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

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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; the results show 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.

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Notes

  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 2016). 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.

  2. For example, a voluminous literature examines the structure and impact of technology transfer offices (Bradley et al. 2013a, b; 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).

  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 USA, following California and Texas, respectively (see www.census.gov, accessed 25 Jan 2015). It is also home to New York City, the largest city in the USA 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; Milken 2013).

  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 (2016) for a more in-depth discussion of how data were collected from TTOs.

  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.

  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 are reliable. The addition of a third coder allows for a decision to be made when there exist 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 % for and 0.800, respectively.

  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 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.

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Acknowledgments

I am grateful to the Ewing Marion Kauffman Foundation for their financial support of this research and also special thanks to Marla Parker and two anonymous reviewers for their helpful comments and suggestions.

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    Christopher S. Hayter

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Hayter, C.S. A trajectory of early-stage spinoff success: the role of knowledge intermediaries within an entrepreneurial university ecosystem. Small Bus Econ 47, 633–656 (2016). https://doi.org/10.1007/s11187-016-9756-3

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