471721
of Leadership & Organizational StudiesMacGregor and Madsen
© Baker College 2013
JLO20410.1177/1548051812471721Journal
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Article
Recovery Following Disruption to an
Ecosystem: The Effects of the Internet
Bust on Community Evolution
Journal of Leadership &
Organizational Studies
20(4) 465­–478
© Baker College 2013
Reprints and permission:
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DOI: 10.1177/1548051812471721
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Nydia MacGregor1 and Tammy L. Madsen1
Abstract
Using data on all organizations operating in California from 1993 to 2006, this article explores the evolution of industries
and communities before and after a disruption to the region (the dot-com bust). Our results indicate that an association
with the Silicon Valley’s high-tech industry clusters explains more of the variance in organizational foundings in communities
located in California after the disruption as compared with the predisruption time period. In contrast, the benefits of a
Silicon Valley location for nascent organizations erode postdisruption. The findings also demonstrate that, pre and post the
dot-com bust, organizational foundings are explained more by an organization’s high-tech industry affiliation than by its
Silicon Valley location.
Keywords
Silicon Valley, disruption, communities, high-tech industry
Work on competitive positioning and value creation is
increasingly informed by analysis that considers the
community context in which organizations operate (e.g.,
Astley, 1985; Audia, Freeman, & Reynolds, 2006; Brittain,
1994; Brittain & Freeman, 1980; Freeman & Audia, 2006).
Organizational communities are “conceptualized as sets of
relations between organizational forms or as places where
organizations are located in resource space or in geography” (Freeman & Audia, 2006, p. 145). In such contexts,
interactions among economic and institutional actors, such
as organizations, suppliers, consumers, regulatory agencies,
and social organizations, influence resource flows and in
turn, the founding and succession of organizations. As a
result, the composition of communities slowly shifts over
time, as organizations and populations (largely analogous to
industries) die off and new organizational forms emerge
(are founded). This pattern of community evolution may
change however, when the region in which communities
reside experiences a significant disruption or catastrophe,
such as the Internet (dot-com) bust that occurred in Silicon
Valley. What explains a region’s development and recovery
following such a disruption?
Environmental, technological, and institutional shocks
to a region may adversely affect its resource base and, in
turn, alter the composition of communities and industries
located in the region. For instance, following a disruption,
the resources of a region’s communities may erode, such as
via the exodus of talented human capital. At the same time,
communities located outside the damaged region’s boundaries may lack the types of resources needed by industries
and organizations that traditionally operate in the focal
region. Since the distribution of resources in space affects
the emergence and viability of organizations, these conditions prompt questions regarding a region’s recovery postdisruption. Specifically, after a disruption to a region, do
organizations primarily start up in communities located in
the region (i.e., the resource-damaged area) or in communities located outside the harmed region’s boundaries (i.e.,
areas with resources that may be less relevant to the region’s
production system)? Furthermore, what types of organizations are more likely to start up in a resource damaged area,
those associated with industries that are part of, or similar
to, the region’s traditional industry clusters, or those associated with industries distinct from the region’s traditional
industry clusters?
Understanding what explains differences in a region’s
recovery following a fundamental disruption can assist organizations and institutional actors in identifying spatial patterns of opportunity development and value creation. Few
studies explore this topic at the community or region levels
1
Santa Clara University, Santa Clara, CA, USA
Corresponding Author:
Nydia MacGregor, Management Department, Leavey School of Business,
Santa Clara University, 500 El Camino Real, Santa Clara, CA 95053, USA.
Email: [email protected]
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Journal of Leadership & Organizational Studies 20(4)
of analysis (exception, Paruchuri & Ingram, 2012).
Moreover, the extant work rarely considers whether the positive effects of geographic and industry proximity on the
spatial distribution of foundings in communities and regions
are robust to major disruptions or shocks. Last, studies at the
community level often focus on one community versus multiple communities, on single, versus multiple, industries or
sectors (e.g., manufacturing), and/or on physical, versus
nonphysical, production-oriented sectors (e.g., Audia et al.,
2006; Audia & Kurkoski, 2012). Yet communities differ in
their resources, industry compositions, and positions in
social and market structures. As members of diverse communities interact, this diversity may promote innovation
through activities such as knowledge recombination. As a
consequence, analysis that considers multiple communities
and their various component industries may enhance our
understanding of community evolution and in turn, a region’s
path of economic development.
To address the above, our analysis covers all organizations, industries,1 and communities in California from 1993
to 2006. The time period allows us to explore the economic
progress of a region, Silicon Valley, pre and post a major disruption (the Internet bust) and thus, sets the stage for a natural
experiment. If organizations are more likely to start up and/or
rebuild in a resource damaged area, then we would expect
communities located in the disrupted region (e.g., Silicon
Valley) to explain more of the variance in organizational
foundings as compared with communities that are located
outside the region’s boundaries. If this holds, we expect that
the positive effect of a community’s regional location on
founding will persist pre- and postdisruption. Second, if a
disruption to a region stains the reputation of the region’s
industries, organizations, and particularly, start-up organizations, may avoid association with these industries. As a result,
we would expect weaker recovery (via fewer organizational
foundings) in the region’s traditional industry clusters (e.g.,
the industries present in the region prior to the disruption)
postdisruption as compared with predisruption.
The article proceeds as follows. We begin with a brief
review of the literature on succession, disruption, and spatial heterogeneity. We then present the theory underlying
our hypotheses. Subsequent sections discuss the data,
method of analysis, and findings. We conclude with the
implications for theory and practice.
Succession and Disruption
Succession refers to the orderly recovery in the composition
and structure of an ecological community after a fundamental disruption or catastrophe (e.g., a fire, landslide, or lava
flow; Pianka, 1994). Similarly, following a shock or disruption, the distribution of organizations and industries operating in a community, or in adjacent communities, also shifts
and some organizations and industries erode whereas others
start anew (Brittain & Freeman, 1980; Wholey & Brittain,
1986). To understand the conditions that explain patterns of
succession or recovery in a region, we investigate how the
communities to which organizations belong (e.g., groups of
industry clusters; a high-tech sector), and their geographic
locations (e.g., in or outside the focal region) affect patterns
of organizational founding in a region that witnessed a significant disruption, such as Silicon Valley following the
dot-com bust. Rates of succession also vary as a function of
the intensity of the disturbance experienced by a community
and by the characteristics of the community and adjacent
communities. As such, our inquiry requires that we examine
foundings in communities located within the region experiencing the catastrophe as well as foundings in communities
located outside the region’s boundaries. As such, we analyze
the nested nature of these relationships by considering how
factors within and across levels, and over time, explain a
region’s recovery.
Similar to the work on succession, extant research
emphasizes that opportunities and constraints arise when
industries, communities, or regions experience major disruptive or transformative events. One stream of research
highlights the positive effects of fundamental technological
change on an industry, illustrating how such change can
destroy barriers to innovation, development, and growth
(e.g., Abernathy & Clark, 1985; Anderson & Tushman,
1990; Jovanovic & MacDonald, 1994; Nelson & Winter,
1982). Other studies demonstrate the adverse effects of
institutional change on firm viability and industry evolution
(e.g., Haveman & Rao, 1997; Haveman, Russo, & Meyer,
2001; Madsen & Walker, 2007; Winston, 1998). A common
conclusion from extant work at the industry level is that,
whether technological or institutional, fundamental change
alters the mix of organizations operating in an industry.
Other studies examine the co-evolution of specific communities, technological change, and institutional events
(Rosenkopf & Tushman, 1994; Van de Ven & Garud, 1994).
In addition, a recent study at the region level finds that
while a major catastrophe to a region (9/11 and New York
City) displaced portions of its resource base, it also freed
organizational actors from factors that previously constrained their behavior. As a result, succession occurred at a
faster rate in communities geographically proximate to the
catastrophe as compared with communities located outside
the focal region (Paruchuri & Ingram, 2012).
Although prior work is informative, it overlooks how
disruptive events affect the spatial systems of production in
which multiple industries and communities are embedded.
In such contexts, bonds of mutual dependence unite different industries and their organizational members (Astley,
1985; Barnett, 1994). Given these ties, a fundamental shock
to a region and its communities and industries may have
systemic implications for the region’s succession. For
instance, a disruption may render existing organizational
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MacGregor and Madsen
forms questionable, reducing their legitimacy, and in turn,
prompting their exit or slow departure from the region.
These events may disturb the structure of resource flows
and social networks in the region, compromising its systems of production. Since the systems of production in and
among a region’s communities influence the opportunity
structure of future entrepreneurs, a disruption to a region
may alter where future entrepreneurs choose to locate their
activities. As a result, understanding the geographic and
industry effects that shape postdisruption patterns of opportunity may help actors define where and when to allocate
their resources.
Heterogeneity in Regional
Recovery: What Matters?
Geographic Effects
Since the distribution of resources in geographic space
influences the locations of industries (Baum & Sorenson,
2003; Cattani, Pennings, & Wezel, 2003; Lomi, 1995;
Sorenson & Audia, 2000), wherein geographic space one
chooses to start up or found an organization matters. Extant
work suggests at least three categories of effects that contribute to differences in patterns of foundings in industries,
communities and regions: spatial relationships (social connections among organizational actors), agglomeration, and
embeddedness (e.g., Audia et al., 2006; Audia & Kurkoski,
2012; Baum & Mezias, 1992; Chung & Kalnins, 2001;
Florida & Kenney, 1988; Marshall, 1920; Saxenian, 1994).
We discuss each category in turn.
The spatial relationships among new and established
organizations influence the frequency of their interactions
and, in turn, their development. New organizations require
a variety of resources to start up, develop, and grow. In
addition, they need to establish organizational legitimacy in
order to attract human and financial capital and to convince
a variety of actors (buyers, suppliers, potential partners,
institutions, advisors) to transact with them. Lacking social
connections to resource holders dampens an entrepreneur’s
ability to develop a new venture. At the same time, work
suggests that these types of social ties rarely transcend the
regional boundaries in which the relevant resource and
knowledge stocks reside (Sorenson, 2003). In addition,
research demonstrates that as the geographic distance
between two organizations decreases, the probability that
they will interact increases and the cost of their interaction
decreases (e.g., Lazarsfeld & Merton, 1964). Spatial proximity thus facilitates building relationships with an array of
resource holders that are anchored in space (Blau, 1977;
Sorenson & Stuart, 2001). It follows then that the distribution of social and institutional resources in geographic
space affects where a new venture locates at founding (e.g.,
Sorenson & Audia, 2000; Stuart & Sorenson, 2003). For
example, historically, venture capital (VC) firms are known
to locate around concentrations of high-tech firms (Florida
& Kenney, 1988) and to invest locally (Sorenson & Stuart,
2001). Other work shows that spatially proximate firms are
more likely to share directors or board members, even after
advancements in transportation technology (e.g., Kono,
Palmer, Friedland, & Zafonte, 1998; Lincoln, Gerlach, &
Takahashi, 1992; Marquis, 2003).
The relationships that emerge among colocated organizational actors in geographic space give rise to informal and
formal networks that facilitate information and knowledge
exchange (Saxenian, 1994) and expose organizations to different sources and types of knowledge. These different
sources and types of knowledge may assist entrepreneurs in
activities critical to an emerging venture, problem solving,
and creating new knowledge combinations. Indeed, several
studies demonstrate that information spillovers decline as
organizations become more spatially distant from each other
(Argote, Beckman, & Epple, 1990; Greve, 1999; Jaffe,
Trajtenberg, & Henderson, 1993). As a consequence, the rate
of information and knowledge transfer between a new organization and established resource holders may increase as the
geographic distance between them decreases. This effect is
particularly strong when the necessary resources are tacit in
nature because geographic distance produces friction in tacit
knowledge transfer across space (Almeida & Kogut, 1999;
Jaffe et al., 1993). Research also shows that context-dependent knowledge or “sticky knowledge” is best exchanged via
direct and repeated contact (Von Hippel, 1994). This type of
knowledge frequently fuels the value created by high-tech
ventures (e.g., Nelson & Winter, 1982; Zucker, Darby, &
Brewer, 1998). It follows that entrepreneurs are likely to be
more attracted to locations with opportunities for frequent
engagement with relevant and established resource holders.
To the extent that such resource holders are clustered within
a region’s boundaries, entrepreneurs should be more likely to
found their ventures in communities that are physically
located in the region as opposed to communities that are situated outside the region’s boundaries.
Although spatial proximity is often linked to greater
competitive intensity, the colocation of organizations may
also yield production enhancing benefits or agglomeration
economies (Marshall, 1920; Feldman, 1999). Agglomeration
economies imply that regional- and local-scale advantages
arise as more organizations in related industries cluster
together in regions or communities, respectively.2 Several
activities underlie these regional effects. First, as firms
operating in a region begin to experience success, they
attract a variety of suppliers, buyers, skilled workers,
potential partners, and investors (Piore & Sabel, 1984;
Rotemberg & Saloner, 2000). This increase in the amount
and variance of resources and capabilities located in a
region not only makes the region more attractive to subsequent
entrants but also lowers their costs of entry. At the same
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Journal of Leadership & Organizational Studies 20(4)
time, the emergence of the necessary infrastructure to
support creative development and production fosters, in
part, stability in a region’s systems of production. A byproduct of the latter is that skilled workers may be more
willing to invest in resources or capabilities relevant to the
region’s production activities (e.g., David & Rosenbloom,
1990). Furthermore, since spatial proximity facilitates
interaction, skilled workers may fuel knowledge diffusion
and spillovers among organizations. These spillovers may
provide cost and value benefits to organizations lacking
production- and innovation-enhancing resources, such as
nascent firms (see, Shaver & Flyer, 2000; Arrow, 1962;
Romer, 1986). As a result, the mutualistic benefits of
agglomeration tend to increase the rate of founding in a
region (Barnett & Carroll, 1987; Chung & Kalnins, 2001;
Sorensen & Sorenson, 2003).
Communities also are embedded in market and institutional structures. These structures may differentially affect
a region’s recovery. First, a major disruption to a region,
whether endogenous or exogenous, typically destabilizes
the markets related to the region, influencing the balance of
supply and demand and in turn, the viability of organizations operating in the region. Consequently, communities
located in the region may experience an outflow of organizations and resources. For instance, suppliers, complementors, intermediaries, or buyers, may exit and, as employment
opportunities erode, skilled workers may depart. These
conditions leave the region with a diluted or damaged
resource base. However, once a region has built up institutional and locational legitimacy in the eyes of various
actors over time, some of this legitimacy may spillover to
the postdisruption context (see, Madsen & Walker, 2007).
For instance, despite a loss of resources, the historical reputation of the Silicon Valley region may help sustain its
legitimacy postdisruption. Entrepreneurs may perceive that
these effects will boost the legitimacy of their ventures in
the eyes of potential investors, consumers, and other organizational actors. If this holds, then communities located in
the disrupted region will retain their attractiveness to
potential entrepreneurs, despite blighted market conditions. Consequently, these communities may account for
more of the variation in founding rates as compared with
communities located outside the region.
Furthermore, although a community’s resources may
dilute following a disruption, some portion of its organizations, and their resources, will remain viable. For example,
organizations vary in their ability to survive a major disruption; some may be more robust and others, more fragile.
Although robust survivors may be more embedded in their
communities and slow to adapt, they also may provide a
resource foundation for the region’s subsequent recovery.
As a result, following a disruption to a region, the institutional and locational legitimacy retained by the region and
the baseline resource foundation sustained in the region’s
communities, will make the region’s communities more
attractive, on the margin, to potential entrepreneurs than to
communities located outside the region that lack an array of
relevant resources.
Building on the above, we argue that after a disruption to
a region, a community’s proximity to the focal region will
explain heterogeneity in founding rates across communities. Formally, we hypothesize,
Hypothesis1a: Following a disruption to a region, a
community’s location in the disrupted region will
explain more of the variance in recovery (via organizational foundings) as compared with a location
outside the region’s boundaries.
The above suggests that although a disruption affects a
region’s composition of communities and organizations,
locating in the region remains important to fledgling organizations, pre- and postdisruption. As a result, we hypothesize,
Hypothesis 1b: The positive effect of a community’s
proximity to the area of disruption on variance
in organizational foundings will persist pre– and
post–regional disruption.
Industry Effects
Actors inside and outside organizations cluster enterprises
into industries. Individuals use category systems, such as an
industry designation, to imbue meaning and construct order
(Berger & Luckmann, 1966; Zuckerman, 1999). Investors,
buyers, suppliers, and other important actors fashion their
expectations of an organization based on the organization’s
membership within an industry. Moreover, these important
actors confer legitimacy to the actions of an organization
based on the expectations set by the categorical designation. Such legitimacy involves a generalized set of assumptions that the actions of an entity are desirable or appropriate
within socially constructed systems of norms, values, and
beliefs (Suchman, 1995). Put another way, legitimacy
defines the parameters in which an organization may act
and still enjoy the approval of their constituencies. As such,
legitimacy is a critical resource for organizations. Indeed,
organizations lacking legitimacy often suffer penalties for
not complying with accepted norms for their group
(DiMaggio & Powell, 1983). As an example, Zuckerman
(1999) demonstrates that organizations not conforming to
the expectations of expert industry analysts achieve lower
stock prices. In other words, analysts’ confusion about an
organization’s category or industry designation results in
discounted value. Similarly, Davis, Diekmann, and Tinsley
(1994) find that the stock market undervalues and the press
denounces organizations using a delegitimized or passé
governance structure.
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MacGregor and Madsen
Consequently, legitimacy is among the many resources
entrepreneurs require for their fledgling organizations
(Aldrich & Fiol, 1994; Hunt & Aldrich, 1996; Starr &
MacMillan, 1990). Under normal conditions, founders need
legitimacy to access social and financial capital (Hannan &
Freeman, 1984; Stinchcombe, 1965). For instance, actors
influential to entrepreneurs, such as VC firms, evaluate the
life-chances for a nascent organization to decide whether to
bestow critical resources on the new organization.
Legitimacy helps reduce the perceived uncertainty surrounding a new organization and, in turn, increases the likelihood that external resources will be funneled to the
venture. But, legitimacy represents a high hurdle that many
new organizations fail to attain (Aldrich & Fiol, 1994;
Hannan & Carroll, 1992).
Borrowing legitimacy simply by association is among
the strategies that founders use to achieve organizational
legitimacy and thereby attain necessary resources. Actors
may attribute legitimacy to new organizations based on the
company that they keep or the memberships they claim to
have. In some instances, interorganizational relationships
transfer legitimacy (Podolny & Stuart, 1995; Stuart &
Sorenson, 2003). As an example, Stuart, Hoang, and Hybels
(1999) evaluate the effect of interorganizational networks
on the ability of new companies to gain necessary funding
and to grow. They find that the legitimacy of alliance partners spills over to their entrepreneurial collaborators such
that the new organizations realize larger valuations and
faster time to initial public offering (IPO) when their partners hold more legitimate positions. It is important to note
that previous research does not demonstrate that the quality
of a new organization is necessarily shaped by its associations. Rather, holding quality constant, association alone
improves the perception of the quality of a new venture
(Podolny & Stuart, 1995).
Taken together, the previous two notions lead us to a
novel suggestion. Specifically, if legitimacy is critical for
new organizations, and that legitimacy, and by extension
illegitimacy, may be bestowed on a firm based on the company that the organization keeps, then the most successful
new organizations are those that strategically avoid identifying with illegitimate groups of organizations. In fact, the
closer that new organizations are to illegitimate groups, the
poorer their performance. Thus, in contrast to geographic
proximity, groups of new organizations that enter illegitimate industry groups will suffer weaker performance as
compared with those that start up in legitimate industry
groups. In the context of Silicon Valley, illegitimate industry groups are those that most directly experienced the disruption. In essence, organizations in these industries were
stained by the disruption. As a result, we expect that postdisruption, new organizations will avoid association with
these “stained” industries to evade potential contagion and
illegitimacy. Building on these arguments, we predict,
Hypothesis 2a: Following a disruption to a region,
industries associated with the region’s traditional
industrial clusters will explain less of the variation
in the recovery (organizational foundings) as compared with industries that are not associated with
the region’s traditional industrial clusters.
The preceding arguments and hypothesis also suggest
that the effects of a community’s industry composition on
the variation in recovery (via organizational foundings) will
differ pre- and postdisruption. Predisruption, the legitimacy
of a community’s industry clusters is not compromised
whereas postdisruption, these clusters may lack legitimacy.
As a result, we expect to find variance in the effects of
industry on recovery pre- and postdisruption as follows:
Hypothesis 2b: Community-based industry effects
will explain less of the variance in founding rates
during the postdisruption time period as compared
with the predisruption time period.
Data
To examine succession (recovery), we analyze the emergence or founding of new organizations in California, home
to Silicon Valley, the epicenter of the dot-com bust. Located
in the southern portion of the San Francisco Bay Area,
Silicon Valley’s distinct industrial system known for innovation and cooperation emerged from early partnerships
between semiconductor firms, the military, and universities
(Saxenian, 1994). The history of Silicon Valley as a technology innovation hub, and the disruption to this region
circa 2000, make it ideal for studying the variation in community and regional development before and after a transformative event. Moreover, California’s industrial diversity
and size provide substantial empirical leverage. In addition
to the high-tech sector, a wide variety of industries (such as
agriculture, arts, media and entertainment, and financial
services) occupy an important presence in the state. In addition, the Californian economy surpassed $1 trillion in 1997,
exceeding all but six nations (Bureau of Economic Analysis,
2012; Legislative Analyst’s Office, 1998). Studying industrial recovery in California, thus, bears valid similarities to
industries across the United States and the world, making
this context highly generalizable.
Several historical events and trends frame our time
period of study and motivate our focus on the Internet bust
as a major disruptive event to Silicon Valley and California.
First, the 1993 introduction of Mosaic Communications
Corporation’s (later Netscape) browser, a consistent method
of web browsing across operating systems, exposed the
public to the vast possibilities of the Internet and is largely
recognized as fueling the Internet’s usage growth.
Furthermore, the Worldwide Web Consortium, a standards
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Journal of Leadership & Organizational Studies 20(4)
Figure 1. Amount of venture capital funded to ventures (in
million dollars) in California and Silicon Valley, 1994-2006.
generating body, and Netscape were both founded in 1994.
Netscape’s IPO in late 1995 mesmerized investors and the
Valley’s tech community; at $1.96 billion, it was the largest
U.S. IPO to date and marked the advent of the Internet
boom (Lashinsky, 2005; Leiner et al., 1997). From 1995 to
2000, IPOs among high-tech companies abounded and VC
funding poured into Silicon Valley and California (see
Figure 1). For example, in 1995, VC investments in Silicon
Valley based ventures totaled ~$1.83 billion across roughly
500 deals (averaging $3.7million per deal); VC investments
in Silicon Valley peaked at $33.75 billion in 2000, spanning
more than 2,100 deals (averaging approximately $16 million per deal and accounting for 33% of all VC investments
in the United States) (National Venture Capital Association
[NVCA], 2012; PricewaterhouseCoopers/NVCA, 2012).
The average compound annual growth rate (CAGR) for VC
investments in California from 1994 to 2000 was 74%; and
Silicon Valley–based ventures accounted for 75% of these
investments, on average annually (NVCA, 2012). The peak
levels of 2000 (both in amount of VC funding and number
of deals) has not been replicated to date. As a comparison
and using comparable time windows of 7 years, the average
CAGR for VC investments in California from 1987 to 1993
was 0.04% and the average for 2001 to 2007 was (−6.6%;
NVCA, 2012). The boom period beginning in 1995 culminated in a bust that began in March, 2000, and was in full
bloom by 2001 (Mahajan, Srinivasan, & Wind, 2002; Net
History, n.d.). For example, more than 500 U.S.-based
Internet firms shut down by the end of 2001 (Fortune &
Mitchell, 2012). As a result and consistent with extant work,
we identify the year 2000 as the year of the “bust” event.
Several sources inform our data. Our primary source is
the National Establishment Time-Series (NETS) data on all
business establishments operating in California from 1993
to 2006. NETS data comprise annual snapshots of the Dun’s
Marketing Information files identifying establishments
active across all industries in January of each year. The
establishment-level data include a firm’s geographic location (city, zip code, longitude and latitude, county affiliation), founding date, dissolution information (where
appropriate), standard industry classification (SIC) code at
the four-digit level, Dun and Bradstreet data universal numbering system (DUNS) number, number of employees, and
sales. We aggregate the number of foundings by industry
(four-digit SIC) for each community (city level). The latter
provides a count of the total number of new entrants or
foundings in each industry within each community. In addition, several data sources inform the geographic components of our nested analysis. First, at the state level,
California specifies nine economic zones and each zone
encompasses multiple counties. This information is combined with the NETS data to affiliate each city and establishment with one of the state’s economic zones. In addition,
we collected annual data on the state unemployment rate
from the State of California Employment Development
Department (http://www.labormarketinfo.edd.ca.gov/) and
amount of VC investments in California from the NVCA
(2012). Next, we reviewed the extant work on Silicon
Valley to define the region’s boundaries. Consistent with
prior work (e.g., Saxenian, 1994), the Silicon Valley region
spans multiple counties and communities; as a result, the
region is defined as including the following cities (listed in
order from, approximately, north to south): Redwood City,
Menlo Park, Palo Alto, Mountain View, Sunnyvale, San
José, Santa Clara, Fremont, Milpitas, Cupertino, Campbell,
Los Gatos, and Los Altos.
The time span of our data, 1993 to 2006, allows for a
natural experiment by covering approximately 7 years
before the dot-com bust of March, 2000, and approximately
6 years after the bust. All time-varying variables in our
study are lagged 1 year to avoid simultaneity thus, we lose
1 year of data values. Given the state of flux following
March 2000, our prebust period is defined as 1994 to 1999
(using values from 1993, to inform the lag structure) and
our postbust period is defined as 2001 to 2006.
Recall that our focal level of analysis is the community
level. Communities include multiple industries. As a result,
our unit of analysis is the industry-community-year. The
total number of unique communities in our analysis is 1,285
and the communities span 391 industries (note: not all
industries have a presence within each community). Given
the above, our analysis covers 322,267 industry-communityyear observations during the prebust time period and
287,008 community-year observations in the postbust time
period. Figure 2 shows the number of founding events, exit
events, and organizations operating (density) in Silicon
Valley during the time period of our analysis. It is interesting to note that the total density builds significantly during
this period, with a spike in foundings around the year 2000,
but no commensurate spike in exits. The bubble and subsequent burst seem to be isolated to high-tech organizations.
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MacGregor and Madsen
Figure 2. Number of foundings, exits, and organizations
(density) in Silicon Valley, all industries, 1994-2006.
Figure 3. Number of organizations (density) in California, hightech industries, 1994-2006.
Indeed Figures 3 and 4 show evidence of this; for all hightech firms in California, foundings peak at about 2000, exits
peak just afterward, and density dips in the same time
frame. The subsequent sections define our variables and
method.
Variables
Dependent variable. Our dependent variable is an annual
sum of the number of organizational foundings in an industry within a community. A founding event occurs when an
establishment first appears in the NETS data.
Geographic effects. We include two variables to address
geographic effects: state level and region level. First, the state
of California divides the state into nine economic regions for
policy implementation and planning as follows: the Bay
Area, Central Coast, Central Sierra, Greater Sacramento,
Lassen, Northern Sacramento, San Joaquin, Southern Border, and Southern California. To address any economic
Figure 4. Number of foundings and exits in California, high-tech
industries, 1994-2006.
region–specific effects that may influence a community’s
development, our analysis includes a class variable indicating a community’s economic zone (based on the community’s location). Second and specific to Hypotheses 1a and 1b,
we classified each community’s geographic location as either
part of the Silicon Valley region (1), or not (0). Building on
prior work (Saxenian, 1994), the communities that constitute
Silicon Valley are (approximately from north to south) Redwood City, Menlo Park, Palo Alto, Mountain View, Sunnyvale, San José, Santa Clara, Fremont, Milpitas, Cupertino,
Campbell, Los Gatos, and Los Altos.
Industry effects. We categorized each industry (at the
four-digit SIC code) according to its association with the
Silicon Valley region’s traditional industry (high-tech) clusters. The categorization is based on the level of technological intensity specific to the industry measured by the ratio
of R&D expenditure to value added and the technology
embodied in purchases of intermediate and capital goods
and, was developed by Hatzichronoglou (1997) for the
Organization for Economic Co-operation and Development
(OECD). As such, the industry effect is a dummy variable
set to 1 if an industry is defined as high tech and 0 otherwise. Examples of high-tech industries include electronic
computers, computer storage, computer peripherals and
equipment, aircraft and space equipment, semiconductors
and related devices, telecommunications equipment, and
instruments, radio, tv and communications equipment, and
medical, precision and optical instruments.
Control variables. Our study also includes a number of
time-varying (annual) control variables. At the state level,
we include the annual amount of VC investments in California as reported by the NVCA (2012). We also control for
the state’s annual unemployment rate. At the community
level, we control for competition effects using industry density defined as the number of organizations operating in an
industry within a community and for industry dynamics
using a sum of the number of exits from an industry within
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Journal of Leadership & Organizational Studies 20(4)
a community.3 The above variables are lagged 1 year to
avoid simultaneity. Last, we include a set of year dummy
variables to address time effects.
Method
We decompose the variance in rates of organizational
founding in industries within communities to explore the
amount of variance explained by geographic (region) and
industry effects pre and post the disruption event. A stream
of strategy research uses variance components and analysis
of variance methods to analyze the contributions of different types of effects (industry, corporate, or business unit) to
differences in performance among firms (e.g., Brush &
Bromiley, 1997; McGahan & Porter, 1997, 2002; Rumelt,
1991). Although this work differs in theoretical focus, we
adopt a similar approach. Importantly, advances in empirical methods allow us to overcome several empirical limitations associated with prior work leveraging variance
components and analysis of variance techniques (see,
Brush & Bromiley, 1997) as well as to analyze variance in
count data. Specifically, we use a multilevel (hierarchical)
mixed effects Poisson specification to test our hypotheses
which is equivalent to a Poisson regression with both fixed
and random effects. This approach allows for nested clusters of random effects; in our analysis, economic zones
represent the third level of effects, subsequent levels
include geographic effects and industry effects. Foundings
are discrete events, independent of each other and at a constant incidence rate and thus, follow a Poisson distribution.
As a result, the number of foundings, y, in industry i in
community j and occurring in time interval t is
(
)
Pr yij = y|u j =
exp ( −µij )µijy
y!
(1)
and µij = exp ( X ij + rij u j ) , where X ij is the vector of
covariates for the fixed effects, rijuj represents the parameters associated with the random effects, and µij is the expectation of y and the variance of y given the fixed and random
effects. As standard practice, we transform the above
specification and analyze the following to estimate the
amount of variance explained by the constructs of interest
using a maximum likelihood estimation:
( )
ln µijt = β0 + γ j + δ j + αi + X ijt −1 + Zt + ε ijt ,
(2)
where µ captures the number of foundings per industry i
within community j at time t; γ is a class variable indicating
a community j’s economic zone based on the state of
California’s classifications; δ captures a community’s geographic effect and is a dummy variable indicating whether a
community is located in the Silicon Valley region; α is the
industry effect parameter specified as a dummy variable
indicating whether or not an industry is associated with the
region’s traditional industry clusters (e.g., whether the
industry is a high-tech industry); X is a vector of control
variables; Z is a vector of year dummy variables; and ε is
the error term. In addition, given we use count data for the
dependent variable, overdispersion may be present as such.
The nested level of our design allows us to address overdispersion using the third level or economic zone random
effects parameter (Rabe-Hesketh & Skrondal, 2005).
Consistent with prior variance decomposition studies,
we begin by estimating a baseline model that includes the
control variables and economic zones. Next, we test two
additional models, adding the geographic effect (Silicon
Valley region effect) to the baseline model and separately,
the industry effect (high-tech effect) to the baseline model.
We then conduct likelihood ratio tests to determine if each
of the effects adds explanatory power to the baseline model.
Our fourth model includes both the geographic and industry
effect variables; we compare the explanatory power of this
model to each of the preceding models. As noted in the data
description section, we apply the above model specification
on two samples: a postdisruption time period (2001-2007)
and a predisruption time period (1994-1999, where the values from 1993 inform the lag structure). Support for
Hypothesis 1a exists if, post disruption, the geographic or
Silicon Valley region effect adds explanatory power to the
model of control variables and the economic zone effects.
Support for Hypothesis 2a exists if the industry effect does
not add explanatory power to the model in the postdisruption time period.
To test Hypotheses 1b and 2b, we use a Wald statistic
(adjusted for differences in sample sizes) to compare the
magnitude of the variance components associated with the
geographic and industry effects pre- and postdisruption.
Recall that Hypothesis 1b predicts that the Silicon Valley
region effect on organizational foundings will not differ
pre- and postdisruption. As a result, support for Hypothesis
1b exists when the Wald test is not significant—indicating
that the variance in foundings explained by a community’s
Silicon Valley regional location does not differ pre and post
the dot-com bust. In contrast, support for Hypothesis 2b
exists when the variance in organizational foundings
explained by the high-tech or industry effect during the predisruption time period is larger in magnitude than that
explained by the industry effect during the postdisruption
time period and the Wald statistic is significant.
Results
Table 1 presents descriptive statistics. Table 2 displays the
results of our multilevel mixed effects Poisson regression
analyses of the annual organizational foundings by industry
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MacGregor and Madsen
Table 1. Descriptive Statistics: Organizations in California
Industry-Communities.
Foundings
Density
Exits
Venture capital
investeda
Unemploymenta
Economic regions
Silicon Valley
Region, 1 = yes
High-tech
industry, 1 = yes
Minimum
Mean
Maximum
SD
0
1
0
1425.4
0.92
8.71
0.60
11603.18
2,262
8,693
999
40666.8
7.00
41.68
3.52
10241.20
0.049
1
0
0.06
6.02
0.03
0.086
9
1
0.01
3.21
0.17
0
0.04
1
0.20
Note: The unit of analysis is industry-community.
a.Variables are based on data aggregated for all of California.
within each organizational community (industry-community
level) in California, pre- and postdisruption. The comparative likelihood ratio tests presented in Table 2 illustrate the
extent to which each additional covariate enhances the
model’s explanatory power. We report the results comparing the variance components for geographic and industry
effects across the pre- and postdisruption time periods in
Table 3. We begin by discussing the findings in Table 2.
Building on a technique used in variance decomposition
studies in strategy, our study examines the influence of two
classes of effects, geographic (region) location and industry,
on variation in organizational foundings at the industrycommunity level pre and post a disruptive event—the dotcom bust. Recall that the geographic (region) effect is
defined as whether a community is located in Silicon Valley
or not. We compare the contribution of each category of
effects to a baseline model that includes effects for communitylevel competition and industry dynamics, state-level factors
(human capital and financial resources), and time (year).
Consistent with prior studies, the results are reported in a
sequential or nested manner to illustrate changes in the
explanatory power of the model as effects are added (e.g.,
see McGahan & Porter, 1997, 2002; Walker, Madsen, &
Carini, 2002). It is important to note that although previous
studies using another form of decomposition technique may
suffer from bias due to the order of covariate entry in model
specifications, our method adjusts for these concerns. Thus,
we are confident that the findings are not biased by the order
in which covariates are entered into our models. The postdisruption results are listed in the top half of Table 2. Consistent
with Hypothesis 1a, the findings indicate that a location in
Silicon Valley explains more of the variance in foundings in
communities as compared with the baseline model; the likelihood (LLHD) ratio test comparing Model 2 with Model 1
is statistically significant (Model 2 vs. Model 1, χ2 = 49.55,
p < .00001). Counter to Hypothesis 2a, high-tech industry
affiliation after the disruption also enhances the explanatory
power of the model; the LLHD ratio test comparing Model 3
with Model 1 is statistically significant (Model 3 vs. Model 1,
χ2 = 4359.5, p < .00001). The LLHD ratio test statistics (χ2
values) imply that a model with the industry or high-tech
effect is superior to a model with the geographic or Silicon
Valley region effect (Model 2 vs. Model 1, χ2 = 49.55, p <
.00001; Model 3 to Model 1, χ2 = 4359.5, p < .00001). This
finding is reinforced by the results of a model (Model 4) that
adds the industry effect to a model (Model 2) with the baseline and region (Silicon Valley) effects (Model 4 vs. Model 2,
χ2 = 4736.08, p < .00001). In sum, although both region and
industry effects explain variance in organizational foundings
in communities following the dot-com bust, the industry or
high-tech effect appears to matter more. The following
expands on this observation by exploring the findings from
a different angle.
To test Hypotheses 1b and 2b, we use a Wald statistic
(adjusted for differences in sample sizes) to compare the
magnitude of the variance components associated with the
geographic and industry effects pre- and postdisruption.
Counter to Hypothesis 1b, the findings indicate that the
Silicon Valley region effect on organizational foundings
erodes from the pre- to postdisruption time periods. As a
result, variance in organizational foundings is explained
more by a community’s Silicon Valley location before the
dot-com bust (11.3% of the variance) as compared with the
postbust time period (.00002%); the Wald statistic comparing the magnitudes of the variance components is significant (χ2 = 231.13, p < .01). The findings are also counter to
Hypothesis 2b; the variance in organizational foundings
explained by the high-tech or industry effect during the
postdisruption time period (53.7%) is larger in magnitude
than that explained by the industry effect during predisruption time period (17.5%) and the Wald statistic is significant
(χ2 = 887.82, p < .01). Thus, the dot-com bust does not appear
to have “stained” the legitimacy of high-tech industries in
California. The subsequent section discusses the implications of our findings.
Discussion
Returning to our research questions, what explains more of
the variation in recovery in a region following a fundamental disruption? Do the influences of geographic location and
industry affiliation on organizational founding predisruption persist to the postdisruption time period or do the
effects differ before and after the event? Our findings indicate that an association with the Silicon Valley’s traditional
industry clusters (e.g., high-tech industry effect) explains
more of the variance in organizational foundings in communities located in California after the dot-com bust as
compared with the prebust time period. In contrast, although
a Silicon Valley location remains relevant postdisruption,
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474
Journal of Leadership & Organizational Studies 20(4)
Table 2. Increment to Explanatory Power by Effect.
Model
Description
Likelihood
ratio test (χ2)
Log Likelihood
Comparisons
−340101.79
−340077.01
−337922.03
−337708.97
Model 2 vs. Model 1
Model 3 vs. Model 1
Model 4 vs. Model 2
49.55*
4359.5*
4736.08*
−296868.2
−296615.3
−296681.18
−296206.99
Model 2 vs. Model 1
Model 3 vs. Model 1
Model 4 vs. Model 2
505.7*
374.04*
816.72*
Postdisruption time period: 2001-2006
1
Baseline + Economic region
2
Model 1 + Region (Silicon Valley)
3
Model 1 + Industry (high-tech)
4
Model 2 + Industry (high-tech)
Predisruption time period: 1994-1999
1
Baseline + Economic region
2
Model 1 + Region (Silicon Valley)
3
Model 1 + Industry (high-tech)
4
Model 2 + Industry (high-tech)
N(postdisruption period) = 287,008; N(predisruption period) = 322,267.
*p < .00001.
Table 3. Hypotheses Tests.
Hypothesis 1b
Hypothesis 2b
Summary of hypotheses
Wald χ2
Region (Silicon Valley) effect
on foundings does not differ
pre- and postdisruption
Industry (high-tech) effect on
foundings postdisruption <
industry (high-tech) effect on
foundings predisruption
231.13*
887.82*
Results
Region (Silicon Valley)
effect on foundings
predistruption
Industry (high-tech)
effect on foundings
predisruption
>
<
Region (Silicon Valley)
effect on foundings
postdistruption
Industry (high-tech)
effect on foundings
postdisruption
*p < .01.
its benefit for nascent organizations erodes following the
disruption. The results also suggest that a high-tech industry affiliation tends to matter more than a regional (geographic) location effect pre- and postdisruption and this
difference is more pronounced in the years following the
disruptive event. The findings have several implications for
extant work on entrepreneurship and ecosystem development as well as previous work using variance decomposition in the strategy field.
Entrepreneurs require a variety of resources to create and
grow their ventures. As a result, they are forced to make
crucial trade-offs in how they allocate their time and scarce
resources. The dot-com bust diluted, in part, the geographicbased resources available to entrepreneurs in Silicon Valley
and, more broadly, in northern California. For instance, the
exit of organizations and human capital from the region
following the dot-com bust affected the region’s system of
production as well as the formal and informal networks
among organizational actors present in the region. As the
region’s networks eroded or diluted, entrepreneurs lost
opportunities for interacting with important organizational
actors and in turn, compromised their abilities to develop
ventures and build legitimacy. Our findings suggest that
under these conditions, nascent organizations will benefit
from where they locate in industry space as opposed to
geographic space. They also call attention to a critical tradeoff for resource-strapped entrepreneurs: Post a fundamental
disruption to a region, entrepreneurs should devote more
resources to developing legitimacy within their industries,
whether established or emerging, rather than developing
location-based legitimacy.
Prior work explores how an industry’s path of development is affected by a shock or disruptive event but directs
less attention to the broader context in which industries are
embedded. We address this void by examining how a disruption to the market for entrepreneurial funding affects a
region’s development pre- and postdisruption. Considering
region-specific effects in combination with industry effects
sheds light on the conditions that foster or hamper ecosystem growth pre- and postdisruption. The findings suggest
that although both categories of effects matter, predisruption, region (location) effects matter more to an ecosystem’s
development than industry affiliation whereas postdisruption, industry affiliation matters more than region (location). These differences imply that when an ecosystem is in
a period of ferment, ventures may increase their likelihood
of success by locating in the ecosystem’s geographic core.
However, following a transformative shock, an ecosystem’s
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MacGregor and Madsen
region-based legitimacy may erode whereas the legitimacy
of the ecosystem’s established industries remains intact.
Under these conditions, ventures trading off investments in
geographic or region-specific knowledge for investments in
industry-specific knowledge may be better off. Future work
might explore whether a pattern of recovery is further conditioned by the source or form of a disruption, such as
whether it is endogenous or exogenous.
Our research also contributes to a stream of work in
strategy that leverages variance decomposition techniques.
Studies in this stream rarely consider how a fundamental
disruption to an industry, sector or region affects heterogeneity in performance (exception, Walker et al., 2002). Our
findings demonstrate that following a major disruption, the
factors explaining variance in organizational foundings
across communities change in their degree of relevance. As
noted above, industry effects explained more of the variance postdisruption as compared with geographic effects.
This observation is informative for extant studies on performance heterogeneity. In particular, omitting transformative
events that affect regions or sectors may obscure important
sources of variance in performance. Subsequent work
examining the influence of industry, corporate, and business effects on performance heterogeneity thus might benefit by considering the role of major regional or industry
disruptions more explicitly.
This study is not without limitations. For one, we examine regional effects using a classification variable indicating
whether a community is located within Silicon Valley or
not. This conservative approach seems appropriate given
our theoretical focus, the distinctive characteristics of the
Silicon Valley region, and analysis of all communities and
organizational foundings in California from 1994 to 2006.
However, other studies, typically in single industries, examine geographic effects using distance measures (such as the
distance from a community or region’s epicenter to an organization’s location). Future work might incorporate a distance-based measure for geographic proximity to further
unravel the drivers of regional recovery in an innovation
hub (see, Paruchuri & Ingram, 2011). Second, to simplify
our exposition, we distinguish high-tech industries from all
other industries. Although the results inform our understanding of the high-tech sector’s evolution in California
pre- and postdisruption, subsequent studies might replicate
the analysis with specific attention to complementary industries and low-tech industries. Future studies also might use
more fine-grained measures of industry or market proximity to enhance our understanding of how the relationships
among industries (high-tech, complementary, low-tech)
affect a region’s (and ecosystem’s) evolution postdisruption
(see, Audia & Kurkoski, 2012). Third, research examining
single industries suggests that agglomeration benefits may
vary by organizational, product, and demand heterogeneity.
While we address unobserved heterogeneity through a
variety of control variables, subsequent work examining
which of these categories of effects contributes the most to
agglomeration economies postdisruption may prove
informative.
Fourth, a region’s attractiveness and average rate of
development may affect its recovery following a disruptive event. For instance, the pull of Silicon Valley may
have attracted an abundance of talent predisruption and
left the region with an oversupply after the disruption.
Although the region experienced an exodus of resources
postdisruption, the “oversupply” may have been sufficiently robust such that the exodus only had marginal
effects on the talent pool. As such, requisite human capital resources may remain available and provide a foundation for the region’s recovery. Our analysis controls for
unemployment rates and exit events, one indicator of a
release of talent and resources in the region; however,
subsequent work might address the effects of resource
dynamics more explicitly by examining how heterogeneity in human capital flows (inflows and outflows) over
time affects variance in regional recovery.
Related to the above, the effects of a disruptive event on
a region’s recovery also may be conditioned on a region’s
state prior to the event. For instance, if Silicon Valley is
typically dynamic, with frequent cyclical ups and downs, a
disruptive event that fuels the exit of firms and resources
may dampen the dynamic character of the region. If this
holds, the disruption could make the region calmer, and perhaps, closer to an equilibrium state. Although this argument
seems plausible, evidence to the contrary suggests that the
dot-com bust destabilized the market for entrepreneurial
funding for several years (see Figure 1). Our findings also
imply that the shift in the distribution of resources allocated
to the region, from the predisruption period to the postdisruption period, dampened recovery dramatically.
Nonetheless, future studies might explore the interactions
among these various effects in more detail to understand the
conditions under which region-specific effects, industryspecific effects, and/or state dynamic effects dominate in
explanations of regional recovery.
In conclusion, understanding what explains differences in
a region’s recovery following a fundamental disruption can
assist entrepreneurs, organizations, and institutional actors in
identifying spatial patterns of opportunity development and
value creation and, in turn, inform research on the dynamics
of industry clusters. The finding that, after a major disruptive
event to a geographic area, the value of a particular unique
location (Silicon Valley) erodes whereas a high-tech industry
affiliation gains prominence offers fodder for future work on
the industry, region, and cluster dynamics.
Acknowledgments
We are grateful to the Pino Audia, Srikanth Paruchuri, and the
special issue editors for their fruitful suggestions on this article.
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476
Journal of Leadership & Organizational Studies 20(4)
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Funding
The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article:
The authors thank the Leavey School of Business for partial
funding in support of this project’s development.
Notes
1. For purposes of this analysis, we exclude the following industries: agriculture, paper printing, furniture manufacturing,
textile, and general services.
2. Research in economics identifies two forms of agglomeration
economies, locational economies and urbanization economies
(Loesch, 1954). Locational economies, or “Marshall–Arrow–
Romer externalities,” involve knowledge spillovers across
firms but internal to an industry within a city or community.
Urbanization economies involve spillovers across industries
but internal to a geographic unit such as a region (for a review,
see Feldman, 1999, p. 14). Our analysis includes industries
located in communities and communities located within
regions, spanning both forms of agglomeration economies.
3. Since we do not observe the entire life histories of industries
with operations in California, the control variables do not
include a density-squared term (for additional details regarding
the theory and operational design of density dependence, see
Hannan & Carroll, 1992).
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Author Biographies
Nydia MacGregor (Ph.D., UC Berkeley) is Assistant Professor
of Management at the Leavey School of Business, Santa Clara
University where she teaches strategic analysis as the capstone
course to undergraduate business majors. Her research interests
include competitive dynamics, industrial agglomerations and geographic issues that influence firm emergence and survival.
Tammy L. Madsen (Ph.D., UCLA) is Associate Professor of
Strategy at the Leavey School of Business, Santa Clara University
where she teaches strategy in the MBA and Executive MBA programs. Tammy’s research interests are at the intersection of strategy, competitive heterogeneity, innovation, entrepreneurship, and
competitive dynamics. Her research has received various awards
from the Business Policy & Strategy (BPS) Division of the
Academy of Management and appears in a variety of journals
including Strategic Management Journal, Organization Science,
Industrial and Corporate Change, Journal of Management
Studies, Journal of Knowledge Management and International
Marketing Review.
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