to performance
differentials among firms in the industry.
Peteraf and Shanley (1997) worked to uncover the identity of strategic groups
and clusters. They viewed strategic groups or clusters as cognitive substructures
35
of an industry in which firms interact to establish a collective understanding of
distinctiveness of the group. They highlight social learning and social
identification as two key processes explaining the emergence of strategic groups
and clusters. As industry environments become complex, managers partition
them through a social identification and categorization process. Coping with
bounded rationality, managers’ cognitive search is targeted at familiar traits and
factors in their immediate environment, such as firms that reside in their
localized identity space (Levinthal & March, 1993). A strategic group or cluster
evolves as firms go through selection, variation and retention processes with
other similar firms (Miner & Haunschild, 1995). To create a win-win situation,
firms competing in the same identity space engage in cooperative relations with
firms that have strategic complementarities or substitutability with the focal
firms. Upon verification of the strategic importance of potential partnering firms,
they begin to align their activities with each other and model their behavior on
referent others (DiMaggio & Powell, 1983). Through this social learning process,
firms interactively strengthen their identification with the cluster they jointly
form. Clusters can also result from the collective struggle of related firms to
control “the production of producers” (Larson, 1977: 49–52).
Approaching from a different angle, organization ecologists propose that firms
sometimes cluster in an area after an initial start-up in order to capture a niche—
36
a region in the resource space that provides the right combination of resources
required to support a given population of organizations (Hannan & Freeman,
1989). Rindova and Fombrun (2000) showed how specialist coffee entrepreneurs
created their own space within an almost zero-growth industry, with virtually no
technical innovation. Thus, rather than discovering new opportunities made
available through exogenous change a la the internet entrepreneurs, these coffee
pioneers actively restructured the existing social understanding of the traditional
coffee industry. Moreover, the creation of the specialty coffee space was the
result of collective action by specialist coffee entrepreneurs. Ingram and Simons
(1999) examined the affect on profitability of co-membership in a formal
organization group in kibbutz agriculture from 1954-1965. They found that
relationships between organizations that shared membership enabled focal firms
to accrue greater profitability than with unrelated ones.
Thus, evidence from the strategic group literature and the organizational ecology
perspective suggests that identity construction also occurs within a particular
technological space. The technological space is dictated by the strategies the
firms pursue and the niche width that members of organizations jointly define.
The technological space that generalists share with each other is very different
from that shared among specialists because each professional identity type
represents divergence in their strategies. Market producers can accordingly be
37
categorized along a generalist-specialist continuum. The patterns of cooperative
ties among firms are likely to be influenced by similarities among their strategic
attributes and shared interests and goals within the market segment or niche
within which they reside. Viewed at the community level, professional identity
segregates members much like technological niches compartmentalize the
market. Similar to competitive processes, collaborative/cooperative tie
formations have a strong tendency to stay localized within a particular
professional identity category. With a market fragmented by various
technological niches, the collaborative community of producers tends to be
segregated into clusters, each of which corresponds to the exchange relations of
producers sharing a technological niche and professional identity. As a result,
the community structure exhibits strong tendencies towards dense connections
within clusters through subgroup identity construction, maintenance, and
reproduction, but relatively sparse connections between clusters (Newman &
Girvan, 2004). Thus, I predict:
H4: The community structure exhibits strong tendencies towards a polycentric, clustered
structure when organizations with similar professional identities collaborate with each
other.
38
Chapter 3: Resource Search and Community Structure
According to institutional (Meyer & Rowan, 1977) and ecological (Hannan &
Freeman, 1989) theories, while organizations collectively reinforce the
boundaries around their shared identity space, individual organizations may try
to outmaneuver each other in gaining and sustaining superior access to resources
(DiMaggio, 1988; North, 1990). The discourse on formation of inter-firm
collaboration has been profoundly influenced by the resource-based view
(Peteraf, 1993). From the resource-based perspective, collaboration provides
firms with opportunities to generate sustained competitive advantage that
requires the synergistic combination of resources that cannot be readily
purchased from the market or developed internally in an efficient manner
(Madhok & Tallman, 1998). A large body of literature has reported the beneficial
outcomes derived from this arrangement (e.g., Gulati, 1998; Podolny, 1994).
In a separate but related literature, organization ecologists have brought both
commensalistic and symbiotic mechanisms into organizational conversations to
explain the evolutionary process of community (Aldrich, 1999; Astley, 1985;
Baum & Rao, 2004; Ruef, 2000). Social organizations are at least as much driven
by cooperation as by competition (Hawley, 1986). Commensalistic mechanisms
allow mutualism and competition between the same species facing similar
39
demands from their environment. Symbiotic mechanisms highlight the mutual
interdependence among different species through their direct or indirect
interactions (Astley & Fombrun, 1983). Together, commensalistic and symbiotic
mechanisms engender a collective self-sufficiency that buffers members of the
same or different populations from environmental constraints (Aldrich, 1999;
Astley, 1985; Barnett & Carroll, 1987; Hawley, 1950, 1986; Monge & Contractor,
2003; Ruef, 2000).
Organization ecologists view increasing carrying capacity as an endogenous
process and propose that collaboration among population members can
collectively expand the pool of resources available to them (Freeman and Audia,
2006; Lomi, et al. 2005). As Dobrev & Kim (2006) point out, “The benefit of
mutualism among organizations occupying the same market segment may
increase monotonically with their density and does not reach a ceiling” (p. 234).
For instance, organizations often create linkages with other organizations to
reduce selection pressure and increase survival chances (Hannan & Freeman,
1977). Studying 1,011 newspaper publishers in Finland from 1771 to 1963, Miner,
Amburgey and Stearns (1990) found that publishers with a greater number of
inter-organizational resource linkages, typically to political parties, had a higher
overall success rate. In studying California banks, Haveman and Nonemaker
40
(2000) found that the modest level of mutualistic relations among multi-market
banks increased the likelihood of entry into local markets.
Symbiotic ties with other organizational populations which do not directly
occupy the same competitive space allow focal organizational populations to
leverage additional resources and capacities from other markets. Such leverage
reduces selection pressures operating in the local market by connecting the focal
population to a new environment where other organizational populations reside.
A new pool of resources and the ability to access them are activated through the
formation of symbiotic ties. Dobbin (1994) found that U.S. railroads between 1825
and 1900 displayed symbiotic relations, as short lines provided larger lines with
reach into small communities. Conversely, the larger lines connected the smaller
ones, allowing them to reach more remote locales. Barnett and Carroll (1987)
found a similar relationship between commercial and mutual telephone
companies in southeast Iowa between 1900 and 1917. The two forms tended to
serve different areas, urban versus rural, and each had a different scale of
operations. The interdependence among the two forms—mutual and commercial
telephone companies—improved the other’s chances of providing long-distance
service. Audia and Rider (2006) compared locally-owned and absentee-owned
organizations’ ties to local communities and found that locally-owned
organizations, being tightly connected with a local community, not only
41
contributed richer information but were more likely to gain access to localized
information flows concerning technical and market development.
As we discussed in Chapter 1, the community is manifested in the form of a
macro network structure of interconnected actors within or across different
populations. The rise or fall, expansion or contraction, stability or interruption of
community can be studied from the perspective of inter-organizational networks.
The evolution of community is inseparable from that of networks which
members of the community forge together. In particular, gathering resources,
expanding into new markets, and producing innovative products are
conditioned on how community structure is configured. At the same time, they
are the indicators of the growth potential of a community and signs of viability to
outsiders. Network theorists posit that network ties serve as pipes and prisms of
a market (Podolny, 2001), transmit information and resources (Stuart, et al.,
1999), and facilitate learning and innovation (Owen-Smith & Powell, 2004). The
abundant evidence accumulated from this research tradition strongly suggests
that exchange relations, motivated by resource complementarities, expand
resource reach for individual firms (Dobrev, Kim, & Hannan, 2001; Gulati, 1999;
Podolny, Stuart, & Hannan, 1996; Sorensen, 2004). Such resource-driven network
ties enable structured flows of resources between concrete organizational
42
populations (Ingram & Roberts, 2000; McPherson, 1983; Owen-Smith & Powell,
2004; Uzzi, 1997).
While evidence of the generative nature of network properties on resource
harnessing is abundant in the organizational literature, little research has
demonstrated how these resource searching mechanisms affect community
structure properties. In this section, we attempt to explain this relationship.
Organizations may be pushed into interdependencies “… because of their need
for resources — not only money, but also resources such as specialized skills,
access to particular kinds of markets, and the like” (Aiken & Hage, 1968: 914-
915). Here, I am particularly interested in two types of resource:
reputation/status resources and operating experience. Reputation resources
enable firms to expand into multiple markets and provide access to superior
material resources. Operating experience embodies specialized skill and
knowledge that serve as a foundational block for building new innovations.
Stuart (2000) examined strategic alliances in the semiconductor industry and
found that larger and more innovative firms played an important role in
conveying endorsement on small focal firms. Stuart concluded that both
innovation rates and scale growth were positively influenced by the extent to
which focal firms maintain strategic alliances with large and innovative partners.
Baum, et al., (2000) examined the performance of startup firms in the Canadian
43
biotech industry, and showed that startup firms involved in alliances with
innovative partners tended to exhibit stronger performance. Nahapiet and
Ghoshal (1998) argue that the process of sharing ideas with innovative alters is
likely to generate new knowledge rather than merely exchange existing
information.
Utilizing a status model of competition and learning mechanisms, I argue that
the search for reputation resources and collective operating experience engender
a fragmented and clustered community structure. The natures of the clusters
produced, however, are different in kind. On the one hand, search for reputation
resources produces clusters centered on high reputation producers and reflects
that market hierarchical status order. On the other hand, search for collective
experience generates clusters in which firms possessing related experience
exchange with each other.
44
Reputation Resource Search
White (1981) argues that production markets generate a ranking of firms in
which consumers infer product quality from the price paid, reversing the
common-sense notion that firms can charge more for products that are
objectively superior. Firms compete for reputational status in an institutional
field (Fombrun & Shanley, 1990). Reputation status signals to the public the
underlying quality of a product or service and produces variations in access to
capital and resources for market producers. As a result, high economic returns
and increased survival chances often accrue to reputable firms (Klein & Leffler,
1981). Reputation status also serves as a form of normative control that directs
firms’ actions and subjects them to broader institutional forces (Shapiro, 1987).
The strategic role of reputation status in shaping market competitive dynamics
has been accentuated in the status-based competition model. Conceptualizing the
market as a hierarchical status order, the status model of competition highlights
the significance of the opportunity structure the order entails, along with
economic advantages high status producers possess though signaling,
legitimacy, and prestige (Podolny, 1993). A number of studies have
demonstrated this central observation. For instance, firms with higher reputation
assume high centrality in resource exchange networks and tend to have a greater
degree of success in political conflicts (Laumann & Knoke, 1987). In the
45
worldwide semiconductor industry, an organization’s life chances are positively
associated with the organization’s status in the industry’s patent network
(Podolny, Stuart & Hannan, 1996).
Research findings have demonstrated that ties to prominent firms can lower
transaction costs, increase prices, enhance sales growth, increase rate of
innovation, escalate market capitalization, and boost an organization’s longevity
(e.g., Baum 1996; Benjamin & Podolny, 1999; Carter & Manaster, 1990; Gulati &
Higgins, 2003; Jain & Kini, 1995; Podolny, et al., 1996; Stuart, et al., 1999). In
answering the question of “Which particular technologies become advanced and
extended, while others are never developed …” Podolny and Stuart (1995)
showed that technologies sponsored by high-reputation actors are more likely to
be rapidly developed than competing ones, and such technologies will thus
appear superior ex post despite the fact that they may not have been superior ex
ante. Podolny and Stuart found that the adoption rate of a technology by
subsequent innovators is a function of the reputation status of the organization
associated with the technology. Reputable innovators tend to attract more
citations than others. As Katz and Shapiro (1985) point out, prestige differences
influence the path-to-market dominance because high status organizations
benefit both from attribution of high quality and from the expectations among
corporate audiences that they will out-compete less recognized rivals. In
46
analyzing the population of US-based, venture capital-backed biotechnology
firms, Stuart, Hoang and Hybels 1999) showed that ties to high status alliance
partners and equity investors enable startup biotech firms to go to IPO faster and
to gain higher market values than new entrants lacking prestige associations.
High-reputation producers influence the direction of resource flow and are more
likely to gain social and economic benefits than less reputable producers; they
also can transfer these advantages to the firms to which they are connected. Ties
to high-status exchange partners provide a focal firm with additional resource
endowments and generate a positive market presence (Benjamin & Podolny,
1999; Podolny, 1994). Such ties provide a basis of expectations and beliefs
influencing the flow of payments and resources that a firm receives (Podolny &
Phillips, 1996). The perceived superior resources of high-status firms help to
create and sustain shared status beliefs that favor resource advantages
(Ridgeway, 1991; Weber, 1968). Thus, I predict organizations with high
reputations tend to attract more collaborative ties from other firms:
H5: High-reputation organizations tend to have more collaborative ties than low-
reputation organizations.
47
While it is plausible that the economic and social advantages accruing to high
reputation producers have a preferential network effect, such an effect has a
ceiling limit. Status transfer arguments suggest that the market status of a focal
firm can be inferred from the status of a firm’s exchange affiliations (e.g.,
Podolny, 1993, 1994; Stuart, 2000). Market exchange often involves not only the
manifest transfer of goods and resources, but the latent transfer of status
(Benjamin & Podolny, 1999). Ties to less reputable firms may cast doubt with
corporate audiences about the nature of the association and cause potential harm
to a high-reputation firm’s established credibility. The key insight of the status
model of competition is that it is the status belief and expectations embedded in
status category about the quality of product or service, not the actual product per
se, which determines opportunities and constraints as well as potential payoffs
faced by each status order. As a result, the stability of a firm’s position in the
reputation status hierarchy is contingent on the degree of congruence between
their actions and the expectations inherent in the status order to which they
belong. Violation of status beliefs and expectations may cause downward
mobility in the status hierarchy.
In addition, the inertial force of a status order discourages the formation of ties
among firms from different status categories and makes status migration
difficult. The reasons are twofold. First, a firm’s position on the reputation status
48
hierarchy translates associated opportunities and constraints into a range of
market outcomes and rewards by inducing “… a flow of resources that causes a
variation in quality across producers’ products consistent with, and thus
confirming, the initial status ordering” (Podolny, 1993: 830). The status hierarchy
circumscribes the producer’s actions by providing a unique and non-transferable
cost and revenue profile for manufacturing a good of a given level of quality.
Non-transferable cost-revenue profiles—opportunities and constraints—derived
from status beliefs and expectations associated with status categories provide
market producers with different incentives to invest in quality (Podolny, 1993).
In their study of the California wine industry, Benjamin and Podolny (1999)
found that affiliations constituting an actor’s status position actually affect the
actor’s choice of quality and its subsequent returns. In addition, status ordering
inhibits high quality, low-status innovations, thereby restricting the range of
potential innovations that may be considered in a given market.
Second, occupancy of a particular status order, once established, can assume a
momentum of its own and becomes loosely coupled to quality and performance.
For example, Washington and Zajac (2005) showed that a basketball team's past
positive association with high status players is a strong indicator of the
likelihood of its potential invitation to a basketball tournament, regardless of
actual performance. In an extreme case, Phillips and Zuckerman (2001) studied
49
financial analysts and found no association between analyst status and forecast
accuracy.
Status order momentum could be attributable to stochastic information diffusion
processes. Podolny (1993) posits that “… the web-like structure of firms’ network
relations developed over time generates ‘information diffusion thickness’ that is
stochastic in nature and, to a certain extent, impedes market status position from
shifting, even if, in reality the actual quality of a firm does not match its
corresponding status order. Status processes not only constrain the expansion of
lower status firms into higher status strata, but also place limits on the higher-
status producer’s extension into the low end of the market” (p. 831). Thus, status
competition appears to be localized, such that a firm competes with those who
are similar in status (Benjamin & Podolny, 1999; Park & Podolny, 2000). In an
analysis of firm pairing in securities offerings in the United States between
1981and 1987, Podolny (1994) found status matching of investment banks under
conditions of uncertainty. He showed that banks are more likely to partner with
other banks of a similar status, independently of their partner firm’s economic
success, because failing to do so would violate the norm that firms should
partner with firms of similar standing.
50
The same mechanisms can explain cooperative dynamics among market
producers. The desire to conform to status beliefs and expectations discourages
producers with high reputations from collaborating with less reputable ones. The
same dynamic also renders difficult lower status producers’ mobility into a
higher status category because the inertial force of status decouples performance
from the status hierarchy. This means, even if a tie to a high status producer
transfers social and economic benefits to the lower status producer, such status
transfer has a ceiling effect because it is difficult to alter corporate audiences’
predetermined perceptions and beliefs of a producer’s market position,
especially its status category. This difficulty reduces the incentives for
collaboration between producers from different status categories. Thus, I expect
status-based cooperation to be localized to the extent that market producers
collaborate with others in the same status/reputation category. As a result,
community structure should be segregated by the status categories within which
producers are densely connected. Clusters would be expected based on status
order categories, with dense intra-status ties and sparse inter-status transactions.
Thus, I predict:
H6: The community structure demonstrates a strong tendency towards a polycentric,
clustered structure when organizations base their collaborative arrangements on search
for reputation resources.
51
Collective Experience
While reputation and status serve as an important buffer against environmental
uncertainties, a firm’s operating experience, gained both from internal learning
and from other interacting organizations, is invaluable for its growth and
development (Argote, et al., 1990; Haunschild & Sullivan 2002; Ingram & Baum,
1997; Miner & Haunschild, 1995). Operating experience improves firms’ internal
efficiency, which helps them gain scale advantages and enables more accurate
understanding of consumer preferences (Darr, et al., 1995; Yelle, 1979). It also
provides firms with a stock of knowledge and expertise for managing crisis,
therefore providing them with a sense of continuity by buffering their activities
from external instability (Sorenson 2003; Thornhill & Amit, 2003).
To succeed in a market, organizations need to exploit their internal competencies
as well as explore new routines and capabilities residing in their external
environment (Levitt & March, 1988). Research suggests that the creation of
knowledge within firms depends heavily on their interactions with other
organizations (Amin & Wilkinson, 1999). Firms forge various forms of
collaborative arrangements with others to facilitate organizational learning and
to expand the scope and depth of their knowledge base (Powell, et al., 1996).
Because knowledge and competencies are dispersed across industry participants,
52
firms engage in collaborative projects with other organizations to develop new
products, solve complex problems and reduce competitive pressure (Erickson &
Rothberg, 2005). Firms’ operating experiences are a source of their internal
competencies and knowledge.
The question, then, is what discernable pattern can we expect to observe when
firms engage in collective learning? Research on collective learning shows that
inter-organizational learning tends to occur between related organizations but
not between those that are unrelated (Ingram, 2002). Henderson and Cockburn
(1996) found spillovers between the research knowledge of pharmaceutical firms
within similar research programs. Other research shows that spillovers in
worldwide semiconductor production were only within families of related chip
designers (Irwin & Klenow, 1994). Darr, Argote, and Epple (1995) found that
pizza stores enjoyed lower costs of production as a function of the operating
experience of other pizza stores, but the benefit was only from the experience of
other related pizza stores, not from every store in the industry. Ingram and
Simons (1999) examined the implication of other organization’s operating
experience on the profitability of kibbutz agriculture from 1954-1965. They found
that kibbutzim were positively affected by the experience of other kibbutzim in
the same federation. Darr and Kurtzberg (2000) found that inter-organizational
learning among fast-food outlets occurred only between stores that employed the
53
same strategy. Darr, Argote and Epple (1995) found that fast-food outlets
benefited from the experience of other stores in the same franchise but not from
the experience of stores in different franchises. Baum and Ingram (1998) found
that Manhattan hotels had lower failure rates as a function of the operating
experience of Manhattan hotels that were related to them through being
members of the same chain, but the experience of unrelated hotels had no
influence. In a similar vein, Foster and Rosenzweig (1995) showed that Indian
farms’ profitability using high-yield seed varieties increased with their
neighbor’s experience with the varieties.
The phenomenon of collective learning is most evident in industry clusters
(Keeble & Wilkinson, 2000; Lawson & Lorenz, 1999). Within a common socio-
economic environment conditions such as localized production system, labor
market, institutional norms and culture embedded in clusters spur dense
interactions among firms within the environment. Malmberg and Maskell (2006)
found that spatially proximate firms are able to generate new knowledge from
their collective experience when they undertake similar and related activities,
because transmission of tacit knowledge becomes easier when economic agents
share similar economic, organizational, geographical and culture conditions
(Lundvall 1992; Lundvall & Johnson, 1994). Researchers have long argued that
inter-organizational networks of varying types contribute to the emergence and
54
substance of technology-based geographic clusters (Almeida & Kogut, 1999).
Saxenian (1994) compared the evolution of Silicon Valley and Route 128 regions
and highlighted the role of multiplex relations at the community level in the
transmission of technical knowledge across firm boundaries.
The empirical evidence discussed above is consistent with the notion of
Markovian organizational adaptation, which describes the process of
organizational change as embedded in searching for new practices that are
closely linked to an organization’s existing routines (Cohen & Levinthal, 1990;
Cyert & March, 1963; Nelson & Winter, 1982; Stuart & Podolny, 1996).
Exploration search entails finding a new combination of knowledge that is the
seed for developing innovations. The search is local in the sense that the “… the
probability distribution of what is found is concentrated on techniques close to
the current ones” (Nelson & Winter, 1982: 211). Past research has shown that
organizations typically innovate within the area of their established expertise
(Rosenkopf & Nerkar, 2001; Sorensen & Stuart, 2000; Stuart & Podolny, 1996).
The new activities and directions they branch into often rest on the core
technological platform currently under use (Kim & Kogut, 1996). Through
increased collaboration activities around the neighborhood of a firm within a
particular niche, the exploration research process not only generates new
55
innovations at the firm level but also shifts the foci of sets of organizations in
similar directions (Ingram, 2002).
Inter-firm collaborations are the main channels for exploration search activities.
Investigating the relationship between technology alliances and the direction of
corporate innovation, Stuart and Podolny (1999) found that alliances with
technologically proximate firms tend to reinforce organizations’ tendencies to
produce innovations that are closely related to their previous endeavors;
alliances that bridge two firms in technologically differentiated niches promote
the production of non-local innovations. Thus, the type of innovations being
developed by a focal firm is contingent on the knowledge and experience of its
collaborative partner. Stuart (1998), utilizing patent citation data to gauge the
level of overlap in the niches of producers, found that alliances are more likely
among pairs of firms with overlapping technological niches. Case studies
(including Henderson & Clark, 1990; Tushman & Anderson, 1986) reveal
increased collaboration activities around the neighborhood of a firm. Mowery
Argote and Epple (1996) show that alliance activities increase the overlap in
partnering firms’ innovation profiles, particularly in deals that include equity
participation. Through a longitudinal analysis of the rate of patenting in the
chemical industry, Ahuja (2000) showed that firms which establish structurally
diverse egocentric alliance networks with non-redundant contacts experience
56
higher rates of innovation. This, in part, can be explained by the diverse input
such advantaged firms harness from their partners, to which other firms have
limited access.
As a result, it can be reasonably argued that innovation, or its prelude, the
process of exploration search, occurs at two levels. At a rudimentary level, an
organization’s search for new experience, knowledge, practices, and routines is
localized to a group of firms possessing similar technological backgrounds. This
represents a closed system of learning in which firms form dense clusters to
share and exchange their experience and knowledge, engage in problem solving,
and pursue new methods of carrying out business operations (Powell & Smith-
Doerr, 1994). As Schilling and Phelps (2007) point out, such dense connections
among similar firms increase the speed of network information transmission and
knowledge transfer; they also produce self-enforcing, informal governance
mechanisms that help to breed shared understanding, trust, and reciprocity
(Coleman, 1988; Dyer & Singh, 1998; Granovetter, 1992).
At a higher level, an organization with structural advantages may actively search
for novel technological experience from firms located in distant technological
niches. By bridging clusters, structurally advantaged firms are able to access new
and diverse knowledge and experience and render possible a higher order of
57
new combinations of both novel and conventional approaches (Uzzi & Spiro,
2005). Fleming (2001) argues that employing novel combinations of antecedent
ideas increases the variance of important new technologies and the likelihood of
producing radical breakthroughs. Combining processes occurring at the
rudimentary and higher levels, I predict that the interaction dynamic at the
community level is significantly affected by a firm’s search patterns for new
experience. Local exploration search acts as a dominant force separating firms
into clusters that are densely connected among firms that benefit from sharing
related experience. At the same time, however, structurally advantaged
organizations play a bridging role to others with diverse knowledge and access
to innovative possibilities. Thus, I argue:
H7: The community structure demonstrates a strong tendency toward polycentric,
clustered structure when organizations base their collaborative arrangements on search
for collective experience.
58
Chapter 4: Overview of the Contemporary American Film Industry
The debut of Stranger Than Paradise (1984) signifies the beginning of a new era in
American film industry. Along with Quentin Tarantino’s Pulp Fiction (1994) and
Steven Soderbergh’s Sex, Lies, and Videotape (1989), these films radically changed
the film business for newcomers and brought independent cinema into the film
industry spotlight. The terms “indie” or “independent” are used primarily to
reflect the insurgence of a new art form and aesthetic strategy (Biskind, 2005;
Merritt, 2001). The rise of independent cinema illuminates complex, ambivalent
and contested moral sentiments in film making and transcends established
industry order.
By and large, the contemporary American film industry has two distinctive
forms: studios and independent producers. The industry contains a handful of
large studios known as the “majors.” These vertically integrated companies have
the capabilities to finance, produce, and distribute their own films. They also
have the means to finance and distribute large-scale works made by others. For
the 2008 calendar year, the six major studios had an 80% market share with a
combined total gross of $7.69 billion (Box Office Mojo). Warner Bros. led the
rankings, followed by Paramount, Sony/Columbia, Universal, 20th Century Fox
and The Walt Disney Company/Buena Vista.
59
Under the studio umbrella, the film industry also includes a similar number of
firms with medium-sized assets known as “mini-majors.” Functioning either as
an internal unit of a studio or as an independently operated entity, they are
largely subject to studios’ supervision and strategic control. For instance, Fox
Searchlight, owned by 20th Century Fox, was established in 1994 to produce
specialty films and art-house fare. Overture Films, a subsidiary of Liberty Media
Corporation, was formed in 2006 to produce and distribute low-budget films
aimed at niche markets (Freebase, 2009). In 2008, the six leading mini-majors had
a 13% market share with a combined total gross of $1.3 billion (Box Office Mojo).
Lions Gate Entertainment Corp. led the mini-major rankings, followed by
Summit Entertainment, Fox Searchlight, MGM/UA, Focus Features and
Overture Films.
Although studios typically concentrate on producing a few expensive but
profitable blockbusters, they also will operate in the independent arena,
producing non-blockbuster films that are relatively inexpensive and which might
not turn a profit. A few of these smaller-budget films can yield considerable
returns, however, making the foray into independents worthwhile. Focus
Features, for example, the independent film division of NBC Universal,
60
produced the 2005 box office hit Brokeback Mountain, which more than repaid the
expense of its production (Box Office Mojo).
The remaining 7% of the American film industry market is shared by many
hundreds of smaller, independent firms that operate outside of the studio
system. Typically referred to as “indies,” these production companies are often
established as one-time corporations, with primary assets consisting of the film
itself (Rusco & Walls, 2004). Films made by indies typically avoid the one-to-
many mass broadcasting model of the studios and instead center on a many-to-
many narrowcasting model (Scott, 2002). In this manner, niche products are
aligned with niche audiences, shifting away from mass-appeal, generic
productions toward individual-targeted films focused on a relatively restricted
audience. Despite their small size, indies often produce money makers of their
own, many times with well-known people at the helm, including, for example,
Quentin Tarantino and Spike Lee (Biskind, 2005). The decades between 1985 and
2005 have seen a considerable rise in the numbers of non-studio affiliated
independents, from 76 in 1985 to 354 in 2005.
Indies are a natural outgrowth of the film industry’s entrepreneurial spirit. Their
evolution and growth is due to more than simply entrepreneurial motivation,
61
however, and can better be understood in light of the film industry’s evolution
(Merritt, 2001).
The traditional studio system that served as the dominate industry logic was
horizontally integrated, with studios controlling all stages of production and
distribution. A typical studio would produce its own films with its own actors,
directors, and money; it would then market and distribute the films and show
them in its own theaters. This system was enormously successful, but its very
success contained the seeds for its demise, a prime victim of the Icarus paradox.
Studios were very powerful in the 1920s and 1930s and were not reluctant to
exercise their power. In order to market their prodigious output, they
introduced “block booking,” which forced exhibitors to purchase not only films
with well-known stars, but films of poor or mediocre quality that might produce
little income from the box office (Sobel, 1974; Warner, 1970). Activities such as
block-booking led the Roosevelt administration to sue eight major Hollywood
studios for anti-trust violations. Fearing a total loss, the studios compromised
and agreed in 1940, among other things, to eliminate block-booking (Thomson,
2006). An even more significant industry change occurred shortly thereafter
when a group of independents joined together as the Society of Independent
Motion Picture Producers and sued Paramount for engaging in monopolistic
practices (Maltby, 2003). The case went all the way to the Supreme Court, which
62
ruled that Paramount and the rest of the major studios were in violation of the
Sherman Antitrust Act by owning both theaters and film distribution. By the end
of the 1940s, the major studios had divested themselves of their theaters
signaling the end to the “Golden Age of Hollywood.”
The Supreme Court’s “Paramount Decision” opened the industry door to
independent film producers (Hall, 2009). It also set the film industry on a solid
trajectory toward vertical disintegration. Between 1968 and 1971, the industry
experienced a financial crisis generated by an overinvestment in feature
production. This hastened the demise of the classical Hollywood system of
industrial production, and replaced it with a much more fragmented system in
which movies were created through a complex arrangement of short-term
contractual agreements. This drastic industry change was fueled by the studios’
shift away from in-house production to contracting with outside film producers,
which as a result became more and more numerous as time progressed. These
outsiders increasingly found it easy to transition from studio helper to
competitor by independently producing films of their own (Merritt, 2001). This
system enabled independent production companies to explore new themes,
forms, and styles outside of the studio system and to gain autonomy in carrying
out their artistic visions.
63
In contrast to the golden age, the modern industrial landscape of the American
film industry is much more fluid and interactive. Neither the majors, the mini-
majors, nor the independents work entirely alone. Alliances are often formed
among them to facilitate financing, film production, or distribution or all three.
These partnerships range from simple pairings between two companies to
complex joint partnerships among several firms. When the partnerships are
considered separate entities in and of themselves, the film industry becomes
substantially larger and more varied. Christopherson and Storper’s (1989: 331)
succinct description of the multifarious nature of today’s film industry is a fitting
end to this brief overview: “… a complex of firms is tied together by an elaborate
structure of transactions, including exchanges of information, material input-
output flows, and personal contacts. These vertically disintegrated industries
have been dubbed ‘flexibly specialized’ because individual firms are specialized
but the complex as a whole is flexible—its mix of outputs can be changed by
altering the group of firms participating in the production of any particular
output ….”
64
Chapter 5: Research Method
Data Collection
The empirical setting for this study is the contemporary American motion
picture industry. A number of characteristics make this industry economically
important. It employs over half a million people in the United States
(Department of Labor, 2004), yields billions of dollars in domestic theatrical
ticket sales, and is the number one American export market. In one recent year,
$43 billion was spent on watching movies worldwide, with ancillary revenues
(e.g., home video) several times higher (Standard & Poor’s, 2006).
The structure of the contemporary motion picture industry is advantageous to
our study. First, the industry is characterized by a few large, dominating
companies—studios and mini-majors—and many smaller firms—independents
or “indies.” Second, studios and indies differ in their moral sentiments and
production ideology and, to a certain extent, the way they organize their work.
Studios often follow the path of producing blockbusters, which cater to the tastes
of a large segment of the population. Independents, on the other hand, often
have an anti-Hollywood sentiment and tend to work outside the system,
generating their own financing and making movies with their own unique
aesthetic (Merritt, 2001). Thus, studios and indies represent two distinctive
65
organizational forms. Third, production companies, especially independents,
often rely on project-based collaborative structures to produce films. This
network form of organizing enables us to observe the effect of micro processes
and mechanisms generating inter-firm ties on various structural tendencies at the
community level.
My data collection is exclusively focused on production companies that have
theatrically released at least one film in 1985 and 2005. A number of small
independent companies do not even release their films to the public and have no
box-office revenues (Rusco & Walls, 2004). These companies are excluded from
our present study. In addition, made-for-television and video movies are not
examined. Only companies producing theatrically released films are considered.
Theatrically released films face stronger market audience scrutiny and have
greater impact on the economic growth of the company. We select 1985 as our
initial data collection year because it signifies the emergence of a new
organizational form –independent film making. During 1985-2005, independent
companies have flourished and acquired a stronger market presence. This time
period represents a significant developmental stage of the U.S. motion picture
industry and signifies the contemporary era of American cinema (McDonald &
Wasko, 2008). By comparing collaboration dynamics at two points in time, we
66
are able to assess the stability of our predictions and to see if they are contingent
on the change in industry dynamics.
Data Sources
Data were collected from four main sources. The first source was trade
publications such as Variety, Hollywood Reporter, and Entertainment Weekly. The
second source was archival documents from the Academy of Motion Picture Arts
and Sciences. Third was the online Internet Movie Database. Finally, production
companies’ individual websites were consulted when necessary for verification
or to fill in information otherwise unavailable.
Data Measures
Film Producer Collaboration Network
The complex, interwoven characteristics of the contemporary film industry
enable us to conceptualize the film producer community as a network of
interrelated firms. The fundamental basis of interrelation is the joint production
of films. Two companies are considered as affiliated if they produced a film
together. On this basis, a collaboration network was developed for 1985 and
2005 respectively, with nodes in the network given a value of 1 if the
corresponding companies had co-produced a film, and zero otherwise. Each
year’s network was a different size because different numbers of production
67
companies existed at those times. Data were assembled into yearly collaboration
networks comprising an aggregated total of 469 production companies involved
in producing 365 feature-length films theatrically released in the U.S. in 1985 and
2005. Appendices B and C contain a list of film companies in 1985 and 2005.
Data were also collected on four main attributes of each production company:
two types of firm identity and two types of firm resources. Identity was
separated into cultural and professional, and resources were separated into
reputational and experience. Measures for each of these attributes were
determined as follows.
Cultural Identity Attribute
Cultural identity is the most restricted element of collective identity embedded in
organizational form and sharpens the form’s distinction as a social category
(Akerlof & Kranton, 2000; Grossberg, 1996). Cultural identity is measured by
type of organizational form in the film industry. For the present study, it was
measured by a firm being either a studio/mini major or not, yielding a binary
variable, with ‘1’ signifying studio/mini major and ‘0’ signifying independent.
68
Professional Identity Attribute
Professional identity is operationalized in terms of the variety of films a
company produces for a particular year. Variety can be measured in a number of
ways (for a review, see Wise, et al., 1997). One of the most widely-adopted
measures is Blau’s index for categorical dissimilarity (Blau, 1977; McPherson,
2004). Blau's categorical index (D) is calculated by
where p is the proportion of films for a given category and N is the number of
different categories. IMDb database reports the genre for each film, out of a total
of 23 categories, such as comedy, drama, action, family, thriller, romance, horror,
animation, and western. Genre data for each production company’s films were
assembled for each year. (For a list of the genres, see Appendix C). To construct a
professional identity vector for a production company, its Blau index was
calculated for a given year, with generalist firms—those producing films in a
wide variety of genres—having relatively high scores compared to specialists.
This measure is consistent with niche width theorizing (Dobrev, Kim & Hannan,
2001). The value of the professional identity measure ranges from 0 to 1.
Measures were lagged by one year before the affiliation matrix.
69
Reputational Resources Attribute
Reputational resources are measured by aggregating the number of nominations
and awards that a production company receives from the films it produced for a
given year. Included were awards from the Academy of Motion Pictures Arts
and Sciences, Deauville Film Festival, Directors Guild of America, Golden Globe,
Writers Guild of America, BAFTA, People’s Choice, and others. A reputational
resource vector for each firm was created by summing the number of awards and
nominations and assigning the number to the firm as its reputational attribute
score. This measure has been used as an indicator of the relative quality of a film
by Lampel and Shamsie (2003). Measures were lagged by one year before the
affiliation matrix.
Experience Resources Attribute
The level of experience a firm possesses reflects resources that can be traded on
the open-market as well as capabilities and knowledge that are implicit and
embedded in its organizational routines (Amit & Schoemaker, 1993; Barney,
1991; Nelson & Winter, 1982). The construct of experience resources as developed
in the present study is meant to encompass both of these aspects. We use the
total number of films produced by a company prior to tie formation to measure
its experience level. As with the previous continuous variables, measures were
lagged by one year.
70
Estimation Procedures
A higher-order exponential random graph model (ERGM) p* is used for
parameter estimation and capturing global network structural tendencies.
Building on Monte Carlo maximum likelihood estimation techniques, the model
incorporates higher-order network transitivity statistics into parameter
estimation processes to reduce the near-degeneracy problem that often occurs in
the Markov random graph model (Snijders, et al., 2006). Such estimation entails
simulation of a distribution of random graphs from a starting set of observed
parameter values, and subsequent refinement of the parameter values by
comparing the distribution of graphs against the observed graph, with this
process repeating until the parameter estimates stabilize. The appropriate
analytical program for Monte Carlo maximum likelihood estimation is PNet
(Wang, et al., 2009), which was used for this study. To gain a graphical sense of
the network under examination, I use Pajek to visualize network data. Pajek is a
powerful program for the visualization of large networks, social or otherwise
(Batagelj & Mrvar, 2003).
To test the proposed hypotheses, I conducted our analysis in two steps. In the
first step, I performed a parameter estimation procedure to test for convergence
of structural parameters. My analysis used the information from the 1985 and
71
2005 film producer collaboration networks as the network file; cultural identity,
professional identity, reputation resources and experiences data are the attribute
file. We performed estimations on two sets of parameters: structural parameters
(edge, alternating K-star, alternating AKT-T, see Appendix E) and actor attribute
parameters (Rb for Attribute 1, R for attribute 1, sum of continuous attribute1,
difference of continuous attribute1). Statistically, good convergence is indicated
by a small statistical t- value, which is less than the absolute value of 0.1. It
suggests parameter estimates are consistent across the different estimation runs.
In the second step, I performed a goodness of fit test to see how well the
converged model globally fits the data. The goodness of fit procedure assesses
the extent to which the structural properties in the observed network are aligned
with the structural properties of the networks generated by the estimated model.
Here, I set all the parameters specified in the estimation model to the values
equal to the estimates derived from our converged model. The goodness of fit
function in PNet provides sample means, standard deviations, and t-statistics for
the observed network. Small t-values [-0. 1, +0.1] for explicitly stated parameters
and t-values of [-2, +2] for non-explicitly estimated parameters in the model
indicate good fit.
72
Results
Table 1 and Table 2 show film production patterns for 1985 and 2005. Majority of
the production companies in 1985 and 2005 produce less than 2 films. This
pattern indicates there are a large number of a small-scaled independent
production companies in the industry. Majority of films are jointly produced by a
collaborative arrangement that constrains less than five production companies.
The dominant mode of collaboration is formed around 2 or 3 companies.
Table 1. Film Production Patterns
Films Per
Company
Number of
Companies
Companies
Per Film
Number of
Films
Count
1985
2005
Count
1985
2005
1 62 308 1 45 68
2 9 41 2 35 53
3 5 12 3 41 54
4 4 8 4 14 19
5 2 5 5 9 14
6 2 1 6 3 4
7 1 0 7 2 1
8 0 1 8 0 1
9 0 1 9 1 1
10 0 2 10 0 0
>10 2 3 >10 0 0
73
Table 2. Summary of Company Ties* (Percentage)
% Firms with Corresponding Number
of Ties
Count
1985
2005
0 14 16
1 41 16
2 23 23
3 10 12
4 5 4
5 3 4
6 0 2
7 3 2
8 0 3
9 0 7
10 0 2
>10 1 2
Notes: * Based on number of all other companies a firm worked
with in co-production for the year.
Table 3 summarizes the estimation results for the 1985 collaboration network. I
discuss them below in terms of the hypotheses of this study.
74
Table 3. 1985 Parameter Estimates of Higher-Order ERGM
Collaboration Network
Parameter Standard
Error
T-statistic
Model 1- Cultural Identity
Edge -3.108922 0.44087 0.06539*
K-Star -1.192160 0.22477 0.06631*
AKT-T 1.969019 0.19455 0.08218*
Rb -2.595615 1.04395 0.03510*
R 1.742736 0.35558 0.07538*
Model 2 - Professional
Identity
Edge -3.148146 0.75316 -0.01986*
K-Star -1.173978 0.23258 -0.02624*
AKT-T 1.911133 0.18976 -0.02105*
Sum of Continuous
Attribute
-2.304997 1.25414 0.00157
Differences of Continuous
Attribute
2.180416 0.92640 0.02355*
Model 3 - Reputational
Resources
Edge -2.738520 0.46971 -0.01981*
K-Star -1.383069 0.24990 -0.04115*
AKT-T 1.922584 0.18965 -0.04839*
Sum of Continuous
Attribute
0.152697 0.03369 -0.01098*
Differences of Continuous
Attribute
-0.105404 0.02744 0.04076*
Model 4 - Experience
Resources
Edge -3.790826 0.54320 0.00168*
K-Star -1.953099 0.30131 0.01468*
AKT-T 2.061109 0.22205 0.06261*
Sum of Continuous
Attribute
0.866967 0.13156 0.01486*
Differences of Continuous
Attribute
-0.420273 0.08472 -0.04057*
75
H1: The community structure exhibits a strong tendency towards cultural identity-based
ties. Model 1 was used to test culture based identity effects. The R parameter for
Attribute1 is positive and significant (1.742736, t= 0.07538*) showing that studios
tend to forge ties with other producers, irrespective of whether they are studios
or not. The Rb parameter for Attribute1 is negative and significant (-2.595615;
t=0.03510*), showing that studios have less tendency to collaborate with other
studios. Thus, H1 is not supported.
H2: The community structure exhibits strong tendencies towards a hierarchical structure
when ties among organizations are based on cultural identity. A positive and
significant AKT-T parameter (1.969019, t=0.08218*) and negative and significant
K-Star (-1.192160, t=0.06631*) show the network has a strong tendency towards
triangulated, polycentric structure, which is contrary to what we had
hypothesized. Thus, H 2 is not supported.
H3: The community structure demonstrates a strong tendency towards professional
identity based ties (i.e., specialists tend to forge ties with specialists and generalists with
generalists). Model 2 was used to test whether professional identity explains
network tendencies. The negative Sum of Continuous Attribute parameter (-
2.304997 , t= 0.00157) combined with the significant and positive Differences of
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Continuous Attribute parameter (2.180416; t= 0.02355*) shows that highly
diversified generalists tend to forge collaborative ties very focused specialists.
Thus, H3 is not supported.
H4: The community structure exhibits strong tendencies towards triangulated structure
when ties are based on professional identity. The positive and significant AKT-T
parameter (1.911133; t=-0.02105*) and negative and significant K-Star (-1.173978;
t=-0.02624*) show that the network has a strong tendency towards triangulated,
polycentric structure when ties are based on professional identities. Thus, H4 is
supported.
H5: The community structure demonstrates a strong tendency towards tie formation
with high reputation actors (i.e., high-reputation actors are more likely to forge ties with
others). Model 3 was used to test the how well reputational resources explain
network structural tendencies. The Sum of Continuous Attribute parameter is
significant and positive (0.152697; t= -0.01098*) showing that there is a greater
tendency for a tie between producers that are both high reputation or between
high reputation actors and lower reputation ones. The Differences of Continuous
Attribute parameter is significant and negative (-0.105404; t=0.04076*), showing
that producers with differences in reputation are less likely to forge ties together.
77
Together, these results suggest that collaborative ties are more likely to occur
when both producers have high reputations. Thus, H5 is supported.
H6: The community structure demonstrates a strong tendency towards triangulated
structure when firms ’ties are based on reputation resources. Model 3 has a significant
and negative K-Star parameter (-1.383069; t= -0.04115*) showing that the
preferential attachment effect is not strong in the observed network. A
significant and positive AKT-T parameter (1.922584; -0.04839*) shows there is a
strong tendency towards triangulation or clustering among producers in the
network. Thus, the community structure is fragmented with multiple clusters
and H6 is supported.
H 7: The community structure demonstrates a strong tendency towards triangulated
structure when firms’ ties are based on collective experience. Model 4 was used to test
the effect of experience resources on network structure. A significant and
positive Sum of Continuous Attribute parameter (0.866967; 0.01486*) shows that
high-experience producers tend to collaborate with other high or low-experience
producers. A negative and significant Differences of Continuous Attribute (-
0.420273; -0.04057*) shows that there is a strong tendency for producers to forge
ties when their experience difference is small. Together, these results suggest that
collaboration is likely to occur between producers with high experience. A
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positive and significant AKT-T parameter (2.061109; 0.06261*) and a positive and
significant K-Star (-1.953099, 0.01468*) show that there is a tendency for cluster
formation in the network. Thus, H7 is supported.
To test the stability of the 1985 results, I ran additional models using the 2005
producer collaboration network, the results of which are summarized in Table 4.
Then, I compared the results from the two time periods.
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Table 4. 2005 Parameter Estimates of Higher-Order ERGM
Collaborative Network
Parameter Standard
Error
T-statistic
Model 1- Cultural Identity
Edge -5.069402 0.05759 -0.07012*
K-Star -2.412965 0.05345 0.00881*
AKT-T 2.900065 0.07272 -0.01050*
Rb 0.190359 0.27967 -0.05110
R 1.045454 0.09253 -0.05878*
Model 2 - Professional
Identity
Edge -4.284993 0.17217 0.01619*
K-Star -1.204686 0.06805 -0.08218*
AKT-T 3.322783 0.08328 0.07692*
Sum of Continuous
Attribute
-4.868105 0.42495 0.07188*
Differences of Continuous
Attribute
1.138066 0.40063 0.02067*
Model 3 - Reputational
Resources
Edge -5.582340 0.06988 0.01648*
K-Star -1.409708 0.08577 -0.06713*
AKT-T 3.459177 0.09414 -0.06777*
Sum of Continuous
Attribute
0.065309 0.00359 -0.02511*
Differences of Continuous
Attribute
-0.048789 0.00369 0.01900*
Model 4 - Experience
Resources
Edge -6.278249 0.13018 0.00528*
K-Star -1.164862 0.06412 0.12191
AKT-T 3.395290 0.08554 0.13048
Sum of Continuous
Attribute
0.549143 0.05829 0.05608*
Differences of Continuous
Attribute
-0.325731 0.04924 0.09468*
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The comparisons of the network structure and actor attribute parameter
estimates for the years 1985 and 2005 are summarized in Table 5. Overall, the
2005 results largely validate the findings generated from the 1985 collaboration
network. Three noticeable differences are worth mentioning, however. The first
is related to the effect of cultural identity on tie formation at the network level.
Positive and significant R for attribute 1 (1.045454, t = 0.09253*) and insignificant
Rb for attribute 1 (0.190359, t=0.27967*) indicate that studios tend to collaborate
with both other studios and Indies. Thus we find partial support for H1 in the
2005 network analysis. The second is related to the dynamics of professional
identity tie formation. The significant and negative Sum of Continuous Attribute
(-4.868105, t=0.07188*) and the significant and positive Difference of Continuous
Attribute (1.138066, t=0.02067*) show that only moderately diversified generalist
tend to forge ties with specialists. The third is related to H7, the relation between
community structure and collective experience. Although the signs remain
positive, both K-Star (-1.164862, 0.06412*) and AKT-T (3.395290, 0.08554*) in
model 4 are not significant in the 2005 network. Because this model fails to
converge with the 1985 model, we cannot make significant inferences from the
results on collective experience estimation. The hypotheses and results are
summarized in table 6. It is worth noting that 2005 estimation results show that
the hypothesized effects largely remain stable between the two times.
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Table 5. Parameter Estimates of the Longitudinal ERGM in Film
Production Networks
Parameter 1985 2005
Model 1- Cultural Identity
Edge -3.108922 (0.44087) -5.069402 (0.05759)
K-Star -1.192160 (0.22477) -2.412965 (0.05345)
AKT-T 1.969019 (0.19455) 2.900065 (0.07272)
Rb -2.595615 (1.04395) 0.190359 (0.27967)
R 1.742736 (0.35558) 1.045454 (0.09253)
Model 2 - Professional
Identity
Edge -3.148146 (0.75316) -4.284993 (0.17217)
K-Star -1.173978 (0.23258) -1.204686 (0.06805)
AKT-T 1.911133 (0.18976) 3.322783 (0.08328)
Sum of Continuous
Attribute
-2.304997 (1.25414) -4.868105 (0.42495)
Differences of
Continuous Attribute
2.180416 (0.92640) 1.138066 (0.40063)
Model 3 - Reputational
Resources
Edge -2.738520 (0.46971) -5.582340 (0.06988)
K-Star -1.383069 (0.24990) -1.409708 (0.08577)
AKT-T 1.922584 (0.18965) 3.459177 (0.09414)
Sum of Continuous
Attribute
0.152697 (0.03369) 0.065309 (0.00359)
Differences of
Continuous Attribute
-0.105404 (0.02744) -0.048789 (0.00369)
Model 4 - Experience
Resources
Edge -3.790826 (0.54320) -6.278249 (0.13018)
K-Star -1.953099 (0.30131) -1.164862 (0.06412)
AKT-T 2.061109 (0.22205) 3.395290 (0.08554)
Sum of Continuous
Attribute
0.866967 (0.13156) 0.549143 (0.05829)
Differences of
Continuous Attribute
-0.420273 (0.08472) -0.325731 (0.04924)
Notes: Parentheses denote standard errors. Significant parameters (at least twice
their standard errors) are printed boldface.
82
Table 6. Summary of Hypothesis Testing Results
Hypothesis
Results
Hypothesis 1: Organizational collaboration
tends to occur among organizations that share
a similar cultural identity.
Not Supported
Hypothesis 2: The community structure
exhibits strong tendencies towards a
hierarchical structure, with a few central
organizations with numerous ties occupying
a core region and less connected
organizations inhabiting a peripheral region,
when organizations collaborate with others to
construct a new culture identity.
Not Supported
Hypothesis 3: Organizational collaboration
tends to occur among organizations sharing a
similar professional identity.
Not Supported
Hypothesis 4: The community structure
exhibits strong tendencies towards a
polycentric, clustered structure when
organizations with similar professional
identities collaborate with each other.
Supported
Hypothesis 5: High-reputation organizations
tend to have more collaborative ties than low-
reputation organizations.
Supported
Hypothesis 6: The community structure
demonstrates a strong tendency towards a
polycentric, clustered structure when
organizations base their collaborative
arrangements on search for reputation
resources.
Supported
Hypothesis 7: The community structure
demonstrates a strong tendency toward
polycentric, clustered structure when
organizations base their collaborative
arrangements on search for collective
experience.
Supported
83
Pajek figures allow us to see in graphical format the overall structural features of
the collaboration networks for 1985 and 2005. The former year consists of 87 U. S.
companies with theatrically released films, and the latter consists of 382 such
companies. In 1985, 11 of the companies were studios or mini-majors, and the
rest were independents. In 2005, the number of studios or mini-majors had
increased to 28; independents had increased to 354. Understanding such large
networks can be greatly facilitated by being able to see a picture of them as
nodes, representing companies, and lines between the nodes, representing ties
between co-producers. Pairs of figures were created for each of the four main
firm attributes: cultural identity, professional identity, reputational resources,
and experience resources. Each of these will be discussed in turn.
Cultural Identity
Figures 1a and 1b show the affiliation matrices for 1985 and 2005, respectively.
Evident in the figures is a lack of homogeneity in cultural identity ties, showing
why H1 was not supported. Heterogeneity is evident by the numerous ties
between red nodes (representing studios/mini-majors) and yellow nodes
(representing independents).
Also evident in Figures 1a and 1b is a general lack of degree-based or hierarchical
structure, showing why H2 was not supported. Although one studio/mini-major
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in 1985 (#16) does have a large number of spokes (11), three nodes (#61, 80, 84)
have only one spoke, and several have just two. Independents show little
hierarchical structure for 1985, but a somewhat more appears in 2005, with node
191 as a notable example. Still, 2005 independents do not generally follow a
hierarchical structure, and in that sense are truly independent, as evident by all
of the singletons spread around the periphery of both figures. Studio/mini-
majors are similar to independents in 2005. Studio/mini-major #380 has 23
spokes, but another (#39) has no affiliations, and several studio/mini-majors in
2005 have only one or two ties. For both cultures (studios/mini-majors and
independents) and for both years (1985 and 2005), then, the network structures
are not generally degree-based or hierarchical.
Figure 1a. 1985 Affiliation Matrix
Studio/Mini-Major
Independent
85
Figure 1b. 2005 Affiliation Matrix
Studio/Mini-Major
Independent
Professional Identity
Figures 2a and 2b show the affiliation matrices for 1985 and 2005, respectively,
with nodes sized relative to firms’ professional identity value (diversification
index), with relatively large nodes representing diversified firms and relatively
small nodes representing specialists. Similar to the figures for cultural identity
discussed just above is the general lack of affiliation homogeneity among pairs of
nodes with respect to professional identity, showing why H3 was not supported.
Graphically speaking, although there are certainly ties between nodes of similar
size, there is also considerable crossover between large-sized nodes and small
ones.
86
Restricting my attention to clusters of nodes in Figures 2a and 2b, and more
specifically, to groups of more than two, the situation is different. Here, I see the
community structure exhibiting tendencies towards polycentric structure with
organizations having similar professional identities collaborating with each other
in groups, showing why H4 was supported. Graphically speaking, this structural
property is shown by clusters of nodes of roughly the same size (disregarding
color) connected together. The 2005 network shows the tendency more clearly
than 1985, with some isolated clusters having almost nothing but specialists tied
together.
Figure 2a. 1985 Affiliation Matrix
(nodes sized relative to professional identity value)
Studio/Mini-Major
Independent
87
Figure 2b. 2005 Affiliation Matrix
(nodes sized relative to professional identity value)
Studio/Mini-Major
Independent
Reputational Resources
Figures 3a and 3b show the affiliation matrices for 1985 and 2005, respectively,
with nodes sized relative to firms’ reputational resources value, with relatively
large nodes representing high-reputation firms and relatively small nodes
representing low-reputation ones.
Disregarding node color, the figures for both years show large-sized nodes
having relatively more ties than smaller ones, showing why H5 was supported,
viz., that high-reputation organizations tend to have more collaborative ties than
those with low-reputation. While there are some instances of larger nodes with
88
few ties (e.g., # 51 in 1985 and #219 in 2005 have none), the overall pattern is
larger node/larger number of ties and smaller node/smaller number of ties.
With respect to clusters of nodes in Figures 3a and 3b, and more specifically, to
groups of size larger than two, I find similarities to professional identity,
discussed above. Here, I see the community structure exhibiting tendencies
towards polycentric structure with groups of organizations having similar
reputational resources collaborating with each other, showing why H6 was
supported. This structural property is shown by the prevalence of nodes of
roughly the same size (disregarding color) connected together. Counter-
examples do exist for both years, but the overall tendency is toward a polycentric
structure based on homogeneity of reputation.
89
Figure 3a. 1985 Affiliation Matrix
(nodes sized relative to reputational resources value)
Studio/Mini-Major
Independent
Figure 3b. 2005 Affiliation Matrix
(nodes sized relative to reputational resources value)
Studio/Mini-Major
Independent
90
Experience Resources
Figures 4a and 4b show the affiliation matrices for 1985 and 2005, respectively,
with nodes sized relative to firms’ experience resources value, with relatively
large nodes representing high-experience firms and relatively small nodes
representing those with low experience. With respect to clusters of nodes in
Figures 4a and 4b, and more specifically, to groups larger than two, I find
similarities to professional identity and reputational resources, discussed above.
Here, I see the community structure exhibiting tendencies towards polycentric
structure with groups of organizations collaborating with each other based on
their collective experiences, showing why H7 was supported.
Figure 4a. 1985 Affiliation Matrix
(nodes sized relative to experience resources value)
Studio/Mini-Major
Independent
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Figure 4b. 2005 Affiliation Matrix
(nodes sized relative to experience resources value)
Studio/Mini-Major
Independent
Finally, to assess how well the converged model globally fits the data, I
calculated summary goodness of fit statistics for 1985 and 2005 as shown in
Tables 7 and 8. Nearly all explicitly specified parameters for 1985 have small t
values [-0.1, +0.1], which allows me to conclude that the model fits the data well
(Anderson, Wasserman & Crouch, 1999). T-values for 2005 are similarly within
the acceptable range for cultural identity. But the remaining attributes for 2005
data do not generally exhibit goodness of fit t-statistics between -0.1 and +0.1,
possibly due to the large size of the network (382 nodes). Thus, careful judgment
must be exercised in interpreting the 2005 results, and the decision on whether to
accept the model is up the individual researcher (Robins, et al., 2007). In this case,
92
it seems reasonable to accept the 2005 results at least on a tentative basis because
they mostly parallel the results obtained for 1985.
93
Table 7. 1985 Summary of Goodness of Fit Statistics (t-values)
Graph Counts
Model 1
Model 2
Model 3
Model 4
Edge -0.1001 0.0714 -0.0057 -0.0921
K-Star -0.1124 0.0687 -0.0401 -0.0869
K-Triangle -0.0698 0.0386 -0.0666 -0.0976
Rb for
Attribute
-0.0977 … … …
R for Attribute -0.1413 … … …
Sum for
Attribute
… 0.0948 -0.0516 -0.0793
Difference for
Attribute
… 0.0747 0.0667 -0.0859
Product for
Attribute
… 0.0880 -0.1154 -0.0251
Degree Distribution
Model 1
Model 2
Model 3
Model 4
Std dev degree
dist
0.7604 0.8718 0.2863 -0.4790
Skew degree
dist
1.9726 2.3517 1.1308 -1.0005
Clustering
Model 1
Model 2
Model 3
Model 4
Global
Clustering
-0.9478 -1.3592 -1.1076 0.4857
Mean Local
Clustering
0.4276 0.8083 1.0642 0.3387
Variance Local
Clustering
1.1091 1.4310 1.3931 0.5423
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Table 8. 2005 Summary of Goodness of Fit Statistics (t-values)
Graph Counts
Model 1
Model 2
Model 3
Model 4
Edge 0.0965 0.0627 -0.1001 -0.4606
K-Star -1.7507 0.4884 1.1690 n.c.
K-Triangle -0.6106 -0.1803 0.6022 n.c.
Rb for Attribute -0.5118 … … …
R for Attribute -0.2106 … … …
Sum for
Attribute
… 0.5170 -0.3770 -0.2302
Difference for
Attribute
… 0.1012 0.0190 -0.0797
Product for
Attribute
… 0.0665 0.3652 -0.0473
Degree Distribution
Model 1
Model 2
Model 3
Model 4
Std dev degree
dist
4.0109 1.6307 1.5550 0.7600
Skew degree
dist
-2.1622 0.7179 3.0989 -2.8448
Clustering
Model 1
Model 2
Model 3
Model 4
Global
Clustering
3.3139 -0.1277 3.6156 57.7217
Mean Local
Clustering
-4.3094 1.7728 -3.8888 81.8234
Variance Local
Clustering
11.6671 9.5477 6.3925 47.3015
Note: n.c. = non-converged.
95
Chapter 6: Discussion and Conclusion
The key goal of my study was to understand how micro interactive processes at
the organizational level affect structural outcomes at the community level. A
community, like any social system, displays integration and differentiation
tendencies. A high degree of integration leads to a centralized, hierarchical
macrostructure, while a high degree of differentiation produces segregation and
decentralized, polycentric structures. Uncovering the origins of these tendencies
helps us understand the rules governing community activities and the forms of
opportunities and constraints.
In this study, I investigated how the mechanisms of collective identity
construction and resource sharing contribute to the formation of these tendencies
at the community level. Here, community is defined as an aggregation of the
network of inter-organizational ties. Community also consists of various
organizational forms connected with commensalistic and symbiotic relations.
These interdependencies manifest into various interaction patterns reflecting the
macrostructure of the community. Using the U. S. film industry as our empirical
context, I analyzed collaboration networks among film producers at two points
in time - 1985 and 2005 – to examine these effects. This time period reflects the
96
emergence and growth of a new organizational form in a hierarchical
organizational field.
I argued that the construction of collective identity entails two simultaneous
processes. On the one hand, cultural identity, representing the imperative
element of collective identity, enforces inter-population boundaries and creates
distinctions. Ties enabled by cultural identity, I posit, produce collective action
and contribute to the growth of organizational forms. Community structure
reflects the mobilization tendencies of these ties and results in a centralized and
hierarchical form in an organizational field controlled by a dominant population.
On the other hand, professional identity, reflecting the role an organization
occupies in the market, transcends population boundaries and produces
fragmented, polycentric structural tendencies at the community level.
My results show that collaboration based on professional identity explains
decentralized, polycentric structural tendencies of the film producer community.
In addition, highly diversified tend not to collaborate with each other. They also
are less likely to collaborate with specialists within a narrow technological space.
Collaboration is most likely to occur among highly or moderately diversified
producers and specialist.
97
Surprisingly, I found that producers differing in cultural identity are not
precluded from collaboration with each other, which is especially true for
member organizations from the dominant population. For instance, I observed
that film studios exhibit a greater tendency to forge ties with other producers
irrespective of their culture categories. These findings suggest the pervasive
power that dominant populations possess. It grants them unencumbered access
to rising markets and deference from the members of emerging populations
through collaborative ties. Even in an industry where ideology and culture
produce sharply contrasted population boundaries, the power dynamic persists.
The solidarity incentive for constructing a new collective identity recedes in the
face of a domination order. My findings resonate with Fligstein’s (2001) political-
cultural approach to the market.
My results also unexpectedly show that when ties are based on cultural identity,
community structure displays triangulated, polycentric structural tendencies
rather than the centralized, hierarchical tendencies that we hypothesized. This
finding is consistent with the observation of Rao, Morrill and Zald (2000) on the
craft-brewing movement. In an effort to establish a new collective identity in
contrast to dominating “industrial brewers,” micro-breweries and brewpubs
initiated a social movement by creating subcultures within the existing field. The
marginalized status of subfields ironically evoked support from craft brewers
98
and consumers, thus, strengthening the authentic collective identity they
represented. Together, these findings shed new light on identity-based
movement in a hierarchical field.
In addition, our results illustrate the effect of resource-sharing mechanisms on
community structure. Utilizing the status model of competition and
organizational learning mechanisms, I argued that the search for reputation
resources and collective operating experience engenders a fragmented and
clustered community structure. The nature of the clusters produced, however,
are different in kind. On the one hand, search for reputation resources produces
clusters centered on high reputation producers and reflects that market
hierarchical status order. On the other hand, search for collective experience
generates clusters in which firms possessing related experience exchange with
each other. I found that high-reputation producers tend to attract more
collaborative partners, but they tend to cooperate with only other high-
reputation partners. Just as status-based competition is localized (Podolny 1993,
1994), status-based cooperation is also localized to the extent that producers tend
to interact with those who are in similar status categories. Community structure
exhibits these tendencies and becomes polycentric and clustered around different
status categories.
99
Experience resources embody an organization’s knowledge and skills. The
learning literature suggests that organizations engage in exploitation and
exploration learning in order to develop innovative products. My results show
that collaborative learning is more likely to occur among highly experienced
producers. The fact that the community is segregated into loosely connected
clusters when producers engage in collective learning implies that exploration
learning is diverse.
The contribution of our research to organizational studies is multifold. By and
large, I offer an expansive account of the micro mechanisms and processes that
breed inter-organizational collaboration and their relation to community
structure. Understanding the sources of integration and differentiation at the
community level enables firms, existing or emerging, to understand the norms
and rules of the market and therefore to navigate the market more efficiently. In
particular, my study demonstrates that the market is a political economy. It is
stratified and influenced by power, status category and knowledge. I observe
that collaboration tends to occur among high status producers and highly
experienced producers. Dominant organizations are able to enter new market
niches being created by marginalized organizational forms. Thus, construction of
new market identity requires successfully mobilizing existing structure along
these dimensions. My research calls attention to studying the sociology of
100
markets and treats these factors as foundational elements of market exchange.
Considering the social and political institutional environment as an exogenous
force is no longer sufficient. These social forces are endogenous to the extent they
are embedded in market exchange and internalized by market producers.
My study is one of the few looking at the entire collaboration network in an
industry production market. By doing so, I control for industry variations in
different market niches and simultaneously take into consideration institutional
forces conditioned by the industry environment. The patterns we observe allow
us to draw deeper and broader implications about the dynamics under study,
especially in cultural industries.
My study contributes to organizational ecology theory through deconstructing
the concept of collective identity into cultural identity and professional identity
and uncovering their effects on community structure. This allows for more fine-
grained analysis on the extent to which organizations are sanctioned for violating
the identity codes embedded in the organizational form to which they belong. It
connects niche width theory (Hannan & Freeman, 1977) and theory of
organizational forms (Polos, et al., 2002) into a coherent whole. In addition,
studying community dynamics through the lens of network enables us to derive
insights that compliment the density dependence model (Aldrich, & Ruef, 2005).
101
My study also contributes to alliance theory by uncovering patterns of alliance
formation at a broad collective level, namely, the industry product market. While
the alliance literature offers an impressive body of knowledge on the antecedents
and consequences of alliance formation, little research has systematically looked
at alliance dynamics at an aggregate level, which cannot simply be explained by
firm level factors. Our research fills this void.
Finally, my study utilized a novel network simulation method. It enables me to
bring agency back into network studies through connecting agent attributes to
overarching network structural tendencies. The method also allows me to make
strong statistical inferences based on the realistic assumption that networks are
not random – they are interdependent rather than independent. The new method
increases the validity of my findings.
My study has several limitations. First, I only study the collaboration networks of
producers in the film industry at two points in time - 1985 and 2005. While this
offers an interesting comparison that proves to be adequate given the industry
context, my findings could be strengthened by testing the stability of the
hypothesized effects over the intervening years for the community. To do this, I
need to gather network data and attribute measures throughout the time period
102
1985-2005 and test whether the effects of identity construction and resource
search mechanisms on community structure remain stable over time. The LPNet
network analytical tool can be use to test transition effects of the parameters
longitudinally. Although data collection at this large scale is very difficult, the
findings from this extended research would allow us to draw more significant
and realistic conclusions than what have been offered under my current study.
Second, my data consist of multiple networks, for 1985 and 2005, that are based
on different kinds of interaction. The multiplicity of the networks under study
implies interdependence among them; identity construction mechanisms may
have an effect on the networks generated by resource search mechanisms and
verse versa. However, my current study fails to consider this interactive effect.
This limitation can be readily resolved by using newly developed XPNet
network technology for testing hypothesized effects set forth in this study.
Third, my research focuses exclusively on a producer market. To gain a fuller
understanding of this industry would entail extending the study to other
organizational forms that are symbiotic to the product market, such as suppliers
and distributors. Once films are produced, they face challenges of finding the
right distributors to maximize their box-office potential. In the cinema
community, distribution deals are as important as, if not more so, than
103
collaboration arrangements for producing a film. In addition, distribution
networks are symbiotic to production networks in the sense that distribution is
functionally different from but complimentary to film production. Thus,
constructing distribution networks and mapping those onto producer networks
would allow us to understand how such symbiotic relations influence
community dynamics at large.
Fourth, for reasons of simplicity, my research examines the content but not the
strength of network ties. It would be fruitful to see what additional insights the
strength of ties might bring to our understanding of market and community. The
strength of ties can be understood through examining repeated ties among
producers. In some cases, collaborative relationships among producers resemble
market-like transactions in which firms join forces to co-produce a film and then
disband when the film is complete. In other cases, the collaborative arrangements
signify a degree of continued friendship and affective connections. For instance, I
observed that some production companies repeatedly interact with each other to
produce films together. To tease out the differences in the strength of the ties
would require interviews with film producers to uncover the rationale behind
partner selection and the motives for collaboration.
104
I intend to overcome this set of limitations in my future research. The central
theme of my research derives from two strands of inquiry. One has to do with
the evolutionary process of the community. Community evolution is affected by
the emergence of new organizational forms, adaptation of existing ones, and
demise of old forms that no longer are compatible with the changing order of the
macro-environment. Organizational ecologists have long argued that
commensalistic and symbiotic interdependencies at the population level affect
the evolution of community (Ashley & Fombrun, 1985). To fully uncover this
process would require an examination of both cooperative and competitive ties
along with their co-evolution over time. In addition, instead of assuming away
the interconnectedness of various inter-organizational micro processes and
mechanisms, I should study the relative salience of these mechanisms in
influencing the evolution process of the community. In particular, I should
examine to what extent the relative salience of these mechanisms is affected by
the strength of the ties among different producers. Moreover, studying
symbiotic complementarities from networks that differ in functionality from
production networks would offer a more complete account of community-level
interactions. This strand of research would require gathering longitudinal data
from the incipient point of the community in order to trace the evolutionary path
of its genesis, ups and downs, expansions and contractions, and in some aspects,
105
its demise. Recent development in network methodology and analytical tools
such as LPNet and XPNet would facilitate an examination of this set of issues.
The second strand of my future research involves understanding the effects of
market stratification on organizational outcomes. This topic is particularly
relevant to emerging entrepreneurial firms. The consensus among
entrepreneurship scholars is that seeking opportunity advantage is the central
quest for entrepreneurship (Covin & Slevin, 2002). Market opportunity, as I have
shown here, is conditioned on the socio-political dimensions of the market.
Understanding the rules governing market exchange would enable
entrepreneurs to position themselves strategically and to gain access to otherwise
unreachable markets. For instance, establishing relationships with high-status
players in the market enables entrepreneurial firms to gain economic and social
advantages over peers who have no such ties. Understanding the conditions that
give rise to such highly beneficial ties and the ways of managing them
successfully to prolong the advantages accruing to the ties are important to
entrepreneurial firms’ survival. In addition, it would be interesting to study to
what extent the ties facilitate or hamper the development of new capabilities in
entrepreneurial firms, given that innovation rests at the core of entrepreneurship.
Network data can be collected longitudinally to study the impact of such ties on
entrepreneurial firms’ performance outcomes. In particular, comparing
106
entrepreneurial firms at peripheral regions to those situated in the core of a
network would enable us to draw inferences on how network position affects
survival chances in the long run. The UCINET network analytical tool would be
applicable to this research direction.
107
References
Abrams, D., & Hogg, M. A. 1990. Social identification, self- categorisation, and
social influence. European Review of Social Psychology, 1, 195-228
Ahuja, G. 2000. Collaboration networks, structural holes, and innovation: A
longitudinal study. Administrative Science Quarterly, 45: 425-455.
Aiken, M. & Hage, J. 1968. Organizational interdependence and intra-
organizational structure. American Sociological Review, Vol. 33, p. 912-930.
Akerlof, G. A. & Kranton, R. E. 2000. Economics and identity. Quarterly Journal of
Economics, August, Vol. 115, No. 3, Pages 715-753
Aldrich, H. 1999. Organizations Evolving. London: Sage Publications.
Aldrich, H. E. & Auster, E. 1986. Even dwarfs started small: Liabilities of age and
size. In B. Staw and L. Cummings (Eds), Research in organizational behavior: 165-
198. Greenwich, CT: JAI Press.
Aldrich, H. E. & Fiol, C.M. 1994. Fools rush in? The institutional context of new
industry creation. Academy of Management Review 19, 4: 645-670.
Aldrich, H. E. & Ruef, M. 1999. Organizations Evolving. Thousand Oaks, CA: Sage.
Aldrich, H. E. & Ruef, M. 2005. Organizations Evolving. 2
nd
ed. Thousand Oaks,
CA: Sage.
Almeida, P. & Kogut, B. 1999. Localization of knowledge and the mobility of
engineers in regional networks. Management Science, 47: 905-917.
Alvarez, S. A., & Barney, J. B. 2001. How entrepreneurial firms can benefit from
alliances with large partners. Academy of Management Executive, 15 (1): 139-48.
Amin, A. & Wilkinson, F. 1999. Learning, proximity and industrial performance:
An introduction. Cambridge Journal of Economics, 23, pp. 121-125.
Amit, R. & Schoemaker, P. 1993. Strategic assets and organizational rent. Strategic
Management Journal, 14: 33-46.
Anderson, C. J., Wasserman, S. & Crouch, B. 1999. A p* primer: Logit models for
social networks. Social Networks, 21: 37-66.
108
Argote, L., Beckman, S. L. & Epple, E. 1990. The persistence and transfer of
learning in industrial settings. Management Science, 36: 140-154.
Astley, W. G. 1985. Administrative science as socially constructed truth.
Administrative Science Quarterly, 30: 497-513.
Astley, W. G. & Fombrun, C. J. 1983. Collective strategy: Social ecology of
organizational environments. Academy of Management Review, 8: 576-587.
Audia, P. G., & Freeman, J. H. 2006. Community ecology and the sociology of
organizations. Annual Review of Sociology, 32: 145-169.
Audia, P. G. & Rider, C. I. 2006. Entrepreneurs as organizational products
revisited. In R. Baum, M. Frese & R. Baron (Eds.), The psychology of
entrepreneurship: 113-130. Mahwah, N. J.: Lawrence Erlbaum Associates.
Baldassarri, D., & Diani, M. 2007. The integrative power of civic networks.
American Journal of Sociology, 3: 735-80.
Barnett, W. P., & Carroll, G. R. 1987. Competition and mutualism among early
telephone companies. Administrative Science Quarterly, 32: 400-421.
Barnett, W. P. & Woywode, M. 2004. From red Vienna to the Anschluss:
ideological competition among Viennese newspapers during the rise of national
socialism. American Journal of Sociology, 109(6): 1452-1499.
Barney, J. B. 1991. Firm resources and sustained competitive advantage, Journal of
Management, 17: 99-120.
Baron, J. N. 2004. Employing identities in organizational ecology. Industrial and
Corporate Change, 13: 3-32.
Batagelj, V. & Mrvar, A. 2003. Pajek program for analysis and visualization of large
networks reference manual. Ljubljana, Slovenia: University of Ljubljana.
Baum J. A. C. 1996. Organizational ecology. In S. Clegg, C. Hardy & W. Nord
(Eds.), Handbook of organization studies: 77–114. London: Sage.
Baum, J. & Oliver, C. 1991. Institutional linkages and organizational mortality,
Administrative Science Quarterly, 36: 187-218.
109
Baum, J. A. C. , Calabrese, T. & Silverman, B. S. 2000. Don't go it alone: Alliance
network composition and startups' performance in Canadian biotechnology.
Strategic Management Journal, 21: 267-294.
Baum, J. A. C. & Shipilov, A. V. 2006. Ecological approaches to organizations. In
S. Clegg, C. Hardy, T. Lawrence & W. R. Nord (Eds.), Sage Handbook for
Organization Studies: 55-110. London & Thousand Oaks, CA: Sage.
Baum, J. A.C. & Rao, H. 2004. Evolutionary dynamics of organizational
populations and communities. In M. S. Poole, A. Van de Ven, et al (Eds.),
Studying organizational change and development: 212-258. Oxford & New York:
Oxford University Press.
Baum, J. A. C. & Ingram, P. 1998. Population-level learning in the Manhattan
hotel industry, 1898-1980. Management Science, 44: 996-1016.
Benjamin, B. A., Podolny, J. M. 1999. Status, quality, and social order in the
California wine industry. Administrative Science Quarterly. September. 44(3): 563-
589.
Biskind, P. 2005. Down and dirty pictures: Miramax, Sundance and the rise of
independent film. London: Bloomsbury.
Blau, P. M. 1977. Inequality and heterogeneity: a primitive theory of social structure.
New York: Free Press.
Bornstein, G., Erev, I. & Rosen, O. 1990. Intergroup competition as a structural
solution to social dilemmas. Social Behaviour, 5: 247 - 260.
Brewer, M. B. & Kramer, R. M. 1986. Choice behaviors in social dilemmas: effects
of social identity, group size and decision framing. Journal of Personality and Social
Psychology, 50: 543-549.
Brewer, M. B. & Brown, R. J. 1998. Intergroup relations. In D. T. Gilbert, S. T.
Fiske & G. Lindzey (Eds.), The handbook of social psychology: 479-501. New York:
Random House.
Burke, P. J. & Reitzes, D. C. 1981. The link between identity and role
performance. Social Psychology Quarterly, 44: 83-92.
Burke, P. J. & Stets, J. E. 1999. Trust and commitment through self-verification.
Social Psychology Quarterly, 62: 347-366.
110
Bush, P. D. 1987. The theory of institutional change. Journal of Economic Issues,
21(3): 1075-1116.
Calhoun, C. (Ed.) 1994. Social theory and the politics of identity. Cambridge, Mass.:
Blackwell.
Carley, K. 1991. A theory of group stability. American Sociological Review, 56: 331-
54.
Carroll, G. R. 1985. Concentration and specialization: Dynamics of niche width
in populations of organizations. American Journal of Sociology, 90: 1262-1283.
Carroll, G. R. 1987. Publish and perish: The organizational ecology of newspaper
industries. Greenwich, CT: JAI Press.
Carroll, G. R. & Hannan, M. T. 2000. The demography of corporations and industries.
Princeton, NJ: Princeton University Press.
Carroll, G.R. & Swaminathan, A. 2000. Why the microbrewery movement?
Organizational dynamics of resource partitioning in the US brewing industry.
American Journal of Sociology, 106: 715 - 762.
Carroll, W.K. & Ratner, R.S. 1996. Master framing and cross-movement
networking in contemporary social movements. Sociological Quarterly, 37: 601-
625.
Carter, R. & Manaster, S. 1990. Initial public offerings and underwriter
reputation. The Journal of Finance, 45(4): 1045-1067.
Castells, M. 1996, second edition, 2000. The Rise of the Network Society. Vol. I.
Cambridge, MA; Oxford, UK: Blackwell.
Caves, R. E. & Porter, M. E. 1977. From entry barriers to mobility barriers:
Conjectural decisions and contrived deterrence to new competition. The Quarterly
Journal of Economics, 91(2): 241-262.
Christensen, C. M. & Raynor, M. E. 2003. The Innovator’s Solution: Creating and
Sustaining Successful Growth. Cambridge, MA: Harvard Business School Press.
111
Christopherson, S. & Storper, M. 1989. The effects of flexible specialization on
industrial politics and the labor market: The motion picture industry. Industrial &
Labor Relations Review, 42(3): 331-348.
Clemens, E. 1997. The People’s Lobby. Chicago: University of Chicago Press.
Cohen, W. & Levinthal, D. 1990. Absorptive capacity: A new perspective on
learning and innovation. Administrative Science Quarterly, 35: 128-152.
Cole, R. E. 1999. Managing Quality Fads: How American Business Learned to Play the
Quality Game. Oxford, New York: Oxford University Press.
Coleman, J. 1988. Social capital and the creation of human capital. American
Journal of Sociology, 94, pp. 94-120.
Coleman, P. T. & Deutsch, M. 1995. The mediation of inter-ethnic conflict in
schools. In W. D. Hawley & A.W. Jackson (Eds.), Toward a common destiny:
Improving race and ethnic relations in America. San Francisco: Jossey-Bass.
Coleman, P. T. 2006. Power and Conflict. In M. Deutsch, P. T. Coleman, E. C.
Marcus (Eds.), The handbook of conflict resolution: Theory and practice: Chapter 5.
San Francisco: Jossey-Bass.
Contractor, N., Wasserman, S., & Faust, K. 2000. Testing multitheoretical,
multilevel hypothesis about organizational networks: An analytic framework
and empirical example. Academy of Management Review, 31, 681-703.
Conway, K. 2008. Small media, global media: Kino and the microcinema
movement. Journal of Film and Video, 60(3-4): 60-71.
Curtis, R. & Zurcher, L. 1973. "Stable resources of protest movements: The multi-
organizational field." Social Forces, 52: 53-61.
Cyert, R. M. & March, J. G. 1963. A Behavioral Theory of the Firm. Englewood
Cliffs, N. J.: Prentice-Hall.
Dacin, M. T., Goldstein, J. & Scott, W. R. 2002. Institutional theory and
institutional change: Introduction to the Special Research Forum. Academy of
Management Journal 45(1): 45-56.
112
Darr, E. D. & Kurtzburg, T. R. 2000. An investigation of partner similarity
dimensions on knowledge transfer. Organizational Behavior and Human Decision
Processes, 82: 28 - 44.
Darr, E. D., Argote, L. & Epple, D. 1995. The acquisition, transfer and
depreciation of knowledge in service organizations: Productivity in franchises.
Management Science, 41: 1750-1762.
Delacroix, J. & Rao, H. 1994. Externalities and ecological theory: Unbundling
density dependence. In J. A. C. Baum & J. V. Singh (Eds.), Evolutionary dynamics
of organizations: 255-68. New York: Oxford University Press.
Department of Labor. 2004. Bureau of Labor Statistics, Career Guide to Industries,
2004-05 Edition, Motion Picture and Video Industries. Washington, D.C.
Diani, M. 2003. Leaders or brokers? In M. Diani & D. McAdam (Eds.), Social
Movements and Networks: 105-22. Oxford & New York: Oxford University Press.
Diani, M. & McAdam, D. (Eds.), Social Movements and Networks. Oxford & New
York: Oxford University Press.
DiMaggio, P. 1988. Interest and agency in institutional theory. In L. G. Zucker
(Ed.), Institutional Patterns and Organizations: Culture and Environment.
Cambridge, MA: Ballinger Publishing.
DiMaggio, P. 1994. Culture and economy. In N. Smelser & R. Swedberg (Eds.),
Handbook of Economic Sociology: 27-57. Princeton and New York: Princeton
University Press and Russell Sage Foundation
DiMaggio, P. & Powell, W. W. 1983. The iron cage revisited: Institutional
isomorphism and collective rationality in organizational fields. American
Sociological Review, 48: 147-160.
DiMaggio, P. & Powell, W.W. 1991. The New Institutionalism in Organizational
Analysis. Chicago: University of Chicago Press.
Dobbin, F. 1994. Forging Industrial Policy: The Untied States, Britain, and France in
the Railway Age. New York: Cambridge University Press.
Dobrev, S., Kim, T-Y., & Hannan, M. 2001. Dynamics of niche width and resource
partitioning. American Journal of Sociology, 106:1299-1337.
113
Dobrev, S. & Kim, T-Y. 2006. Positioning among organizations in a population: A
model of mutualism and competition. Administrative Science Quarterly, 51: 230-
261.
Dyer, J. & Singh, H. 1998. The relational view: Cooperative strategy and sources
of interorganizational competitive strategy. Academy of Management Review, 23:
660-679
Eisenhardt, K. M. & Schoonhoven, C. B. 1996. Resource-based view of strategic
alliance formation: Strategic and social explanations in entrepreneurial firms.
Organization Science, 7(2): 136-150.
Ellemers, N., Spears, R. & Doosje, B. (Eds.). 1999. Social identity: Context,
commitment, content. Oxford: Blackwell.
Erickson, S. & Rothberg, H. 2005. From knowledge to intelligence: Creating
competitive advantage in the next economy. Boston: Butterworth-Heinemann
Elsevier.
Fernandez, R. & McAdam, D. 1988. Multiorganizational fields and recruitment
contexts. Sociological Forum, 3(3): 357-382.
Fernandez, R. M. & Weinberg, N. 1997. Sifting and sorting: Personal contacts and
hiring in a retail bank. American Sociological Review, 62: 883-902.
Fleming, L. 2001. Recombinant uncertainty in technological search. Management
Science, 47: 117-132.
Fombrun, C. & Shanley, M. 1990. What's in a name? Reputation building and
corporate strategy. The Academy of Management Journal, 33(2): 233-258.
Foster, A. D. & Rosenzweig, M. R. 1995. Learning by doing and learning from
others: Human capital and technical change in agriculture. Journal of Political
Economy, 103(6): 1176-1209.
Fourcade-Gourinchas, M. & Healy, K. 2007. Moral views of market society.
Annual Review of Sociology, 33: 285-311.
Frank, O., & Strauss, D. 1986. Markov graphs. Journal of the American Statistical
Association, 81: 832-842.
114
Freebase. 2009. http://www.freebase.com/view/en/overture_films. Accessed
4/30/09.
Freeman, J. H. & Lomi, A. 1994. Resource partitioning and the founding of
banking cooperatives in Italy. In J. Baum & J. Singh (Eds.) Evolutionary Dynamics
of Organizations: 269- 293. New York: Oxford University Press.
Freeman, J. H. & Audia, P. G. 2006. Community ecology and the sociology of
organizations. Annual Review of Sociology, 32: 145-169.
Friedman, D. & McAdam, D. 1992. Identity incentives and activism: Networks,.
choices and the life of a social movement. In C. Mueller & A. Morris (Eds.),
Frontiers in Social Movement Theory. New Haven: Yale University Press.
Galaskiewicz, J. 1976. Social Networks and Community Decision-Making: A Study of
Corporate Actors. Ph. D. Thesis, University of Chicago.
Gerlach, L. P. 2001. The structure of social movements: Environmental activism
and its opponents. In J. Arquilla & D. Ronfeldt (Eds.), Networks and netwars: The
future of terror, crime, and militancy: 300-320. Santa Monica, CA: RAND.
Giles, H. & Johnson, P. 1987. Ethnolinguistic identity theory: A social
psychological approach to language maintenance. International Journal of the
Sociology of Language, 68: 69-99.
Gould, R. V. 2002. The origins of status hierarchies: A formal theory and
empirical test. American Journal of Sociology, 107: 1143-1178.
Grabher, G. 2002. The project ecology of advertising: Tasks, talents and teams.
Regional Studies, 36(3): 245-262.
Granovetter, M. 1992. Problems of explanation in economic sociology. In N.
Nohria & R. G. Eccles (Eds.), Networks and Organizations. Boston, MA: Harvard
Business School Press.
Greve, H. R., Posner, J. & Rao, H. 2006. Vox populi: Resource partitioning,
organizational proliferation, and the cultural impact of the insurgent microradio
movement. American Journal of Sociology, 112: 802–837
Grossberg , L. 1996. Identity and cultural studies - is that all there is? In S. Hall &
P. Du Gay (Eds.), Questions of cultural identity: 87-107. New York: Sage.
115
Gulati, R. 1998. Alliances and networks. Strategic Management Journal, 19: 293-317.
Gulati, R. 1999. Network location and learning: The influence of network
resources and firm capabilities on alliance formation. Strategic Management
Journal, 20(5): 397–420.
Gulati, R. & Higgins, M. C. 2003. Which ties matter when? The contingent effects
of interorganizational partnerships on IPO success. Strategic Management Journal,
24: 127–144.
Gulati, R., Nohria, N. & Zaheer, A. 2000. Strategic Networks. Strategic
Management Journal, 21(3): 203-215, Special Issue: Strategic Networks.
Hagedoorn, J. 1993. Understanding the rationale of strategic technology
partnering: International modes of cooperation and sectoral differences. Strategic
Management Journal, 14: 371-383.
Hall, P. 2009. The History of Independent Cinema. Albany, GA: BearManor Media.
Hall, R. 1992. The strategic analysis of intangible resources. Strategic Management
Journal, 13: 135-144.
Hall, R. 1993. A framework linking intangible resources and capabilities to
sustainable competitive advantage. Strategic Management Journal, 14: 607-618.
Hannan, M. T. & Freeman, J. 1977. The population ecology of organizations.
American Journal of Sociology, 82(5): 929-964.
Hannan, M. T. & Freeman, J. 1984. Structural Inertia and Organizational Change.
American Sociological Review, 49: 149-164.
Hannan, M. T. & Freeman, J. 1987. The ecology of organizational founding:
American labor unions, 1836-1985. The American Journal of Sociology, 92: 910-943.
Hannan, M. T. & Freeman, J. 1989. Organizational Ecology. Cambridge, MA:
Harvard University Press.
Hannan, M. T., Pólos, L. & Carroll, G. R. 2007. Logics of Organization Theory:
Audiences, Codes, and Ecologies. Princeton, NJ: Princeton University Press
116
Haunschild, P. R. & Sullivan, B. 2002. Learning from complexity: Effects of
airline accident/incident heterogeneity on subsequent accident/incident rates.
Administrative Science Quarterly, 47: 609-643.
Haveman, H. A., & Nonemaker, L. 2000. Competition in multiple geographic
markets: The impact on growth and market entry. Administrative Science
Quarterly, 45: 232–267.
Hawley, A. H. 1950. Human ecology: A theory of community structure. New York:
Ronald Press.
Hawley, A. H. 1986. Human ecology: A theoretical essay. Chicago: University of
Chicago Press.
Hedstrom, P. 2005. Dissecting the Social: On the Principles of Analytical Sociology.
Cambridge: Cambridge University Press.
Henderson, R. M. & Clark, K. B. 1990. Architectural innovation: The
reconfiguration of existing product technologies and the failure of established
firms. Administrative Science Quarterly, 35(1): 9-30.
Henderson, R. & Cockburn, I. 1996. Scale, scope, and spillovers: Determinants of
research productivity in the pharmaceutical industry. RAND Journal of Economics,
27(1): 32-59.
Hendricks, G. 1966. The Kinetoscope: America's first commercially successful motion
picture exhibitor. New York: Theodore Gaus' Sons.
Hogg, M.A. & Abrams, D. 1993. Towards a single process uncertainty reduction
model of social motivation in groups. In M.A. Hogg, D. Abrams (Eds.), Group
motivation: Social psychological perspectives. Englewood Cliffs, NJ: Prentice Hall.
Hogg, M. A. & Terry, D. J. 2000. Social identity and self-categorization processes
in organizational contexts. The Academy of Management Review, 25(1): 121-140.
Hoskisson, R.E., Hitt, M.A., Wan, W.P. & Yiu, D. 1999. Theory and research in
strategic management: Swings of a pendulum. Journal of Management, 25(3): 417-
46.
Hsu, G. 2006a. Jacks of all trades and masters of none: Audiences’ reactions to
spanning genres in feature film production. Administrative Science Quarterly 51:
420-450.
117
Hsu, G. 2006b. Evaluative schemas and the attention of critics in the film
industry. Industrial and Corporate Change, 15: 467-496.
Hsu, G. & Hannan, M. T. 2005. Identities, genres, and organizational forms.
Organization Science, 16(5): 474-90.
Ingram, P. 2002. Interorganizational learning. In J. A.C. Baum (Ed.), Companion to
organizations: 642-663. New York: Blackwell.
Ingram, P. & Baum, J. A.C. 1997. Opportunity and constraint: Organizations'
learning from the operating and competitive experience of industries. Strategic
Management Journal, 18: 75-98, (Summer Special Edition).
Ingram, P. & Inman, C. 1996. Institutions, intergroup competition, and the
evolution of hotel populations around Niagara Falls. Administrative Science
Quarterly, 41(4): 629-658.
Ingram, P. & McEvily, B. 2007. Sharper in relief. Opposition, identity and the
maintenance of social movement organizations. Working Paper, Columbia
Business School.
Ingram, P. & Rao, H. 2004. Store wars: The enactment and repeal of anti-chain-
store legislation in America. American Journal of Sociology. 110(2): 446–87.
Ingram, P. & Roberts, P. W. 2000. Friendships among Competitors in the Sydney
hotel industry. American Journal of Sociology, 106: 387-423.
Ingram, P. & Simons, T. 2000. State formation, ideological competition, and the
ecology of Israeli workers' cooperatives, 1920-1992. Administrative Science
Quarterly, 45: 25-53.
Ingram, Paul & Yue, L. Q. 2008. Structure, affect and identity as bases of
organizational competition and cooperation. Academy of Management Annals, 2:
275-303.
Irwin, D. A. & Klenow, P. J. 1994. High tech R&D subsidies: Estimating the
effects of Sematech. NBER Working Papers 4974, National Bureau of Economic
Research, Inc.
118
Jain, B. A. & Kini, O. 1995. Venture capitalist participation and the post-issue
operating performance of IPO firms. Managerial and Decision Economics, 16: 593—
606.
Johnston, H., Laraña, E. & Gusfield, J. R. 1994. Identities, grievances, and new
social movements. In E. Laraña, H. Johnston, & J. R. Gusfield (Eds.), New social
movements: From ideology to identity: 3-35. Philadelphia: Temple University Press.
Katz, M. & Shapiro, C. 1985. Network externalities, competition and
compatibility. American Economic Review, 75(3): 424-440.
Keeble, D. & Wilkinson, F. 2000. High-technology clusters, networking and collective
learning in Europe. Aldershot, Hampshire, U. K.: Ashgate Publishing.
Kim, D. J. & Kogut, B. 1996. Technological platforms & diversification.
Organization Science, 7: 283–301
Klein, B. & Leffler, K. B. 1981. The role of market forces in assuring contractual
performance. Journal of Political Economy, 89(4): 615-41.
Lampel, J. & Shamsie, J. 2000. Critical push: Sources of strategic momentum in
the motion picture industry. Journal of Management, 26(2): 233-257.
Lampel, J. & Shamsie, J. 2003. Capabilities in motion: New organizational forms
and the reshaping of the Hollywood movie industry. Journal of Management
Studies, 40(8): 2189-2210.
Larson, M. S. 1977. The rise of professionalism: A sociological analysis, Berkeley, CA:
University of California Press.
Laumann, E. O., Galaskiewicz, J. & Marsden, P. V. 1978. Community structure as
interorganizational linkages. Annual Review of Sociology, 4: 455-84. New York:
Annual Reviews, Inc.
Laumann, E. O. & Knoke, D. 1987. The organizational state: A perspective on national
energy and health domains. Madison: University of Wisconsin Press.
Lawson, C. & Lorenz, E. 1999. Collective learning, tacit knowledge and regional
innovative capacity. Regional Studies, 33(4): 305-317.
Levinthal, D. A., March, J. G. 1993. The myopia of learning. Strategic Management
Journal, 14: 95-112.
119
Levitt, B. & March, J. G. 1988. Organizational learning. Annual Review of Sociology,
14: 319-338.
Lichterman, P. 1996. The search for political community: American activists
reinventing commitment. Cambridge: Cambridge University Press.
Lomi, A. 1995. The population ecology of organizational founding: Location
dependence and unobserved heterogeneity. Administrative Science Quarterly, 40:
111-144.
Lomi, A., Larsen, E. R. & Freeman, J.H. 2005. Things change: Dynamic resource
constraints and system-dependent selection in the evolution of populations of
organizations. Management Science, 51: 882-903.
Lounsbury, M. & Ventresca, M. 2003. The new structuralism in organizational
theory. Organization, 10(3): 457-480.
Lowe, P., & Goyder, J. 1983. Environmental groups in British politics. London: Allen
and Unwin.
Lundvall, B-Å. 1992. National systems of innovation: Towards a theory of innovation
and interactive learning. London: Pinter Publishers
Lundvall, B-Å. & Johnson, B. 1994. The learning economy. Journal of Industry
Studies, 1(2): 23-42.
Madhok, A. 1995. Revisiting multinational firms' tolerance for joint ventures: A
trust-based approach. Journal of International Business Studies, 26: 117–137.
Madhok, A. & Tallman, S. B. 1998. Resources, transactions and rents: Managing
value through interfirm collaborative relationships. Organization Science, 9(3):
326-339.
Malmberg, A. & Maskell, P. 2006. Localized learning revisited. Growth and
Change, 37(1): 1-18.
Maltby, R. 2003. Hollywood cinema. Oxford: Blackwell Publishing Co.
McAdam, D. 1982. The decline of the civil rights movement. In J. Freeman (Ed.),
Social movements of the sixties and seventies: 279-319. New York: Longman.
120
McAdam, D. 2003. Eehh, what’s up (with) DOC? Mobilization, 5: 126-134.
McAdam, D., McCarthy, J. & Zald, M. 1996. Opportunities, mobilizing structures,
and framing processes: Toward a synthetic, comparative perspective on social
movements. In D. McAdam, J. McCarthy & M. Zald (Eds.), Comparative
perspectives on social movements: 1-20. New York: Cambridge University Press.
McAdam, D. & Sewell, W. Jr. 2001. Temporality in the study of social movements
and revolutions. In R. Aminzade, et al. (Eds.), Silence and voice in the study of
contentious politics: 89-125. New York and London: Cambridge University Press.
McCarthy, J. D. & Wolfson, M. 1992. Consensus movements, conflict movements
and the co-optation of civic and state infrastructures. In A. D. Morris, C. M.
Mueller (Eds.), Frontiers in social movement theory: 273–297. New Haven, CT: Yale
University Press.
McDonald, P. & Wasko, J. (Editors) 2008. The contemporary Hollywood film
industry. New York: Wiley-Blackwell.
McKendrick, D. G., Jaffe, J., Carroll, G. R., Khessina, O. M. 2003. In the bud? Disk
array producers as a (possibly) emergent organizational form. Administrative
Science Quarterly, 48: 60-83.
McPherson, J. M. 1983. An ecology of affiliation. American Sociological Review, 48:
519-532.
McPherson, J. M. 2004. A Blau space primer: Prolegomenon to an ecology of
affiliation. Industrial and Corporate Change, 13(1): 263-280.
McPherson, J. M., Smith-Lovin, L. & Cook, J. 2001. Birds of a feather: homophily
in social networks. Annual Review of Sociology, 27: 415-444.
Melucci, A. 1989. Nomads of the present: Social movements and individual needs in
contemporary society. London: Century Hutchinson.
Melucci, A. 1996. Challenging codes: Collective action in the information age.
Cambridge: Cambridge University Press.
Merritt, G. 2001. Celluloid mavericks: A history of American independent film. New
York: Thunder's Mouth Press.
121
Meyer, J. W. & Rowan, B. 1977. Institutionalized organizations: Formal structure
as myth and ceremony. American Journal of Sociology, 83: 333-63.
Meyer, J. W. & Scott, W. R. 1983. Organizational environments: Ritual and
rationality. Beverly Hills: Sage.
Miner, A. S. & Haunschild, P. R. 1995. Population level learning. In L. L.
Cummings & B. M. Staw, (Eds.), Research in organizational behavior: 115-166.
Greenwich, CT: JAI Press.
Miner, A. S., Amburgey, T. L. & Stearns, T. 1990. Interorganizational linkages
and population dynamics: Buffering and transformational shields. Administrative
Science Quarterly, 35: 689-713.
Minkoff, D. C. 1997. The sequencing of social movements. American Sociological
Review, 62(5): 779-799.
Monge, P. R. & Contractor, N. S. 2003. Theories of communication networks. New
York: Oxford University Press.
Montgomery, J. 1998. Toward a role-theoretic conception of embeddedness.
American Journal of Sociology, 104: 92-125.
Moreno, J. L., & Jennings, H. H. 1938. Statistics of social configurations.
Sociometry, 1: 342-374.
Mowery, D. C., Oxley, J. E. & Silverman, B. S. 1996. Strategic alliances and
interfirm knowledge transfer. Strategic Management Journal, 17 (Winter Special
Issue): 77-92.
Nahapiet, J. & Ghoshal, S. 1998. Social capital, intellectual capital, and the
organizational advantage. Academy of Management Review, 23(2): 242-266.
Nelson, R. R. & Winter, S. 1982. An evolutionary theory of economic change. London:
The Belknap Press of Harvard University.
Newman, M. E. J. & Girvan, M. 2004. Finding and evaluating community
structure in networks. Physical Review E, 69(2): 026113+.
Nohria, N. & Garcia-Pont, C. 1991. Global strategic linkages and industry
structure, Strategic Management Journal, 12 (Summer Special Issue): 105-124.
122
North, D. 1990. Institutions, institutional change and economic performance.
Cambridge University Press.
Oakes, P. J., Haslam, A., Turner, J. C. 1994. Stereotyping and social reality. Oxford:
Blackwell.
Oliver, P. E. & Myers, D. J. 2003. The coevolution of social movements.
Mobilization, 8(1): 1-24
Osa, M. 2003. Solidarity and contention: Networks of Polish opposition. Minneapolis,
MN: University of Minnesota Press.
Owen-Smith, J. & Powell, W. W. 2004. Knowledge networks as channels and
conduits: The effects of spillovers in the Boston biotechnology community.
Organization Science, 15(1): 5-21.
Park, D. Y. & Podolny, J. M. 2000. The competitive dynamics of status and niche
width: U. S. investment banking, 1920-1949. Industrial and Corporate Change, 9(3):
377-414.
Pattison, P. E., & Wasserman, S. 1999. Logit models and logistic regressions for
social networks, II. Multivariate relations. British Journal of Mathematical and
Statistical Psychology, 52: 169-194.
Peteraf, M. A. 1993. The cornerstones of competitive advantage: A resource-
based view. Strategic Management Journal; 14(3): 179–191.
Peteraf, M. A. & Shanley, M. 1997. Getting to know you: A theory of strategic
group identity. Strategic Management Journal, 18: 165-186.
Phillips, D. J. & Zuckerman, E. W. 2001. Middle status conformity: Theoretical
restatement and empirical demonstration in two markets. American Journal of
Sociology, 107: 379-429.
Podolny, J. M. 1993. A status-based model of market competition. American
Journal of Sociology, 98: 829–872.
Podolny, J. M. 1994. Market uncertainty and the social character of economic
exchange. Administrative Science Quarterly, 39(3): 458-483.
Podolny, J. M. 2001. Networks as the pipes and prisms of the market. American
Journal of Sociology, 107(1): 33-60.
123
Podolny, J. M. 2003. A picture is worth a thousand symbols: A sociologist’s view
of the economic pursuit of truth. American Economic Review, 93(2): 169-174.
Podolny, J. M. & Baron, J. N. 1997. Resources and relationships: Social networks
and mobility in the workplace. American Sociological Review, 62: 673–693.
Podolny, J. M. & Phillips, D. J. 1996. The dynamics of organizational status.
Industrial and Corporate Change, 5(2): 453-471.
Podolny, J. M., & Stuart, T. 1995. A role-based ecology of technological change.
American Journal of Sociology, 100(5): 1224-1260.
Podolny, J. M., Stuart, T. E & Hannan, M. T. 1996. Networks, knowledge, and
niches. A sociological examination of worldwide competition in the
semiconductor industry. American Journal of Sociology, 102: 659-689.
Polletta, F. & Jasper, J. 2001. Collective identity in social movements.
Annual Review of Sociology, 27: 283-305.
Pólos, L., Hannan, M. T. & Carroll, G. R. 2002. Foundations of a theory of social
forms. Industrial and Corporate Change, 11: 85-115.
Porac, J. F., Thomas, H., Wilson, F., Paton, D. & Kanfer, A. 1995. Rivalry and the
industry model of Scottish knitwear producers. Administrative Science Quarterly,
40(2): 203-227.
Porter, M. E. 1980. Competitive strategy, New York: Free Press.
Porter, M. E. 1985. Competitive advantage, New York: Free Press.
Powell, W. W. & DiMaggio, P. J. 1991. The new institutionalism in organizational
analysis. Chicago, IL: University of Chicago Press
Powell, W. W. & Smith-Doerr, L. 1994. Networks and economic life. In N. J.
Smelser & R. Swedberg (Eds.), The handbook of economic sociology: 368-402.,
Princeton, NJ: Princeton University Press.
Powell, W. W., Koput, K. W. & Smith-Doerr, L. 1996 . Interorganizational
collaboration and the locus of innovation: Networks of learning in
biotechnology. Administrative Science Quarterly, 41: 116-145.
124
Powell, W.W., White, D. R., Koput, K. W. & Owen-Smith, J. 2005. Network
dynamics and field evolution: the growth of interorganizational collaboration in
the life sciences. American Journal of Sociology, 110: 1132–205
Pozner, J. & Rao, H. 2006. Fighting a common foe: Enmity, identity and
cooperative strategy. In J. A.C. Baum, D. Stanislav & A. van Witteloostuijn,
(Eds.), Ecology and Strategy. Advances in strategic management, Volume 23.
Rao, H. 1994. The social construction of reputation: Contests, credentialing and
legitimation in the American automobile industry; 1895-1912. Strategic
Management Journal, 15: 29-44.
Rao, H. 1998. Caveat emptor: The construction of non-profit watchdog
organizations. American Journal of Sociology, 103: 912-961.
Rao, H. 2002. Tests tell: Constitutive legitimacy and consumer acceptance in the
American automobile industry; 1985-1912. In P. Ingram & B. Silverman (Eds.),
The new institutionalism in strategic management: 307-339. Stamford, CT: JAI Press.
Rao, H., Davis, G. M. & Ward, A. 2000. Embeddedness and social identity: Why
organizations leave Nasdaq and join NYSE? Administrative Science Quarterly, 45:
268-292.
Rao, H., Monin, P., Durand, R. 2003. Institutional change in Toque Ville:
Nouvelle cuisine as an identity movement in French gastronomy. American
Journal of Sociology, 108: 795-843.
Rao, H., Morrill, C. & Zald, M. 2000. Power plays: How social movements and
collective action create new organizational forms. In B. Staw and L. Cummings
(Eds.), Research in organizational behavior, 22: 237-281.
Rao, H. & Giorgi, S. 2006. Code breaking: How entrepreneurs exploit cultural
logics to generate institutional change. In B. Staw and L. Cummings (Eds.),
Research in organizational behavior, 27: 269-304.
Ridgeway, C. L. 1991. The social construction of status value: Gender and other
nominal characteristics. Social Forces, 70: 367–386.
Rindova, V. & Fombrun, C. 2000. The growth of the specialty coffee niche in the
U.S. coffee industry. In K. Bird- Schoonhoven, & E. Romanelli (Eds.), The
entrepreneurship dynamic. Stanford, CA: Stanford University Press.
125
Robins, G., Snijders, T., Wang, P., Handcock, M. & Pattison, P. 2007. Recent
developments in exponential random graph (p*) models for social networks.
Social Networks, 29(2): 192-215.
Robins, G. L., Pattison, P. E. & Wasserman, S. 1999. Logit models and logistic
regressions for social networks, III. Valued relations. Psychometrika, 64: 371-394.
Rosenkopf, A. & Nerkar, L. 2001. Beyond local search: Boundary spanning,
exploration and impact in the optical disc industry. Strategic Management Journal,
22(3): 287-306.
Rosenthal, N., Fingrutd, M., Ethier, M., Karant, R. & McDonald, D. 1985. Social
movements and network analysis: A case study of nineteenth-century women's
reform in New York state. The American Journal of Sociology, 90(5): 1022-1054
Ruef, M. 2000. The emergence of organizational forms: A community ecology
approach, American Journal of Sociology, 106(3): 658-714.
Ruef, M. 2004. The demise of an organizational form: Emancipation and
plantation agriculture in the American South, 1860 – 1880. American Journal of
Sociology, 109, 1365-1410.
Rusco, F. W. & Walls, W. D. 2004. Independent film finance, pre-sale agreements,
and the distribution of film earnings. In V. Ginsburgh (Ed.), The economics of art
and culture: Chapter 2. Contributions to Economic Analysis No. 260, Amsterdam:
Elsevier Science.
Salancik, G. R. 1995. Wanted: A good network theory of organization.
Administrative Science Quarterly, 40, 345-349
Saxenian, A. L. 1994. Regional advantage: Culture and competition in Silicon Valley
and Route 128. Cambridge, MA: Harvard University Press.
Schilling, M. & Phelps, C. 2007. Interfirm collaboration networks and knowledge
creation: The impact of large scale network structure on firm innovation.
Management Science, 53: 1113-1126.
Scott, W. R. 1995. Institutions and organizations. Thousand Oaks, CA: Sage.
Scott, A. J. 2002. A new map of Hollywood: The production and distribution of
American motion pictures. Regional Studies, 36: 957-975.
126
Shapiro, S. P. 1987. The social control of impersonal trust. American Journal of
Sociology, 93(3): 623-58.
Simons, T. & Ingram, P. 2003. Enemies of the state: Interdependence between
institutional forms and the ecology of the kibbutz, 1910-1997. Administrative
Science Quarterly, 44: 592-596.
Sine, W. & Lee, B. 2005. Institutional geography and the emergence of new
economic sectors: Antecedents of entrepreneurial activity in the emerging wind
power industry. Presented at the annual meeting of the American Sociological
Association, Philadelphia, PA.
Smith, E. R., & Henry, S. 1996. An in-group becomes part of the self: Response
time evidence. Personality and Social Psychology Bulletin, 22, 635-642.
Snijders, T. A. B., Pattison, P. E., Robins, G. L, & Handcock, M. S. 2006. New
specifications for exponential random graph models. Sociological Methodology, 36:
99-153.
Snow, D. & Oliver, P. 1995. Social Movements and Collective Behavior. In K.
Cook, G. Fine & J. House (Eds.), Sociological perspectives on social psychology: 571-
600. Boston: Allyn and Bacon.
Sobel, R. 1974. The entrepreneurs: Explorations within the American business tradition.
New York: Weybright & Talley.
Sorensen, J. B. 2004. Recruitment-based competition between industries: A
community ecology. Industrial and Corporate Change, 13: 149-170.
Sorensen, J. B. & Stuart, T. E. 2000. Aging, obsolescence, and organizational
innovation. Administrative Science Quarterly, 45: 81-112.
Sorenson, O. 2003. Interdependence and adaptability: Organizational learning
and the long-term effect of integration. Management Science, 49: 446-463.
Sorenson, O., McEvily, S., Ren, C. R. & Roy, R. 2006. Niche width revisited:
organizational scope, behavior and performance. Strategic Management Journal,
27(10): 915–936.
Sperling, C. W., Milner, C. & Warner, J. Jr. 1998. Hollywood be thy name: The
Warner Brothers story. Lexington: University Press of Kentucky.
127
Standard & Poor's. 2006. Movie and entertainment industry survey. New York:
Standard & Poor's Equity Research Services.
Stinchcombe, A. 1965. Social structure and organizations. In J. G. March (Ed.),
Handbook of Organizations: 142-193. Chicago: Rand McNally & Co.
Stuart, T. E. 1998. Network positions and propensities to collaborate: An
investigation of strategic alliance formation in a high-technology industry.
Administrative Science Quarterly, 43(3): 668-698.
Stuart, T. E. 2000. Interorganizational alliances and the performance of firms: A
study of growth and innovation rates in a high-technology industry. Strategic
Management Journal, 21: 791–811.
Stuart, T. E., & Podolny, J. M. 1996. Local search and the evolution of
technological capabilities: Evolutionary perspectives on strategy. Strategic
Management Journal, 17: 21-38.
Stuart, T. E., & Podolny, J. M. 1999. Positional consequences of strategic alliances
in the semiconductor industry. In S. Andrews & D. Knoke (Eds.), Research in the
sociology of organizations, Vol. 16: 161-182. Greenwich, Conn.: JAI Press, Inc.
Stuart, T. E., Hoang, H. & Hybels, R. C. 1999. Interorganizational endorsements
and the performance of entrepreneurial ventures. Administrative Science
Quarterly, 44: 315-349.
Suchman, M. C. 1995. Managing legitimacy: Strategic and institutional
approaches. Academy of Management Journal, 20(3): 571-610.
Suddaby, R. & Greenwood, R. 2005. Rhetorical strategies of legitimacy.
Administrative Science Quarterly, 50(1): 35-67.
Swaminathan, A. & Wade, J. B. 2001. Social movement theory and the. evolution
of new organizational forms. In C.B. Schoonhoven & E. Romanelli (Eds.), The
entrepreneurship dynamic in population evolution: 286-313. Stanford, CA: Stanford
University Press.
Tajfel, H. 1982). Social psychology of intergroup relations. Annual Review of
Psychology, 33: 1-39.
128
Tajfel, H. & Turner, J. C. 1979. An integrative theory of intergroup conflict. In W.
G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations: 33-47.
Monterey, CA: Brooks-Cole .
Tajfel, H. & Turner, J. C. 1986. The social identity theory of intergroup behavior.
In S. Worchel & W. G. Austin (Eds.), Psychology of intergroup relations: 7-24.
Chicago: Nelson Hall.
Teske, Nathan 1997. Political activists in America: The identity construction model of
political participation. New York: Cambridge University Press.
Thomson, D. 2006. The whole equation: A history of Hollywood. New York: Vintage.
Thornhill, S. & Amit, R. 2003. Learning about failure: Bankruptcy, firm age and
the resource-based view. Organization Science, 14(5): 497-509.
Tilly, C. & Wood, L. 2003. Contentious connections in Great Britain, 1828-1834. In
M. Diani & D. McAdam (Eds.), Social movements and networks: Relational
approaches to collective action. New York: Oxford University Press
Turner, J. C. 1985. Social categorization and the self-concept: A social cognitive
theory of group behaviour. In E. J. Lawler (Ed.), Advances in Group Processes, vol.
2: 77-122. Greenwich, CT: JAI press.
Turner, J. C. 1991. Social influence. Pacific Grove, CA: Brooks/Cole.
Turner, J. C., Hogg, M. A., Oakes, P. J., Reicher, S. D. & Wetherell, M. S. 1987.
Rediscovering the social group: A self-categorization theory. Oxford ; New York :
Blackwell.
Turner, J. C., Oakes, P. J., Haslam, S. A. & McGarty, C. 1994. Self and collective:
cognition and social context. Personality and Social Psychology Bulletin, 20(5): 454-
463.
Tushman, M. L. & Anderson, P. 1986. Technological discontinuities and
organizational environments. Administrative Science Quarterly, 31: 439-65.
Uzzi, B. 1997. Social structure and competition in interfirm networks: The
paradox of embeddedness. Administrative Science Quarterly, 42: 35-67.
Uzzi, B. & Spiro, J. 2005. Collaboration and creativity: The small world problem.
American Journal of Sociology, 111: 447-504.
129
Wang, P., Robins, G. & Pattison, P. 2009. PNet program for the simulation and
estimation of p* exponential random graph models: User manual. Melbourne:
Department of Psychology, University of Melbourne.
Warner, J. L. 1970. My first hundred years in Hollywood. New York: Random
House.
Washington, M. & Zajac, E. J. 2005. Status evolution and competition: Theory
and evidence. Academy of Management Journal, 48: 282-296.
Wasserman, S., & Pattison, P. E. 1996. Logit models and logistic regressions for
social networks: I. An introduction to Markov graphs and p*. Psychometrika, 61:
401-425.
Weber, M. 1968. Economy and society. G. Roth & C. Wittich (Eds.). New York:
Bedminister Press.
Weber, K., Heinze, K. & DeSoucey, M. 2008. Forage for thought: Mobilizing
codes in the movement for grass-fed meat and dairy products. Administrative
Science Quarterly, 53(3): 529-567.
White, H. C. 1981. Production markets as induced role structures. Sociological
Methodology, 12: 1-57.
Yelle, L. E. 1979. The learning curve: Historical review and comprehensive
survey. Decision Sciences, 10(2): 302–328.
Zald, M. & McCarthy, J. D. 1987. Social movements in an organizational society:
Collected essays. New Brunswick, CT: Transaction Books.
Zuckerman, E. W. 1999. The categorical imperative: Securities analysts and the
illegitimacy discount. American Journal of Sociology, 104(5): 1398–1438.
Zuckerman, E. W. & Kim, T-Y. 2003. The critical trade-off: Identity assignment
and box-office success in the feature film industry. Industrial and Corporate
Change, 12: 27-67.
130
Appendix A:
Figure A-1
Ideal Types of Community Structure
Above: Degree-based Hierarchical Structure
Above: Triangulated Polycentric Structure
131
Appendix B:
Table B-1
Film Production Companies in 1985
1 20th Century Fox
2 A&M Films
3 AAR Films
4 Amblin Entertainment
5 American Broadcasting Company (ABC)
6 Aspen Film Society
7 Avenging Venture
8 Blackhawk Productions
9 Bountiful Film Partners
10 Cannon Films
11 Cannon Group
12 Cannon Pictures
13 CBS Entertainment Production
14 Channel
15 Cinema 84
16 Columbia Pictures Corporation
17 Crystalite Productions
18 David Foster Productions
19 De Laurentiis Entertainment Group (DEG)
20 Debra Hill Productions
21 Delphi III Productions
22 Delphi IV Productions
23 Desert Hearts Productions
24 Dino De Laurentiis Company
25 Double Play
26 Elsboy Entertainment
27 Embassy Film Associates
28 Embassy Home Entertainment
29 Embassy Pictures Corporation
30 Empire Pictures
31 Engelberg-Sumner-Cheikes
32 Entertainent Events
33 Famous Films N.V.
34 Fifteen
35 Film Development Fund
36 FilmDallas Pictures
37 Fogbound Inc.
38 Fogwood Films Ltd.
39 Fox Films Ltd.
40 Geffen Pictures
41 Girls
42 Guber-Peters Company, The
43 Hemdale Film Corporation
44 Home Box Office (HBO)
45 Industrial Light & Magic (ILM)
132
46 iPictures
47 Kings Road Entertainment
48 Lantana
49 M Square
50 Marty Katz Productions
51 Metro-Goldwyn-Mayer (MGM)
52 Mirage Entertainment
53 Monument Pictures
54 National Broadcasting Company (NBC)
55 NBC Productions
56 New Century Productions
57 New Visions Pictures
58 New World Pictures
59 Night Life Inc.
60 Northbrook Films
61 Orion Pictures Corporation
62 Paramount Pictures
63 Pendragon Film
64 Pfeiffer/Blocker Production
65 Pluperfect
66 Prize Productions
67 Rastar Films
68 Rastar Pictures
69 Re-Animator Productions Inc.
70 Republic Entertainment International
71 Samuel Goldwyn Company, The
72 Silver Pictures
73 Silver Screen Partners
74 Silver Screen Partners II
75 SLM Production Group
76 Stone Group Pictures
77 Summa Entertainment Group
78 Tambarle
79 The Malpaso Company
80 Touchstone Pictures
81 TriStar Pictures
82 Turman-Foster Company
83 Vivid Entertainment
84 Walt Disney Pictures
85 Warner Bros. Pictures
86 WW Production
87 Zoetrope Studios
133
Appendix C:
Table C-1
Film Production Companies in 2005
1 20th Century Fox
2 21 Laps Entertainment
3 A&E
4 A&E Indiefilms
5 Alberta Film
Entertainment
6 Alcon Entertainment
7 Alliance Atlantis
Communications
8 Alloy Entertainment
9 Aloha Pictures
10 Alter Ego
Entertainment
11 Amblin Entertainment
12 Ambush Entertainment
13 American Empirical
Pictures
14 American Film
Foundation
15 Anarchos Productions
Inc.
16 Andrew Lauren
Productions (ALP)
17 Antidote Films
18 Apatow Productions
19 Appaloosa Pictures
20 ARTE
21 Autonomy Inc.
22 Avenue Pictures
Productions
23 Avery Pix
24 Barry Mendel
Productions
25 Bay Films
26 Beacon Pictures
27 Bedford Falls
Productions
28 Bee Season Productions
Inc.
29 Belladonna
Productions
30 BenderSpink
31 Bergman Lustig
Productions
32 BET Pictures
33 Big Beach Films
34 Big Beach Productions
35 Big Screen
Entertainment Group
36 Biscayne Pictures
37 Black Gold Films
38 Black Water Films
39 Blue Sky Studios
40 Blue Yonder Films
41 Bob Yari Productions
42 Bona Fide Productions
43 Branded
Entertainment/Batfilm
Productions
44 Bravo Cable
45 Brick Dust Productions
LLC
46 Bridget Johnson Films
47 BronWa Pictures
48 Buena Vista Pictures
49 Bull's Eye
Entertainment
50 Cactus Three
51 Castle Rock
Entertainment
52 Cataland Films
53 Catch 23 Entertainment
54 Catfish Productions
55 Cent Productions Inc.
56 Charlie Guidance
57 Charlotte Street Films
58 Cherry Road Films
59 Cho Taussig
Productions Inc.
60 Cinema-Go-Go
61 Cineville
62 Class 5 Films
63 Coach Carter
64 Coalition Films
65 Code Entertainment
66 Collision
Entertainment
67 Colossal Pictures
68 Columbia Pictures
Corporation
69 Comedy Central Films
70 Complex Corporation
71 Coquette Productions
72 Court TV
73 Craven-Maddalena
Films
74 Creep Entertainment
International
75 Cruise/Wagner
Productions
76 Crunk Pictures
77 Cube Vision
78 Cyan Pictures
79 Damage Control
Productions
80 Daniel Bobker
Productions
81 Dark Horse
Entertainment
82 David LaChapelle
Studios
83 DC Comics
84 Deep River
Productions
85 DEJ Productions
86 Departure
Entertainment
87 Departure Studios
88 Desperate Pictures
89 Destination Films
90 Detour Filmproduction
91 Deuce Three
Productions
92 Devil's Rejects Inc.
93 Di Bonaventura
Pictures
94 Diary of a Woman Inc.
95 Diary of a Woman
Productions Inc.
96 Dimension Films
97 DiNovi Pictures
98 Discovery Docs
99 DisneyToon Studios
100 Displaced Films
134
101 Dog Pond
Productions
102 Donners' Company
103 Double Down
Entertainment
104 Double Edge
Entertainment Inc.
105 Double Feature Films
106 DreamWorks
Productions LLC
107 DreamWorks SKG
108 Duplass Brothers
Productions
109 Dusted Productions
110 Earthship Productions
111 Easy Mañana
112 EAT Films LLC
113 ECR Productions
114 Eden Rock Media
115 El Camino Pictures
116 Element Films
117 Elevation Filmworks
118 Endgame
Entertainment
119 Entache
Entertainment
120 entitled entertainment
121 Epoch Films
122 Escape Artists
123 ESPN
124 Every Tribe
Entertainment
125 Everyman Pictures
126 Fader Films
127 Fairway Films Ltd.
128 Film Threat DVD
129 Firm Films
130 Flan de Coco Films
131 Flavor Unit
Entertainment
132 Flux Films
133 Focus Features
134 Forensic Films
135 Fortis Films
136 Fox 2000 Pictures
137 Fox Searchlab
138 Fox Searchlight
Pictures
139 Funny Boy Films
140 Furst Films
141 Galen Films
142 Gaylord Films
143 Geller/Goldfine
Productions
144 Gerber Pictures
145 Geyer Kosinski
146 Ghost House Pictures
147 Gladiator Pictures
148 Glass Eye Pix
149 Go Fish Pictures
150 Gold Circle Films
151 Good Machine
152 Gorilla Factory
Productions
153 Got Films
154 Green Room Pictures
155 GreeneStreet Films
156 GreenHouse Pictures
157 Guide Company
Films Inc.
158 Halestorm
Entertainment
159 Hammer & Tongs
160 Happy Madison
Productions
161 Haven Films Inc.
162 HBO Documentary
Films
163 HBO Films
164 HBO/Cinemax
Documentary
165 HDNet Films
166 Hey Jude Productions
167 Hibiscus Films
168 High Line
Productions
169 High Tide
Entertainment
170 Hock Films Inc.
171 Holedigger Films Inc.
172 Home Box Office
(HBO)
173 Homegrown Pictures
174 Hoodwinked
175 Horseshoe Bay
Productions
176 HSI Productions
177 Hypnotic
178 i5 Films
179 IFC Films
180 IFC Productions
181 ImageMovers
182 Imagine
Entertainment
183 In Her Shoes c/o The
Lot
184 Indelible Pictures
185 Independent Dream
Machine
186 InDigEnt
(Independent Digital
Entertainment)
187 Initial Entertainment
Group (IEG)
188 Insomnia
Entertainment
189 InterAL Inc.
190
Interscope/Shady/Afterm
ath Films
191 iPictures
192 Irish DreamTime
193 J & C Entertainment
194 Jersey Films
195 Jet Tone Films
196 Jigsaw Productions
197 John Wells
Productions
198 Just One Productions
199 Katalyst Films
200 Keep Your Head
201 Kevin Messick
Productions
202 Kids in America LLC
203 King Cobra Films
204 Konrad Pictures
205 L. Driver Productions
206 L.I.F.T. Production
207 Lakeshore
Entertainment
208 Lamp-Post
Productions
209 LaSalleHolland
210 Launchpad
Productions
211 Lawrence Bender
Productions
135
212 Legendary Pictures
213 Liaisons Films
214 Linson Films
215 Lions Gate Films
216 Loggerheads LLC
217 Loki Films
218 Lookalike Productions
LLC
219 Lucasfilm
220 MacDonald/Parkes
Productions
221 Magnolia Pictures
222 Mandalay Pictures
223 Mandeville Films
224 Map Point Pictures
225 Marc Platt
Productions
226 Marty Katz
Productions
227 Marvel Enterprises
228 MD Films
229 Media 8
Entertainment
230 Media Talent Group
231 Meno Film Company
232 Merchant Ivory
Productions
233 Metro-Goldwyn-
Mayer (MGM)
234 Metropolitan
235 Michael London
Productions
236 MID Foundation
237 Mighty Cheese
Productions
238 Mile High
Productions LLC
239 Minority Films LLC
240 Mint Pictures
241 Miramax Films
242 Mockingbird Pictures
243 Moe Greene
Associates Inc.
244 Morgan Creek
International
245 Mosaic Media Group
246 MTV Films
247 MTV Productions
248 Muse Productions
249 Mutant Enemy
250 NBA Entertainment
251 NetFlix
252 Neverland Films Inc.
253 New Deal
Productions
254 New Harmony
Pictures
255 New Line Cinema
256 New Regency Pictures
257 Nick Wechsler
Productions
258 Nickelodeon Movies
259 Nina Saxon Film
Design
260 NOW Productions
261 NPV Entertainment
262 Nuance Productions
263 Odd Lot
Entertainment
264 Original Film
265 Original Media
266 Out of the Blue...
Entertainment
267 Outerbanks
Entertainment
268 Outlaw Productions
(I)
269 Outlaw Victoria
Productions Inc.
270 Overbrook
Entertainment
271 Paramount Pictures
272 Pariah
273 Parkwood East
Productions
274 Participant
Productions
275 Pasidg Productions
Inc.
276 Patalex Productions
277 Perdido Productions
278 Peter
Newman/Interal
Productions
279 Picture Entertainment
Corporation
280 Pie Films Inc.
281 Plan B Entertainment
282 Plum Pictures
283 Priddy Brothers
Entertainment
284 Prime Film
Productions LLC
285 Primetime Pictures
286 Production/Zone Inc.
287 Purple Demon
Productions Inc.
288 Radar Pictures
289 Radar Pictures Inc.
290 Rainforest Films
291 Rake Films
292 Rebel Park Pictures
293 Red Claw Inc.
294 Red Wagon
Productions
295 Regency Enterprises
296 Reinventing the
Wheel
297 Releve Entertainment
298 REN-Mar Studios
299 Rent Productions LLC
300 Reuben Cannon
Productions
301 Revere Entertainment
302 Revolution Erie
Productions Ltd.
303 Revolution Studios
304 Rice/Walters
Productions
305 Rising Star
306 River Road
Entertainment
307 Robert Simonds
Productions
308 Roberts/David Films
Inc.
309 Rogue Pictures
310 Romano Films
311 Runteldat
Entertainment
312 S.K.G. Productions
LLC
313 Sarah Green Film
Corp.
314 Scared Little Animals
LLC
315 Section Eight
316 Serenade Films
317 Seven Hills Pictures
318 ShadowCatcher
Entertainment
136
319 Shoreline
Entertainment
320 Showtime
Independent ilms
321 Silver Pictures
322 Silver Plane Films
323 Silverstar Productions
324 Sixth Way
Productions
325 Slippery Chicken
Pictures
326 Sly Dog Films
327 Sneak Preview
Entertainment
328 Solaris
329 Sony Pictures
Entertainment
330 Spring Creek
Productions
331 Spyglass
Entertainment
332 Squid and Whale Inc.
333 State Street Pictures
334 Stonehaven Media
335 Stratus Film Co.
336 Street Legal Cinema
337 Subdivision
Productions
338 Sunflower
Productions LLC
339 Sunlight Productions
340 Susie Q Productions
341 Syncopy
342 Tag Entertainment
343 Tall Trees Productions
344 Tapestry Films
345 Team Todd
346 Teitler Film
347 Telegraph Films
348 The Documentary
Campaign
349 The Family Stone
350 The Javelina Film
Company
351 The
Kennedy/Marshall
Company
352 The Rainbow Film
Company
353 The Real Baxter Inc.
354 The Virginia
Company LLC
355 The Weather Man
356 This Is That
Productions
357 Time Productions Inc.
358 Tiny Dancer Films
359 Tollin/Robbins
Productions
360 Tornado Productions
Inc.
361 Touchstone Pictures
362 Transition
Productions
363 Traveling Pants
Productions Inc.
364 Tree Line Films
365 Trent Othick
Productions
366 Tribeca Productions
367 Triumphant Pictures
368 Troublemaker Studios
369 TV2 Danmark
370 Twisted Pictures
371 Two Careys
Productions LC
372 Twopoundbad
373 Tyler Perry Company,
The
374 Unclaimed Freight
Productions
375 Ush Entertainment
376 Vinyl Films
377 Walt Disney
Company, The
378 Walt Disney Feature
Animation
379 Walt Disney Pictures
380 Warner Bros. Pictures
381 Zanuck Company,
The
382 Zee Films
137
Appendix D:
Table D-1
Genres
Action
Adventure
Animation
Biography
Comedy
Crime
Documentary
Drama
Family
Fantasy
History
Horror
Music
Musical
Mystery
Reality
Romance
Science Fiction
Sport
Thriller
Total
War
Western
138
Appendix E:
Figure E-1
Model Parameter Description
Note: Model parameter description is adopted from PNet reference guide.
Abstract (if available)
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Asset Metadata
Creator
Chai, Lin
(author)
Core Title
Community structure as collective identity construction and resource search
School
Marshall School of Business
Degree
Doctor of Philosophy
Degree Program
Business Administration
Publication Date
09/11/2011
Defense Date
07/27/2009
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
collaboration networks,community structure,identity,OAI-PMH Harvest,resource exchange
Place Name
USA
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Language
English
Contributor
Electronically uploaded by the author
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Advisor
Rajagopalan, Nandini (
committee chair
), Kennedy, Mark T. (
committee member
), Monge, Peter (
committee member
)
Creator Email
ccummin2@csulb.edu,chai@marshall.usc.edu
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-m2602
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Chai, Lin
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Tags
collaboration networks
community structure
resource exchange