A trajectory of early-stage spinoff success: The role of knowledge intermediaries within an entrepreneurial university ecosystem

Abstract

Universities play a well-established role in regional economic growth, one contribution to which is academic entrepreneurship, the establishment and support of faculty and graduate student spinoff companies based on university research. A vibrant literature examines the general contributions of universities within regional innovation ecosystems while another strain of literature examines individual intermediaries, such as technology licensing offices and incubators, in support of the university's economic development mission. Little research exists, however, that conceptualizes the structure and function of an entrepreneurial university ecosystem. This paper seeks to address this gap in the literature by examining the composition, contributions, and evolution of social networks among faculty and graduate student entrepreneurs and the role of knowledge intermediaries therein. While our investigation supports an emerging literature that finds academic entrepreneurs are typically limited by their own homophilous social networks, we also find that spinoff success relies upon academic and non-academic contacts who connect faculty and students to other social networks important to spinoff success. We investigate how by creating a taxonomy of social network evolution among spinoffs and find that the contributions of universities depend on the existence and interrelationship of loosely-coordinated, heterogeneous knowledge intermediaries guided by a strong collective ethos to encourage and support academic entrepreneurship. Implications for policy and research are discussed.

Full text

A Trajectory of Early-Stage Spinoff Success:
The Role of Knowledge Intermediaries within an Entrepreneurial University Ecosystem
Christopher S. Haytera
aCenter for Organization Research and Design, School of Public Affairs,
Arizona State University, Phoenix, AZ 85004, USA
Abstract
Universities play a well-established role in regional economic growth, one contribution to which
is academic entrepreneurship, the establishment and support of faculty and graduate student
spinoff companies based on university research. A vibrant literature examines the general
contributions of universities within regional innovation ecosystems while another strain of
literature examines individual intermediaries, such as technology licensing offices and incubators,
in support of the university’s economic development mission. Little research exists, however,
that conceptualizes the structure and function of an entrepreneurial university ecosystem. This
paper seeks to address this gap in the literature by examining the composition, contributions, and
evolution of social networks among faculty and graduate student entrepreneurs and the role of
knowledge intermediaries therein. While our investigation supports an emerging literature that
finds academic entrepreneurs are typically limited by their own homophilous social networks, we
also find that spinoff success relies upon academic and non-academic contacts who connect
faculty and students to other social networks important to spinoff success. We investigate how
by creating a taxonomy of social network evolution among spinoffs and find that the
contributions of universities depend on the existence and interrelationship of loosely-coordinated,
heterogeneous knowledge intermediaries guided by a strong collective ethos to encourage and
support academic entrepreneurship. Implications for policy and research are discussed.
Keywords: University Spinoffs, Social Networks, Entrepreneurship, Innovation
Ecosystems, Innovation Policy, Entrepreneurial Universities

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1 Introduction
The recent financial crisis and an increasingly competitive global marketplace have
heightened interest among policymakers and scholars alike regarding the potential economic
development role of colleges and universities. While higher education institutions play a well-
understood role in the production of new knowledge and human capital (Utterback 1994; Romer
1990), Audretsch (2014) argues that the role of universities is to provide leadership necessary to
advance the entrepreneurial society. Thus, only by realizing this leadership role, manifest
through the creation of entrepreneurial thinking, actions, institutions, and entrepreneurial capital,
can universities fulfill their economic and social potential.
One important contribution of the entrepreneurial university relates to academic
entrepreneurship, the establishment of new spinoff companies based on technologies stemming
from university research (Shane 2004). University spinoffs provide an important vehicle to
generate new innovations, accelerate productivity, and create jobs and prosperity for regional
economies (van Praag and Versloot, 2007; Shane, 2004; Doutriaux 1987). Further, academic
entrepreneurship can provide an important signal to future academic entrepreneurs as well as
surrounding regions of a university’s commitment to entrepreneurship. For our purposes,
spinoffs also serve as a window through which the specific entrepreneurial contributions of
universities may be examined (Urbano and Guerrero 2013; Svensson et al. 2011).
The entrepreneurial development of a university spinoff—and potential commercial
success—offers an outcome-based measure of regional economic development, yet research
shows that success among academic entrepreneurs is anything but guaranteed (Druilhe and
Garnsey 2004; Franklin et al. 2001; Hayter 2013a; Hayter 2015a; Mosey and Wright 2007).
Specifically, the entrepreneurial decision and spinoff establishment—a proxy of entrepreneurial
propensity—are often tied to factors internal to the university, including faculty motivations
(Hayter 2011), academic norms (Franklin et al. 2001), and internal culture (Feldman and
Desrochers 2004). Universities, regions, and national governments have also created myriad
policies and programs in an effort to encourage and support academic entrepreneurship (Bradley
et al. 2013a). Specific programs range from technology incubators (Mian 1997; 1996) and
entrepreneurship education programs (Pittaway and Cope 2007) to the more recent emergence of
university proof-of-concept centers (PoCCs) (Hayter and Link 2015; Bradley et al. 2013b).

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What is missing within the literature is a conceptualization of what we term entrepreneurial
university ecosystems: the strategic and collective actions of various organizational
components—what we term knowledge intermediaries—in order to maximize both the
entrepreneurial and innovative contributions of universities. In an effort to bridge this auspicious
gap within the literature, this paper will compare the composition and contributions of social
networks among academic entrepreneurs and examine how these networks co-evolve with the
developmental trajectory of university spinoffs. Further, it examines the specific impact of
policies and programs intended to encourage the formation of university spinoffs and support
their success.
To do this, we take a mixed-method, methodological approach (Creswell 2002),
administering a social network analysis (SNA) survey to a theoretically relevant population of
nascent graduate student and faculty entrepreneurs1 who have established spinoff companies
based on technologies stemming from federally-funded research. These graduate student and
faculty entrepreneurs are (or were) from engineering schools located at universities within New
York State, an area of critical economic and scientific importance. Subsequently, two rounds of
interviews—approximately two years apart—are conducted with SNA survey respondents in
order to understand the specific contributions of their network contacts and how their networks
evolve over time. Network data are concurrently compared to the developmental status of each
spinoff by employing Vohora and colleagues’ (2004) critical juncture framework.
Our paper makes four distinct contributions to our understanding of the entrepreneurial
university: (i) following the extant literature, we affirm the importance of organizational ‘cross
logics’ for obtaining valuable resources and contacts within the unique context of academic
entrepreneurship, (ii) due to this context academic entrepreneurs must rely on knowledge
intermediaries—network boundary spanners—to improve the developmental chances of their
1 This definition of university spinoff differs slightly from Shane (2004), for example, who defines a spinoffs as a
company established by faculty based on technologies licensed from their respective university. First, our inquiry is
not limited to university faculty; emerging research shows that graduate students play a critical role in the
establishment and management of university spinoffs (e.g. Boh et al. 2012; Hayter 2015b). Second, all spinoffs in
the sample are based on technologies that were disclosed to their respective university’s TTO. However, four
spinoffs in the sample do not have licenses. In two cases the TTO could not find a licensee for patented technology
and spinoffs were established once these technologies were released to the inventor. In the other two cases, the
university decided not to pursue a patent, releasing the technologies back to the inventor. We nonetheless posit that
spinoffs went through the ‘formal’ technology transfer process and remain a critical vehicle for the dissemination
and commercialization of new knowledge.

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spinoff, (iii) related, the specific structure and contributions of intermediary networks relate to
the likelihood and speed of spinoff development, and (iv) though more research is needed, the
collective and strategic actions of multiple academic and non-academic knowledge
intermediaries appears to be the foundation for vibrant entrepreneurial university ecosystems,
compared to other, single intermediary structures. We discuss these interrelated elements and
create a taxonomy of social network evolution to illustrate our findings.
Toward this end, we discuss the academic literature related to business and university
ecosystems, conceptualizations of knowledge intermediaries, and network-related factors of
success for university spinoffs in Section 2. In Section 3 we introduce our methodology while
our empirical results are presented in Section 4, including a taxonomy for an entrepreneurial
university ecosystem. Section 5 includes an in-depth discussion of the findings while Section 6
provides conclusions for research and public policy.
2 Previous Research
2.1 Business and Regional Innovation Ecosystems
Moore (1993) is credited with coining the term business ecosystem, the co-evolution of
capabilities among multiple companies working “cooperatively to support new products, satisfy
customer needs, and eventually incorporate the next round of innovations (p. 76).” Christensen
and Rosenbloom (1995) build on Moore’s work by describing business ecosystems as nested
commercial systems where each player contributes a specific component of an overarching
solution. In turn, ecosystems are comprised of inter-organizational networks that provide
entrepreneurial firms with resources and information to navigate a constantly changing
competitive environment (Zahra and Nambisan 2012). Further, business ecosystems can vary by
technology, network intensity, and organizational variety, while cooperative and collaborative
relationships exist simultaneous (Adner and Kapoor 2010; Iansti and Levien 2004; Moore 1993).
Ecosystem dynamics lead to “an economic community supported by a foundation of interacting
organizations—the organisms of the business world” (Moore 1996, p. 23).
Concurrent research in the economic geography literature broadly defines regional
innovation ecosystems primarily as the location-based transmission and absorption of knowledge
(Saxenian 1994; Piore and Sabel 1984). These views seek to explain industrial dynamism and
the corresponding economic success (or failure) of regions through a variety of conceptual lenses,

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including ‘entrepreneurial support networks’ (Kenney and von Burg 1999), ‘incubator regions’
(Schoonhoven et al., 1990), the ‘social structure of innovation’ (Florida and Kenney 1988), or an
innovation or entrepreneurial ‘ecosystem’ (Bahrami and Evans 2000). Recent research, however,
questions the primacy of clustering effects, especially within the life sciences, an area for which
universities are particularly well-suited to contribute (Kenney and Patton 2005).
Powell et al. (2009) relate the emergence and institutionalization (or lack thereof) of
innovative regions within the life science industry to cross-boundary transposition: the
conversion and transportation of status and experience gained from one organizational network
to another (domain). The authors find that while successful life-science clusters (i.e. Boston,
Bay Area, and San Diego) once started with different anchor organizations (and, thus, evolved
differently), they have all developed robust inter-organizational affiliations buttressed by labor
mobility and cultural change (also see Whittington et al. 2009; Casper 2007). Other, less-
successful life science-focused regions (e.g. Los Angeles, New York City, Seattle) also
possess(ed) anchor organizations but have yet to develop robust inter-organizational ties even
though individual organizations in these regions may be well-connected to organizations outside
the region.
Transposition is made possible by the ability of organizations to bridge weakly connected—
or unconnected—networks. Core explanatory factors are the (1) the presence of an anchor
organization that provides an initial infrastructure for connections and field formation needed for
collective growth and (2) the existence of heterogeneous organizational forms from which
different practices, strategies, and rules can emerge. Relating to anchor organizations, Powell et
al. (2009) find that research universities are critical for successful cluster formation because they
contribute to the continuing advance of science and technology but often make poor commercial
partners; the commercialization of technology is unlikely without a strong private sector
presence within a region (Powell et al. 2009; Owen-Smith and Powell 2004).
In their recent investigation of the Flanders region of Belgium, Clarysse and colleagues
(2014) differentiate between business ecosystems and knowledge ecosystems. The authors find
that knowledge ecosystems are typically disconnected from business ecosystems needed to apply
and commercialize new knowledge. Further, technology transfer offices (TTOs) and regional
public venture funds often reinforce the academic nature of university spinoffs rather than bridge
the two disparate ecosystems. In short, according to the extant literature on ecosystems, regional

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economic dynamism and growth is a function of the interconnectivity of disparate, yet
collectively supportive organizations (Whittington et al. 2009).
2.2 University Knowledge Intermediaries
While business ecosystem literature remains largely silent on the role of universities, the
regional ecosystem perspective views research universities—and its supporting policy and
programmatic infrastructure—as a critical component (Bahrami and Evans 2000; Saxenian 1994).
As mentioned, Clarysse et al. (2014) takes an expansive view of knowledge ecosystems,
including universities. A separate line of research explores the role of so-called knowledge
intermediaries, organizations critical to the exchange of new knowledge between universities and
society, especially market-based firms (Yusef 2008).
Yusef (2008) articulates four categories of knowledge intermediary: (1) general purpose:
organizations that produce and disseminate knowledge with research universities and their core
capabilities representing the most common type; (2) specialized: organizations, such as a
technology transfer office, that seeks out new forms of knowledge and aids in its transmission
vis-à-vis licensing to commercial users; (3) financial: organizations such as venture capitalist or
angel investor that supplies risk capital and provides management know-how, technical skills,
and links to other supportive contacts; and (4) institutional: organizations, often public agencies,
that offer incentives to encourage knowledge transfer and facilitate interaction among
researchers and firms (but may not have the necessary financial assets needed to be a financial
intermediary).
Applying Yusef’s (2008) knowledge intermediary framework to academic entrepreneurship,
examples of related general purpose intermediary activities might include the development of a
formal entrepreneurship curriculum and research agenda, among other initiatives, that relate to a
university’s core teaching, research, and service mission (Jong 2008; Pittaway and Cope 2007).
Activities might also include a willingness to create, transform, and sunset related academic
entrepreneurship intermediaries writ large: to what extent does the mission of a university
promote entrepreneurship (Audretsch 2014; Urbana and Guerrero 2013; Youtie and Shapira
2008; Feldman and Derochers 2001)?
A robust literature reviews the emergence and impact of specialized knowledge
intermediaries as it relates to academic entrepreneurship, including technology transfer offices

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(Bradley et al. 2013; Phan and Siegel 2006), technology incubators (Mian 2011; 1997; 1996);
science parks (Mian et al. 2012; Link and Scott 2007; 2006; Siegel et al. 2003); university early-
stage capital funds (Croce et al. 2014), and PoCCs (Link and Hayter 2015; Bradley et al. 2013).
Specialized research-oriented intermediaries, such as cooperative research centers (Gray and
Boardman 2010) and industry-consulting vehicles (O’Gorman et al. 2008) can socialize faculty
and students to market-oriented dynamics important for motivating the spinoff decision.
The importance of financial intermediaries, such as angel and venture capitalists, to academic
entrepreneurship is well established within the literature (Hayter 2013a; Wright et al. 2007;
Shane and Stuart 2002). Research on institutional intermediaries is less developed though recent
studies examine the impact of government funding schemes, such as the Small Business
Innovation Research (SBIR) program in the United States (e.g. Link and Ruhm 2009) and
government venture capital funds (e.g. Colombo et al. 2014). Further, regional institutional
intermediaries, such as the San Diego CONNECT program, are especially important for building
strong social ties among the academic, business, and venture capital communities located within
specific regions (Walcott 2002).
While research on the efficacy of the aforementioned intermediaries is at various levels of
maturity 2, most scholars find ‘mixed’ results; the impact of knowledge intermediaries is
dependent on a number of contextual, managerial, and resource factors (e.g. Mian 2011).
Important for our investigation, scholars rarely examine the relationship among the knowledge
intermediaries outlined above. In other words, how do the knowledge intermediaries reviewed
above ‘fit’ together, what are the strategies and techniques used to conceptualize and manage
these components as a group, and what is their collective impact on spinoff success?
2.3 Networks and Spinoff Success
As discussed above, social networks serve as a proxy for vibrant interrelationships important
for economic dynamism and prosperity (Powell et al. 2009; Whittington et al. 2009). However,
aside from a few recent exceptions (Hayter 2015a, 2015b; Rasmussen et al. 2011; forthcoming),
few studies empirically examine the specific structure and related contributions of social
2 For example, a voluminous literature examines the structure and impact of technology transfer offices (Bradley et
al. 2013; Phan and Siegel 2006) while scholars have yet to empirically examine the structure and impact of
individual PoCCs, a relatively new policy innovation (Hayter and Link 2015).

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networks to spinoff success, much less the role of knowledge intermediaries in network
evolution.
A robust literature instead shows networks to be conceptually important for academic
entrepreneurship (e.g. Hayter 2013a; Nicolaou and Birley 2003; Shane and Cable 2002). Vohora
et al. (2004) posit that networks are pathways through which opportunity recognition—market
insights motivating spinoff establishment—is achieved. According to Wright et al. (2007),
‘social resources’—to whom network contacts are connected—enable firms to obtain
technological, human, and financial resources needed for spinoff development and success
(Shane and Cable 2002). Shane and Stuart (2002) similarly find that venture and angel investors
are more likely to invest in spinoffs they know or to which they have been referred by reliable
sources; mutual network connections and trust are important determinants for the receipt of
resources (Coleman 1988; Krackhardt 1999).
Recent research follows the broader entrepreneurship network research finding that networks
can also have a detrimental impact on entrepreneurial performance (Gulati et al. 2000;
Johannison and Monsted 1997; Ruef et al. 2003). Academic entrepreneurs come from the ranks
of university faculty, a specific professional identity and culture subject to high levels of
homophily (Ruef et al. 2003; Bozeman et al. 2001; Crane 1972), which can create a barrier to
entrepreneurial success (Hayter 2013a; 2015a). Specifically, academic entrepreneurs embedded
within non-commercial, academic research environments typically lack the skills and
experiences important for spinoff success (Mosey and Wright 2007; Wright et al. 2007; Druilhe
and Garnsey 2004; Franklin et al. 2001).
Conversely, university scientists who have ties to industry, receive industry funding, or
possess industry experience are more likely to patent, license, and establish a university spinoff
(O’Gorman et al. 2008; Dietz and Bozeman 2005; Gulbrandsen and Smeby 2005). Hayter
(2013a), for example, finds that the commercialization success among a sample of university
spinoffs in the United States is dependent on the presence of “external knowledge networks,”
including external licenses; joint ventures with other companies; experienced, professional
managers; and the presence of faculty entrepreneurs with consulting experience.
In addition to the aforementioned opportunities to empirically investigate the composition
and contributions of social networks, few studies have empirically investigated entrepreneurship
networks from an evolutionary perspective in order to explain differences in entrepreneurial

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development (Jack 2010; Slotte-Kock and Coviello 2010). From a regional economic
perspective, the ecosystem literature does little to advance our understanding of how and why
knowledge exchange occurs, especially relating to research universities (Braunjerhelm et al.
2010; Ács et al. 2009), not to mention the role of knowledge intermediaries therein (Yusef 2008).
A recent comprehensive review of the empirical entrepreneurship network literature (Hayter
2013b) suggests that scholars investigate entrepreneurship networks employing a knowledge-
based conceptual view, a charge embraced by the present investigation.
3 Research Approach
This study investigates the role of knowledge intermediaries and their impact on the
development of university spinoffs within New York, a state of critical importance to the U.S.
economy.3 It does so by investigating the contact composition, contribution, and evolution of
business networks among faculty and graduate student entrepreneurs whose spinoffs are in the
early stages of spinoff development. We assume that a knowledge intermediary is an
organization comprised of one or more individuals dedicated to the dissemination and
commercialization of new knowledge generated within universities. The sections below explain
the specific elements of our research approach.
3.1 Conceptualizing Entrepreneurial Development
We conceptualize entrepreneurial development in terms of critical junctures (Vohora et al.
2004), multiple, iterative development milestones. Figure 1 illustrates the four critical junctures,
including opportunity recognition, entrepreneurial commitment, credibility and sustainability, as
well as the associated resource and network elements associated with each. Entrepreneurial
development and therefore success is reflected in the forward progression of university a spinoff
though each critical juncture, with the eventual goal of achieving enterprise sustainability.
3 While ranked only 27th out of 50 American states in geographic area, New York ranks fourth in population and has the third
largest economy within the U.S., following California and Texas, respectively (see www.census.gov, accessed 25 January 2015).
It is also home to New York City, the largest city in the U.S. and global center for finance, fashion, and media and entertainment.
Despite its economic and cultural importance, the state also contains several regions, especially in the north (i.e. Upstate),
that have been in relative decline. These regions include Buffalo, Rochester, and Syracuse. A legacy of their former industrial
success, many Upstate regions (along with New York City) enjoy the presence of internationally-renown research universities
(see, for example, Table 1) that attract high levels of sponsored research dollars; the state ranks second in total federal R&D
funding. However, the state also scores relatively low on measures of innovation and high-tech employment resulting in what
many state policymakers have termed New York’s ‘innovation gap’ (see, for example, ITIF, 2012 and Milken, 2013).

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Forward progression through a critical juncture is adopted as a proxy for entrepreneurial success
within the present investigation. Vohora et al. (2004) defines each critical juncture as a barrier to
growth, potentially preventing spinoff transition from one development phase to the next.
<Insert Figure 1 about here>
3.2 Data Collection
According to Vohora et al. (2004), the initial venture champion—the academic
entrepreneur—plays a key role in initial startup development. Further, the initial founder(s) play
a particularly important role in the development of the company, especially university faculty
and graduate student entrepreneurs who possess relatively unique backgrounds, motivations, and
growth ambitions compared with other types of entrepreneurs (Boh et al. 2012; Hayter 2011;
Pittaway and Cope 2007).
In order to investigate these entrepreneurship networks, we obtain contact information for
nascent graduate student and faculty entrepreneurs affiliated with engineering schools located at
research universities within New York State. We focus on engineering-relating spinoffs because
engineering disciplines are typically well linked to practical outcomes and constitute a significant
portion of intellectual property generated in universities (Argawal and Henderson 2002). Further,
we focus on spinoffs established no more than two years prior in order to understand the
evolution of social networks among nascent academic entrepreneurs—and account for potential
recall bias within our population (Carter et al. 2003; Ruef et al. 2003).
Spinoff contact information was obtained from university technology transfer offices as well
as the New York State Energy Research and Development Authority (NYSERDA) and New
York Economic Development Commission.4 Illustrated in Table 1, spinoffs are drawn from nine
research universities, public and private, constituting a wide range of engineering areas and
locations. As noted, all spinoffs were established based on technologies derived from federally-
funded research.
4 Two technology transfer offices did not respond to our request for information. Contact information for several spinoffs located
at these universities is public information, made available by a state entrepreneurship support organization. See Hayter (2015b)
for a more in-depth discussion of how data were collected from TTOs.

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We obtained contact information for 27 faculty and graduate student entrepreneurs who’s
spinoffs met our criteria above. A total of 23 spinoffs participated fully in our study.5 Founding
academic entrepreneurs interviewed for the study included seven graduate students, three
assistant professors, nine associate professors, and four full professors. Further, we interviewed
a total of 15 other spinoff employees as well as TTOs, university entrepreneurship personnel,
advisors, and funders in order to supplement and validate data reported by academic
entrepreneurs. Data were collected between 2010 and 2013.
<Insert Table 1 about here>
To academic entrepreneurs agreeing to participate in the study, we first administered an SNA
survey. According to Borgatti and Foster (2003), SNA has emerged as an effective method for
analyzing networks. SNA enables scholars to view individual or collective agents as social
entities embedded within a web of relationships thereby fitting the goals of our investigation
(Scott 2000). Ego-centric network data are collected using a so-called name generator technique
(Renzulli and Alrich 2005). Accordingly, academic entrepreneurs within our sample were asked
to list their most important “business contacts” with whom “you have collaborated for the
purpose of establishing your company and/or commercializing your company’s technology.”6
For each contact, respondents were also asked to include the full name, position, and
organization. Respondents are asked to differentiate between strong ties (defined as the receipt
of valuable resources) and weak ties with network contacts (Jack 2010). Applying Bozeman and
Corley’s (2004) concept of cosmopolitanism7 to entrepreneurship networks, contact location is
also requested.
After social network surveys were completed, we conducted separate interviews among 23
academic entrepreneurs and selected spinoff employees during two separate research phases. As
5 While twenty-five academic entrepreneurs initially agreed to participate in our study, two individuals did not respond fully to
our data collection efforts.
6 Several studies in the management literature ask respondents to list their five (5) most important contacts (e.g. Nicolaou and
Birley 2003). However, given that there have been few, if any, studies on network differences between faculty entrepreneurs and
other types of entrepreneurs, we opt for a more open-ended request: we do not limit the number of network contacts reported.
7 Bozeman and Corley (2004) find that connections with individuals outside of one’s research group, university, or region—so-
called cosmopolitan networks—positively impact publishing productivity among faculty researchers. Related, Kenny and Patton
(2005), in their study of spinoffs that have achieved an initial public offering (IPO), find that extra-regional “entrepreneurial
support networks”, including venture capitalists, lawyers, and accountants, are critical in the biotech industry, just as Davenport
(2005) and Gertler and Levitte (2005) find that firms are increasingly sourcing ideas internationally.

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noted, we also interviewed TTOs, university and state entrepreneurship support personnel,
entrepreneurship advisors, and funders. The first phase (phase I) occurred immediately after the
social network survey was administered, while the second (phase II) occurred approximately two
years after the initial interviews. Data were collected in person or over the phone during two
separate time periods, utilizing an open-ended interview template based on the literature review
and accompanying research questions above. Interviews ranged in length from one-half hour to
two and half hours; most were approximately one hour in duration.
Interviews were guided by a narrative approach (Polkinghorne, 1988) whereby respondents
were asked to describe the establishment of their spinoff and its subsequent development,
including its current developmental state, guided by the critical junctures framework (Vohora et
al., 2004). Referring to their respective social network survey, academic entrepreneurs were also
asked to describe the specific contributions provided by their respective business contacts in
order of importance. During the second round of interviews, respondents were asked to provide
an update of how their spinoff had developed and, separately, update their SNA survey,
describing how social networks and their corresponding contributions had evolved over time.
3.3 Data Analysis
From the social network survey, we identified the position (role), affiliated organization, and
location of each individual reported by academic entrepreneurs as a business contact. From the
subsequent interviews, responses were recorded and transcribed; transcripts were read as data
were collected. Once each round of data collection was completed, responses were coded
inductively, according to procedures recommended by Kuckartz (2014) and Saldana (2012).8
After data were analyzed, respondents were asked to validate the specific contributions of their
network contacts.
From each interview, we were able to reconstruct a narrative of the entrepreneurial process
and thus determine the current relative developmental state of each academic entrepreneur’s
spinoff company according to the prescribed critical juncture framework. Further, a comparison
8 A total of three research team members coded the data, including the author and two colleagues. According to Krippendorff
(2004), agreement among multiple coders increases the likelihood that data is reliable. The addition of a third coder allows for a
decision to be made when there exists divergent interpretations of binary data between the two other coders. Further, a critical
element of data validity is intercoder reliability, the extent to which independent coders evaluate reported data and reach the same
conclusion. Using (1) percent agreement and (2) Krippendorff’s alpha, we find that all coded variables exceed accepted
thresholds of intercoder reliability, 90 percent for and 0.800, respectively.

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of responses among academic entrepreneurs yielded multiple emergent themes regarding the
specific contributions of network contacts. Finally, social network data were compared to the
relative entrepreneurial developmental state—critical juncture—for each spinoff. The results are
reported in Section 4 below.
4 Empirical Results
4.1 Network Composition and Evolution
Each spinoff is associated with an academic entrepreneur who has a social network of
business contacts. The 23 faculty entrepreneurs in the sample reported a total of 79 and 107
contacts during the first and second research phases, respectively. The number of reported
business contacts range from a minimum of 3 to a maximum of 7. Figure 2 presents reported
contacts by frequency. For the first phase, a large majority (65 or 82.3 percent) of reported
contacts are academic, defined as individuals employed by a university, including other faculty,
students, TTOs, and university entrepreneurship support personnel. The remaining 14 contacts
are comprised of advisors, state and local entrepreneurship services, two professional managers,
and an angel investor.
In the second phase, the overall number of reported contacts rises to 107; the social networks
of respondents have expanded. Though academic contacts (62) still constitute a majority within
the sample, the number of reported TTOs (8) and university entrepreneurship support personnel
(6) decline (4 and 4, respectively) while additional students are reported. The number of faculty
(30) remain the same, though a few individual faculty contacts are different. More notably,
academic entrepreneurs report 31 additional non-academic contacts (45 total; 42.1 percent of all
contacts), including advisors (10), company partners (10), state/regional entrepreneurship
services (9), and angel investors (7). New contact types are also reported, including a federal
government representative (Department of Defense), attorney, and a corporate venture capitalist.
Also of note, two contacts reported as advisors in phase I are subsequently designated as
managers, constituting a total of five managers in the second research phase. In short, social
networks within our sample have evolved quickly between the two research phases growing
mostly in terms of additional non-academic contacts.
<Insert Figure 2 about here>

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4.2 Specific Network Composition and Contributions by Research Phase
Table 2 illustrates the specific network contacts and primary resource contributions during
the two data collection phases, with new contacts (and phase I contacts that are not longer
included in phase II networks) highlighted in bold. Changes in contact contributions are
designated with an asterisk. Table 2 also indicates the associated developmental stage of each
spinoff: in the first phase, 22 spinoffs are in the commitment critical juncture while one spinoff
has achieved credibility; 17 spinoffs remain in the commitment phase during phase II, while 5
additional spinoffs (6 total) have progressed to the credibility critical juncture. No spinoff in the
sample has progressed beyond the credibility phase. Table 3 further summarizes the primary
contributions of each contact type.
<Insert Table 2 about here>
<Insert Table 3 about here>
In keeping with the entrepreneurship network literature, nearly all contacts provide advice as
well as connections to other contacts. Advice and connections received from faculty connections
primarily relate to research assistance and other facets associated with the early stages of spinoff
establishment. Further, network connections made through other faculty are often limited to
intra-university contacts, such as the TTO or university entrepreneurship support personnel.
For faculty-established spinoffs, contributions from graduate students relate primarily to their
role as co-founder, their commitment to entrepreneurial action (“influence to establish”), and role
as spinoff manager. For student-established spinoffs, fellow students typically serve as co-
founders. Discussed below, both TTOs and university entrepreneurship support personnel were
deemed important for advice and connections, though the latter offered more services, including
education programs, physical space, and funding programs to support spinoff success. Similarly,
local, federal, and state/regional entrepreneurship support programs offer advice, connections,
funding, technical assistance, and physical space.
Among the contributions of non-academic contacts, respondents deem management (from
managers and advisors) and funding (from angel investors) as critical resources for spinoff

15
development. Further, company partners were important for (joint) applied research projects,
technology development assistance, prototype development, and manufacturing.
4.3 Explaining Network Evolution: The Role of Knowledge Intermediaries
From the sections above, we find that the contributions of faculty and students focus on
motivating and undertaking spinoff establishment as well as more basic research functions.
Beyond faculty and students, network contacts provide an array of financial, technical, and
human resources as well as social resources—ties—to other contacts who can provide resources
important for spinoff development. Further, we find that the contributions of knowledge
intermediaries reported in our sample vary substantially. Based on this data and the sections
above, we construct three conceptual models (Figures 3-5) to explain how social networks
among academic entrepreneurs (co)evolve with the development of university spinoffs and the
role of knowledge intermediaries therein.
Each figure represents the ego-centric social networks of spinoffs within the entrepreneurial
commitment phase (left side) compared to the network of spinoffs that have progressed to the
credibility phase (right side). The large circle in the bottom left-hand corner side of each
diagram (within each figure) represents an academic entrepreneur. Further, ties to faculty
researchers and graduate students are ubiquitous among academic entrepreneurs in the sample
represented in the bottom right-hand side. As mentioned, contacts in the social network can
possess three possible types of ties with other contacts: strong ties (dark, thick lines), weak ties
(thinner, dashed lines), or non-existent ties.
Single Academic Intermediary Model
The majority of spinoffs in our sample had yet to progress to the credibility phase during our
three-year study represented in the left diagram within Figure 3. Academic entrepreneurs are
also connected to academic contacts, such as TTOs and university entrepreneurship support
personnel represented by Tie 1A. From an organizational perspective, we designate these
academic contacts constituents of first-order academic intermediaries to reflect their potential
connective role within the network: TTOs and other academic contacts may or may not possess
relationships with non-academic contacts (Tie 1B) useful to spinoff success. Regardless, the

16
social networks of academic entrepreneurs in the commitment phase do not extend beyond first-
order academic intermediaries represented by Tie 1C.
In the single academic intermediary model, spinoff progression is dependent on both the
resources (such as access to early stage funding) and the quality of that individual’s ties to non-
academic contacts (represented by Tie 1D). While no spinoff in the model had yet to progress to
the credibility phase, an academic contact in at least one university within our sample has
connected academic entrepreneurs to non-academic contacts that can potentially be helpful to
spinoff development (Tie 1E). For at least one spinoff, the non-academic contact possessed an
existing relationship with a local angel funder (Tie 1F) and connected this individual to the
academic entrepreneur (Tie 1G), though no funding has been received thus far. We deem this
secondary network connection second-order.
<Insert Figure 3 about here>
Single Non-Academic Intermediary Model
Figure 4 represents the single non-academic intermediary model whereby, in addition to
academic contacts (Tie 2A), entrepreneurship networks include at least one non-academic
contact (Tie 2B). Existing connections to non-academic contacts were first established by
academic entrepreneurs while working in cooperative research centers, consulting with industry,
prior industry experience, friends, and chance meetings. In at least three cases, student academic
entrepreneurs connected other spinoff founder(s) (faculty and/or other students) with non-
academic contacts with whom they had worked on student research projects (Tie 2C).
For spinoffs in Figure 4 that progress to the credibility phase, relationships with non-
academic contacts (Ties 2E and 2F) strengthen as ties to academic intermediaries become
relatively weaker (Tie 2D). Further, the single non-academic contact not only provides an
important resource (e.g. management), it also possesses connections to with other non-academic
contacts (Tie 2G) and are deemed first-order. Thus, a non-academic contact can connect the
academic entrepreneur(s) to other individual that can provide resources to the academic
entrepreneur (e.g. angel funding) important for entrepreneurial development (Tie 2H).
<Insert Figure 4 about here>

17
University Ecosystem Model
Figure 5 represents networks of academic entrepreneurs in what we deem the university
ecosystem model. Among spinoffs that have yet to achieve the credibility phase, academic
entrepreneurs (faculty and students) are connected to multiple academic contacts (Ties 3A and
3C), including representatives from entrepreneurship ‘boot camps’, entrepreneurship
coordinators, and entrepreneurs-in-residence. Contacts can also be non-academic and include
state entrepreneurship support personnel, advisors, managers, company partners, and angel
investors. In cases where academic entrepreneurs do not possess strong ties to contacts, they
often know these individuals and are familiar with their services (Tie 3B). Further, academic
entrepreneurs observed that network contacts seem to know and work with each other,
represented by Tie 3D. For example, academic entrepreneurs working with one university’s
entrepreneurship coordinator—what we term an entrepreneurship ombudsman (represented in
this case by tie 3A)—are typically introduced to a representative from a state entrepreneurship
support program. Similarly, engineering students attending a proof-of-concept/product
development workshop are introduced to a ‘management matchmaker’ within that university’s
business school.
We also found that academic entrepreneurs drew from the ties of their co-founders to connect
with contacts important to the success of their spinoff. For example, a faculty entrepreneur
established a spinoff with a graduate student who, during establishment, introduced their co-
founder to a retired company executive. The graduate student previously met and closely
worked with this ‘advisor’ during a year-long, applied capstone project required for graduation.
Contacts in the ecosystem model have ties of varying strength to non-academic contacts,
given their different functions (represented by Ties 3E and 3F). For example, a student business
plan competition coordinator will likely have different contacts compared the manager of a
technology-focused incubator, just as contacts may also differ by technology and application.
The availability of useful non-academic contacts will depend on the characteristics of the region
surrounding the university as well as the institutional mechanisms that exist for engaging these
individuals. Non-academic contacts may or not know each other (Tie 3G).
<Insert Figure 5 about here>

18
Networks among spinoffs in the ecosystem model that progress to the credibility phase are
distinct from the other two models. First, academic entrepreneurs are exposed to a variety of
entrepreneurship support mechanisms prior to the establishment of their spinoff. For students,
some of these mechanisms include ‘formal’ classes (i.e. part of a university’s degree program) on
product development, entrepreneurship, and business plan development as well as the
aforementioned team-oriented, multi-disciplinary, problem-focused capstone projects. Other
support mechanisms include business plan competitions, networking events, ‘TED talks’ by
successful academic entrepreneurs, hack-a-thons, and entrepreneurship clubs that not only
provide greater familiarity with entrepreneurship, they are also venues used to engage non-
academic contacts, typically within surrounding communities.
Another key difference is the relationships among academic and non-academic contacts (Tie
3H and 3K); as mentioned, academic entrepreneurs observed that university program managers,
funders, professional managers, and state entrepreneurship support personnel know and often
‘hang out’ with each other. Each of these relationships becomes a potential point of network
entry for academic entrepreneurs. Academic entrepreneurs are often referred to different
services and offerings, depending on their specific interests and needs (Tie 3I). For spinoffs
progressing to the credibility phase, these (multiple) interactions helped collectively socialize
faculty and students to the technical, funding, and managerial realities of entrepreneurship, a
world to which few academic entrepreneurs have been exposed.
Contacts do this through specific services—and concurrent engagement with relevant, non-
academic contacts. In turn, non-academic contacts have an opportunity to observe and work
with academic entrepreneurs, allowing them to build trust among these individuals before
providing valuable resources (Tie 3J). Once non-academic individuals feel comfortable with the
relative developmental state of a spinoff, they may introduce academic entrepreneurs to other
non-academic contacts (such as technologists and venture capitalists) who can help aid
entrepreneurial development (Tie 3K). Interestingly, positive interactions between non-
academic contacts and university contacts helped build trust and positive working relationships
important for future academic entrepreneurs (Tie 3L).
5 Discussion

19
In keeping with the extant entrepreneurship network literature, our research shows that social
networks are critical pathways through which academic entrepreneurs access resources and other
contacts important to the development of their spinoff. However, unique network-related
challenges exist for university spinoffs relative to other, non-academic entrepreneurial ventures.
Specifically, academic entrepreneurs must bridge a yawning social gap between traditional,
academic social networks and more market-oriented entrepreneurial networks needed to progress
to the credibility phase—our proxy for nascent entrepreneurial success.
We find that academic entrepreneurs in the early stages of spinoff establishment define
“business contacts” in a way that reflects their primary professional environment as faculty and
graduate students. Nearly two-thirds of their network contacts in the first research phase are
comprised of other academic contacts, especially faculty researchers and students from their
home institution. Faculty colleagues and graduate students provide influence, advice, and
management support important to motivate and support the initial establishment of a university
spinoff. This finding comports with a robust literature that shows that social capital is critical to
the exchange of information and resources within an in intra-organizational network context
(Coleman 1988; Krackhardt 1999). However, within an academic context, homophilous
networks may also constrain future entrepreneurial development if academic entrepreneurs
cannot access contacts embedded within other social networks important to the development of
their spinoff.
Asking how ‘network bridging’ occurs, we created a taxonomy of social network evolution to
illustrate our findings. Supporting previous literature on absorptive capacity (Cohen and
Levinthal 1990) and more recent work on network competence (Ritter and Gmunden 2003) or
capability (Walter et al. 2006), we found that first-order academic and non-academic contacts are
important for entrepreneurial development. When successful, contacts and their associated
knowledge intermediaries help socialize faculty entrepreneurs to market-oriented motivations,
values, and practices that they may not otherwise receive in an academic environment. Further,
bridging contacts connect academic entrepreneurs with (other) non-academic contacts who can
provide resources and other contacts important for the development of their spinoff.
For most academic entrepreneurs in our sample, TTOs or university entrepreneurship support
services were the first point of contact related to the establishment and development of their
spinoff; these knowledge intermediaries were viewed as a primary source of (nominal) resources

20
and connections, in many cases years after spinoff establishment (i.e. during our second research
phase). Deemed the single academic intermediary model, spinoff development is largely
dependent on the capability of one individual (e.g. a technology transfer officer) and the size and
composition of his or her respective social networks. At its best (i.e. University G), this model is
functional when universities are embedded within entrepreneurial regions and the academic
contact is well connected and has prior business experience.
However, in most cases, spinoffs located at universities embodying the single academic
intermediary model were limited in their development. Although single academic intermediaries,
especially TTOs, are conceptualized in the literature as the critical path for entrepreneurial
development (Bradley et al. 2013), our research finds that affiliated individuals often lack social
networks required to enable spinoff success. At its worst, the single academic intermediary
model can slow or even inhibit the growth of entrepreneurship networks by focusing on licensing
and equity issues in lieu of ways to best enable spinoff development.
While most respondents viewed single academic intermediaries positively (or at least
agnostically), several respondents indicated that they had avoided working with their respective
TTO for a number of reasons, including their predominant focus on life science technologies,
preference for large-company licensing over startup support, greed, slow response times, and
lack of entrepreneurship support capability. Reported examples of ‘TTO avoidance’ include the
development of core spinoff IP that doesn’t relate to their university license, encouraging student
co-founders to claim IP within the university (within a university that allows graduate students to
retain their IP), and refusing to consult with potential external IP licensees.
Entrepreneurship network relationships ascribed to the single non-academic intermediary
model are more ad hoc and develop despite the presence of a single academic intermediary. The
majority of ties among spinoffs located at universities ascribing to this model came from prior
working relationships with industry through joint research projects and consulting. Further,
reliance on non-academic contacts were seen as a way to avoid single academic contacts. Other
non-academic relationships formed after TTOs released intellectual property back to the inventor
once they were unable to license the technology. Finally, visible in two cases within our sample,
spinoffs can be established without a formal intellectual property relationship with their home
university. The connective role of single non-academic contacts seems to highlight an resesarch
opportunity within Yusef’s (2008) framework: what is the role of advisors, company personnel,

21
and other non-financial intermediaries important to academic entrepreneurs within this study but
not defined as such in the literature?
The ecosystem model seems to offer a superior approach for bridging disparate social
networks important for spinoff success. Within the model, network ties co-evolve, beginning
long before spinoffs are established—with different co-founders often developing ties with
different academic and non-academic contacts. Several universities within our sample have
established multiple knowledge intermediaries that offer students and faculty formal courses,
workshops, product/technology development seminars, mentoring, funding, and networking
services designed to promote and support academic entrepreneurship.9 These services are
largely open access creating a high level of intra-university dynamism: most individuals
interested in entrepreneurship are welcome. Further, engagement of myriad non-academic
contacts among disparate knowledge intermediaries creates multiple opportunities for ‘mixing’; a
diverse constellation of first-order academic and non-academic contacts allow academic
entrepreneurs to connect with other important (2nd order) non-academic contacts, including
professional managers, company researchers, and angel investors. Thus, the combination of
connections to important non-academic contacts and parallel improvement in the entrepreneurial
skills of academic entrepreneurs (and related development of their technologies and products)
together create an endorsement effect reducing resource investment risk for other network
contacts, especially angel and venture capitalists (Shane and Stuart 2002). In other words, the
more opportunities that academic entrepreneurs have to interact and—importantly—build
substantive relationships with non-academic contacts, the more likely they are to have access to
resources and other contacts important to spinoff success.
In addition to the dynamic and multi-faceted nature of contacts within the ecosystem model,
another key difference is the presence of what we term an ‘entrepreneurship ombudsman.’ The
purpose of this individual is to act as a neutral coordinator to promote the interests of academic
entrepreneurs, remove barriers to their success, and connect these individual to entrepreneurship
support mechanisms both inside and outside the university.10 Further, entrepreneurship
9 Interestingly, while several single academic intermediary model universities also offered entrepreneurship support mechanisms,
access to these resources was largely at the discretion of one ‘gatekeeper’, the TTO, and often only in exchange for an equity
stake in the spinoff. To be sure, ecosystem model universities have TTOs but they are but one of a constellation of resource
providers.
10 Two universities in our sample have created these positions. Administrators interviewed during the project posited that
coordinator positions were created to differentiate the encouragement and support of academic entrepreneurship from how those
support mechanisms (and the university writ large) are funded. At one university, an administrator spoke about prior conflict of

22
ombudsman are tasked with the creation of an entrepreneurship strategy whereby curricular and
entrepreneurship support mechanisms are aligned in support of academic entrepreneurship thus
building an entrepreneurial culture. In practice, entrepreneurship ombudsman do not replace the
compliance and intellectual property management function of TTOs but—conceptually—
represents a first-order specialized intermediary solely focused on improving the entrepreneurial
success of university spinoffs (Yusef 2008).
In summary, our university ecosystems model most closely approximates Christensen and
Rosenbloom’s (1995) ecosystem definition: nested, loosely-organized academic and non-
academic intermediaries that work collectively and strategically to promote and support
academic entrepreneurship. An entrepreneurial mission drives action, not one ‘lead’
intermediary nor a priority, for example, to earn licensing revenue. Entrepreneurial university
ecosystems therefore constitute a specific subsystem within the larger context of business and
regional innovation ecosystems that is critical to economic and social development.
6 Conclusion
As mentioned, spinoffs are one vehicle through which entrepreneurial universities can
contribute to regional economies (Audretsch 2014; Shane 2004; Slaugter and Rhoades 2004).
What differentiates universities in our sample—and perhaps their contributions to economic
development—is how they view and manage academic entrepreneurship vis-à-vis knowledge
intermediaries. In other words, how do research universities view, manage, and create (or
sunset) knowledge intermediaries in furtherance of academic entrepreneurship? We investigate
this question by inductively examining the composition, contributions, and evolution of social
networks among academic entrepreneurs to explain the development of university spinoffs and
the role of knowledge intermediaries therein.
Most universities within our sample view academic entrepreneurship as a primary
responsibility of TTOs—a single, specialized knowledge intermediary—possibly supported by a
few ‘add on’ support services, such as an early-stage venture fund or incubator. This finding
aligns with Clarysse and colleagues’ (2014) recent finding that knowledge ecosystems are often
disconnected from the business ecosystems needed to apply and commercialize new university
interest situations between TTOs and the interests of academic entrepreneurs, a specific scenario that the university had hoped to
avoid in the future with the creation of the coordinator position.

23
knowledge; single academic contacts can reinforce the academic nature of university spinoffs. In
other words, traditional university knowledge intermediaries and their constituent representatives
may not be capable of fully bridging the social gaps between academic and non-academic
(industry) networks.
Further, our research provides support for how Powell and colleagues’ (2009) cross-realm
transposition occurs. Specifically, a vibrant entrepreneurial university ecosystem conceives of
spinoff performance and its associated economic impact in terms of social networks—or in terms
of an overall network architecture—needed to provide the resources and connections important
for entrepreneurial success. While universities can act as anchor organizations within a region,
their entrepreneurial contributions depend on the existence and interrelationship of loosely-
coordinated, heterogeneous knowledge intermediaries guided by a strong ethos to encourage and
support academic entrepreneurship. Thus, the critical contribution of entrepreneurial university
ecosystems, as conceptualized here, lies in their ability to facilitate network bridging in order to
disseminate and commercialize new knowledge vis-à-vis university spinoffs. Taking Clarysse
and colleagues’ (2014) by example, the structure and underlying social networks of knowledge
intermediaries may explain macro-level disconnections between knowledge and business
ecosystems in Flanders. Reconciling macro-level economic observations with micro-level
investigations of knowledge intermediaries and the associated social networks of affiliated
individuals no doubt presents a promising research opportunity for scholars.
Building off recent work (Lubynsky 2013; Boh et al. 2012; Pittaway and Cope 2007),
another contribution of this paper is to highlight the role of graduate students in academic
entrepreneurship. Not only are graduate students important for motivating and undertaking
spinoff establishment, we find that they often connect other academic entrepreneurs to academic
and non-academic contacts that they met during previous entrepreneurship programs and
experiences. Scholars would do well to understand better the role of students in university
spinoffs, including the establishment and success and contributions of student-run spinoffs, their
strengths and weaknesses.
Based on these findings, universities and the regions in which they are embedded could work
together to view social networks as a strategic asset important for technology commercialization
and economic development. Individual universities could investigate the extent to which
existing entrepreneurship support systems favor a single intermediary or ecosystem model,

24
examining the nature and contributions of networks among current and aspiring academic
entrepreneurs. Further, universities could implement alternative intellectual property policies
that deemphasize a single academic intermediary model approach (vis-à-vis the TTO) while
creating and strengthening other opportunities to engage non-academic contacts in other
education, research, outreach, and entrepreneurship support efforts. For their part, states and
regions could assist universities to understand existing business and external ecosystems while
creating knowledge intermediaries to connect them with entrepreneurial university ecosystems.
For example, though limited to ‘cleantech’ within New York State, NYSERDA’s grant,
technology development, and entrepreneur-in-residence programs provided both tangible
resources and connections valuable to the success of spinoffs within our sample.
Future investigations will hopefully overcome some of the data limitations of the paper. First,
while networks are conceptualized dynamically, the paper relies upon a relatively small sample
of academic entrepreneurs limited to one region of the United States. Further, our investigation
focuses on spinoffs from engineering schools. Scholars would do well to investigate spinoffs
within other disciplines such as energy and the life sciences, comparing how network
composition, contributions, and evolution might differ. And while we chose to sample nascent
academic entrepreneurs, methodological concerns about recall bias nonetheless remain,
including the propensity to view the past with ‘rose-colored lenses’ (Carter et al. 2003).
Future empirical work might seek to validate and strengthen our conceptual models of
entrepreneurial university ecosystems. Further, scholars could undertake additional
examinations of the composition, contributions, and evolution of entrepreneurship networks
within the context of other universities within other regions of the United States. One obvious
area of neglect was examining the extent to which the regional presence of useful non-academic
contacts determines the structure of university ecosystems. Rhetorically, to what extent does a
university’s geographic location necessitate a specific entrepreneurship support strategy—and
what does this strategy look like?
Finally, future investigations should also examine the extent to which entrepreneurial
university ecosystems and the regions in which they are embedded collectively foster social and
geographic proximity between academic entrepreneurs and contacts valuable to the future of
their spinoff (Tartari et al., 2014). Put differently, to what degree can academic entrepreneurship
networks be engineered, especially given the technical and geographic heterogeneity among

25
university spinoffs? Armed with this research university leaders and policymakers will be better
equipped to help the entrepreneurial university reach its full economic and social development
potential.
Acknowledgements
I am grateful to the Ewing Marion Kauffman Foundation for their financial support of this
research. Also, special thanks to Marla Parker and two anonymous reviewers for their helpful
comments and suggestions.
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31
Table 1: New York Research Institutions Represented in the Sample
Institution
City
Ownership
Enrollment
(2012)
Medical
School (=1)
Engineering
School (=1)
Columbia University
New York City
Private
28,824
1
1
Cornell University
Ithaca
Public/Private
22,400
1
1
New York University
New York City
Private
43,911
1
1
Rensselaer Polytechnic
Institute
Troy
Private
6,999
0
1
Rochester Institute of
Technology
Rochester
Private
18,063
0
1
State University of New
York at Albany
Albany
Public
17,142
0
1
State University of New
York at Buffalo
Buffalo
Public
28,952
1
1
State University of New
York at Stony Brook
Stony Brook
Public
24,149
1
1
Syracuse University
Syracuse
Private
21,029
0
1
University of Rochester
Rochester
Private
10,510
1
1

32
Table 2: Evolution of Networks Among Respondents (Changed Contacts in Bold; Asterisk
[*] denotes changed contribution)

Spin-
off
Phase I Network
Contact
Contributions
Juncture
Phase II Network Contact
Contributions
Juncture
A1
Faculty
Advice, research
Commitment
Faculty
Advice, research
Commitment
Faculty
Research
Faculty
Research
TTO
Advice, connections
TTO
Advice, connections
Faculty (out of region)
Research
A2
Student
Management, Influence to
establish
Student
Management, influence to
establish
Commitment
TTO
Advice, connections
TTO
Advice, connections
Faculty
Research
B1
Faculty
Advice, research
Commitment
Faculty
Advice, research
Commitment
Faculty (out of region)
Advice
Faculty (out of region)
Research, connections
State/Regional Entre.
Support
Connections
Manager
Management, connections
Attorney
Legal services, connections
Angel Investor
Connections
B2
Student
Co-founder, management
Commitment
Student
Co-founder, management
Credibility
Faculty
Research
Faculty
Research
University Entre. Svc.
Establishment assistance,
connections
Angel investor
Funding, advice, management
Company partner
Development asst.
Company partner
Manufacturing asst.
C1
Faculty
Advice, connections
Commitment
Faculty
Advice, connections
Commitment
Faculty
Advice, research
Faculty
Advice, research
Company Partner
Research
Company Partner
Research
State Entre. Svc.
Connections, Advice
State Entre. Support*
Connections, advice, funding*
Corporate Venture
Capitalist
Connections, Technical
Assistance
C2
Student
Co-founder, Management
Commitment
Student
Co-founder, management
Commitment
Student
Co-founder
Student
Co-founder
Faculty
Co-founder, research
Faculty
Co-founder, research
Local Entre Svc.
Advice, connections
Local Entre Svc
Advice, connections, space*
C3
Faculty
Co-founder
Commitment
Faculty
Co-founder
Commitment
Faculty (out)
Research, Advice
Faculty (out)
Research, Advice
Student
Employee
Student
Employee

34
Uni. Entre. Svc.
Advice, connections
D1
Student
Co-founder
Commitment
Student
Co-founder
Credibility
Student
Co-founder
Student
Co-founder
Student
Co-founder
Student
Co-founder
Advisor
Advice, connections
Advisor-Manager*
Management, CEO*
Advisor
Advice, connections
Company Partner
Development asst.
Univ. Entre. Support
Advice, connections,
funding, education
Angel Investor
Funding, advice, connections
D2
Faculty
Co-founder
Commitment
Faculty
Co-founder
Commitment
Student
Co-founder, influence to
establish
Student
Co-founder
Student
Co-founder, influence to
establish
Student
Co-founder
Faculty (out of region)
Research, advice
Advisor
Advice, connections
Univ. Entre. Support
Advice, connections, funding,
education
Angel funder
Advice, connections
D3
Faculty
Co-founder
Commitment
Faculty
Co-founder
Commitment
Faculty
Advice
Faculty
Advice
Univ. Entre Support
Advice, education*
Univ. Entre. Support
Advice, connections*
E1
Student
Co-founder
Commitment
Student
Co-founder
Credibility
Student
Co-founder
Student
Co-founder
Faculty
Advice, connections
Faculty
Advice, connections
Manager
Management, Advice
Company Partner
Research, Development asst.
State Entre. Support
Connections, funding
E2
Student
Co-founder
Commitment
Student
Co-founder
Commitment
Student
Co-founder
Student
Co-founder
Faculty
Advice, connections
Student (out of region)
Advice
Advisor (parent)
Advice
F1
Student
Co-founder
Commitment
Student
Co-founder
Commitment
Student
Co-founder
Student
Co-founder
Faculty
Advice, connections
Advisor
Advice, connections

35
Faculty
Advice, connections
Local Entre. Support
Space, advice
G1
Faculty
Co-founder
Commitment
Faculty
Co-founder
Commitment
TTO
Advice, connections,
funding
Advisor
Advice, connections
Angel
Advice, connections
Company partner
Technical Assistance
G2
Faculty
Co-Founder
Commitment
Faculty
Co-Founder
Commitment
Student
Co-founder, Influence to
establish
Student
Co-founder, Influence to
establish
TTO
Advice, connections
Advisor
Advice, connections
H1
Advisor
Advice, connections
Credibility
Advisor/Manager
Management*, Connections
Credibility
Student
Co-founder
Student
Co-founder
Student
Co-founder
Student
Co-founder
Angel Investor
Advice, connections,
funding
Angel Investor
Funding
Company Partner
Prototyping asst.
Univ. Entre Svc
Advice, connections,
space, education*
Company Partner
Development asst.
H2
Faculty
Co-founder
Commitment
Faculty
Co-founder
Commitment
Faculty
Co-founder, research
Faculty
Co-founder, research
Student
Co-founder, influence to
establish
Student
Co-founder, influence to
establish
Univ. Entre. Svc.
Advice, connections,
education*
Univ. Entre. Svc.
Advice, connections, funding*
State Entre. Assistance
Advice, connections, funding
Company Partner
Development asst.
Advisor
Legal services
H3
Faculty
Co-founder
Commitment
Faculty
Co-founder
Credibility
Manager
Co-founder, management,
advice
Manager
Co-founder, management,
advice
Univ. Entre. Svc.
Connections, education
Univ. Entre. Intermediary
Connections, education
Company Partner
Development asst.
Company Partner
Development asst.
Regional Entre. Support
Connections, funding
I1
Faculty
Co-founder, Research
Commitment
Faculty
Co-founder, research
Commitment
TTO
Advice, connections
Faculty (out of region)
Advice, research
Advisor
Advice, connections
TTO
Advice, connections

36
State Entre. Assistance
Advice, connections
Advisor
Advice, connections
State Entre Assistance
Advice, connections
I2
Faculty
Co-founder
Commitment
Faculty
Co-founder
Commitment
Faculty
Advice
Faculty
Advice
TTO
Advice, connections
TTO
Advice, connections
Advisor
Advice, connections
J1
Faculty
Advice, Influence to
Establish
Commitment
Faculty
Advice, Influence to Establish
Commitment
Faculty (out of region)
Advice
Faculty (out of region)
Advice
Advisor
Advice, connections
Advisor
Connections, Management*
Company Partner
Development asst.
State Entre. Support
Advice, Funding
J2
Faculty
Co-founder
Commitment
Faculty
Co-founder
Commitment
Student
Co-founder, Management
Student
Co-founder, Management
TTO
Advice, connections
J3
Manager
Co-founder
Commitment
Manager
Co-founder*
Credibility
Faculty
Research
Faculty
Co-founder, research
Student
Employee
Student
Co-founder
TTO
Advice, connections
Advisor
Advice, connections
Advisor
Management, Connections*
State Entre. Support
Advice, connections
State Entre. Support
Funding, Connections*
Angel Investor
Advice, connections
Fed. Government Rep.
Development asst.,
Connections, funding

Table 3: Primary Contact Contributions
Contact
Contributions
Faculty Researcher
 Advice
 Research
 Co-founder
 Influence to Establish
Student
 Co-founder
 Influence to Establish
 Management
 Employee
TTO
 Advice
 Connections
Uni. Entre. Services
 Advice
 Education
 Connections
 Establishment Assistance
 Funding
 Space
Advisor
 Advice
 Connections
 (Later) Management
State/Regional Entre.
Services
 Connections
 Advice
 Funding
 Space
Manager
 Advice
 Management
 Connections
Local Entre. Service
 Advice
Company Partner
 Advice (related to commercialization)
 Research
 Development Assistance
 Prototyping and manufacturing
Angel Investor
 Advice
 Connections
 Funding
 Management
Federal Gov’t Rep.
 Technical Assistance
 Connections
 Funding
Attorney
 Legal Services (specialized advice)
 Connections
Corporate Venture
 Advice

38
Investor
 Connections

Technical Assistance

39
Figure 2: Reported Contact Frequency by Research Phase

40
Figure 3: Single Academic Intermediary Model

41
Figure 4: Single Non-academic Intermediary Model

42
Figure 5: Ecosystem Intermediary Model