Harnessing University Entrepreneurship for Economic Growth

Abstract

University spin-offs are an important vehicle for knowledge dissemination and have the potential to generate jobs and economic growth. Despite their importance, little research exists on spin-off performance or impact, especially from the perspective of academic entrepreneurs. Using logit regression, this article makes a scholarly contribution by testing the relationship between spin-off success—defined here as technology commercialization—and multiple factors derived from the extant literature. Several significant variables are found to enable commercialization success within the sample, including venture capital, multiple and external licenses, outside management, joint ventures with other companies, previous faculty consulting experience, and—surprisingly—a negative relationship to post-spin-off services provided by universities. The results have important implications for public policy and management, supporting an overall “open innovation” approach to spin-off success.

Keywords

entrepreneurship technology transfer economic development

Introduction

The recent financial crisis and an increasingly competitive global marketplace have heightened interest among policy- makers and scholars alike regarding the economic impact of university entrepreneurship (Rothaermel, Agung, & Jiang, 2007). While universities play a well-understood role in the production of new knowledge and human capital (Romer, 1986; Solow, 1956; Utterback, 1994), governors, legislators, and university presidents now promote the establishment of university spin-offs believing that these new companies will generate new innovations, accelerate productivity, and create jobs and prosperity for regional economies (Carree, 2002; Carree & Thurik, 2006; National Governors Association, 2004; Shane, 2004; van Praag & Versloot, 2007).

University spin-offs are defined as new firms established by faculty members based on intellectual property generated from their research (Shane, 2004). Given the embryonic nature of many university technologies, university spin-offs offer these academic entrepreneurs an alternative pathway, among others, for disseminating and commercializing their research (Audretsch, 1995; Lowe, 2002; Shane, 2004). Given that new knowledge tends to be bounded within the region where it was created (Audretsch & Feldman, 1996), university spin-offs are a local phenomenon and are therefore important to industry formation and economic dynamism (Lowe, 2002; Pressman, 2002; Shane, 2004; Tornatzky et al., 1995).

Given what Shane (2005, p. 34) calls “emerging fragmentary evidence,” the specific economic development contributions of university spin-offs are substantial and myriad. Pressman (1999), for example, estimates the direct economic impact of university spin-offs from 1980 to 1999 was $33.5 billion or approximately $10 million per company. Much of this economic impact is in the form of job creation, purchasing, and production—the so-called multiplier effect—which has a critical impact on the regional economies where spin-offs are located (Pressman, 2002; Tornatzky et al., 1995).

Specific to job creation, university spin-offs created some 280,000 jobs from 1980 to 1999, exceeding employment rates among other new firms (Cohen, 2000; Pressman, 1999). Furthermore, spin-off employment exceeds the job creation levels of larger, established companies that license university technology. Pressman, Guterman, Abrams, Geist, and Nelson (1995) find that though university spin-offs from the Massachusetts Institute of Technology (MIT) only account for 35% of the university’s licensees, they account for more than 70% of employment linked to MIT technology licenses.

Specific to individual spin-offs, once established, these new firms generally have a high propensity for survival (Lowe, 2002; Mustar, 1997; Pressman, 2002), have a high likelihood of attracting early-stage finance such as angel or venture capital (Shane, 2004), and of going public (Goldfarb & Henrekson, 2003). Policymakers have therefore sought to encourage the formation and growth of university spin-offs through numerous policies and programs.

Given their perceived importance in economic development, a robust literature has emerged investigating factors responsible for spin-off creation. Most quantitative studies of university spin-offs have relied on data collected in the annual Association of University Technology Managers (AUTM) and, as a result, focus on numbers of university spin-offs and their relationship with institutional or environmental factors (Phan & Siegel, 2006; Rothaermel et al., 2007). Although useful, this research typically does not empirically investigate whether or not individual spin-offs actually succeed and, if they do, their specific economic impact on regional economies (Rothaermel et al., 2007; Shane, 2004, 2005).

The main challenge for researchers lies in the lack of empirical and systematic, longitudinal data needed to produce studies for premier journals. Among the empirical studies that do exist, data on individual entrepreneurs is rare at best; in their review of the broader university entrepreneurship literature, Rothaermel et al. (2007) find only five studies where the individual entrepreneur is the unit of analysis. Furthermore, academic entrepreneurship research is often limited to individual universities, typically elite institutions such as MIT (Roberts, 1991; Shane, 2004), or has been criticized for failing to use or build theory, incorporate insights from multiple disciplines, or employ sophisticated methods and sampling frames (Mustar et al., 2006; O’Shea, Allen, Chevalier, & Roche, 2005; Rothaermel et al., 2007).

This article seeks to address these challenges by exploring factors of success—defined here as technology commercialization—among university spin-offs. Technology commercialization offers an intermediate outcome-based measure of success that explicitly links spin-off activity and economic development. The article is one of the first to empirically examine spin-off success from the perspective of academic entrepreneurs.

This article makes specific contributions to the literature while addressing concerns among scholars that extant research is more descriptive and lacks generalizability (Mustar et al., 2006), noncumulative (O’Shea et al., 2005), or are “less sophisticated in sampling frames, hypotheses development . . . or statistical analysis” (Rothaermel et al., 2007, p. 699). It does this not only by testing the relationship between technology commercialization and individual, university, and firm-specific factors, it is also the first to include federal, state, and university entrepreneurship support services and programs.

The structure of this article is as follows. The next section reviews the business and economics literature related to factors of success for new firm startups. The third section sets forth the theoretical and methodological foundation for the article. The fourth section presents the data, research methodology, variables, and descriptive statistics. The fifth section presents the article’s findings, including an econometric analysis of the data. The article concludes a discussion of the results and their implication for policy and future research.

Theory, Methodology, and Literature Review

Researchers from disparate disciplines have introduced numerous entrepreneurial success factors that may be relevant to university spin-offs. Although financial resources are of particular interest among these studies, especially those employing a Resource-Based View (RBV) theoretical lens, it follows from the literature that spin-off success is subject to multiple, complex factors (Heirman & Clarysse, 2004; Penrose, 1959; Rothaermel et al., 2007).

Given this multitude of factors and the R&D intensity of university spin-offs, the fledgling Knowledge Spillover Theory of Entrepreneurship (KSTE) offers a useful theoretical lens to examine university spin-off success (Acs, Audretsch, Braunerhjelm, & Carlsson, 2004; Audretsch, Keilbach, & Lehmann, 2006). Although KSTE embraces Romer’s (1986) assumption that new knowledge is the source of innovation, productivity, and economic growth, it assumes that not all knowledge is necessarily economically useful or automatically spills over. Knowledge is instead subject to institutional, geographic, and cost constraints (Almeida & Kogut 1999; Jaffe, 1989; Jaffe, Trajtenberg, & Henderson, 1993) known collectively as the “knowledge filter” (Acs et al., 2004; Audretsch et al., 2006).

Important to the knowledge filter is the notion of tacit knowledge, often referred to as know-how; it is not easily codified and is typically embodied in individuals, organizations, and processes (Audretsch & Feldman 1996). Therefore, an understanding of how the aforementioned factors impact spin-off success may provide a deeper understanding of the diffusion, commercialization, and economic impact of new knowledge generated in universities.

This article seeks to investigate barriers and enablers to commercialization and therefore potential contributions to economic development, while hopefully addressing the previously mentioned gaps in the literature by examining the following question:

Question 1: What are the main factors that contribute or detract from the success of the university spin-off?

Success factors are derived from the extant literature; the sections below capture success factors from three primary perspectives—that of the entrepreneur, the university, and the firm—along with a section on related, cross-cutting policy instruments. Hypotheses are also derived for each section and include specific success later used to construct a conceptual framework for the study.

Individual Entrepreneur-Related Factors

A robust literature finds that professional experience and networks enable individuals to recognize commercial value in new knowledge and therefore recognize entrepreneurial opportunities (Nerkar & Shane, 2003; Vohora, Wright, & Lockett, 2004). University scientists who collaborate with industry, receive industry funding, or possess industry experience have a higher propensity to patent, license, consult, and establish a company (Audretsch, Lehmann, & Warning, 2005; Dietz & Bozeman, 2005; Gulbrandsen & Smeby, 2005; O’Gorman, Byrne, & Pandya, 2008; Roberts, 1991). Conversely, faculty members with long academic careers and few interactions outside of the university typically lack the business acumen and experience needed to successfully manage a company (Franklin, Wright, & Lockett, 2001; Murray, 2004; Nicolaou & Birley, 2003; Radosevich, 1995). Sources of research funding—public, private, or combinations thereof—may also affect the propensity of faculty members to spin off a company and affect its subsequent success (Dietz, 2000; Dietz & Bozeman, 2005).

The combined elements help determine a faculty member’s network; the quality, depth, and diversity of faculty networks also affects spin-off success (Johansson, Jacob, & Hellstrom, 2005; Murray, 2004; Rothaermel et al., 2007). Formal networks can help counterbalance a faculty member’s lack of industry experience providing assistance to write and develop a business plan, raise early-stage financing, commercialize technology, and develop links with potential partner firms and customers (O’Gorman et al., 2008; Rappert, Webster, & Charles, 1999). Furthermore, informal networks often facilitate more formal linkages that facilitate collaborative research, spin-off, and licensing arrangements with established firms (Landry, Amara, & Oumit, 2002; Martinelli, Meyer, & von Tunzelmann, 2008; Meyer-Krahmer & Schmoch, 1998).

Hypothesis 1: University faculty members who consult with industry, conduct industrial R&D, and have previously established a company are more likely to have successful spin-offs.

University-Related Factors

Researchers have also used the university as a unit of analysis to explore academic entrepreneurship. Examinations of academic culture, for example, find that entrepreneurship and industry partnerships often clash with “traditional” academic norms manifested in tenure, peer review, and public research support (Bercovitz & Feldman, 2004; Chiesa & Piccaluga, 2000; Franklin et al., 2001; Samson & Gurdon, 1993; Slaughter & Rhoades, 2004). Entrepreneurial role models and administrative support from within the institution and from the surrounding business community may positively affect university culture and provide an informal curriculum to, for example, find venture capital and start a firm (Bauer, 2001; Feldman & Desrochers, 2004; Hayter, 2011; Hsu & Bernstein, 1997; Shane, 2004).

Faculty and intellectual property (IP) policies also affect entrepreneurial behavior through faculty promotion and rewards (O’Gorman et al., 2008; Siegel, Waldman, & Link, 2003), institutional emphasis on federal R&D support (Colyvas et al., 2002; Markman, Phan, Balkin, & Gianiodis, 2004), and leave and conflict-of-interest policies (Bekkers, Gilsing, & van der Steen, 2006; Kenney & Goe, 2004; Renault, 2006; Tornatzky et al., 1995). For IP, university equity investments—as opposed to other, short-term fees—encourage inventor involvement and promote spin-off success (DiGregorio & Shane, 2003; Jensen, Thursby, & Thursby, 2003; Shane, 2004). A robust research also finds that some university technology licensing offices provide important guidance and services (Shane, 2004), whereas others are seen as obstacles or predisposed to encourage technology licensing in lieu of entrepreneurship (Audretsch et al., 2005; Markman et al., 2004; Phan & Siegel, 2006; Steffensen, Rogers, & Speakman, 2000).

Hypothesis 2: Spin-off success is more likely among universities that make equity investments, have “non-obstructionary” technology transfer offices, and have faculty and an administration supportive of entrepreneurship.

Firm-Related Factors

Many researchers take a firm-centric approach to academic entrepreneurship with specific attention to early-stage finance, spin-off management, and industry factors. Financial resources are typically seen as the most important factor, allowing spin-offs to hire staff, conduct research, and pursue commercialization (Hellmann & Puri, 2000; Heirman & Clarysse, 2004; Shane & Stuart, 2002). Studies of the Boston metropolitan region finds that venture capital, angel capital, bank loans, and friends and family all play an important role (Roberts, 1991; Roberts & Easley, 2009).

Industry experience, capability, and knowledge of firm managers are also critical factors to the success of a spin-off (Roberts, 1991; Rothaermel et al., 2007). Unfortunately, many, if not most, spin-off management teams lack these capabilities, negatively affecting their ability to recruit new employees and attract early-stage finance (Clarysse & Moray, 2005; O’Shea et al., 2005; Shane & Stuart, 2002). To compensate, Franklin et al. (2001) and Radosevich (1995) recommend that academic entrepreneurs take a leadership role in the establishment of the company and scientific development of products, but cede management to an experienced, “surrogate” manager.

Spin-off success is more dependent on technological development, at least initially, than marketing, sales, or distribution (Perez & Sanchez, 2003). With regard to specific technologies, significant differences exist among spin-offs in information technologies, the life sciences, energy, software, and others that have implications for ease of commercialization and therefore, spin-off success (Bekkers et al., 2006; Golub, 2003; Gulbrandsen & Smeby, 2005; Lowe, 2002; Shane, 2004).

Recent research within the corporate management literature (Chesbrough, 2003) also suggests the importance of “Open Innovation” to technology development. Open Innovation highlights the importance of firm strategies that focus on solving technical problems by using research and intellectual property outside the organizational boundaries of a firm (Chesbrough, 2003). Although most of this literature focuses on innovation strategies within large multinational corporations, recent research suggests that Open Innovation may also be applicable to other organizations such as universities and spin-offs in the form of outside licenses, multiple sources of IP, joint ventures, and informal partnerships (Chesbrough, 2009).

Hypothesis 3: Spin-off success depends on a number of firm-specific factors, including professional management, IP from external sources, joint ventures, venture capital funding, and the industry of the spin-off.

Policy Instruments

Though limited, an emergent scholarly literature examines the impact of cross-cutting policy instruments on university spin-offs (Phan & Siegel, 2006; Rothaermel et al., 2007). Programs and policies have emerged at the university level in the form of science parks, incubators, proof of concept centers, and venture funds (Blair & Hitchens, 1998; Gulbranson & Audretsch, 2008; Siegel et al., 2003). Although several states have initiated programs to encourage and support technology commercialization, the federal government plays a much larger role with programs such as the Small Business Innovation Research (SBIR) grants and the now-defunct Advanced Technology Program (Lerner, 1999; National Governors Association, 2007; Shane, 2004).

Hypothesis 4: University, state, and federal spin-off support programs, university entrepreneurship services, state early-stage investment funds, and SBIR awards will positively affect spin-off success.

Spin-off Success

Previous studies define spin-off success as new firm establishment; given the literature review above along with related lines of inquiry (Hayter, 2011; Link & Ruhm, 2009), success is framed in terms of commercialization (Link, Siegel, & Bozeman, 2007). This (purposely) narrows the focus of the conceptual model to what Vohora et al. (2004) term the credibility phase of spin-off development and the factors responsible for commercialization represented in Figure 1.

Figure 1.

Conceptual model based on the extant literature.

While Audretsch et al. (2006) and Acs et al. (2004) suggest that cultural and structural barriers attenuate the disclosure and licensing of university technology, a second barrier (filter) to knowledge dissemination from the university may exist after the firm has been established. Furthermore, a deductive multifactor approach must be taken to understand its composition and to help build entrepreneurship theory (Davidsson, 2004; Roberts, 1991). The assumption is made that when a spin-off commercializes technology, it not only penetrates the knowledge filter(s), it also signals its capability to contribute to economic development.

Data and Research Methodology

The Database

To analyze factors of success among academic entrepreneurs, a contact database was constructed. Davidsson (2004) recommends that researchers obtain data from a sample of cases that are theoretically relevant, reflect the critical unit of analysis, reflect relevant variances in phenomenon characteristics, and are “workable” from a practical point of view. Therefore, the database is populated with university spin-offs of diverse institutions from different states, emphasizing a substantial degree of variance including different stages of development and technological focus, varying in location and environmental factors.

Constructed in late 2008, the database contains contact information for 193 academic entrepreneurs from 18 different states representing all 5 main geographic regions of the United States. A total of 117 individuals responded to the author’s request for interview, yielding an effective response rate of 60.6%. Academic entrepreneurs were interviewed in person or over the phone; all questions in the survey template were answered.

Measurement of Variables

Dependent variable

Commercialization is a self-reported binary indicator of commercial success where 1 equals some level of commercialization, while 0 indicates that the spin-off has not to date commercialized. The mean value of the dichotomous commercialization variable (comm.) is .444, indicating that the likelihood that a spin-off within the sample will commercialize their technology university spin-off is slightly less than 50%.

Success variables

Based on the literature review above and previous, the independent success variables are operationalized, where possible, through the variables presented in Table 1.

Table 1.

Description of Independent Variables.

Variables	Description
facconsult	Binary variable, for academic entrepreneur indicating that they have consulted with industry, facconsult = 1
facindrd	Binary variable, for academic entrepreneur who has conducted industrial R&D, facindrd = 1
outceo	Binary variable, for spin-offs with a nonfaculty CEO, outceo = 1
multlic	Binary variable, for spin-off that has sourced intellectual property beyond the original spin-off license(s), multlic = 1
vc	Binary variable, for spin-offs that have received venture capital, vc = 1
ttoobs	Binary variable, for academic entrepreneur indicating their TTO was “obstructionary,” ttoobs = 1
unisrv	Binary variable, for spin-offs that receive initial entrepreneurship services from the university, unisrv = 1
jv	Binary variable, for spin-off that has participated in at least one joint venture with another company, jv = 1
equity	Binary variable, for academic entrepreneurs whose home university has taken equity stake in the spin-off, equity = 1
peers	Binary variable, for academic entrepreneur indicating that their peers are “supportive” of entrepreneurial activities, peers = 1
sbir	Binary variable, for spin-offs that have received at least one SBIR award, sbir = 1
prevspin	Binary variable, for academic entrepreneur who has previously established a spin-off, prevspin = 1
statef	Binary variable, for spin-offs that have received funding from state entrepreneurship support programs, statef = 1
bio	Binary variable, for spin-off in the bioscience industry, the dominant industry in the sample, bio = 1

Control variables

Age of Spin-off (age)—This variable indicates the age of the spin-off to control for differences in technology development factors. A positive coefficient would suggest that the likelihood of commercialization may increase over time with the maturity of the spin-off.

R&D Ratio (rdratio)—To control for university incentives to spin off, this continuous variable is added to reflect the proportion of extramural government research funding received (in 2005) by the spin-off’s home university compared with that university’s overall research budget during that same year. Given that most federal research funding supports basic research at universities, a negative coefficient is expected; a more basic research orientation may indicate less of an internal focus on commercialization.

Regions—Four different binary location variables take on the value of one for the northeast (neast), which includes all states north of Washington, D.C., including Maryland, Connecticut, and Maine; the Midwest (midwest), including Illinois, Iowa, Ohio, Kansas, and Nebraska; the northwest (nwest), including Washington, Oregon, and Montana; and the southwest (swest), including California, Utah, and Arizona. The base region is the southeast and includes Virginia, Kentucky, North Carolina, and Georgia. Those regions that tend to have a culture more supportive of academic entrepreneurship such as the southwest and northeast might be expected to have a positive coefficient (Saxenian, 1994).

Descriptive Statistics

Table 2 provides a simple comparison of means for selected independent variables; other descriptive statistics are available on request. The last column of the table shows p values; the null hypothesis is tested to see if the sample means for the two groups are the same. Large, significant differences are observed.

Table 2.

Selected Sample Means by Commercialization Status.

Variable	Not commercialized (n = 65)	Commercialized (n = 52)	p Value of difference
R&D Ratio	0.713	0.736	.351
Faculty Consulting	0.385	0.769	<.001
Industrial R&D	0.415	0.558	.128
Venture Capital Funding	0.077	0.192	.065
Outside CEO	0.308	0.865	<.001
Multiple/External Tech	0.246	0.769	<.001
TTO Obstruction	0.154	0.077	.206
Equity	0.108	0.173	.311
University Services	0.231	0.192	.618
Joint Ventures	0.154	0.500	<.001
Peer Support	0.292	0.481	.037
SBIR Funding	0.462	0.462	1.000
Life Sciences Industry	0.569	0.077	<.001
Previous Spin-off	0.492	0.577	.367
State Funding	0.231	0.250	.811
Age of Spin-off	5.769	6.423	.361

Note. Table shows sample means separately for commercialized and noncommercialized projects. The last column shows p value for the null hypothesis that the sample means are the same for the two groups.

The most important findings are that spin-offs that have commercialized their technology are more than seven times likely to not be in the life sciences industry (57 vs. 8). They are about three times as likely (50 vs. 15) to have participated in a joint venture with other companies, have sourced multiple and external technologies (77 vs. 25), and have hired an outside CEO (87 vs. 31). Academic entrepreneurs whose spin-offs have commercialized their technology are more likely to have participated in formal or informal consulting (77 vs. 39) and have the support of their peers (48 vs. 29). Finally, spin-offs that have commercialized their technology are more likely to have received venture capital funding (19 vs. 8, though only significant to the .10 level).

Results

In this section, the empirical results for spin-off factors of success are reported. Given the binary nature of the dependent variable indicating commercialization success, logistic regression is employed. Four derivations of the model are run with no missing variables. Before running the model, the author tests for multicollinearity; the measures indicate that, in general, the model may be used.

Table 3 presents the results: logit coefficients, robust standard errors (in parentheses), and calculated average marginal effects [in brackets]. For binary variables, the average marginal effects are interpreted as the likelihood of commercialization resulting from discrete changes in the explanatory variable. For logit regression in STATA, the margeff command defines quantities of interest as the probability of a positive outcome, unity (Bartus, 2005).

Table 3.

Logit Regression Estimates^a (Robust standard errors in parentheses, calculated marginal effects are in brackets).

Variable	1	2	3	4
rdratio	1.696	−4.779	−5.917	−15.74
	(2.467)	(5.087)	(5.615)	(12.1892)
	[0.1426]	[−0.2208]	[−0.2682]	[−0.6299]
ccconsult	1.738	3.066	3.097	5.756
	(0.7856)**	(1.438)**	(1.461)**	(2.867)**
	[0.1628]	[0.1406]	[0.1401]	[0.1856]
facindrd	−1.288	−3.392	−3.174	−6.320
	(0.8513)	(1.539)**	(1.607)**	(3.789)**
	[−0.1010]	[−0.1492]	[−0.1362]	[−0.1477]
outceo	3.276	5.504	5.154	8.506
	(0.8346)***	(1.667)***	(1.752)***	(3.777)***
	[0.3637]	[0.3363]	[0.3086]	[0.2896]
multlic	2.319	3.729	4.038	9.221
	(0.7785)***	(1.427)***	(1.548)***	(5.016)**
	[0.2278]	[0.2082]	[0.2218]	[0.2897]
vc		4.009	4.491	9.024
		(1.792)**	(2.000)**	(4.625)*
		[0.2044]	[0.2188]	[0.2579]
ttoobs		−0.1156	−0.3258	−1.186
		(2.221)	(2.206)	(2.609)
		[−0.0053]	[−0.0146]	[−0.0458]
unisrv		−3.940	−4.320	−6.055
		(1.584)**	(1.709)**	(2.881)**
		[−0.1796]	[−0.1957]	[−0.1746]
jv		3.559	3.655	5.892
		(1.427)**	(1.513)**	(3.069)*
		[0.1826]	[0.1820]	[0.2073]
equity			−0.6850	−3.389
			(2.051)	(2.941)
			[−0.0298]	[−0.0987]
peers			0.8142	1.007
			(1.250)	(1.549)
			[0.0373]	[0.0420]
sbir			0.8362	2.624
			(1.296)	(2.397)
			[0.0390]	[0.0987]
prevspin				3.438
				(2.462)
				[0.0966]
statef				1.538
				(2.390)
				[0.0545]
age				0.5823
				(0.3665)
				[0.0233]
bio	−4.275	−6.973	−7.314	−12.487
	(1.047)***	(1.926)***	(2.107)***	(5.880)**
	[−0.4318]	[−0.4190]	[−0.4195]	[−0.3724]
midwest	−0.0953	0.1208	−0.3935	−2.500
	(1.008)	(1.374)	(1.463)	(2.675)
	[−0.0080]	[0.0056]	[−0.0177]	[−0.0891]
nwest	1.069	2.259	2.191	2.076
	(1.143)	(2.278)	(2.596)	(3.296)
	[0.0874]	[0.1106]	[0.1043]	[0.0793]
swest	1.007	2.906	2.909	4.609
	(0.9359)	(1.515)*	(1.713)*	(2.744)*
	[0.0827]	[0.1352]	[0.1302]	[0.1576]
neast	−1.430	−2.716	−3.185	−5.675
	(1.359)	(3.123)	(3.332)	(12.202)
	[−0.1194]	[−0.1163]	[−0.1355]	[−0.1528]
LR χ²	96.89	125.08	125.86	131.42
Pseudo-R²	0.6027***	0.7781***	0.7830***	0.8175***
N	117	117	117	117

Note. Robust standard errors in parentheses; calculated marginal effects are in brackets.

Significant at 10%. **Significant at 5%. ***Significant at 1%.

Entrepreneur-Specific Factors

The results provide evidence of the generally positive commercialization benefits of working with industry. Spin-offs whose founding academic entrepreneurs participate in outside consulting arrangements with industry are more likely to commercialize their technology; the results are positive and significant at the .05 level. A test of intrauniversity engagement vis-à-vis industry-sponsored research agreements yields an unexpected negative sign and is significant in three of the four model versions above. Previous establishment of a spin-off by academic entrepreneurs within the sample does not necessarily guarantee success in subsequent spin-off commercialization efforts.

University-Specific Factors

While they may be important in the spin-off decision (Phan & Siegel, 2006), university-related factors had little impact on postestablishment commercialization in the sample with the exception of university entrepreneurship services discussed in the policy instrument section below. Specifically, neither an “obstructionary” TTO nor the university taking an equity stake in the spin-off has a significant impact on spin-off success. Though positive, peer culture is insignificant.

Firm-Specific Factors

University spin-offs that receive venture capital funding (vc) have a 20% to 26% higher likelihood of commercialization compared with spin-offs that do not (significant at .05 and .1 levels). However, deciding to establish a spin-off in the biosciences industry (bio) spin-off reduces the likelihood of technology commercialization by 37% to 43% compared with non–bioscience companies at the .01 level (p = .001) for all versions of the model. Large average marginal effects are also observed for having a nonfaculty CEO (outceo) and taking advantage of multiple and outside sources of intellectual property (multlic). With the exception of the last version of the model, both are significant at the .01 level. Spin-offs participating in joint ventures (jv) also increase their chances of commercialization as much as 20%, significant at the .05 and .1 levels.

Policy Instruments

The findings on how university entrepreneurship services affect the probability of spin-off commercialization are new; to the author’s knowledge, there has been no prior attempt to empirically examine the relationship between university services and their impact on postspin-off commercialization. Table 3 shows the surprising result: spin-offs that rely primarily on a university for entrepreneurship services are 17% to 20% less likely to commercialize their technology than those that do not, significant to a .05 level among all four model derivations. Development funding from the SBIR program (sbir) or state government (statef) does not significantly improve commercialization.

Controls

The age of a spin-off (age) does not increase (or decrease) the likelihood of commercialization among spin-offs in our sample; though negative, the home university’s R&D ratio (rdratio) is also insignificant. Three regional control variables (neast, midwest, nwest) are not significant. However, in three of the four versions of the model, spin-offs located in the southwest (swest) are more likely to commercialize their technology, significant to a .1 level. This finding corresponds with case studies of dense entrepreneurial networks in the San Francisco Bay area (Saxenian, 1994) and anecdotal reports of strong institutional and state support for academic entrepreneurship in these regions (National Governors Association, 2007).

Concluding Discussion

This article not only contributes to the literature, it is of great relevance to scholars of economics and entrepreneurship, as well as federal, state, and university policymakers interested in harnessing spin-offs for economic development. By empirically testing the conceptual model among a theoretically relevant sample of academic entrepreneurs from public universities across the United States, it finds that all spin-offs are not created equally. In short, spin-offs in the sample with access and strong external linkages to new technologies, ideas, funding, management, and ideas are more likely to commercialize their technologies compared with those that are without.

Several words of caution are offered when interpreting these results. First, the sample is small with 193 academic entrepreneurs in the contact database whose spin-offs were established over the past 24 years; only 117 complete responses were received. By some estimates, more than 3,000 university spin-offs (as defined in this article) have been established since 1980, including those from private universities. Furthermore, the sample only includes spin-offs that have formal IP agreements with their universities overlooking other types of spin-offs (Link et al., 2007) or other forms of “university entrepreneurship” and commercialization (Phan & Siegel, 2006; Rothermael et al., 2007). The contact database is not a probability sample, remains painfully small, and is thus subject to sampling error.

Another challenge is that the data rely on a point-in-time snapshot among academic entrepreneurs and subsequent spin-off outcomes are not available without extensive follow-on research. Although previous research suggests that motivations and growth ambitions may influence spin-off success, it is unclear as to the strength of this influence (Hayter, 2011). A point-in-time approach, especially one that examines relatively new firms, may also overlook more dynamic aspects of firm development such as the impact of external investors or managers; emerging market opportunities; or shifting priorities, capabilities, or outlooks of academic entrepreneurs.

These considerations aside, this article provides empirical support for a “nonlinear,” network-centric perspective of spin-off success analogous to Chesbrough’s (2003, 2009) Open Innovation paradigm. Specifically, external sources of technology, outside management, industry experience among academic entrepreneurs, and venture capital are shown to be important factors for commercialization within the present sample of university spin-offs. Venture capitalists, who provide critical financial resources and mentoring, are also recognized among respondents for their networking value, helping connect spin-offs to other technologies, professional management, and services (Wright, Clarysse, Lockett, & Binks, 2006).

If these results are generalizable to broader populations of academic entrepreneurs, then policies and programs designed to spur academic entrepreneurship should establish and strengthen dense networks of funders, professional managers, support services, potential customers, and a variety of innovation sources to improve commercialization. Approaches that focus singularly on financing or university entrepreneurship services are subject to diminished efficacy. These findings seem to complement the findings of Powers and McDougall (2005) and Degroof and Roberts (2004) who show that spin-offs in entrepreneurial regions require little support from the university and vice versa. A far greater challenge, however, is supporting commercialization among university spin-offs located in less entrepreneurial regions.

To address these challenges, researchers should investigate new networking and institutional models that attempt to mitigate challenges associated with rural or less entrepreneurial regions. More broadly, researchers should explore challenges and enablers for social networks connecting academic entrepreneurs and university researchers to industry, finance, and other supporting sectors. Future studies should also test the supporting factors in this study among larger populations of academic entrepreneurs, over time, and within different types of institutions. Finally, spin-offs that do not use formal IP licensing agreements with universities should also be investigated.

University spin-offs may not automatically lead to new jobs and prosperity, but through this research and that which hopefully follows, policymakers will be better equipped to fashion programs and policies to improve the likelihood of commercialization and, therefore, economic development.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article:

I am grateful to the Ewing Marion Kauffman Foundation for their financial support of this research.

Author Biography

Christopher S. Hayter is Executive Director of the Policy Evaluation and Transformation Group at New York Academy of Sciences. He works with governments in the United States and abroad to evaluate and restructure science and postsecondary policies and institutions. His research agenda focuses on higher education management and culture, student and faculty entrepreneurship, and the effectiveness of public–private partnerships to advance human capital and economic development.

References

Acs

Audretsch

Braunerhjelm

Carlsson

(2004). The missing link: The knowledge filter and endogenous growth (Discussion paper). London, England: Center for Economic Policy Research.

Almeida

Kogut

(1999). Localization of knowledge and the mobility of engineers in regional networks. Management Science, 45, 905-917.

Audretsch

(1995). Innovation and industry evolution. Cambridge: MIT Press.

Audretsch

Feldman

(1996). R&D spillovers and the geography of innovation. American Economic Review, 86, 630-640.

Audretsch

Keilbach

Lehmann

(2006). Entrepreneurship and economic growth. London, England: Oxford University Press.

Audretsch

Lehmann

Warning

(2005). University spillovers and new firm location. Research Policy, 34, 1113-1122.

Bartus

(2005). Estimation of marginal effects using margeff. Stata Journal, 3, 309-329.

Bauer

(2001, April 17). Effects of patenting and licensing on research. Presentation to the National Academies Board on Science, Technology, and Economic Policy Committee on Intellectual Property Rights in the Knowledge-Based Economy.

Bekkers

Gilsing

van der Steen

(2006). Determining factors of the effectiveness of IP-based spin-offs: Comparing the Netherlands and the U.S. Journal of Technology Transfer, 31, 545-566.

10.

Bercovitz

Feldman

(2004). Academic entrepreneurs: Social learning and participation in university technology transfer (Mimeo). Toronto, Ontario, Canada: University of Toronto.

11.

Blair

Hitchens

(1998). Campus companies—U.K. and Ireland. Aldershot, England: Ashgate.

12.

Carree

(2002). Industrial restructuring and economic growth. Small Business Economics, 18, 243-255.

13.

Carree

Thurik

(2006). The lag structure of the impact of business ownership on economic performance in OECD countries. Small Business Economics, 30, 101-110.

14.

Chesbrough

(2003). Open innovation: The new imperative for creating and profiting from technology. Boston, MA: Harvard Business School Press.

15.

Chesbrough

(2009, March). Open innovation and universities. Presentation at seminar “What Industry Wants From Universities,” sponsored by the Ewing Marion Kauffman Foundation, San Diego, CA.

16.

Chiesa

Piccaluga

(2000). Exploitation and diffusion of public research: The case of academic spinoff-off companies in Italy. R&D Management, 30, 267-281.

17.

Cohen

(2000, January 1-5). Taking care of business. ASEE Prism Online. Retrieved from http://www.prism-magazine.org/jan00/html/coverstory.cfm

18.

Colyvas

Crow

Gelijns

Mazzoleni

Nelson

Rosenburg

Sampat

(2002). How do university inventions get into practice? Management Science, 48, 61-72.

19.

Davidsson

(2004). Researching entrepreneurship. New York, NY: Springer.

20.

Degroof

J. J.

Roberts

(2004). Overcoming weak entrepreneurial infrastructure for academic spin-off ventures. Journal of Technology Transfer, 29, 327-357.

21.

Dietz

(2000). Building a social capital model of research development: The case of EPSCOR. Science and Public Policy, 27, 137-147.

22.

Dietz

Bozeman

(2005). Academic careers, patents, and productivity: Industry experience as scientific and technical human capital. Research Policy, 34, 349-367.

23.

Di Gregorio

Shane

. (2003). Why do some universities generate more startups than others? Research Policy, 32, 209-227.

24.

Feldman

Desrochers

(2004). Truth for its own sake: Academic culture and technology transfer at the Johns Hopkins University. Minerva, 42, 105-126.

25.

Franklin

Wright

Lockett

(2001). Academic and surrogate entrepreneurs in university spin-out companies. Journal of Technology Transfer, 26, 127-141.

26.

Goldfarb

Henrekson

(2003). Bottom-up versus top-down policies towards the commercialization of university intellectual property. Research Policy, 32, 639-658.

27.

Golub

(2003). Generating spin-offs from university-based research: The potential of technology transfer (Unpublished doctoral dissertation). Columbia University, New York.

28.

Gulbrandsen

Smeby

(2005). Industry funding and university professors’ research performance. Research Policy, 34, 932-950.

29.

Gulbranson

Audretsch

(2008). Proof of concept centers: Accelerating the commercialization of university innovation. Kansas City, MO: Ewing Marion Kauffman Foundation.

30.

Hayter

(2011). In search of the profit-maximizing actor: Motivations and definitions of success from nascent academic entrepreneurs. Journal of Technology Transfer, 36, 340-352.

31.

Heirman

Clarysse

(2004). How and why do research-based startups differ at founding? A resource-based configurational perspective. Journal of Technology Transfer, 29, 247-268.

32.

Hellmann

Puri

(2001). Venture capital and the professionalism of start-up firms: Empirical evidence (Research Paper No. 1661). Palo Alto, CA: Graduate School of Business, Stanford University.

33.

Hsu

Bernstein

(1997). Managing the university technology licensing Process. Journal of the Association of University Technology Managers, 9, 1-33.

34.

Jaffe

(1989). Real effects of academic research. American Economic Review, 79, 957-970.

35.

Jaffe

Trajtenberg

Henderson

(1993). Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics, 108, 577-598.

36.

Jensen

Thursby

(2003). Disclosure and licensing of university inventions: The best we can do with the s**t we get to work with. International Journal of Industrial Organization, 21, 1271-1300.

37.

Johansson

Jacob

Hellstrom

(2005). The strength of strong ties: University spin-offs and the significance of historical relations. Journal of Technology Transfer, 30, 271-286.

38.

Kenney

Goe

(2004). The role of social embeddedness in professorial entrepreneurship: A comparison of electrical engineering and computer science at UC Berkeley and Stanford. Research Policy, 33, 691-707.

39.

Landry

Amara

Oumit

(2002). Research transfer in natural science and engineering: Evidence from Canadian universities. Paper presented at the 4th Triple Helix Conference, Copenhagen, Denmark.

40.

Lerner

(1999). The government as venture capitalist: The long-run effects of the SBIR program. Journal of Business, 72, 285-318.

41.

Link

Ruhm

(2009). Bringing science to market: Commercializing from NIH SBIR awards. Economics of Innovation and New Technology, 4, 381-402.

42.

Link

Siegel

Bozeman

(2007). An empirical analysis of the propensity of academics to engage in informal university technology transfer. Industrial and Corporate Change, 16(4), 1-15.

43.

Lowe

(2002). Invention, innovation and entrepreneurship: the commercialization of university research by inventor-founded firms (Unpublished doctoral dissertation). University of California at Berkeley.

44.

Markman

Phan

Balkin

Gianiodis

(2004). Entrepreneurship from the ivory tower: Do incentive systems matter? Journal of Technology Transfer, 26, 233-245.

45.

Martinelli

Meyer

von Tunzelmann

(2008). Becoming an entrepreneurial university? A case study of knowledge exchange relationships and faculty attitudes in a medium-sized, research-oriented university. Journal of Technology Transfer, 33, 259-283.

46.

Meyer-Krahmer

Schmoch

(1998). Science-based technologies: University-industry interactions in four fields. Research Policy, 27, 835-851.

47.

Murray

(2004). The role of academic inventors in entrepreneurial firms: Sharing the laboratory life. Research Policy, 33, 643-659.

48.

Mustar

(1997). Spin-off enterprises, how French academics create high-tech companies: Conditions for success or failure. Science and Public Policy, 24(1), 37-43.

49.

Mustar

Renualt

Colombo

Piva

Fontes

Lockett

Moray

(2006). Conceptualizing the heterogeneity of research-based spin-offs: A multi-dimensional taxonomy. Research Policy, 35, 289-308.

50.

National Governors Association. (2004). A governors guide to strengthening state entrepreneurship policy. Washington, DC: U.S. Department of Education.

51.

National Governors Association. (2007). Investing in innovation. Washington, DC: U.S. Department of Education.

52.

Nerkar

Shane

(2003). When do startups that exploit patented academic knowledge survive? International Journal of Industrial Organization, 21, 1391-1410.

53.

Nicolaou

Birley

(2003). Social networks in organizational emergence: The university spinout phenomenon. Management Science, 49, 1702-1725.

54.

O’Gorman

Byrne

Pandya

(2008). How scientists commercialise new knowledge via entrepreneurship. Journal of Technology Transfer, 33, 23-43.

55.

O’Shea

Allen

Chevalier

Roche

(2005). Entrepreneurial orientation, technology transfer, and spin-off performance of U.S. universities. Research Policy, 34, 994-1009.

56.

Penrose

(1959). The theory of the growth of the firm. New York, NY: Oxford University Press.

57.

Perez

Sanchez

(2003). The development of university spin-offs: Early dynamics of technology transfer and networking. Technovation, 23, 823-831.

58.

Phan

Siegel

(2006). The effectiveness of university technology transfer: Lessons learned. Foundations and Trends in Entrepreneurship, 2(2), 77-144.

59.

Powers

McDougall

(2005). University start-up formation and technology licensing with firms that go public: A resource-based view of academic entrepreneurship. Journal of Business Venturing, 20, 291-311.

60.

Pressman

. (1999). AUTM licensing survey: FY 1999. Northbrook, IL: Association of University Technology Managers.

61.

Pressman

(2002). AUTM licensing survey: FY 2002. Northbrook, IL: Association of University Technology Managers.

62.

Pressman

Guterman

Abrams

Geist

Nelson

(1995). Pre-production investment and jobs induced by MIT exclusive patent licenses: A preliminary model to measure the economic impact of university licensing. Journal of Association of University Technology Managers, 7, 49-81.

63.

Radosevich

(1995). A model for entrepreneurial spin-offs from public technology sources. International Journal of Technology Management, 10, 879-893.

64.

Rappert

Webster

Charles

(1999). Making sense of diversity and reluctance: Academic-industrial relations and intellectual property. Research Policy, 28, 873-890.

65.

Renault

(2006). Academic capitalism and university incentives for faculty entrepreneurship. Journal of Technology Transfer, 31, 227-239.

66.

Roberts

(1991). Entrepreneurs in high technology. New York, NY: Oxford University Press.

67.

Roberts

Easley

(2009). Entrepreneurial impact: The role of MIT. Kansas City, MO: Ewing Marion Kauffman Foundation.

68.

Romer

(1986). Increasing returns and long-run growth. Journal of Political Economy, 94, 1002-1037.

69.

Rothaermel

Agung

Jiang

(2007). University entrepreneurship: A taxonomy of the literature. Industrial and Corporate Change, 16, 691-791.

70.

Samson

Gurdon

(1993). University scientists as entrepreneurs: A special case of technology transfer and high-tech venturing. Technovation, 13(2), 63-71.

71.

Saxenian

(1994). Regional advantage. Boston, MA: Harvard Business School Press.

72.

Shane

(2004). Academic entrepreneurship: University spin-offs and wealth creation. Northampton, MA: Edward Elgar.

73.

Shane

(2005). Government policies to encourage economic development through entrepreneurship: The case of technology transfer. In Shane

(Ed.), Economic development through entrepreneurship: Government, university, and business linkages (pp. 33-46). Northampton, MA: Edward Elgar.

74.

Shane

Stuart

(2002). Organizational endowments and the performance of university start-ups. Management Science, 48, 154-170.

75.

Siegel

Waldman

Link

(2003). Assessing the impact of organizational practices on the productivity of university technology transfer offices: An exploratory study. Research Policy, 32(1), 27-48.

76.

Slaughter

Rhoades

(2004). Academic capitalism and the new economy: Markets, state and higher education. Baltimore, MD: Johns Hopkins University Press.

77.

Solow

(1956). A contribution to the theory of economic growth. Quarterly Journal of Economics, 70, 65-94.

78.

Steffensen

Rogers

Speakman

(2000). Spin-offs from research centers at a research university. Journal of Business Venturing, 15, 93-111.

79.

Tornatzky

Waugaman

Casson

Crowell

Spahr

Wong

(1995). Benchmarking best practices for university-industry technology transfer: Working with start-up companies. Atlanta, GA: Southern Technology Council.

80.

Utterback

(1994). Mastering the dynamics of innovation. Boston, MA: Harvard Business School Press.

81.

van Praag

Versloot

. (2007). What is the value of entrepreneurship? A review of recent research. Small Business Economics, 29, 351-382.

82.

Vohora

Wright

Lockett

(2004). Critical junctures in the development of university high-tech spin-out companies. Research Policy, 33, 147-175.

83.

Wright

Clarysse

Lockett

Binks

(2006). University spin-out companies and venture capital. Research Policy, 35, 481-501.