The productivity of licensing endeavors at entrepreneurial universities
Keywords:innovation ecosystems, university-industry relationships, licensing, productivity, less productive entrepreneurship
(Formal) technology-transfer activities at entrepreneurial universities (Etzkowitz et al., 2000) - such as licensing of university technologies - are seen as conduits for knowledge-based economic development (Rothaermel et al., 2007, Mowery and Ziedonis, 2015; Bercovitz et al., 2019). The licensing activity is a bridge between business and universities - two different innovation ecosystem actors relied around common ‘artifacts’ (Granstrand & Holgersson, 2020); in our case university technologies.
Entrepreneurial activities with the potential to ‘yield new businesses or products’, i.e. those that Baumol (1990) categorizes as ‘productive entrepreneurship’, are often deemed in advance to increase value. At first glance, we can reasonably construe that the licensing activity when leading to dissemination whilst giving the licensees the potential to appropriate value, should bring well-being, i.e. positive societal outcomes. However, Lucas and Fuller (2017) warn that (social) value need not be created by those engaging in seemingly ‘productive’ activity particularly when these activities are publicly funded, however, this is hard to evaluate as there is often a lack of feedback information.
Using licensing revenues as a proxy for such feedback, we ask ourselves which factors contribute to licensing that cannot be deemed as ‘productive’? The lack of revenue can be seen as a signal of not utilizing the licensed technology. Using detailed micro-license data we examine the factors that contribute to licensing of university technologies not delivering returns and thus not fitting with the notion of productive entrepreneurship.
We consider several reasons for why such licensing activities exist. Firstly, prior literature points to the embryonic nature of the licensed technology (Thursby et al., 2001). If this is combined with the lack of additional developmental commitment, it prevents the technologies’ implementation, and causes a lack of licensing revenue.
Secondly, the reasons can be connected to failed opportunity assessment of the licensee about the technology potential, hence unwillingly contributing to what we cannot deem as ‘productive entrepreneurship’. Thirdly, the licensee can have strategic reasons to gain a license, e.g. to shelf it or to prevent others to use it, which is in line with Baumol’s ideas of ‘unproductive activities’. Fourthly, the technology transfer offices (TTO) might also have chosen the wrong licensee - one that is either unable or unwilling to use the technology. Fifthly, in line with the institutional logic theories (Thorthon and Ocasio, 2008), the TTO strategies related to licensing, i.e. its institutional context, might push for certain licensing practices. On one hand, TTO might excessively seek an increase in the number of licenses, since those are often an alternative measure of the TTOs ‘success’. Thursby et al. (2001) point out that larger numbers of licensing agreements do generate more income, however, due to large amounts of patented technologies, generation of ever-more licensed technologies might become unsustainable due to resource constraints. On the other hand, many TTOs follow the idea that one needs many deals to get results, in line with the notions of a ‘licensing lottery’.
Our paper provides a nuanced view of university licensing activities by focusing on activities that cannot be deemed as ‘productive’. By unpacking the black box of university licensing, and thereby uncovering inefficiencies in university licensing, we add to the lacking area of micro-foundational analyses and answer to the call by Cunningham and O’Reilly (2018) for more micro studies with respect to the technology transfer, and in particular licensing.
We acquired micro-level administrative data on licensing and connected it to licensing revenue streams, despite a recorded lack of availability and accessibility of such data (Bercovitz et al., 2019). The university exhibits strong licensing performance. Our dataset includes information on licensing revenues (10yhrs), combined with licensing agreements data and hand-curated data on licensees. Our unit of observation are licensing agreements to particular licensees related to particular patents, i.e. patent families (n=385).
Following a descriptive analysis, we conduct a preliminary analysis in which we specify several attributes for every observation. We determine attributes related to patent and technology (international patenting, granted patents, joint patenting, embryonic, patent field, inventors), to institutional context (TTO size, coordinators attributes), to licensing (licensee size and type, agreement types), and to revenue (size, time, type). We consider the cases with licensing revenue being zero as not fitting the definition of being productive. We also conduct robustness checks: i) including licenses with trivial revenues, and ii) including only typical licensing contracts (n=193).
Results and implications
The focal university transfers approximately one fifth of all their patents. We confirm the existence of less productive licensing activities by documenting that 82 (21,2%) of all licensing cases generate no revenue. This non-negligible result indicates resource inefficiencies stemming from university resources, and implies potential for distorting dissemination.
The licensing attempts with no generated revenue are less often connected to a corporate licensee. However, the licensing process is longer than for those that have received some revenue. This could imply lesser demand for these particular technologies. There is a significant difference in whether a running royalty has been agreed – indicating there is a different understanding of the risk related to the applicability of a certain license. Other results contrast with conventional logic and prior literature; e.g. licenses with no revenues more often include patents that have been invented by larger groups. Furthermore, these licenses less often include an experienced ‘star PI’ as the primary investigator; contrasting with the literature depicting these PIs as efficient substitute brokers for technology transfer. We take these insights to further investigate reasons for less productive licensing activities by using panel data and survival analysis techniques.
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