Content uploaded by Marc-David L. Seidel
Author content
All content in this area was uploaded by Marc-David L. Seidel on Jul 10, 2016
Content may be subject to copyright.
Network Opportunity Emergence and Identification
by
Marc-David L. Seidel
University of British Columbia
Sauder School of Business
2053 Main Mall
Vancouver, BC V6T 1Z2
E-mail: seidel@mail.ubc.ca
Running head: Network Opportunity Emergence and Identification
Network Opportunity Emergence and Identification - 2
ABSTRACT
In this paper I propose a theory of network opportunity emergence. The core of
the argument is that as an overall industry network structure becomes centralized
opportunities emerge for new entrants. As the institutional environment evolves towards a
centralized network flow structure, innovators can identify newly emerged rich resource
niches which serve as the perfect breeding ground for an entrepreneurial start-up. While
the framework is an aggregate level conceptualization of market opportunities, it also
identifies specific actionable opportunities at a very micro level. Examples from the
networks of the airline industry illustrate the logic. I conclude by discussing the
innovation and entrepreneurship implications for a wide variety of industries and network
tie types, calling for utilization of the framework to answer a broad variety of research
questions.
Keywords: Network opportunity, opportunity identification, innovation,
entrepreneurship, structural holes, niche
Classification: Conceptual paper
Network Opportunity Emergence and Identification - 3
INTRODUCTION
Opportunity identification is a key aspect of entrepreneurial start-up. The bulk of
the literature on opportunity identification focuses on individual characteristics of the
nascent entrepreneurs themselves such as prior experience, social network connections,
personality traits, and alertness (Ucbasaran, Westhead and Wright 2009; Shepherd and
DeTienne 2005; Ardichvili, Cardozo and Ray 2003; Gaglio and Katz 2001). This
literature tends to focus on the individual successes and failures of entrepreneurs, and not
on the emergence and identification of emergent opportunities. On the more macro side,
the organizational ecology literature focuses on aggregate characteristics of industry
structure to identify conditions that predict organizational foundings (Freeman and Audia
2011), and the institutional theory literature focuses on contestation of alternative logics
(Thornton and Ocasio 1999; Greenwood, Raynard, Kodeih, Micelotta and Lounsbury
2011) to determine ultimate identity and form outcomes. Yet the bulk of this literature
currently fails to identify the emergence of specific entrepreneurial opportunities and
instead explains founding and survival rates more broadly. In fact, nascent entrepreneurs
misinterpret population level dynamics regularly (Sorensen and Sorenson 2003) when
perceiving opportunities, creating a need for better opportunity identification methods. In
this paper I build upon this macro level framing and develop a more nuanced theory of
network opportunity emergence, the emergence of opportunities in the structure of
industry networks, to help better inform the entrepreneurial identification literature and
provide theoretical grounds for specific emergent opportunity identification methods.
In their seminal theoretical article, Hannan and Freeman (1977) ask, “Why are
there so many different types of organizations?” The theoretical perspective known as
Network Opportunity Emergence and Identification - 4
organizational ecology (Hannan and Freeman 1977; Hannan and Freeman 1989) attempts
to answer this question by studying the long term changes in populations of
organizations. The most developed model within this perspective, density dependence
(Hannan and Carroll 1992), proposes that the number organizations in a population
(density), is directly related to two main evolutionary forces: legitimation (which
increases density) and competition (which decreases density). Numerous studies have
found support for the density dependence model (see Hannan et al 1995 for review). The
base model describes the long-term growth trajectories of organizational populations
reasonably well. But it does not fully explain periods of decline or resurgence, which
have been routinely observed in mature populations. The model was therefore refined by
asking the question, “What accounts for secondary waves of founding in a population that
has started to decline in density?” The resource partitioning process (Carroll 1985)
attempted to addresses this question.
Resource partitioning theory holds that as large generalist organizations come to
dominate markets and develop economies of scale, they cannot exploit all aspects of the
market. Specialized organizations are founded by aspiring entrepreneurs to take
advantage of untapped resources they have identified. This prediction has received strong
empirical support in a variety of industries (Carroll 1985; Powell 1985; Carroll 1987;
Carroll and Swaminathan 1992; Hannan and Carroll 1992:156-167; Swaminathan 1995;
Reis, Negro, Sorenson, Perretti, and Lomi 2012). In these studies, market dominance
among the generalist organizations is conceptualized as market concentration. The studies
find that as an industry becomes concentrated, generalist organizations experience
increased mortality. Simultaneously, they show that smaller specialist organizations have
Network Opportunity Emergence and Identification - 5
improved life chances. In other words, as markets become concentrated entrepreneurs are
better able to identify true opportunities.
An important question currently left unanswered by the research on resource
partitioning is, “What are the dynamics of concentration that lead to the observed
effects?” I argue here that for numerous cases, the centralization of industry networks is
the driving force behind the observed concentration effect. A centralized network is one
in which most network traffic passes through a few nodes. Or, in other words, many
nodes of the network do not have a direct connection. Burt (1992) suggests that as
networks become centralized, “structural holes” develop. A structural hole is the lack of a
direct connection between two nodes in a network. As structural holes develop, actors in
a central position compete more directly to satisfy a brokerage role. This directness of
competition leads to the observed resource partitioning effects of concentration. Picture
an market where every company must communicate and transact via particular brokerage
firms, a classic centralized market making broker such as the New York Stock Exchange.
Compare it to a market where every entity can communicate to every other entity
directly, a C-Form organization (Seidel and Stewart 2011) such as the worldwide
community of developers who build and maintain the Linux operating system. These are
respectively centralized and decentralized transaction networks.
Networks can describe a wide variety of industry structures and flows. They are
not just limited to economic transactions per se. The content of the network tie can be
trust, friendship, financial transaction, information flow, movement of physical objects or
people, among many other tie types. The goal of this paper is to craft a network
opportunity identification framework agnostic to the content of the ties and focusing on
Network Opportunity Emergence and Identification - 6
the structural aspects of the overall network regardless of the type of tie. I integrate
structural hole theory with resource partitioning theory to develop several propositions,
dealing with the differing effects of network centralization on generalist and specialist
firms. The propositions especially pertain to the conditions fostering specialist emergence
and growth. I use examples from the airline industry to illustrate the concepts in the case
of ties moving physical objects or people, and finally demonstrate the application of the
framework to a wide variety of industries and types of ties.
Niche width and the resource partitioning process
Organizational ecology’s treatment of niches focuses on the organizational
population. Niche is not a characteristic of an individual organization, but a set of
environmental conditions in which a population of organizations can survive (Hutchinson
1978; Hannan and Freeman 1989).1 The theory defines niche space as a continuous space
in N-space, with N being the number of dimensions used to define various characteristics
of the environment. The niche is defined as a continuous range on each dimension. The
intersection of each range is the niche space. The theory goes on to predict that when a
set of environmental conditions exists in the niche space that can sustain a form of
organizations, the probability of an organization being founded that uses the resources of
the niche increases (Hannan and Freeman 1989:125). Similarly as environmental
conditions change, and a niche space shrinks, organizations that use the resources of the
niche face an increased probability of failure.
1 Following Hutchinson (1978) Hannan and Freeman (1989) distinguish between fundamental and realized
niches. A fundamental niche is defined as the set of environmental conditions under which a population’s
growth rate is non-negative (Hannan and Freeman 1989:96). The fundamental niche applies to growth rates
of isolated populations. When populations interact and compete for the same resources through overlapping
fundamental niches, the resulting restricted set of conditions in which each population’s growth rate is non-
negative is the realized niche (Hannan and Freeman 1989:97).
Network Opportunity Emergence and Identification - 7
A single industry can sometimes be characterized by several populations or
subpopulations -- different types of organizations which thrive in different environments
or on different resources. A common distinction is that of specialist and generalist
organizations, a distinction of niche width (Hannan and Freeman 1977; Carroll 1985).
Generalist organizations operate in a wide range of environmental conditions. As
organizations they typically maintain excess capacity, take advantage of economies of
scale through lower marginal costs, are not as sensitive to environmental shocks, and are
slow to change. Specialist organizations can exist in only a small part of the environment.
Organizationally they utilize almost all of their capacity, gain expertise in specific
competencies, have lower fixed costs, and are highly sensitive to environmental shifts
(Hannan and Freeman 1989:106). The majority of new start-ups fall into the specialist
category by definition, particularly when entering established industries. Theory predicts
that these two types of organizations fit ideally with different environments. A rapidly
changing environment that is populated by specialists will have a high turnover rate, as is
common in industries high in entrepreneurial activity. As the environment shifts away
from the current specialist organizations' focus, they will fail and new ones will be
founded. These two types of organizations also use different resources. If resource bases
change, the different populations or subpopulations will be affected differently.
Niche width ideas were first developed by Carroll (1985) to examine what has
come to be called resource partitioning. This theory demonstrates the different effects of
market concentration on generalist and specialist organizations. As an organizational
population develops each organization attempts to position itself towards the center of the
market. Organizations that can exploit economies of scale use their market power to
Network Opportunity Emergence and Identification - 8
eventually push other organizations away from the center of the market, resulting in
failure or peripherization. The remaining generalist organizations come to dominate the
center of the market. They grow and become even more general in their appeal, becoming
too general for smaller customer groups. This new environment can sustain more
specialist organizations in the periphery than before because as the overall market size
grows the generalist organizations start to neglect the peripheral market segments. So
resource partitioning theory predicts that as an industry grows and becomes more
concentrated, the life chances of specialists are increased, but the life chances of
generalists are decreased. The effects of concentration on generalists and specialists are
thus opposite in direction.
Carroll (1985) originally found support for the theory in a study of seven
randomly selected local newspaper populations over the period 1800-1975. He analyzed
mortality rates of newspapers in those markets and found that as concentration increased
generalist mortality rate increased while specialist mortality rate decreased. Carroll and
Swaminathan (1992) found similar support in a study of the U.S. beer brewers over the
period 1975-1990. They found that as industry concentration increased the mortality rate
of microbreweries (the specialists) decreased. Support for this model, and its refinements,
has been found in a wide variety of industries including newspaper publishing (Carroll
1985), beer brewing (Carroll and Swaminathan 1992; Hannan and Carroll 1992:156-167),
television broadcasting (Reis, Negro, Sorenson, Perretti, and Lomi 2012), hospitals (Al-
Amin, Zinn, Rosko, and Aaronson 2010), audit firms (Boone, Meuwissen, and
Witteloostuijn 2009), and wine making (Swaminathan 1995).
Network Opportunity Emergence and Identification - 9
These predictions carry over to the entrepreneurial founding process as well.
Carroll and Swaminathan (1992) found that as industry concentration increased the
founding rate of both microbreweries and brewpubs increased. Aspiring entrepreneurs
identified the opportunities and founded specialized start-ups. Further support was found
for the founding predictions by Swaminathan (1995) who found that as industry
concentration increased the founding rate of farm wineries increased in his study of
wineries over the period of 1941-1990. These studies show that as markets become
concentrated the life chances of specialists and generalists move in opposite directions,
and the key is that these industry conditions provide fertile ground for entrepreneurs to
enter the market. The competition becomes stronger for generalists decreasing their life
chances, while the specialists that find a pocket of resources on the periphery have
increased life chances.
Resource partitioning explains the dynamics of long term industry evolution, but
specific market identification is analyzed post-hoc. As such, it is difficult to use the
theory in its current form to actively identify emergent entrepreneurial opportunities.
Digging under the surface and understanding the structure that leads to these
concentration effects is the next logical theoretical step to develop a broader theory of
emergent opportunity identification. This is similar to what has been done in terms of
physical geographic space and market entry decisions in the Tokyo banking industry
(Greve 2000), but geographic proximity is just one type of network tie. I argue that the
deeper theoretical construct of network structure can provide a broader generalized set of
potentially untapped niches in the overall market. In the next section, I explore how a
generalized focus on the network structure of the industry, using the well developed
Network Opportunity Emergence and Identification - 10
concepts of network theory, appears to be a promising method of contemporaneous
specific opportunity identification.
Network theory and the niche
Early work on networks focused on the theoretical development of network
concepts and methods (White, Boorman, and Breiger 1976; Freeman 1979). The concepts
developed later were applied to a wide range of substantive research questions outside of
the field of network theory itself (Borgatti, Mehra, Brass, and Labianca 2009). Yet
network theory still has many insights to provide in applications of a well developed set
of concepts to research questions of interest to a broader audience, particularly to the
domain of emergent opportunity identification by entrepreneurs.
Operationalizing the concept of niche by using network concepts is one such
application, one which can also provide a stronger more contemporaneous direct link to
opportunity identification than the current theory of concentration used in the resource
partitioning literature. Network concepts such as centralization can theoretically be used
in a similar way to the market concentration measure used by Carroll (1985), but provide
more specific tools to identify specific actionable emergent opportunities. A centralized
network is one in which most network traffic moves through a limited number of nodes.
In the simplest case of a three actor network, if two of the actors are connected to the
third, but do not have connections to each other, the network flow needs to go through the
central actor (the third actor). If, however, all actors are connected to each other it is a
decentralized network where all actors can directly reach each other.
Network Opportunity Emergence and Identification - 11
There are several operationlizations of the centralization concept. For instance,
Burt’s theory (1992) suggests that as networks become more centralized structural holes
develop. Burt defines structural holes as a relationship of nonredundancy between two
contacts (Burt 1992:18). He defines redundancy in two ways: cohesion, and structural
equivalence. Cohesion is a strong tie between two contacts. Let us return to our simple
three person network to illustrate the concept. If two strongly tied actors are in a focal
actor’s network, then there is no structural hole. However, if these two actors did not have
a strong tie, then there is a structural hole between them. In other words, the lack of a
strong tie is a cohesion structural hole “signature.” This directly parallels the concept of
centralization. Now turning to the structural equivalence definition, two actors are
structurally equivalent to the extent they have the same network connections. Structural
equivalence provides redundancy through the same network ties. If two actors have ties
to different groups of actors, then there is a structural hole between them because of their
lack of structural equivalence. To illustrate this point, let us add a fourth actor to our
simple network. If the fourth actor has connections to the two originally disconnected
actors, then he has structural equivalence with the third actor. They both maintain the
same set of ties, without being tied to each other. But they serve the same brokerage role
for the four person network. Neither of the two brokers would receive an additional
network benefit from being tied to each other, because such a tie would lead to redundant
contacts.
Moving the level of analysis up to the population level makes a new set of
predictions possible. The number of structural holes in a population should in theory
predict opportunities for specialized brokerage. As overall network centralization rises,
Network Opportunity Emergence and Identification - 12
those actors in a central position compete more directly to serve a structurally equivalent
brokerage role. They in essence become generalist brokers. Non-central actors are left
with fewer choices to connect them to the rest of the network. The intense direct
competition will cause some generalist brokers to fail. When this occurs, the surviving
generalists may acquire some of the failed brokers’ links. However, some will be left
unclaimed creating new structural holes. Thus as generalist organizations centralize their
competitive networks, new structural holes emerge as potential opportunities for a
specialist broker organization to operate successfully. As network centralization rises, the
generalist actors compete more directly and the failure rate of generalists rises. However,
as network centralization rises and generalists fail, the additional emerging structural
holes improve opportunities for specialists. The effects of network centralization on
generalists’ and specialists’ vital rates are opposite in direction, similar to the effects of
concentration in resource partitioning theory.
Figures 1 and 2 illustrate why the survival chances for each individual specialist
organization improve. In Figure 1 two organizations use non-centralized networks to
connect the nodes on the left and the nodes on the right. While all nodes on the left are
connected to all nodes on the right, there is no competition to move from the left to the
right. Figure 2 shows the same two organizations utilizing a concentration point to
centralize their individual networks. There is still no direct competition to move from the
left to the right. However, there are now structural holes between the points on the left
and the points on the right. These holes are a niche that can sustain specialist non-
centralized organizations.
Network Opportunity Emergence and Identification - 13
On a similar note, as generalist organizations become more centralized they
compete more directly with each other. In Figure 3 the same two organizations add one
new link to their concentration point. There is now direct competition from all nodes on
the left to all nodes on the right. The concentration point in the center is different, but the
overall effect is the same -- both organizations can link nodes on the left to nodes on the
right. These centralized organizations now compete directly with each other for that
brokerage role.
As an example, imagine two libraries located on the same street with different
print collections. The first stocks only those books published by Harvard University
Press. The second only stocks those from Cambridge University Press. The structure of
competition is centralized with the two organizations not directly competing through
overlapping network roles, as in Figure 2. If an individual needs to find a particular book
from Harvard University Press, then she must go to the first library. Figure 3 represents
the situation where each library starts to carry the collections of both presses. The
structure of competition is centralized with competing central actors. Both libraries
compete for the brokerage role and they are now in direct competition. One of these
organizations may eventually gain some advantage -- maybe it has a better inventory
system to keep track of collections. Because of scale economies (e.g. the larger library
might be able to purchase books more cheaply or reduce costs through better inventory
management technology), the direct competition will eventually lead to either a failure or
peripherization of the other organization in a competitive market. While these central
organizations are focusing on their competition they likely will be missing other major
Network Opportunity Emergence and Identification - 14
opportunities such as other publishing houses, electronic media, and new forms of
content delivery not reliant upon publishers or libraries at all.
Drawing upon the resource partitioning logic, for specialist organizations, such as
an all electronic publishing platform that directly connects content creators with content
consumers, the structural shift in time from conditions represented in Figure 1 to those of
Figures 2 & 3 will reduce the failure rate as well as increase the founding rate, leading to
propositions 1 and 2:
P1: As the structure of competition becomes centralized in network terms, the
probability of specialist failure decreases.
P2: As the structure of competition becomes centralized in network terms, the
founding rate of specialists increases.
While these propositions are necessary to build the groundwork for understanding
the overall dynamics of emergent network opportunity structure, the real strength of this
perspective comes in identifying specific opportunities instead of general macro trends of
positive or negative conditions. Carrying through the logic above, one can utilize a
dynamic network map and identify both structural holes, and more specifically
emergence of structural holes as organizations centralize their networks. This tracking of
the dynamic shift can lead quickly to new market opportunity identification, leading to
proposition 3:
P3: As the structure of competition becomes centralized in network terms,
new structural holes emerging in the shifting network maps are strong
potential opportunities for new market entrants.
As these emerging structural holes are strong potential opportunities for new
specialized market entrants, we can make a very specific set of testable predictions
regarding the dynamics of specialist organizations attempting to fill such holes:
Network Opportunity Emergence and Identification - 15
H1: Specialists have a higher probability to be founded to fill a structural hole
than in a non-structural hole network position.
H2: Specialists founded in a previous structural hole have better survival
chances than those founded in a non-structural hole network position.
For those specialist organizations that are founded in a position that does not fill a
structural hole, H2 predicts life chances are poorer overall. Experiencing such negative
pressures can either lead to failure or change. Those organizations are likely to make
changes prior to failure. But the quality of those changes will impact their subsequent life
chances. Based upon the logic presented above, those whom pivot to fill a structural hole
will have better life chances than both those who remain static and those who make a
change to another non-structural hole position, leading to:
H3: Specialist organizations founded in a non-structural hole that then pivot to
fill a structural hole have better survival chances than those who do not
pivot to fill a structural hole.
An illustrative example: The airline industry
The transportation network aspects of the airline industry make it an excellent
context for illustrating the link between network centralization and opportunity for start-
ups. While developing the logic such obvious network industry characteristics are useful,
but the application is wide beyond such types of industries. As such, I will first work
through the logic using the easy to understand airline network structures, and then later in
the paper further develop the concept and make the linkage to other types of industries
and network ties.
The route network represents one of the single most important aspects of the
airline business. It determines what types of equipment to purchase, whom to hire, what
types of customers they will serve, what types of operational difficulties the airline will
Network Opportunity Emergence and Identification - 16
encounter (snow, ice, fog, etc.), what types of additional market opportunities the airline
has, and what types of alliances to enter. Airlines are defined by their route network.
An airline makes money by having its planes in the air with paying passengers on
board while simultaneously optimizing aircraft, crew, and ground facility utilization
(Greve and Seidel 2015). While convenient for the passenger, non-stop service creates
some yield problems in the form of non-utilization. Prior to deregulation most airline
service was conducted on a point-to-point basis, a decentralized network. Planes
frequently were flying with a large percentage of empty seats, a low load factor. Prior to
deregulation in the United States, the average load factor was a mere 55% (Davies 1988),
meaning that 45% of the seats went empty. These low load factors were not a problem in
a regulated industry because the carriers did not face strong direct competition. As the
industry was deregulated carriers started to compete more directly and in response to
these new competitive pressures airlines started to focus on efficient operation.
They introduced the hub-and-spoke system in an attempt to gain economies of
scale through network centralization. The hub-and-spoke system increases load factors by
aggregating multiple “feeder” flights in a hub city, concentrating passengers from several
cities onto continuing connection flights to a spoke (Greve and Seidel 2015). Each new
spoke attached to a large hub adds numerous new market pairs. If an airline currently has
nine spokes from a hub, and adds one additional spoke, ten additional markets are opened
(one from the spoke to the hub, and nine from the new spoke to the other spokes). The
strategy diffused and was quickly adopted throughout the industry, resulting in the
emergence of large hub-and-spoke networks. American Airlines was early on the scene,
with an internal analysis completed by Mel Olsen on “Depeaking Dallas/Fort Worth.”
Network Opportunity Emergence and Identification - 17
Olsen demonstrated the profit potential of adding flights to peak periods and single-
handedly convinced American Airlines to centralize its network and build a “fortress
hub” at Dallas/Fort Worth (Reed 1993). The practice diffused worldwide quickly. Other
major carriers followed suit, moving quickly to centralize their networks through hub-
and-spoke strategies (Wells 1994).
As the existing airlines centralized their networks through the development of
hub-and-spoke structures, a niche for specialists emerged. When the large carriers moved
to a centralized hub-and-spoke system, their service became more and more general. That
is, instead of serving specific non-stop markets, the generalist carriers were able to take a
passenger from almost any city to almost any other city via a single hub connection.
Their service products became very similar. It did not matter much to people living in,
say, Buffalo if on the way to Miami, they changed planes in Philadelphia or Washington
DC. Rather than a single carrier providing non-stop service from Buffalo to Miami,
numerous generalist carriers competed, each with a different single connection service. In
network terms, the generalist carriers became structurally equivalent. Additionally since
they were competing more directly, these generalist carriers tended to have very similar
organizational structures characterized by: high fixed costs, strong labor unions, internal
maintenance facilities, internal catering facilities, internal computer reservation systems,
a large percentage of equipment owned instead of leased, and hierarchical management
(Davies 1988, Reed 1993, Peterson and Glab 1994, Wells 1994).
Although the new hubs connected many previously unserved city pairs, certain
previously non-stop markets were no longer served without a connection (e.g. Buffalo-
Miami and Boston-Orlando). The generalist organizational form -- with its high fixed
Network Opportunity Emergence and Identification - 18
costs and lack of flexibility -- was not well suited to such markets. This development
created an environment with an opportunity for specialized non-stop service for two
reasons. First, the added inconvenience of connecting through a hub may be less
desirable than would slightly more expensive non-stop service. Second, the efficiencies
of a hub-and-spoke system can only be realized on mid range to long haul trips. For a
short trip, such as Los Angeles to San Francisco, connecting through San Jose drives up
costs prohibitively.
In other words, as the major airlines started to centralize their service networks by
building large fortress hubs, an environmental condition emerged in the point-to-point
markets as an opportunity for entrepreneurial entrants. These entrants would have many
characteristics different from the existing carriers. Specialist entrants would have low
fixed costs. To operate a simple point to point service, only a single gate is needed at each
airport served, reducing fixed gate costs. The ground services for that single gate are
smaller and easier to maintain, reducing fixed ground operations costs. A highly
competitive specialized subpopulation -- characterized by frequent replacement of
organizations -- is conducive to weak or non-existent labor unions. Since specialist
organizations are smaller, they can handle many of their requirements by contracting
arrangements. These would include contracted maintenance agreements, contracted or
non-existent catering services, and contracted space on computer reservation systems.
Since specialist organizations are by definition less complex, they can operate with a
reasonably small and flat management structure.
One might ask why the large carriers did not simply serve the non-stop markets
that had enough demand? Large carriers have high fixed costs (labor, and hub operation),
Network Opportunity Emergence and Identification - 19
and thus could offer lower fares on connecting service (Reed 1993). Most importantly,
hub-and-spoke service requires different organizational routines than point-to-point
service. Large carriers did not focus on fast turn-around (amount of time a plane sits on
the ground at a stop), which is the key to being a successful point-to-point carrier. Fast
turn-around is not the primary source of yield efficiencies for a hub-and-spoke carrier,
and thus is not as much of a focus for creation of a generalist organizational routine.
Hub-and-spoke carriers also possess many routines which are unnecessary and
inefficient for point-to-point service. Examples include: connecting passengers,
connecting baggage, complex itinerary booking, complex reservations capability,
complex pricing structure, diverse maintenance requirements, and diverse training
requirements. For a point-to-point carrier, any type of connection yields inefficiencies.
Connecting passengers and baggage drive up turn-around times substantially. Passengers
on hub-and-spoke carriers frequently have complex itineraries stopping in many cities
and making many connections. Serving them requires organizational routines and
information systems that can handle complex bookings. For a point-to-point carrier this is
inefficient. A hub-and-spoke carrier has numerous types of airplanes to serve the various
types of markets (Greve and Seidel 2015). These aircraft all have their own unique
maintenance and staffing requirements. The training and staffing functions are thus more
complex than for a point-to-point carrier with only one equipment type.
While the centralized hub-and-spoke system efficiently connects the greatest
number of city pairs, it has inefficiencies as well. First, connecting service inherently
takes longer for the passenger. Second, for an airline to transport a person from point A to
point B, two flights must be operated. For long haul travel this adds only a marginal
Network Opportunity Emergence and Identification - 20
amount of time to the overall trip. A six hour non-stop trip would take seven or eight
hours. However, for a short trip of an hour, adding a connection to the service doubles or
triples the travel time. Third, as hubs grow in size they become difficult to manage. Just
as the number of possible spoke to spoke trips increases geometrically, so too does the
number of possible routings for both baggage and passengers passing through the hub.
The delayed openings in the 1990’s of the Hong Kong, Kuala Lumpur, and Denver
airports due to baggage routing problems illustrate this point well. Fourth, physical limits
of airspace and gate space are reached. In an effort to reduce connecting times for
passengers the hub-and-spoke strategy is managed by using time banks. A time bank is a
window of connection times in a hub where multiple flights arrive and depart. For
example a wave of east bound flights will arrive between 4:45 and 5:15 p.m. in Chicago
for the 5 p.m. eastbound bank. Starting around 5:45 these flights all continue to their
eastern destinations after having exchanged passengers, crew, baggage, and cargo. Only
so many flights can depart a given airport in a given time bank. As connecting time banks
are enlarged, the time for the connecting bank window must also be increased. This adds
to passenger inconvenience and aircraft ground time. All of these inefficiencies will
increase the price for passengers as the added cost for the airlines are passed on to the
consumer. Thus, a specialist niche with non-stop and low cost service emerged as it was
identified by entrepreneurial start-ups.
Network analysis helps to conceptualize this development in the structure of
competition. Imagine each city as a network node. For the purposes of the airline
industry, the structural hole definition using cohesion is more appropriate.2 This definition
2 The structural equivalence of Boston and New York does not outweigh the underlying purpose of air
transport -- to get people and cargo from point A to point B. If an airline attempted to route a passenger to
Boston instead of New York citing “structural equivalence” they would quickly go out of business. Thus for
the purposes of this paper I used the cohesion definition of structural holes (Burt 1992:18).
Network Opportunity Emergence and Identification - 21
uses direct ties to examine network structure. A strong tie can be characterized as non-
stop service. As a hub-and-spoke system develops, network centralization rises, and the
number of structural holes between nodes (cities) rises. Rising centralization spurs the
emergence of a specialist niche opportunity. That is, centralization creates structural holes
which are the location of resources that can sustain the growth of a specialist population.
The cities of Buffalo, Boston, Raleigh-Durham, and Miami illustrate this point.
Prior to the development of a hub-and-spoke system, Buffalo, Boston, and Raleigh-
Durham all had non-stop service to Miami (see Figure 4). A hub was created at Raleigh-
Durham. If all non-stop service from Boston and Buffalo to Miami had been converted to
a connection through Raleigh-Durham, then structural holes between Boston-Miami and
Buffalo-Miami would have developed (as in Figure 5). Demand for non-stop Boston-
Miami and Buffalo-Miami flights would sustain specialized non-stop service should this
hole be large enough, and thus examining the network structure could facilitate specific
emergent opportunity identification by a potential entrepreneurial entrant.
Generalists and specialists - A sample direct network tie implemented
If there is enough demand for non-stop service, then why would the generalist
carrier operating through Raleigh-Durham not compete effectively with a specialist
serving Boston to Miami by adding a direct network tie with non-stop service? For a
moment let us assume that a generalist carrier attempts to compete directly with a
specialist on the Boston to Miami route. Let us follow a passenger through both carriers
on a trip from Boston to Miami. Suppose passenger A chooses the specialist carrier
(Airline S) and passenger B chooses the generalist carrier (Airline G).
Network Opportunity Emergence and Identification - 22
Airline S has an efficiency advantage in the reservations process. Passenger A
searches for a ticket on the carrier’s website from Boston to Miami. The site offers
several non-stop flight options based on the passenger’s time preference and fares
available. The passenger chooses the flight and the ticket is booked. Meanwhile
passenger B visits Airline G’s website. Airline G needs to have higher powered servers
with more complex search software that is more difficult to maintain and keep up to date
because of the complex nature of many generalist connecting tickets. The airfare database
needs to have multiple yield formulas being optimized to keep pricing on all possible
network hub connections balanced and efficient on every route. When conducting the
search, the passenger is offered more options than passenger A, and those options are
likely to change more frequently to regularly balance yield on multiple route pairs. They
would be offered non-stop, or a better fare potentially with connecting service through
Raleigh-Durham. After sorting through the options, the passenger chooses the flight (or
flights) and the ticket is booked. The additional cost of maintaining the complex
reservations system for the generalist carrier, in addition to the added cost of more
complex itinerary building (and the resulting need of additional database and website
upkeep), puts Airline G at a disadvantage during the reservations process. Airline G’s
reservation system needs to calculate the optimal pricing of seats from Raleigh-Durham
to Miami for passengers connecting from both Buffalo and Boston. (The price will most
likely be different depending on specific market conditions.) This too adds complexity to
the process for Airline G, and requires the build-up of larger pricing departments as well
as more expensive upkeep for both hardware and software. Should the passenger’s travel
plans change or be disrupted due to weather the rerouting of the passenger is more time
Network Opportunity Emergence and Identification - 23
consuming and complex for Airline G as the complexity of changes cascades throughout
the system with many unexpected impacts and costs.
Airline S has an advantage in customer service ground operations at the departure
airport. Let us assume that both passengers choose non-stop flights. When the passengers
arrive at the airport, the Airline S check-in counter requires fewer agents than Airline G’s,
and can rely more heavily on automated check-in processes for the simpler travel
itineraries they deal with for point to point flights. For live agents, Airline S only needs to
check baggage to a few direct destinations. Airline G’s agent needs to have the capability
to check baggage worldwide. An average check-in process will take longer for the Airline
G agent because of the added complexity, and more need to agent intervention to handle
complex bookings and resolve technical issues from the more complex itineraries that fail
automated check-in. More passengers will be unable to use automated check-in due to the
complexity of the trips. On average, Airline G will carry more checked luggage.
Passengers that are traveling on connecting flights are less likely to carry their bags on
board. Once the passengers have checked their baggage, the baggage sorting operation is
more complex for Airline G. Airline G’s baggage personnel need to sort the baggage into
connecting groups to make “down line” operation more efficient. Airline S can just load
the bags as they arrive without concern for sorting for connecting flights. Airline G is
again at a disadvantage.
Airline S has an advantage in equipment costs, training costs, and personnel costs.
Since Airline G maintains the capability to fly in many distinct markets, it requires more
variety in types of equipment. Aircraft are profitable on different types of routes
depending on distance, load, and weather. By maintaining this capability, Airline G must
Network Opportunity Emergence and Identification - 24
have spare parts, trained mechanics, trained flight crew, and diverse gatespace for
multiple equipment types. This again puts Airline G at a disadvantage.
Airline G and Airline S are well suited to different types of operations. The
additional organizational routines that Airline G maintains to serve the generalist hub-
and-spoke markets drive up the costs on point to point routes without regaining the cost
benefit of concentrating traffic through a hub. This puts Airline G at a distinct
disadvantage when attempting to compete with Airline S on a non-stop market. Both
types of carriers can co-exist, but they have advantages in different types of market pairs.
Airline G continues to maintain an advantage in connecting market pairs that would not
be viable to be connected directly.
Network opportunity identification of the specialist niche
Network structure data are aggregate measures of network ties. While industry
level networks can be conceptualized quite differently depending on industry
characteristics, in the case of airline traffic the network ties represent traffic flows
between cities. Each city serves as a node in the overall population network. An airline
hub is a central node. As more traffic flows through a subset of nodes, the network takes
on a centralized structure. This centralized hub structure leaves structural holes in the
overall network between non-central nodes. So, the amount of traffic flowing through
central hubs is a direct measure of the network opportunity for a potential entrant in a
specialized niche. As the centralization of the overall network increases network
opportunities emerge for start-up entry of specialists. Specialist niche size is a
characteristic of the population network. Population-wide network data are required to
Network Opportunity Emergence and Identification - 25
measure the specialist niche. The construct NetworkOpportunity is the average percentage
of network flow passing through a network hub. It is defined as:
t
M
it
H
j
jt
t
M
S
a
ortunityNetworkOpp
t
t
∑∑
=
=
=
1
1
where
M
is the number of major organizations,
H
is the number of network hubs for a
given organization,
a
is the traffic through a given hub for a given organization,
S
is
overall system traffic for a given organization, and
t
is the time period. As major
organizations become more hub oriented, this approaches 1. It is an aggregate measure on
the population level.
It can theoretically range from 0 to 1. As the measure approaches 1, the number of
structural holes in the overall network increases. It thus serves as a population level
measure of the network opportunity for specialist niche entry. Returning to the airline
example, if the major carriers all moved every passenger via a hub the measure would be
1. This is obviously in the extreme. But considering that major carriers have revenues at
least 100 times larger than medium regionals, a slight shift in the overall network
structure of the majors creates a large resource pool for potential entrants, thus creating a
network opportunity.
To understand founding processes on the industry level it is critical to have an
aggregate level measure such as this of specialist niche size. This measure of specialist
niche size differs from those used in previous studies. This is a population-level measure
rather than a firm-specific measure. Baum and Mezias (1992), in a study of the
Network Opportunity Emergence and Identification - 26
Manhattan hotel industry, utilize Euclidean distance to measure directness of competition.
They argue that as spatial distance increases between competitors that the intensity of the
rivalry decreases. The Euclidean distance measure they use is calculated for each
organization, instead of on a population wide basis. Similarly, Baum and Korn (1996), in
a study of commuter airlines in California, calculate a measure of market domain overlap
on a focal firm basis. They argue that firms face differing levels of competition
depending on which markets they enter, and which firms they face in those markets.
This proposed aggregate measure also differs from those typically used in
previous resource partitioning studies. The direct measure of the specialist niche size
relies on objective operational network measures. The concepts of network theory
provide a clearer more specific picture than concentration measures of the structure of
competition. These types of measures build in the specific network strategy of the major
organizations as opposed to simply their market share. The measure of specialist niche
size is an aggregate strategic measure for the entire industry in a given time period. Yet
unlike concentration, since the measure includes strategic choices made by the
incumbents, the building blocks of this measure can also serve as specific market analysis
for opportunity identification in specific specialized niche markets. I elaborate on this
logic of opportunity identification below.
DISCUSSION – NETWORKS AS NICHE
If we view the industry competitive framework as a network, the specialist niche
network opportunity can be conceptualized as a structural hole in an overall network
structure. Theoretical predictions about resource partitioning can be restated in network
Network Opportunity Emergence and Identification - 27
terms. As network centralization rises, organizations in a central position compete more
directly to satisfy a brokerage role. The risk of central brokers holding structurally
equivalent brokerage positions increases, leading to more head to head competition
between the generalist central brokers. This intense direct competition will cause some
generalists to fail. When this happens, the surviving generalists acquire some of the failed
generalists’ network links. However, some are left unclaimed creating emerging structural
holes in the network. These structural holes are a network opportunity niche in which
specialists can emerge and thrive. As the structure of competition becomes centralized in
network terms, the failure rate for specialists decreases. Similarly, as the structure of
competition becomes centralized in network terms, the founding rate of specialists
increases due to the increased opportunities in the network structure.
Ideally to analyze this process a network matrix is required for each time period.
These matrices could then be used to calculate a measure of specialist niche size that
varies over time. While any measure of network centrality could be used, one
straightforward one is degree centrality (Freeman 1979) which is defined as:
1,,
1
1
>≠
−
=∑
=
Cji
C
a
Degree
C
j
j
i
Centra lit y
where C is the number of nodes served by all organizations during the period in question,
and a equals 1 when there is a direct network link. Averaging these individual centrality
scores from each node yields:
Average Degree C
a
Ci j C
Centrality
i
C
ij
j
C
( ) , ,=−
≠ >
==
∑∑
11
11
Network Opportunity Emergence and Identification - 28
Using this network level measure of centralization, we can measure overall specialist
niche size as:
Niche
a
C C i j C
Size
ij
j
C
i
C
= − −≠ >
== ∑∑
1 1
11
2
, ,
This measure is similar to the measure of network density commonly used in network
analysis, but is subtracted from 1. Compared to concentration measures, this more direct
measure of specialist niche size allows an even finer grained approach to understanding
the link between structural holes and niche.
Industries that have logistical network components (airlines, railroads, bus,
trucking, shipping, cargo, delivery, taxi and limousines), communication network
components (newspapers, magazines, telephones, computer networks, television, radio
stations, film, search engines, content aggregators and social media), or brokerage
network components (investment banks, venture capital firms, labor unions, job
placement services, dating services, auctioneers, insurance companies, commercial
banks, savings and loans, art galleries, advertising agencies, real estate agents, and
content curators) are well suited to this type of more fine grained network centralization
type of analysis. Table 1 shows how the network centralization perspective can be applied
in various industries using different network tie types.
Let us take the newspaper industry of Carroll’s original resource partitioning work
(Carroll 1985) as an example and view it through the network opportunity lens. What is a
newspaper? It is a centralized broker of information. Prior to newspapers information was
transmitted from person to person in a manner similar to point-to-point service in the
airline industry. Small regional newspapers served as “regional hubs” of information. As
Network Opportunity Emergence and Identification - 29
newspapers grew the network structure of information flows became centralized. As the
information flows became centralized, a niche was created for specialist newspapers to
connect groups of people who were no longer connected conveniently through a large
central newspaper. Ethnic groups, for example, could not communicate effectively
through a large general city paper. Similarly, business people could not effectively
communicate through general city papers. Specialized ethnic and business newspapers
sprung up as a result. Yet even such specialized newspapers still left structural holes
between individuals around topics or geography and social media capitalized upon that
direct connection niche. In the case of newspapers, conventional measures of market
concentration, as used by Carroll (1985), can be seen as proxies of the network
centralization of information flows, and the specific structural holes appearing in the
network matrix can be seen as potential entrepreneurial opportunities.
Using the network opportunity framework, we can rigorously identify
opportunities in a systematic manner. In fact the entire trend of disintermediation in the
finance, marketing and legal literatures (Fang, Ivashina and Lerner 2015; Nordin,
Brozovic and Holmlund 2013; Cunningham 2015) is built upon filling those structural
holes and removing the broker from the transaction. Using the tools of network analysis
to systematically identify structural holes in an industry network provides a clear method
to narrow down potential disintermediation opportunities to explore for innovation to
emerge and flourish into entrepreneurial activity.
Similarly, research on marginality and innovation suggests that marginal and
peripheral actors, previously considered to be less obvious sources of innovation, provide
better solutions to technological and scientific challenges (Jeppesen and Lakhani 2010).
Network Opportunity Emergence and Identification - 30
Conceptualizing any specific field of knowledge using the types of network opportunity
frameworks developed in this paper could systematically help to identify specific
innovative actors that may be highest at risk of developing innovative solutions to
identified structural holes. As such these tools could also be used systematically by
entrepreneurs scanning the knowledge environment for technology transfer possibilities
and other sources of unique innovation opportunities from the knowledge network.
Directions for future research
A promising direction for future research is dynamic network analysis of these
proposed dynamics. By aggregating structural holes at the population level, we can study
a wide range of population level questions in a dynamic network setting. The network
opportunity structure, or specialist niche, represents the resource base that entrepreneurs
and existing organizations can systematically exploit. Applications include understanding
the true competitive framework of entrepreneurial opportunity structure, understanding
opportunities for market entry for existing organizations, and further understanding the
link between large organizations and small organizations in the competitive landscape.
Studying how aggregate networks evolve on a population level will serve as the
foundation for these studies.
But the true strength of this perspective comes from the components that build
into the overall aggregate network measures. These components can be used to develop
tools to systematically identify specific market entry opportunities, particularly after a
shift in overall network dynamics opens up new emerging markets. This logic could be
used to build a dynamic model identifying and predicting emerging market opportunities
Network Opportunity Emergence and Identification - 31
which are ripe for innovation and market entry. This is a practical implication of the
theory, and one which could be developed into a systematic mechanism of specific
opportunity identification directly applicable to the entrepreneurial community. Such
tools could also be used by organizations in the innovation ecosystem to rigorously
evaluate, using data about overall industry network structure, which ideas to provide
resources to, invest in, and ultimately help achieve their full growth potential. The clear
research question to be tested based upon this theory is to examine industries from a
network perspective, identify appropriate network measures to yield very specific
network position measures of structural holes. Using those measures researchers can then
test H1-H3. The theory predicts that specialist foundings will be higher in filling
structural holes, have better survival chances if founded in structural holes, and have
better survival chances if changing from non-structural holes to structural holes.
While the logistical network of the airline industry was used as the primary
example in this paper, the methodology applies equally well across a wide variety of
network tie types including logistical, communication, and brokerage as outlined in Table
1. These are all ripe for testing by researchers. Just as the centralization of an airline
network can predict the opportunity for direct point to point entry between city pairs, the
centralization of communication through television stations could have predicted the
emergence of platforms such as YouTube to directly connect video content producers to
video content consumers. Similarly, the centralization of the brokerage network among a
few major auction houses could have predicted the emergence of platforms such as eBay.
Going forward, taking any established industry and characterizing the primary structure
of it in network terms can yield similar predictions of where the true opportunities are
Network Opportunity Emergence and Identification - 32
emerging. These opportunities can emerge slowly in slowly changing network structures
or more quickly after dramatic shifts to network structure. In either case analyzing an
industry as a series of network flows helps to systematically identify both industries that
are ripe for innovation on the aggregate level, as well as the specific niche in which
innovations are most likely to flourish. Such analyses, being both rigorous and relevant,
would answer the call by Tushman and O’Reilly (2007) for more Pasteur’s Quadrant
research. The analyses provide both a better theoretical understanding of the dynamics of
entrepreneurship as well as a very practical set of tools that can be used to create
successful innovations based upon predictions of where innovations are best positioned to
emerge and thrive.
ACKNOWLEDGEMENTS
Network Opportunity Emergence and Identification - 33
I am grateful to Lyda Bigelow, Ron Burt, Glenn Carroll, Lisa Cohen, Bill Foster, John
Freeman, Michael Gerlach, Henrich Greve, Mike Hout, Charles O’Reilly, Trond Petersen,
Ishak Saporta, Roy Suddaby, Anand Swaminathan, and Albert Teo for comments and
discussions on various aspects of this work. The work benefitted from discussion at the
Administrative Sciences Association of Canada Meetings, the Management Theory
Conference, as well as with seminar attendees at Berkeley, Chicago, Madison, Michigan,
Stanford, and UT Austin. All remaining errors are my own.REFERENCES
Al-Amin, M., Zinn, J., Rosko, M. & Aaronson, W. (2010). Specialty Hospital Market
Proliferation: Strategic Implications for General Hospitals. Health Care
Management Review, 35(4), 294-300.
Allen, O.E. (1981). The Airline Builders. Alexandria, Virginia, USA: Time-Life Books.
Ardichvili, A., Cardozo, R. & Sourav, R. (2003). A Theory of Entrepreneurial
Opportunity Identification and Development. Journal of Business Venturing,
18(1), 105-123.
Barnett, W. & Carroll, G.R. (1993). How Institutional Constraints Affected the
Organization of Early US Telephony. Journal of Law, Economics, and
Organization, 9(1), 98-126.
Baum, J.A.C. & Korn, H.J. (1996). Competitive Dynamics of Interfirm Rivalry.
Academy of Management Journal, 39, 255-291.
Baum, J.A.C. & Mezias, S.J. (1992). Localized Competition and Organizational Failure
in the Manhattan Hotel Industry, 1898-1990. Administrative Science Quarterly,
37, 580-604.
Network Opportunity Emergence and Identification - 34
Boone, C., Meuwissen, R. & van Witteloostuijn, A. (2009). Resource-partitioning
Processes in the Audit Industry. Strategic Organization, 7(3), 307-338.
Borgatti, S.P., Mehra, A., Brass, D.J. & Labianca, G. (2009). Network Analysis in the
Social Sciences. Science, 323, 892-895.
Burt, R. S. (1992) Structural Holes: The Social Structure of Competition. Cambridge,
Massachusetts, USA: Harvard University Press.
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, Connecticut, USA: JAI Press.
Carroll, G.R. & Swaminathan, A. (1992). The Organizational Ecology of Strategic
Groups in the American Brewing Industry from 1975 to 1990. Industrial and
Corporate Change, 1, 65-97.
Cunningham, L.A. (2015). Berkshire's Disintermediation: Buffett's New Managerial
Model. Appearing in volume 50 of the Wake Forest Law Review (2015); GWU
Law School Public Law Research Paper No. 2015-16; GWU Legal Studies
Research Paper No. 2015-16. Available at SSRN:
http://ssrn.com/abstract=2602825
Davies, R.E.G. (1988). Airlines of the United States Since 1914. Washington, DC, USA:
Smithsonian Institution Press.
Fang, L., Ivashina, V. & Lerner, J. (2015). The Disintermediation of Financial Markets:
Direct Investing in Private Equity. Journal of Financial Economics, 116(1), 160-
178.
Network Opportunity Emergence and Identification - 35
Freeman, J. & Audia, P.G. (2011). Community Context and Founding Processes of
Banking Organizations. Research in the Sociology of Organizations, 33, 253-282.
Freeman, L. (1979). Centrality in Social Networks: Conceptual Clarification. Social
Networks, 1, 215-239.
Gaglio, C.M. & Katz, J.A. (2001). The Psychological Basis of Opportunity Identification:
Entrepreneurial Alertness. Small Business Economics, 16(2), 95-111.
Greenwood, R., Raynard, M., Kodeih, F., Micelotta, E.R. & Lounsbury, M. (2011)
Institutional Complexity and Organizational Responses. Academy of Management
Annals, 5, 317-371.
Greve, H.R. (2000). Marketing Niche Entry Decisions: Competition, Learning, and
Strategy in Tokyo Banking, 1894-1936. Academy of Management Journal, 43(5),
816-836.
Greve, H.R. & Seidel, M.D.L. (2015). The Thin Red Line Between Success and Failure:
Path Dependence in the Diffusion of Innovative Production Technologies.
Strategic Management Journal, 36(4), 475-496.
Hannan, M.T. & Carroll, G.R. (1992). Dynamics of Organizational Populations. New
York, USA: Oxford University Press.
Hannan, M.T. & Freeman, J.H. (1977). The Population Ecology of Organizations.
American Journal of Sociology, 83, 929-984.
Hannan, M.T. & Freeman, J.H. (1989). Organizational Ecology. Cambridge,
Massachusetts, USA: Harvard University Press.
Hutchinson, G.E. (1978). An Introduction to Population Ecology. New Haven,
Connecticut, USA: Yale University Press.
Network Opportunity Emergence and Identification - 36
Jeppesen, L.B. & Lakhani, K.R. (2010). Marginality and Problem-solving Effectiveness
in Broadcast Search. Organization Science, 21(5), 1016-1033.
Nordin, F., Brozovic, D. & Holmlund, M. (2013). Disintermediation in Business-to-
Business Service Channels: Mechanisms and Challenges. Journal of Business-to-
Business Marketing, 20(4), 179-192.
Peterson, B.S. & Glab, J. (1994). Deregulation and the Shakeout in the Airlines Rapid
Descent. New York, USA: Simon & Schuster.
Powell, W. (1985). Getting Into Print. Chicago, Illinois, USA: University of Chicago
Press.
Reed, D. (1993). American Eagle: The Ascent of Bob Crandall and American Airlines.
New York, USA: St. Martin’s Press.
Reis, S., Negro, G., Sorenson, O., Perretti, F. & Lomi, A. (2012). Resource Partitioning
Revisited: Evidence from Italian Television Broadcasting. Industrial and Corporate
Change, 22(1), 1-29.
Seidel, M.D.L. & Stewart, K. (2011). An Initial Description of the C-form. Research in
the Sociology of Organizations, 33, 37-72.
Shepherd, D.A. & DeTienne, D.R. (2005). Prior Knowledge, Potential Financial Reward,
and Opportunity Identification. Entrepreneurship Theory and Practice, 29(1), 91-
112.
Sorensen, J.B. & Sorenson, O. (2003). From Conception to Birth: Opportunity Perception
and Resource Mobilization in Entrepreneurship. Advances in Strategic
Management, 20, 89-117.
Network Opportunity Emergence and Identification - 37
Swaminathan, A. (1995) The Proliferation of Specialist Organizations in the American
Wine Industry, 1941-1990. Administrative Science Quarterly, 40, 653-680.
Thornton, P. H. & Ocasio, W. (1999). Institutional Logics and the Historical Contingency
of Power in Organizations: Executive Succession in the Higher Education
Publishing Industry. American Journal of Sociology, 105(3), 801-843.
Tushman, M. & O’Reilly III, C. (2007). Research and Relevance: Implications of
Pasteur’s Quadrant for Doctoral Programs and Faculty Development. Academy of
Management Journal, 50(4), 769-774.
Ucbasarana, D., Westhead, P. & Wright, M. (2009). The Extent and Nature of
Opportunity Identification by Experienced Entrepreneurs. Journal of Business
Venturing, 24(2), 99-115.
Wells, A.T. (1994) Air Transportation: A Management Perspective. Belmont, California,
USA: Wadsworth Publishing Company.
White, H., Boorman, S.A. & Breiger, R.L. (1976). Social Structure from Multiple
Networks. I. Blockmodels of Roles and Positions. American Journal of
Sociology, 81, 730-780.
1
1
2
2
Network Opportunity Emergence and Identification - 38
Figure 1: A non-centralized network with 2 organizations
1 1
1
2 2
2
Figure 2: A centralized network with 2 generalist organizations that do not directly
compete
(Solid lines are actual ties. Dotted lines are structural holes.)
Network Opportunity Emergence and Identification - 39
1 1
1
2 2
2
2
1
Figure 3: A centralized network with 2 generalist organizations that directly compete
(Solid lines are actual ties. Dotted lines are structural holes.)
Network Opportunity Emergence and Identification - 40
Boston
Raleigh-Durham
Miami
Buffalo
Figure 4: Point to Point service from 3 cities to Miami
Boston
Raleigh-Durham
Miami
Buffalo
Figure 5: Hub and Spoke service from 3 cities to Miami.
(Solid lines are service. Dashed lines are structural holes.)
Network Opportunity Emergence and Identification - 41
Table 1: Specific industry examples applying the network centralization as specialist niche
concept
Industry Nodes Generalists Specialists Nature of Tie
Airline, Railroad,
Bus
Cities Hub-and-spoke
carriers
Point-to-point
carriers
Logistical
Trucking Cities Multi-load carriers Single-load
carriers
Logistical
Shipping,
Cargo, Delivery
Addresses Hub-and-spoke
carriers
Specialized
courier services
Logistical
Taxi,
Limousine
Addresses Multi passenger
shuttle services
Point-to-point
service
Logistical
Newspapers Interest groups Major national and
city papers
Ethnic and
specialized topic
papers
Communication
Magazines Interest groups Major national and
international
general topic
magazines
Specialized
content
magazines
Communication
Telephones Individuals Major providers Specialized
international
destination
carriers
Communication
Computer
networks
Individuals Internet backbone
providers
LAN, WAN,
and ISP
providers
Communication
Television Interest groups Major networks Specialized
topic cable TV
channels,
Youtube, Vlog
Communication
Radio stations Interest groups Major networks Specialized
content stations,
Spotify
Communication
Film Interest groups Major studios Independent
film, Youtube,
Vlog
Communication
Investment
banks and
venture capital
Capital and
ventures
Major banks, Major
VCs
Industry specific
focus, angel
funding
Brokerage
Labor unions Workers and
management
Multi-industry
unions
Industry or site
specific focus
Brokerage
Art Artists and buyers Major catalogs Artist specific or
genre specific
focus
Brokerage
Auctioneers Sellers and buyers General collection Item type
focused
Brokerage
Job placement Employees and
employers
General placement Headhunters,
industry focused
Brokerage
