University Technology Transfer

Universities technology transfer offices work to transition university-developed technologies into wider commercial use products. Researchers seek to learn more about how processes of social and normative influences among aspiring academic entrepreneurs interact with incentive structures set up by universities and firms. The changing nature of how knowledge is transferred between academic and industrial sectors is also a notable area of study.


Academic scientific research has made significant contributions to technological innovation and the American economy more generally since at least the beginning of the last century. Throughout its history, the orientation of U.S. university research has been towards the resolution of "practical" problems of concern to local industry. (Mowery, Nelson, Sampat, Bhaven and Ziedonis 2004) For example, the University of Akron became a leader in research on the processing of rubber, and later in polymer chemistry, both important to the local tire industry. In the 1920s and 1930s, university research at agricultural experimentation stations formed the foundation of the hybrid corn revolution. One of the first licensed patents arising from university research was for the electrostatic precipitator, invented by Frederick Cottrell of the University of California, Berkeley in the early 1900s to control pollution from local San Francisco Bay Area smokestacks. In the 1920s, Dr. Harry Steenbock of the University of Wisconsin developed a method for increasing the vitamin D content in foods, an important invention patented and licensed by the licensing arm of the university to Quaker Oats for the production of cereals. More recently, several industries such as the digital computers, lasers, and biotechnology can track their origins to research conducted in university laboratories. For example, Nelson (2015) chronicles research at Stanford University by musicians, engineers, computer scientists, and psychologists that underpinned the development of digital music as an academic field and new industry.

The commercial impact of academic scientific discoveries is clearly visible in the life sciences. A survey of 77 major firms from 7 industries conducted in the 1990s found that over 30 percent of the new drugs and medical products commercialized between 1986 and 1994 could not have been developed (without substantial delay) in the absence of recent academic research. (Mansfield 1998)  Mowery et al. (2001) report that in 1995, licensing income was highly concentrated among biomedical technologies for the top five earning inventions at three leading university licensors, Columbia University (94%), Stanford University (85%), and the University of California (100%).

The mechanisms that facilitate such an impact vary, but to take one specific example, young biotech firms founded by university professors often act as knowledge brokers between universities and larger pharmaceutical firms. (Stuart, Ozdemir and Ding 2007)

The commercialization of academic science can be challenging, however. For example, university research tends to be fundamental and not directly amenable to technology development. Furthermore, academic scientists face multiple goals and must balance applied technology development and transfer with their mission to teach and to conduct basic academic research.

This second concern is mitigated somewhat by recent empirical evidence that suggests that commercial pursuit does not necessarily crowd out university scientists' effort towards research. However, other evidence suggests that his/her research shifts in a more commercial direction after the commercial engagement. The decision to engage in commercial activity is undoubtedly influenced by financial incentives, but research on scientists' career choices has shown that scientists often are not purely driven by financial considerations.(Stern 2004)

Geography also often affects the commercialization of university research.  For example, "star" university scientists often play a critical role in the geographical location decisions of startups in biotechnology. (Zucker, Darby and Brewer 1998)  Moreover, academic scientists' pursuit of commercial activities (academic entrepreneurship) is often a locally clustered phenomenon, (Zucker, Darby and Brewer 1998) with the social influence through collaboration and collegial ties play a strong role in the transition process. (Stuart and Ding 2006) More generally, geographic proximity affects the operation of market and non-market channels of university technology transfer. (Audretsch and Stephan 1996) (Mowery and Ziedonis 2014)

We know less about how processes of social and normative influences among aspiring academic entrepreneurs (e.g., networks) interact with incentive structures set up by universities and firms, as well as with the changing nature of knowledge that has been transferred between academic and industrial sectors. We also know little about how channels by which scientists in the academic and industrial sectors are connected, whether and how academic scientists move between sectors, and the implications of such connections and mobility patterns on the way knowledge travels between academia and industry.

Finally, little is known about the economic returns from public and private investment in scientific research and technology transfer. Similarly, the tradeoffs associated with various ways to organize technology transfer remain little understood. The answers to these questions have public policy implications towards university technology transfer and commercialization of academic research.


Overview of the Process

There is a large body of literature examining the university technology transfer process, primarily focused on institutions that facilitate commercialization such as technology transfer offices (TTOs) or offices of innovation and commercialization (OICs). Many of these studies measure commercialization success by counts of patents, licenses, and spinoffs or startups. The process of technology transfer from invention to commercialization, however, is often assumed to be something of a black box. The extant literature is replete with depictions of traditional models of the technology transfer process, but for the most part these are oversimplified linear models.

The linear representation of the technology transfer process has probative value as many scholars have focused exclusively on one or two elements of the model. It does oversimplify the overall technology transfer process, however.

The linear model of the technology transfer process begins with the process of discovery by a university scientist or inventor. The scientist discloses the invention to the university's TTO. Once the invention is disclosed, the TTO evaluates the invention and decides whether or not to pursue acquiring a patent. The TTO must consider the commercial potential of the invention, as well as prospective interest from the private sector. If the TTO decides to invest in the invention, the next step is the patent application process. If the patent is awarded, the TTO markets the technology to organizations and entrepreneurs. The goal of this marketing effort is to match the technology with an organization or entrepreneur that/who can best utilize the technology and provide opportunities for revenues to the university. When a suitable partner is found, the university negotiates a licensing agreement with the organization or entrepreneur. (Some universities allow firms to take an "option," on the technology, which provides the firm a period in which it may evaluate "in-house" the technology before arriving at a licensing decision. (Ziedonis 2007) The licensing agreement typically includes a royalty to the university, an equity stake in the startup, or other compensation. The original invention typically undergoes extensive development during commercialization. The inventing scientist may be involved in the development process.

The inventor's decision to disclose a discovery to the TTO is influenced by the university's reward systems and culture. If there are significant perceived barriers and disadvantages to involving the TTO, the inventor may choose not to disclose and circumvent university's formal process and pursue commercialization independently. (Thursby, J Fuller and Thursby M 2009)

The source of the funding underpinning the scientific research generating the invention may affect the path to commercialization.  If the discovery results from a federally-funded research project, as is most common, one of two paths might be followed: the university can decline to retain title or the university can retain title to the invention.

If the university decides to hold title to the invention, it must decide how to proceed with commercialization. The processes of marketing the invention, acquiring IP protection, and negotiating licensing agreements and pecuniary returns do not necessarily follow a linear path.  The invention can be marketed before IP protection is acquired, that is, if the university wants to gauge market interest before investing significant time and resources to protecting the invention. Or, if the invention seems especially promising, the university might choose to apply for patents, copyrights, etc. before or even as they are marketing it to potential investors. The university could commence licensing negotiations before the IP protection process is completed.

Macro Impact

University research is associated with the creation of tremendous economic value—it can potentially generate revenues for universities, create research connections between academia and industry, and enhance regional economic growth and development. Over the past two centuries, academic laboratories have played a critical role in the birth of entire industries including the synthetic dye industry, (Murmann 2003) the digital computer industry, (Rosenberg and Nelson 1994) and the biotechnology industry. (Zucker, Darby and Brewer 1998) In fact, large-scale empirical studies have found positive relationships between academic research and technology development (Jaffe 1989) as well as between academic research and productivity growth. (Adams 1990)

Nature of Academia's Impact

The available stock of scientific knowledge constitutes the equivalent of a map guiding the invention process and increasing its efficiency. (Nelson 1962) (Mokyr 2002) (Fleming and Sorenson 2004) In addition, scientific discoveries sometimes directly open the door to the development of new technologies. (Stokes 1997) (Murray 2002) Using large patent datasets, researchers have found that academic patents and those that use scientific knowledge tend to be of higher quality. Moreover, technology appears to be increasingly intertwined with science, and university patenting has boomed. (Narin, Hamilton and Olivastro 1997) (Henderson, Jaffe and Trajtenberg 1998) (Mowery, Sampat and Ziedonis 2002) (Branstetter 2005)


The commercialization of academic science can occur through a variety of channels. (Agrawal and Henderson 2002) (Cohen, Nelson and Walsh 2002) Those include exposure to scientific publications and informal interactions (e.g., Allen 1978), contract research (e.g., Lacetera 2009), collaboration (e.g., Zucker, Darby, and Armstrong 2002), patent licensing (e.g., Hellmann 2007), entrepreneurship (e.g., Shane 2001) and the hiring of students trained in academic labs (e.g., Furman and MacGarvie 2007). Returns from the licensing of university inventions are highly skewed and depend on a number of factors including the status of the scientists and their university (Sine, Shane, and Gregorio) (Elfenbein 2007) as well as their incentive to get personally involved in the process of technology transfer. (Jensen and Thursby 2001) (Lach and Schankerman 2008)  A few studies have compared the commercialization of university technology by start-ups vs. established firms. For example, in a study of licensing activity by MIT, Shane (2002) found that in scientific fields where patent effectiveness was weak, inventor founded licensees (start-ups) were more likely to terminate a license and less likely to introduce a new product based on the licensed technology than an established licensee, leading him to conclude that licensing to an inventor-founded start-up is less promising than licensing to an existing firm. Examining data on licensing from the University of California system, however, Lowe and Ziedonis (2006) found that start-ups took longer to terminate a failed technology, and that economic returns from licenses to start-ups were similar to those to more established firms. They also find that the majority of returns associated with the technologies licensed to start-ups are realized subsequent to the acquisition of the start-up by an established firm, leading them to conclude that many academic start-ups serve as a "transitional" organizational form in the market for university technology commercialization.

Division of Labor

University-based scientific research often is fundamental in nature and thus may have little immediate economic value (an important exception may be "use-inspired" basic research occurring within "Pasteur's Quadrant," a term coined by Donald Stokes in his description of research that seeks fundamental scientific knowledge while at the same time having practical benefits—biomedical research is often thought to meet this definition). (Stokes 1997) Some scholars have argued that a division of innovative labor exists in which university scientists focus on basic science and firms conduct more applied work. (Rosenberg and Nelson 1994) (Aghion, Dewatripont and Stein 2008) Survey evidence supports that view by finding that academic scientists tend to not only focus on more basic science, but that the usefulness of their discoveries varies considerably across disciplines and across industries. (Klevorick, Levin, Nelson and Winter 1995) (Mansfield 1998) (Cohen, Nelson and Walsh 2002) (Sauermann and Stephan 2013)

Technology transfer activities have brought academic and industrial scientists closer in many ways, however. To the extent that scientists often work with both university labs and industrial labs, they can bridge gaps in knowledge flow, communication patterns, management techniques, as well as norms of the academia and industrial sectors. For example, Ding's (2009) research examined the adoption of open science, an unconventional practice that allows firms' research staffs to publish discoveries generated from corporate-funded research projects. She analyzed the adoption records in a sample of over 500 young biotechnology firms between 1972 and 2002 and found that firms founded by entrepreneurs with Ph.Ds are more likely to form pro-open-science perceptions and adopt the practice at their firms, and that they are less sensitive to the risks of their research being scooped by competitors, compared to firms founded by entrepreneurs with other types of training. More recent work by Stuart and Liu (2014) also reveals interactions between industrial and academic scientists through an analysis of email communications of industrial scientists in a major pharmaceutical firm. These scholars show that bonuses and the managerial attention allocated to this firm's industrial scientists are tied to their publications and those that publish in academic journals are much better connected to external (to the company) members of the scientific community. These findings are consistent with earlier accounts of absorptive capacity of industrial firms, (Cohen and Levinthal 1990) and findings by Cockburn and Henderson (1998) on how pharmaceutical firms acquire knowledge from public research.

Institutional Factors

The university environment is not always favorable for technology transfer. The institutional logic of academia (Merton 1973) (Dasgupta and David 1994) (Gittelman and Kogut 2003) can conflict with attempts to commercialize academic discoveries and inventions. (Argyres and Liebeskind 1998) (Murray and Stern 2007) (Murray 2010) As a result, academic scientists' involvement with patenting is highly heterogeneous. Mid-career male scientists are particularly likely to patent, (Azoulay, Ding and Stuart, 2007) and patenting activity is in part determined by local norms. (Bercovitz and Feldman 2008)

Scholars have long noted the gradual evolution of social norms within academia regarding scientific effort towards commercially related research. Merton (1965) noted that the ethos of science—namely, communism, universalism, disinterestedness, and organized skepticism—are the pillars of academic institutions. Several scholars have documented the erosion of the ethos in universities where commercial activities are prevalent among scientists, however (Etzkowitz, 1998; Louis, et al. 1989). A large-scale longitudinal study by Stuart and Ding (2006) found that academic entrepreneurship to be a locally clustered process, as aspiring academic entrepreneurs influence each other through various forms of local ties. Kenny and Goe (2004) compared connection patterns among scientists at the University of California, Berkeley and Stanford University and found notable differences between the two universities due to the difference in locally embedded norms.

Potential Negative Effects

There has been a heated debate among scholars and policymakers on whether commercial activity by university scientists negatively influences their research. Mowery, Nelson, Sampat, and Ziedonis (2004) conducted a historical analysis of university-industry interaction in scientific research and technology development throughout the 20th century and raise concerns of the consequences for university research of an increasing proprietary emphasis. They examine the consequences of the Bayh-Dole Act of 1980, which facilitated the patenting and licensing by universities of federally funded research outcomes, and conclude that while Bayh-Dole did contribute to the expansion of commercialization activities by U. S. universities, other concurrent factors, such as the strengthening of intellectual property rights as well as scientific advances in biomedical research also played a role.

Several studies have examined the relationship between patenting by academic scientists and their research output. Agrawal and Henderson (2002) estimated effect of patenting over a 15-year period by 236 scientists in two MIT departments and found that patenting did not affect publishing rates. Fabrizio and Di Minin (2008) examined the number of published articles by 166 academic patenters to a matched sample of non-patenting scientists, finding a positive relationship between patenting researchers' patent stocks and their publication counts. A similar positive relationship was also found in a third study by Stephan et al. (2007) using a survey of doctorate recipients. In a fourth paper, Azoulay et al. (2009) (Azoulay, Ding and Stuart 2007) addresses the same question using a research design that account for the thorny issue of selection—i.e., certain types of scientists choosing to patent—that renders traditional fixed-effect models inadequate for solving the endogeneity problem in estimating patenting effect on research productivity. Using inverse-probability-of-treatment weights in estimations to account for scientists' self-selection into patenting, they confirm a complementary relationship between patenting and the rate of publications (as opposed to a finding that patenting crowds out public research effort). Patenting also has a weak positive effect on the quality of research in this study. However Azoulay et al. do find that a scientist's research direction moves to be closer to those of commercial interest on average.

Heller and Eisenberg (1998) raise concerns over the insertion of proprietary intellectual property rights in early stage biomedical research, arguing that competing patent rights could lead to blocking patents and an "anticommons," thus deterring follow-on research. Murray and Stern (2007) provide some support for an anticommons effect, finding that journal citation rates after the grant of a patent declined between 9-17 percent compared with a control group of articles published in the journal Nature Biotechnology whose related invention was not patented.  In follow-on work, Fehder, at al. (2014) find that this negative effect is concentrated in the early years subsequent to a new scientific journal's founding.

A recent pair of studies based on evidence from the University of California have examined the effect of licensing on research and commercial outcomes. Drivas, Lei and Wright (2015) address concerns that exclusive licensing of university discoveries may deter non-licensee firms from conducting their own research within the licensed technical area.  Examining the patent citations by non-licensee firms to licensed University of California patents, they find that citations by non-licensees actually increase, suggesting that such licenses act as "signposts," signaling potential commercially valuable pathways to non-licensees. Thompson, Ziedonis and Mowery (2015) examine journal citations to published scientific articles linked to patented discoveries.  Compared to a matched sample of articles linked to non-licensed discoveries, they fail to find an effect of licenses on the citation of published articles for their overall sample.  However for the subset of licensed inventions that are research tools (i.e., used as inputs to future research), they find that licenses are associated with a decrease in journal citations to linked articles. This finding raises the possibility that licensing may restrict the flow of inputs to further scientific research among researchers.

Overall, this stream of research suggests that although concerns over the direction of academic scientific research after the onset of patenting may be warranted, there is little evidence that patenting has a negative effect on scientists' research productivity or quality, with the important exception of research tools, in which licensing may negatively affect follow-on scientific research.


The literature has made considerable progress over the past few decades, thanks in part to access by scholars to data from university technology transfer operations, increasing availability of large publication and patent datasets (e.g., NBER patent dataset and Harvard's patent data project), as well as survey data (e.g., AUTM dataset, the 1994 Carnegie Mellon Survey of Industrial R&D). Identification and measurement issues have prevented researchers from answering important questions, however. For example, considerable debates exist about the returns on public and private investment in academic research, about whether academic knowledge might be underutilized by firms, about the tradeoffs associated with various organization of technology transfer and about the impact of technology transfer on the conduct of more fundamental research.

Future Research

  • The economic returns from public and private investment in scientific research and technology transfer remain poorly understood. What are the key drivers of performance in technology transfer? What are the economic returns from investment in basic versus applied science?
  • Although many channels of technology transfer exist, the tradeoffs of using one over the other remain little understood. What are the benefits and costs of collaboration, contract research, licensing, or academic entrepreneurship? Under what circumstances does it make sense to use one over the other?
  • The differences in institutional logic between academia and industry are now well understood. However, despite efforts to understand these differences, the consequences for technology transfer remain unclear. How do they impact the division of innovative labor? How can managers and policy makers take advantage of these differences in their effort to innovate?
  • There is a little scholarly work on the entrepreneurial paths that university scientists and researchers take after retiring from the university.
  • To better understand the micro-foundations underlying the technology transfer process, more research endeavor that compares, contrasts, or integrates various approaches are necessary. We will need to understand further the patterns and types of connectivity among and between academic and industrial researchers, and how knowledge flows through the connected paths. We also need to understand how the connectivity patterns interact with incentives system and institutional structure to shape the knowledge transfer process. These questions are better answered by incorporating both economic and sociological perspectives.

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