Part III - The Way Forward

10 Policies and Practices for Supporting Successful Knowledge Transfer from Public Research to Firms

Anthony Arundel

10.1 Introduction

In the last decades, governments in many countries have added a third goal of community engagement to the university goals of teaching and research. Although there are many types of engagement, a primary focus is to encourage universities to support the commercialization of university-produced knowledge by private sector firms, with the expectation that this will improve competitiveness, living standards, and employment. This also requires universities to adopt some of the goals of public research institutes such as the Fraunhofer Institutes in Germany, which were established to fulfill this role. The combination of universities and publicly funded research institutes are referred to in this chapter as “public research” or “public research organizations.”

Multiple types of policy and practice are involved in successful knowledge transfer and commercialization. Successful transfer results in products or processes, derived in part on discoveries or inventions made by researchers in the public research sector, that are either introduced onto the market and acquired by users or implemented in the business processes or functions of firms or government organizations. Successful transfer is difficult to identify (see Chapter 12) and consequently many pre-commercial metrics are used as a proxy, such as the licensing of public research inventions or the establishment of startups.

The discussion of policies and practices in this chapter draws on the published literature and six national case studies, three of which are for high-income countries (Germany, the Republic of Korea, and the United Kingdom) and three from middle-income countries (Brazil, China, and South Africa). These six countries show a range of policies and practices for knowledge transfer and a variety of contextual conditions that influence success, including different industrial structures and levels of technological competence within the public research sector and the business sector. In the last few decades, all six countries have undergone major changes in national policies with the goal of improving rates of knowledge transfer and commercialization.

Section 10.2 evaluates the context for successful knowledge transfer and commercialization, exploring the effects of linear and nonlinear models of innovation and how these models influence our understanding of the demand-side requirements for knowledge transfer. Section 10.3 draws on the literature and the case studies to identify “what works” and uses the case studies to illuminate the contextual factors that influence outcomes. Section 10.4 provides brief descriptions of changes in knowledge transfer policy practices in each of the six case countries and an evaluation of the causes of the changes. Section 10.5 draws conclusions and recommendations for supporting knowledge transfer.

10.2 Models of Knowledge Transfer

Knowledge transfer can occur via multiple channels, as discussed in Chapter 2. Different methods for knowledge transfer can result in equally successful results, indicating equifinality, in which multiple causal paths can lead to the same desired outcome (Ordanini et al., 2014). The probability of a successful outcome is affected by many contextual factors that are not the direct target of knowledge transfer practices, such as the national industrial structure, the firm’s main sector of activity, the national or regional level of economic development, the type of research conducted by public research organizations, and the technological and innovation capabilities of both the public research sector and private sector firms.

The type of research varies by the domain or field of science, but also between basic and applied research. Basic research is expected to have long time lags between discovery and commercialization, whereas applied research is closer to the market and therefore has shorter time lags. The widely disparaged but still powerful “linear model” of innovation assumes that basic research, conducted by universities and some public research institutes, is followed by applied research, either by public research organizations or firms, that results in commercial products and processes. The linear model, or the “mode 1” conception of knowledge transfer (Gibbons et al. 1994), underpins the American Bayh-Dole Act of 1980.

The linear model has two assumptions. First, it views knowledge flows as unidirectional, flowing from public research organizations to firms. Second, it assumes that there is an ample supply of firms that are capable of taking university discoveries and further developing them into commercial products and processes, but unwilling to invest in further research because of a lack of patent protection on inventions. The Bayh-Dole Act permits universities to provide the necessary patent protection.

The assumption of an ample supply of firms with the absorptive capacity to develop university inventions into products and processes probably reaches its closest approximation to reality in the United States of America (U.S.), where there is a larger pool of firms that are close to the technological frontier than in many other countries. Firms in science-based industries are also more likely to successfully use university inventions within a mode 1 linear model because they have the necessary capabilities to work within this model. However, this model does not hold in many countries and is also unlikely to be true in some regions of the U.S., in sectors where innovation is not based on science, or among specific types of firm, such as SMEs that lack advanced technological or scientific capabilities.

The mode 1 linear model of innovation assumes that there is always sufficient demand from national firms that are capable of using knowledge produced by universities. This has led to national policy reports in almost every developed country lamenting that excellent research results produced by national universities fail to be picked up and developed by national firms, with the blame placed on the universities or on the lack of programs to transfer knowledge from universities to capable firms. An example is a South African White Paper that states:

Whilst South Africa has many examples of good R&D work, it has only managed to commercialise and exploit the research results in a few instances. Part of the problem is undoubtedly the absence of mechanisms to ensure that industry benefits maximally from the [output of public research] and other basic and/or applied research performers.

(cited in Kahn 2017: 12).

The “mode 2” model (Gibbons et al. 1994) revises the original linear model based on technology push by introducing the need for universities to conduct applied research and consequently provide firms with inventions that are closer to the market. Market proximity has been measured through “technology readiness levels” or “proof of concept” (Heder 2017; Munari et al. 2017). Yet the mode 2 model is still insufficient because it fails to integrate the other half of the knowledge transfer process: the absorptive capacity of firms. Caryannis and Campbell (2009) and Miller et al. (2016) extend the mode 2 model by recognizing the need for demand pull from firms to the public research system, such that public research scientists are aware of industry needs and are able to act on this knowledge by altering their research programs. In the South African case, Kaplan (2008) argues that this occurs infrequently because there are few incentives for researchers to change or adjust their research programs to meet domestic needs. Furthermore, government officials in South Africa have understood a failure to transfer knowledge as a network failure, where there is a lack of bilateral communication between university academics and the managers of firms, or as a financial problem, with insufficient early-stage funding for startups or incentives for university researchers, instead of a possible “mismatch between demand and supply” (Kahn 2017: 28).

This is not only a problem for South Africa – in many countries, academics are comfortable within a technology-push model because it requires less involvement and provides more independence, permitting academics to conduct the type of research that they want to do and in the way they want to do it. This model does not require academics to conduct research that meets the needs of industry. This goes deeper than arguments over the “different cultures” of academics and firms, which often revolve around deadlines and confidentiality and arise when academics are involved in a collaborative research project with industry. The greater issue is the willingness of public researchers to engage with industry in the first place. O’Shea et al. (2008) note that there are large differences among academic researchers in their interest in engaging with a variety of stakeholders, while Arque-Castells et al. (2016) find that approximately one-third of Spanish and Portuguese academics that hold a patent for an invention are not interested in working with firms, even with financial incentives from a possible share of future royalty income.

The “mode 3” model for knowledge transfer assumes that effective transfer requires a pool of firms with sufficient absorptive capabilities (Hallam et al., 2014) and that there is a reverse knowledge flow whereby firms provide public research scientists with information on their needs and that this information influences the research projects of public research scientists. Miller et al. (2016) argue that this “demand pull” is the dominant factor in the process of effective knowledge transfer. It is likely to be of critical importance to collaborative research between the public research sector and firms. Mode 3 therefore follows a nonlinear model that is aligned with theories of national innovation systems (Lundvall 1992; Hallam et al. 2014).

In many countries, an awareness of demand pull has existed for decades and was met through public research institutes that conducted applied research for local industries, but universities were often outside this system. An example is Germany, which maintains a clearly defined basic research infrastructure, including universities, the Max Planck Institutes, and, to a certain extent, the Helmholtz Institute. Researchers at Max Planck do not see knowledge transfer as part of their role and have been largely unaffected by the trend, in many countries, to introduce third-pillar “community engagement” into public research organizations (see Chapter 5). Conversely, other public research institutes such as the Fraunhofer Institutes and the Leibnitz Institute view knowledge transfer as an important part of their role.

Out of the six country case studies, the United Kingdom has probably experimented the most with policies to encourage demand pull. Since the early 2000s, UK policy identified the disadvantages of too much focus on IP as part of a technology-push model and encouraged universities to become active players within a complex ecosystem of innovation characterized by collaboration and knowledge exchange (see Chapter 4). This was supported by financial incentives that allocated 9 percent of total government research funding on the basis of the income universities obtained from knowledge transfer activities, along with research subsidies to firms to participate in collaborative research with universities.

A “mode 3” model based on an understanding of national innovation systems recognizes the roles of both technology push and demand pull, with a focus on both public research and the capabilities and needs of national firms. Both public science and the private sector play strong roles, such that the failure to transfer knowledge could be due to a range of deficiencies on each side. Furthermore, mode 3 includes knowledge transfer intermediaries, such as university “technology transfer” and “knowledge transfer” offices, that play a greater role than simply preparing patent applications and licensing contracts. Instead, effective knowledge intermediaries need to actively find firms that could benefit from public research and encourage informal and formal contacts and collaborations between public research scientists and firms (Garengo 2019).

The terminology for knowledge intermediaries reflects the different conceptions of how knowledge flows. The original concept of a “technology transfer” office is based on the linear model, whereby knowledge flows in one direction from public research to firms. The update to “knowledge transfer” offices remains within this paradigm, with the exception that “knowledge” includes nontechnical knowledge such as works protected by copyright. The most recent term, although still rarely used for practical purposes, is “knowledge exchange,” which views the process as involving a bidirectional flow of knowledge. This also includes cocreation as part of “open innovation” (Chesbrough 2003; Miller et al. 2016), where researchers from firms and public research organizations jointly develop inventions, often through collaborative research projects.

10.2.1 The Knowledge Capabilities Gap

While the linear model assumes that there is a pool of capable firms that can make use of results flowing out of a “public research pipeline,” mode 3 models assume that a pool of capable firms may not exist: national firms might lack the absorptive capacity to use the outputs of the public research sector. This can be captured through the concept of a knowledge (or technological) capability gap between firms and public research.

The effect of a knowledge capability gap has been identified in several contexts. Haas et al. (2015), in an analysis of 952 problems posted on an online forum, find that knowledge providers are more likely to allocate time to solving a posted problem if the problem matches their expertise. In addition, they find an inverse “U” relationship between the novelty of a problem and the probability that solution providers will respond. Chan et al. (2018) examine the adoption of novel ideas obtained from a firm’s customers through crowdsourcing and find that the adoption of the idea by the firm declines with the novelty of the idea, as measured through newness, distinctiveness, and originality. Criscuolo et al. (2017) also find an inverse “U” relationship between the novelty of 556 research proposals for R&D funding and the share of requested funding received.

A study by Kotha et al. (2013) provides an empirical example of the effect of a knowledge gap on the licensing of invention disclosures from an unidentified American university between 2001 and 2006. Out of 3,776 invention disclosures, 874 inventions were patented, of which 38 percent (339) were licensed, while 14 percent (416) of the non-patented inventions were also licensed, giving a total of 755 licensed inventions. Of note, more licenses were given to non-patented inventions than to patented inventions. The authors calculate the scientific “distance” or technological complexity of each invention disclosure, measured by the number of knowledge domains used for the invention and the prevalence of cross-disciplinary research between the domains. Inventions with low technological complexity are likely to provide minor increments to existing knowledge, while very technologically complex inventions are likely to represent major inventions. The authors use survival analysis to determine the probability of each invention being licensed. Similar to Criscuolo et al. (2017) and Chan et al. (2018), Kotha et al. (2013) find an inverse “U”-shaped relationship between the probability of licensing and scientific distance. Inventions of medium complexity are more likely to be licensed than inventions of both low and high complexity. This effect is mediated by the inventor team’s experience with licensing. Greater experience increases the probability of licensing all types of invention, while low experience decreases the probability of licensing more technologically complex inventions.

The implication of this research is that a large gap between the technological complexity or novelty of an invention or idea and the capabilities of potential users decreases the probability that an invention or idea will be taken up, probably because potential users lack the absorptive capacity to understand and adapt an invention or idea for their own uses. Conversely, inventions or ideas with low complexity or novelty are also less likely to be taken up, possibly because firms are capable of developing similar solutions. In the Kotha et al. study, university inventions with low complexity may be less likely to be licensed because firms can work around the patent, saving the cost of taking out a license. The positive effect of the previous licensing experience of the inventors could increase the probability of licensing complex inventions because it signals to firms that the inventors are willing to assist firms in understanding and further developing complex inventions into commercially useful products or processes.

The gap in capabilities between university inventors and a firm is likely to vary between countries. For instance, the average absorptive capacity of the potential pool of licensees in a technologically leading economy such as the U.S. is likely to be greater than in a middle-income economy such as Brazil. In addition, inventions by universities in middle-income economies are also likely to be less novel or complex than they are in the U.S. Nevertheless, the literature suggests that what matters is the gap in technological capabilities between university academics and domestic firms, rather than the absolute level of complexity of the university invention. The gap needs to be sufficiently large to provide inventions that firms could not develop themselves, but not so large that firms are unable to understand and commercialize them. Of course, there may be islands of competence where the technological gap between public research and firms is within a “sweet spot” for licensing, as shown by the high technological capabilities and close linkages with public research of aircraft manufacturers in Brazil (De Negri and Rauen 2017) or petrochemical firms such as Sasol in South Africa (Kahn 2017).

The technological gap can also occur in the other direction, with universities operating at a lower level of technological competence than firms. In this case, firms have little interest in licensing inventions from universities. One example is large firms in the Republic of Korea, which benefited from public research from the 1970s until the 1990s. However, after the late 1990s, the capabilities of large firms in the Republic of Korea exceeded the capabilities of the public research sector (see Chapter 6), with one consequence being a shift in policy to encourage public research institutions to support technologically lagging SMEs.

The concept of a knowledge gap applies not only to licensing IP, but also to involvement in collaborative research. When the knowledge is gap is high, firms might resort instead to contracting out research to public research organizations.

Out of the six country case studies, four identify barriers to knowledge transfer as a result of a knowledge gap where the capacities of universities exceed those of firms (Brazil, China, South Africa, and the United Kingdom), and the Republic of Korea identifies a knowledge gap where the capacities of firms gradually exceeded those of universities. Germany is the only case study where a knowledge gap does not appear to be a significant issue, either because of the well-developed infrastructure of public research institutes that serves the requirement for applied research by German firms, or because of a lack of comprehensive data for Germany on knowledge transfer channels other than those based on patents (see Chapter 5).

The technological gap between universities and firms can be imagined as a situation where knowledge must be “pumped uphill” to overcome the deficit in the absorptive capacities of firms. The “pump” can consist of demand-side activities such as investments by firms in absorptive capacity, the active assistance of academics in helping firms to understand their inventions, or closer collaboration between firms and academics so that the last are more knowledgeable about the problems that firms face. Policy can contribute to the pump by subsidizing the R&D activities of firms, providing subsidies for collaborative research between firms and universities, or supporting university practices that encourage inventors to assist firms, for instance, by taking up short-term contracts with firms to assist with knowledge transfer.

10.3 Appropriate Policies and Practices

Successful knowledge transfer from public research depends on context: the technological and related capabilities of firms and public research organizations, the gap between these capabilities, and the industrial structure of a country, among other factors.

From the perspective of the mode 3 model, there are three main actors in knowledge transfer: the public research organization (a university or public research institute), intermediaries, particularly knowledge transfer offices, and firms. Figure 10.1 charts the relationships between these three nodes and identifies the main factors for each actor that can influence successful knowledge transfer.

Figure 10.1 Factors that influence knowledge transfer

Source: Authors

The set of factors that promote knowledge transfer are likely to differ depending on the knowledge transfer channel (startups, contract research, collaboration, or IP licensing), interactions between policies, and interactions with other knowledge transfer channels. The systems perspective underlying the mode 3 model of knowledge transfer emphasizes the need for policies and practices to bridge the knowledge gap between public research and firms and to support knowledge exchange in addition to knowledge flows from academia to firms.

The question for policy is which factors need to be further developed and which factors are functioning adequately? Table 10.1 provides a basic framework for answering this question, based on the concept of a knowledge gap between public research and firms. Table 10.1 should be interpreted in respect to specific knowledge domains, for instance it could refer to knowledge on food manufacturing (safety, shelf life, processing, packaging, etc.) or to pharmaceutical manufacturing.

Table 10.1 Policies to support knowledge transfer for differing capabilities of public research organizations and firms

		Level of firm capabilities
		High	Low
Level of public research capabilities	High	A Ensure knowledge flows through flexible licensing and contracting rules; incentives for public research scientists to disclose inventions and assist firms	B Bridge the gap through polices to build firm capabilities and incentives for the public research sector to interact with firms
	Low	C Bridge the gap through policies to build public research capabilities and incentives for firms to interact with the public research sector	D Improve public research capabilities (supply), firm capabilities (demand), and knowledge exchange between them

Source: Authors

Successful knowledge flows require motivating all three partners (researchers, KTO intermediaries, and firms) to participate in knowledge transfer. In cell A of Table 10.1, where the capabilities of both public research and firms are high (and with a suitable knowledge gap somewhere near the top of the inverse “U” distribution), the role of policy is to ensure that there are appropriate incentives for interactions and capabilities in place for the three main actors in knowledge transfer. For cells B, C, and D, additional policies to either build supply capabilities in the public research sector or demand capabilities in firms are likely to be required in addition to the policies identified in cell A. For example, when public research capabilities are high but firm capabilities are low (cell B), incentives are required to encourage public research academics to interact with firms, in addition to R&D or other types of subsidy to build firm capabilities. Cell C provides the opposite case where firm capabilities are high but public research capabilities are low. Here, incentives could be required to encourage firms to interact with the public research sector, in addition to supply-side policies to improve the capabilities of public research academics.

Policies and practices can be usefully divided into two groups: those that directly address knowledge transfer, such as incentives, funding for KTOs, etc., and those that affect contextual factors such as the technical capabilities of firms or the industrial structure. Most of the existing literature on policies and practices to support knowledge transfer is relevant to cell A in Table 10.1 and concerns direct methods to improve knowledge transfer. Nevertheless, this literature is of use to all other conditions because it identifies practices that support interactions between public research and firms. These direct policies and practices are discussed below for each of the three main actors: public research organizations, knowledge intermediaries, and firms.

10.3.1 Policies, Practices, and Characteristics of Public Research Organizations

A history of previous linkages between a public research organization and firms increases the interest of researchers in knowledge transfer activities and consequently the probability of knowledge transfer (D’Este and Patel 2007; Libaers 2012; Padilla-Meléndez and Garrido-Moreno 2012; Grimpe and Hussinger 2013; Agiar-Diaz et al. 2016). In addition, academics can be motivated to collaborate with firms by previous involvement in applied research and the importance of applied research to career advancement (Abreu and Grinevich 2013; Abreu et al. 2016; Zhang et al. 2016). In contrast, practices such as a requirement to disclose inventions with commercial potential have only a small effect on the involvement of academics in knowledge transfer (Abreu et al. 2016).

Combining informal and formal knowledge transfer channels can have a positive effect on the innovation activities of firms (Siegel et al. 2003; Grimpe and Hussinger 2013). Informal channels build up relationships and trust between academic researchers and firms that can lead, over time, to research relationships that produce IP (Weckowska 2015). The use of both informal and formal channels could be especially important to spinoffs (Hayer 2016).

Another type of linkage is when public research organizations and firms coinvent through a collaborative research agreement. This can result in corporate patents that include university inventors as a contributor through formal or informal channels. Walsh (2016) reports that 4 percent of corporate triad patents held by American firms between 2001 and 2004 included formal or informal input from universities.

An important factor for encouraging knowledge transfer via IP-mediated methods is financial incentives for academic staff to disclose inventions and participate in the knowledge transfer process (Walter et al. 2013). The size of the financial reward has a positive effect, either through a one-off lump sum or a share of ongoing royalties (Friedman and Silberman 2003; Siegel et al. 2003; Lach and Schankerman 2004; Debackere and Veugelers 2005; Walter et al. 2013), although in Brazil an increase in status and recognition has also been a driver for increased academic interest in knowledge transfer activities (Closs et al. 2013).

Studies of academics find that their interest in participating in knowledge transfer can also be increased by including knowledge transfer activities in performance measures (Siegel et al. 2003; Closs et al. 2013; Ranga et al. 2016) and permitting academics to take time off to work with a firm.

Barriers to researcher interest in knowledge transfer include personal characteristics that create a lack of interest in knowledge transfer or in financial incentives, teaching and other responsibilities that reduce the time available for academics to engage in knowledge transfer (Closs et al. 2013), concern over delays in publishing knowledge linked to IP, a lack of financial support (for instance, when the academic must cover the patenting costs, which can be an issue in middle-income countries), a lack of research ideas with commercial potential, limited experience with interactions with firms (D’Este and Patel 2007), and differences between academic and business cultures, although this may be less important than commonly believed. In a UK survey of both businesses and academics, less than 7 percent of both groups cited cultural differences as an important barrier to interactions. In comparison, the most commonly cited barrier was “insufficient internal resources,” cited by 42 percent of businesses and 28 percent of academics (Hughes and Kitson 2012).

Bureaucratic and inflexible rules for knowledge transfer activities can act as a barrier to the participation of both academics and firms in knowledge transfer (Muscio et al. 2016). Knowledge transfer via licensing is supported by clear IP regulations that provide guidance to staff (Baldini et al. 2006) and a flexible approach on the part of the public research organization to licensing (Lerner 2005; Okamuro and Nishimura 2013; Barjak et al. 2015; Shen 2016).

Policies that contribute to knowledge transfer via the establishment of startups include dedicated programs (support for developing business plans, etc.) and facilities (such as an incubator) (Berbegal-Mirabent et al. 2015; Muscio et al. 2016) and employment conditions that permit academics to take leave to work with startups. High licensing income for inventors has been found to reduce the number of startups, possibly because it provides a less demanding source of income (Markman et al. 2004; Barjak et al. 2015). However, a European study that evaluated the effect of multiple policies on the establishment of startups found that the share of license income retained by inventors had a positive effect on the number of startups (Barjak et al. 2015).

10.3.2 Policies, Practices, and Characteristics of KTOs

The experience of the KTO, often estimated by the number of years that the KTO has been active, has a significant positive effect on many knowledge transfer outcomes (Friedman and Silberman 2003; Conti and Gaule 2011; WIPO 2011; Berbegal-Mirabent and Sabate 2015). The effect is due to a positive relationship between KTO age and institutional experience with knowledge transfer activities.

To be effective at knowledge transfer tasks, KTOs require highly skilled staff. Relatively low salaries, as noted in the case studies for Brazil and the Republic of Korea, can result in a failure to attract skilled employees. In addition, policies that do not permit KTOs to retain a percentage of license revenues can limit the ability of KTOs to offer benefits to staff.

Several of the case study countries provide regional or national KTOs or technology exchanges that can serve multiple public research organizations (China, Brazil, Germany). However, the preference of larger public research organizations is to retain their own KTO instead of using the services of a regional KTO. This suggests that proximity between a KTO and its institution is a strong advantage (see Chapter 4). This could be due to the ability of proximate KTOs to develop close working relationships with researchers and local firms.

10.3.3 Policies, Practices, and Characteristics of Firms

Two consistent results from the literature are that firms dislike rigid rules over IP and firm involvement in knowledge transfer from public research organizations increases with the firm’s R&D intensity (an indicator of technological capabilities) (Okamuro and Nishimura 2013; Maria et al. 2014; Kafouros et al. 2015). Okamuro and Nishimura (2013) also find that firm involvement with universities increases with the number of universities in a region, possibly because it improves the probability of a good match between the needs of firms and what universities can offer, or because greater competition between universities increases the flexibility of academic and KTO staff.

A major policy challenge is to create demand pull from firms, which requires firms with sufficient absorptive capacity to take an interest in public research inventions. Demand pull can be created through subsidies for R&D and innovation activities within firms, subsidies to permit firms to hire trained graduates from public research organizations and to thereby interact with these institutes, such as the THRIPS program in South Africa, or subsidies for consulting, contract research, or collaboration with universities or public research institutes.

A second issue related to demand is ensuring that firms are aware of research projects and inventions developed in the public research sector. Several countries have established national technology exchanges for this purpose (e.g., China and Brazil), but the effectiveness of these exchanges appears to be limited. This could be because firms have many other methods of identifying interesting projects or capabilities, such as reading the scientific literature or searching patent databases. Alternatively, KTOs can publish relevant information that is oriented to the needs of local firms. Due to the importance of proximity in firm–university contacts, this could be an important complement to other sources of information used by firms.

10.3.4 National versus Institutional Policies and Practices

Knowledge transfer programs can be supported at the national level and at the institutional level. Munari et al. (2016), in an analysis of European programs at 125 KTOs to fund the gap between invention and the development of a commercially viable prototype, report a shift over time in national centralized programs to decentralized activities at the level of the institution or region, which then shifts back again to a centralized program. The authors suggest that centralization is high at the start of policies to initiate and encourage knowledge transfer activities, which are then replaced by local experimentation that builds on in-depth knowledge of the needs of local firms. Over time this is then replaced by further centralization to “refine and complement local initiatives with measures promoting critical mass and selectivity.”

A similar pattern appears to have occurred in the United Kingdom (see Chapter 4). The 2009 UK survey of university academics found that academics in regional areas were more intensively involved in university–industry linkages than academics in the metropolitan regions and that teaching-oriented universities were also very active in these linkages (Zhang et al. 2016). However, a policy of using knowledge transfer for local economic development (supported by regional development authorities) was abandoned in 2010. This was followed by a shift to finding the highest bidder for university IP, no matter where located.

In many countries there is an unavoidable tension between a national goal to maximize income from IP and goals to use knowledge transfer to improve the competitiveness of domestic or regional firms (Kassicieh 2012; Rosli and Rossi. 2014). Until recently, the Republic of Korea explicitly followed a policy of encouraging universities to support the local economic development of SMEs, at the cost of reduced IP income (Lee and Shin 2017). SMART specialization platforms can help to overcome these problems by focusing on promoting regional strengths and providing mechanisms whereby firms can influence public sector research through demand pull. This can require an open knowledge exchange environment to assist in building effective relationships and for KTOs to actively support cocreation (Miller et al. 2016).

10.4 Policies and Practices for Knowledge Transfer: Case Study Results

Several of the case studies identify a common pattern: direct policies to support knowledge transfer were implemented to address one of the players in the knowledge transfer system without sufficient steps to ensure that all players could participate, including a failure to adequately address a knowledge gap between public research and firms. As a result, direct policies that increased the output of patented inventions in Brazil, the Republic of Korea, China, and South Africa were not matched by an equivalent increase in patent licensing. Over time, the mix of policies and practices were changed to address inadequacies in existing policies (China, Brazil, South Africa, and the United Kingdom), changing circumstances (Republic of Korea) and changes in political goals (United Kingdom).

10.4.1 Brazil (Chapter 7)

The Innovation Act of 2004 addressed the knowledge gap, a lack of incentives for public researchers to work with firms, and the need for knowledge intermediaries. It allowed the government to provide grants to firms to invest in innovation (thereby building capabilities) and created a framework for university–firm interactions, including the right for universities to sign exclusive licensing agreements with firms and provide staff with financial compensation. The Act also required all universities and public research organizations to have a KTO or use the services of a shared KTO. The Act appears to have increased university patenting. Between 2000 and 2012, Brazilian universities increased their share of total patents tenfold, from 0.38 percent to 3 percent. However, the Act had several flaws: it failed to provide sufficient funding to KTOs, required KTO staff to be public servants, and did not specify the specific mechanisms by which researchers could receive a share of license income for their patents. In 2016 the Act was replaced by a new Act that addressed many of the shortcomings of the 2004 Act, but it did not resolve the issue of financial incentives for university researchers.

10.4.2 Republic of Korea (Chapter 6)

The Government of the Republic of Korea established public research institutes in 1973. Since then, public research institutes have played a greater role than universities in public R&D and knowledge transfer. In the late 1990s, policies were introduced to improve the role of universities in knowledge transfer, culminating in the Technology Promotion Act of 2000, which required universities to have KTOs and shifted ownership of IP from the government or individual professors to KTOs. The number of KTOs increased from seventeen in 2003 to 263 by the mid-2010s. This generated a large increase in university patents, but little additional commercialization. The government has also tried to engineer a shift in the role of universities and public research institutes from supporting large firms to supporting knowledge transfer to SMEs. Large firms no longer required public research support because their own internal capabilities exceeded those of public research institutes.

The shift in the role of public research institutes to support SMEs and regional economic development has not succeeded due to the funding model for salaries of researchers. Financing is linked to the number of projects, which compels researchers to conduct many projects within a short period of time. The result is that projects are completed before a discovery reaches a level of development that is appropriate for SMEs. Given a large knowledge gap between public research institutes and SMEs, there is a need to make sure that new technologies are developed to the level of demonstrated prototypes in operational environments (technology readiness level 7).

In the late 2000s, the government made several further changes to the knowledge transfer system, by providing funding for universities to revitalize regional economies and by relaxing restrictions on exclusive licensing and permitting universities to license to foreign firms. KTOs were also instructed to obtain more information on the needs of firms in order to create demand pull. The government also increased the rate of funding for KTOs from 1.3 percent of research expenditures in 2010 to 3.3 percent in 2015, with the expectation of improving the skills and quality of KTO staff. To date there is little evidence that the policy revisions have paid off in an increase in commercialization via licensing. The share of total license income out of total R&D expenditures for public research institutes and universities combined was 1.38 percent in 2009 and 1.35 percent in 2014.

The example of the Republic of Korea suggests that government policy has lagged behind the needs of industry (public research institutes failed to maintain an optimal knowledge gap with both large firms and SMEs). Policy also appears to have dropped the focus on domestic industry in favor of increasing the amount of license income earned by public research institutes. This is an imperfect measure of successful knowledge transfer in a country where most licenses are based on lump sum payments instead of running royalties on the actual sales of products based on licensed inventions.

10.4.3 China (Chapter 8)

The patenting activity of Chinese public research organizations has increased substantially, but both patent applications and licensing are concentrated in a small number of research universities. The main barrier to licensing is low demand from domestic firms, suggesting a continuing knowledge capability gap. Half of licenses go to foreign-owned firms.

To address these issues, the 1996 law on knowledge transfer for universities and public research institutes was amended in 2015 to support the knowledge transfer capabilities of public research organizations and the technological capabilities of firms. Before 2015 the transfer of university IP had to obtain the approval of the Ministry of Finance, and income from knowledge transfer also went to the Ministry of Finance. The 2015 amendment gave full control of IP and related income to universities and public research institutes and allowed these organizations to give much larger financial incentives to researchers and KTO personnel. Other Chinese policies supported demand pull by involving firms in research cooperation with universities. Firms participate in 90 percent of national R&D projects and lead approximately half of science and technology projects.

10.4.4 South Africa (Chapter 9)

South Africa has world-class universities that focus on leading-edge research. A substantial share of all research expenditures (24.5 percent) is for basic research as part of “Big Science.” Research programs are primarily driven by academic interest. With the important exception of several industry-focused public research institutes that serve the petrochemical, pulp and paper, wine, and mining sectors and excellent linkages between university agricultural research and the agricultural sector, the South African research system is not designed to produce applied research of relevance to the majority of South African firms. A major challenge for knowledge transfer in South Africa is to improve the technological capabilities of South African firms outside of several sectors of excellence. Other challenges include shifting from a mode 1 to a mode 3 model for knowledge exchange on the part of public research organizations, for instance, by building closer relationships between firms and academics so that demand-pull influences are incorporated into research programs.

10.4.5 United Kingdom (Chapter 4)

The public research sector in the United Kingdom is dominated by universities, with 80 percent of research expenditures conducted by universities compared to a 20 percent share for public research institutes. Policies and practices for knowledge transfer in the United Kingdom have tracked academic research on how knowledge transfer occurs. Up until the early 2000s, practices followed the mode 1 model of a linear flow of knowledge from public research organizations to firms, with an emphasis on IP-mediated licensing. Currently, knowledge transfer is viewed as part of a “complex ecosystem of innovation characterized by collaboration and knowledge exchange among many actors” (Chapter 4). The importance of flexibility in negotiations between firms and universities is also recognized, with flexible policies on IP licensing, including the amount received by the inventor.

Policy for universities recognizes four types of knowledge transfer activities: commercialization (patenting, licensing, consulting, spinoffs), problem solving (collaborative research, contractual research, access to university facilities), people-based (conferences, invited lectures, enterprise education, etc.) and community-based (social enterprises, museums, public exhibits, open lectures, etc.). With the possible exception of community-based activities, all are relevant to the economic activities of firms. Universities vary in the depth of their activities in each of the four knowledge transfer activities, with teaching and regional universities more active in people-based and community-based activities and research-intensive universities more active in commercialization. This partly explains the high concentration of IP licensing. In fiscal year 2014–15, twenty-five universities produced 80 percent of university patent applications and twenty-seven universities earned 80 percent of contract income. In the late 2000s, IP income was approximately 3–4 percent of total income from all knowledge transfer activities.

KTO experience and learning over time has improved efficiency, with a decline in the number of patent applications since the mid-2000s to patents with a higher commercial potential. The quality of spinoffs has also improved, with an increase in the share that survive for three or more years. Demand-side policies include R&D tax credits, Smart Programme grants to firms, the Small Business Research Initiative (SBRI) and support for venture capital.

10.4.6 Germany (Chapter 5)

Germany has a well-developed knowledge transfer system with clear delineations between public research institutes that specialize in basic research and public research institutes and universities of applied sciences that specialize in applied research of commercial interest to firms. Surveys of researchers from institutes that conduct applied research show that they give a high level of importance to knowledge transfer activities.

The German case emphasizes how knowledge transfer can form a functioning innovation system that can be difficult to change, due to the many actors and networks involved. Ownership of IP was changed in 2002 from the inventor to the inventor’s institution to emulate the U.S. Bayh-Dole model. The switch was expected to increase the number of startups and patent applications by academics. Instead, up to 2008 the change in policy reduced the number of patents by university academics by 17 percent and had no effect on the number of startups (Czarnitzki and Licht 2017). These poor outcomes could be temporary effects that may dissipate after sufficient time to adjust to the new model.¹

10.5 Conclusions

Over time, the conceptual model behind policies to support knowledge transfer has shifted from a mode 1 linear pipeline model to a mode 3 model that involves multiple actors in an innovation system, including different types of public research organization, knowledge intermediaries such as knowledge transfer offices, and private businesses. The mode 3 model recognizes the role of both supply-side activities on the part of public research and demand-side activities on the part of firms. A knowledge capability gap between public research and firms is required for public research results to be useful to firms, but too much of a gap will prevent firms from being able to acquire public research results, closing off demand. Current best practice recognizes that all actors in the system must have sufficient capabilities and incentives to participate in knowledge transfer activities including consulting, contractual research, collaborative research, and IP licensing.

Research finds that activities that create demand for knowledge produced by public research organizations, including both informal contacts and the participation of firms in contractual relationships with public research organizations, increases the probability of knowledge transfer and IP licensing. In addition, IP licensing can occur without any previous linkages between public research and firms. Nevertheless, knowledge transfer systems can benefit considerably from incorporating demand pull, for instance, by building close relationships between firms and research institutes to ensure that the needs of firms are included in applied research.

Best practice for public research organizations includes providing sufficient financial incentives and time for researchers to participate in knowledge transfer and to include knowledge transfer activities in career evaluations. Successful knowledge transfer can require researchers to expend considerable time on the process, from developing a patent application to working with firms or spinoffs to ensure follow-on development of an invention. KTOs need adequate financing to ensure that they develop sufficient expertise, the ability to hire and retain staff with a variety of necessary skills, incentives for successful transfer, and freedom to pursue a range of knowledge transfer activities, in addition to IP licensing. Firms must have the absorptive capacity to adapt and use knowledge and inventions to create product and process innovations. Best practice includes policy support for R&D and other innovation-related activities and incentives for firms to work closely with public sector researchers for problem solving and commercialization.

A significant barrier to knowledge transfer in middle-income countries is the knowledge gap between the public research sector and firms. Overcoming this gap can require public research organizations to take inventions to the prototype stage or for public sector researchers to work closely with firms to assist follow-on development.

IP licensing is a minor but not unimportant part of knowledge exchange between public research and firms that facilitates knowledge transfer by protecting investments in follow-on research from imitation. It can also provide an additional funding stream for public research organizations, although this is likely to be a small share of total research expenditures. Best practice for IP licensing includes flexibility in drawing up IP contracts, negotiating skills on the part of KTO staff, and outreach activities to identify potential licensees. However, in many contexts, successful IP licensing is dependent on other good practices that support demand pull and research outputs that are relevant to the needs of firms.

Comment 10.1

Henri J.M. Theunissen

The current chapter studies how to successfully transfer knowledge from public research organizations to companies. Clearly, this depends, among other things, on the technological and related capabilities of firms and public research organizations, the gap between these capabilities, and the industrial structure of a country.

In this commentary I would like to provide some insight on the basis of my experience in the past in science, business and valorization at the University of Maastricht and the Brightlands Maastricht Health Campus.

Valorization is a term that is used in the Netherlands and some other countries to indicate the process through which knowledge from academic institutions is made available and relevant to society. In our practice, we attempt to translate this knowledge in the form of licenses, spinoffs and alliances with companies, or a combination of these. In fact, a license is always provided to the company, be it a spinoff of the university itself or a third party (i.e., a company not affiliated to the university). In this respect, licensing is crucial to the knowledge transfer process. It may be given either as a “standalone” asset in the form of a licensing agreement or in the context of a broader collaboration agreement.

The decision whether to grant a license to an already existing company or to a yet-to-be-established spinoff of the university is taken based on many factors, such as the mere availability of a licensee, bargaining power, match between the parties, preferences of stakeholders at the university and the valorization office, the nature and maturity of the technology, etc. The overriding argument is, however, the estimated overall probability of success that the technology will actually get to market in favor of customers or – in our case – patients. Hence, in principle, we would prefer to license our IP to firmly established companies that would develop the technology to maturity and bring it to market, while we receive a reasonable return on our investment in the research and the IP ensuing from it.

However, the chance of finding such a perfect licensee is slim, and, in practice, we have seen only a few such examples. This may be related to the subject of the science and the licensing opportunities that emerge from our knowledge institution. Indeed, we have in the past generated relatively few technology platforms, let alone ones that are able to generate products, such as monoclonal antibodies for therapy. That situation is gradually changing and hence may result in more straightforward licensing deals. Instead, the most frequent form of valorization of IP occurs via licensing to our own spinoff companies.

Having said this, we would very much like to increase our performance in terms of licensing to companies. There are many activities one might undertake to enhance the probability that companies would license our IP, such as putting even more effort into showcasing our opportunities, e.g., via websites, portals, and other marketing tools.

One aspect that makes straightforward licensing difficult is tacit knowledge. It often occurs that there is a long scientific and maybe even business history behind an emerging licensing opportunity. The moment the company is asked to have a look at the opportunity there is a huge lack in knowledge, understanding, and experience with the matter at hand. This seriously hampers the closing of a deal based on just one piece of information (i.e., the IP or the patent). In my daily practice at the company I worked for, I never licensed any isolated piece of IP from an academic partner outside the realm of an established collaboration. Also, besides the tacit knowledge issue there is yet another very practical problem: if a company scientist tells management that the IP at hand is interesting and should be licensed, it implies a risk that external technology may be better than their own and that the latter may be abandoned!

Based on my previous experience in the pharma industry, I believe that establishing an alliance with an industrial partner is the preferred route toward effective IP licensing. This would then be a separate paragraph in the collaboration agreement in which the company has an option to license or even acquire IP emerging from the collaboration. This would enable the company to develop and sell products covered by that IP. This should give the company sufficient comfort to use the results from the collaboration for their benefit.

It goes without saying that such an arrangement would require a reasonable return for the knowledge institution. Depending on the nature and value of the IP and the preference of the partners, this could be in the form of (additional) sponsored research or upfront, milestone, or royalty payments. In practice, it turns out that royalties are sometimes a no-go for companies. This is the most cumbersome part of the negotiation, as it directly affects product margins. However, it is reasonable as well as realistic to address this issue during negotiations. Both parties should make an effort to discuss this matter in good faith. Also, they should keep in mind that IP is a means, not a goal, and therefore should be treated with proportionate priority, especially when it comes to early IP emerging from an academic collaboration.

It is important that the inventor of the IP and their department gets a fair share of the return made on IP revenues. In our institution we split any revenues from IP in three equal parts, i.e., a third each for the inventor(s), department(s) and the valorization organization itself. Such an incentive is important to motivate scientists to go the extra mile, often after daily work or at the weekend. At the same time, it is a great deal for the department as well, as revenues are spent on new research that, once again, could generate novel IP.

Even though I have described the experiences and practices that we have at our institution in the Netherlands, I believe that the main issues that I have mentioned are similar in many other developed countries. The operational implementation of these issues and policies will, however, be different from one country to another. In any case, the overall guiding principle is that IP is a major driving force for valorization, knowledge transfer, and innovation across the globe!

Comment 10.2

Kerry Faul

South Africa (the “Rainbow Nation”) is a vibrant dynamic country of 57 million people speaking at least one of the eleven official languages, spread across nine provinces. It is a country rich in natural resources with a number of well-established industries, including mining, manufacturing, and agriculture, with strong financial, transport, and communication infrastructure but with significant weaknesses in areas of labor, health, and primary education. South Africa is strongly characterized in our National Development Plan (NDP; Vision for 2030) by the three permeating challenges of unemployment, inequality, and poverty. The NDP acknowledges that science and technology, and, indeed, innovation are a means to “fundamentally alter the way people live, connect, communicate, and transact, with profound effects on economic development,” with “the ability to innovate and learn by doing by investing public funding to help finance research and development in critical areas” being required. Public research institutes and universities are integral in the approach to address a number of the challenges experienced, not just as a third stream of income for the public research institute or university but also as a combination of commercialization and utilization of research results for societal benefit. South Africa experienced the same global trend in that it required a policy intervention to shift the focus at our public research institutes and universities from pure academia and teaching to knowledge transfer. In South Africa, it was the impetus of the Intellectual Property Rights from Publicly Financed Research and Development Act (IPR Act; No. 51 of 2008) that mandated the shift from “publications” to “innovations.”

There is no doubt that a critical intervention that can be classified as a direct means of facilitating knowledge transfer was the legislative provision for support, including in financial terms, for the establishment of Offices of Technology Transfer. Through the “Office of Technology Transfer (OTT) Support Fund” the salaries of individuals within the offices are paid for by the South African government, through the National Intellectual Property Management Office, for a three-year period, giving the institution an opportunity to motivate staff for these positions to be included on their payroll thereafter. Should they not succeed, the government steps in again to ensure that capacity is not lost and provides funding for a further three-year period, but this time on a sliding scale. The funding under the OTT Support Fund also provides a ring-fenced budget for training such as licensing, technology evaluation, and later valuation, as well as marketing techniques and tips, and most recently has been expanded to now support knowledge transfer-related activities, such as IP audits, business case development, techno-economic feasibility analyses, etc. A survey conducted in 2014 and to be run every five years revealed that the South African system has just over 100 full-time equivalents and that the level of outcomes from each OTT was directly proportional to the experience of the individuals within the office.

The growing capacity at institutions has, however, been overshadowed by the “pushback” received from both academia and, in particular, industry, due to the fundamental changes the legislation brought about. For the first time, academics are now being held, to some extent, accountable for the outputs of publicly financed research and development. The impact of new legislative framework on formal knowledge transfer between academia and industry was significant as the IPR Act prescribes who owns the intellectual property. As such, industry is no longer able to instruct an institution and pay a portion of the costs and walk away with the intellectual property. The so-called default position is “s/he who creates shall own” as opposed to having the option to contractually own all IP created. This shift necessitated an effective change-management strategy as industry balked at instructing institutions to do research on their behalf amid the uncertainty of who would own and have access to the resulting intellectual property, and worries over whether industry would be held to “ransom,” so to speak. Informal communications have revealed that the more experienced the knowledge transfer professionals, the more willing researchers are to work with them and the more productive the relationships with industry partners are. In addition, these relationships are often made or broken depending on how nimble the institution is in processing disclosures and concluding research collaboration agreements, for example. A reputation for “doing the deal” versus “making as much money as possible” also appears to strongly impact on the success of an OTT and the strength of the relationships built with the industry partner. This is evident in that one might put all the right direct support into the system, but if the indirect knowledge transfer channels are not operational or optimal, the ability to move the technology into the market or public space is negatively impacted.

Nine years later, the implementing office, NIPMO, continues to “demystify” the IPR Act to players across the triple helix as a critical intervention to assisting everyone to understand the clear framework that the IPR Act establishes.

In spite of these challenges, which are typically anecdotal, hence subjective, and can only be measured qualitatively, a quantitative analysis of the inputs, outputs, outcomes, and early impacts of the publicly financed research and development system in South Africa, a subset of the National System of Innovation, shows some positive upward trends. Over the period 2008 to 2014 there has been an increase in the outputs in the form of the number of disclosures received by knowledge transfer offices at institutions and an increase in the number of patent applications filed. When normalized against research and development expenditure, the increase in the outputs outstrips the growth in the inputs, namely, research and development funding. Furthermore, the conversion of these outputs into outcomes, namely, licensing arrangements or spinoff companies also increased over the period, albeit from a low base. In line with the vision of the NDP, employment is being realized with a doubling in the number of full-time equivalents employed by these SMMEs between 2008 and 2014.

As with any partnership or collaboration (or any other synonym), the core determinant of the success and longevity of the relationship depends, almost solely, on trust and a sound almost watertight contract. It is clear that over time, increases are observed in a number of formal knowledge transfer metrics, but it is the informal knowledge transfer metrics that are at the core. What is encouraging is the increase observed in the level of awareness and understanding about intellectual property and the associated rights among academia, industry, and within government. As we are enveloped by the so-called fourth industrial revolution, the role of government to fund highly risky technologies in our public research organizations will come to the fore again, providing the platform for, when they are ready, industry partners to step in and, in the words of Professor Mariana Mazzucato, “roar”! With the baseline now established in South Africa, time will tell whether we are indeed able to achieve critical mass in the system and thereby harness the public research system to bring innovation solutions to local problems in an emerging economy sitting at the end of the African continent.

11 Policy Recommendations Aiming for Effective Knowledge Transfer Policies in High- and Middle-Income Countries

Suma Athreye and Federica Rossi

11.1 Introduction

Policy interventions supporting the transfer of knowledge from public research organizations, including universities and public research institutes, to industry, have been adopted in many countries around the world since the 1980s. This has led to a marked convergence in policies supporting knowledge transfer from the public science base in different countries. However, implementing similar policies in different innovation systems is full of pitfalls. Drawing on the six case studies in this book, which range from high- (United Kingdom, Germany, Republic of Korea) to middle-income countries (China, Brazil, South Africa), we show that, because the innovation systems in these countries were different, the implementation of similar knowledge transfer policies was supported by different sets of complementary polices. In fact, many middle-income countries were forced to compensate for institutional deficiencies with supporting policies that differed from those adopted by high-income countries.

In this chapter, we identify the different sets of complementary knowledge transfer policies implemented in high- and middle-income countries, evaluate their implications for the success of knowledge transfer processes, and develop policy recommendations. Briefly, we show that in high-income countries with mature national innovation systems, patterns of interaction between university and industry already existed, and the policy convergence merely incentivized the rearrangement of outcomes in the vector of possible outcomes that was outlined in Chapter 2. Thus, commercialization through patent licensing in Germany and the United Kingdom often replaced other established knowledge transfer channels. The policy challenge in these economies is to ensure all channels of knowledge transfer are appropriately nurtured. In middle-income economies, where patterns of interaction with industry are still developing, policy convergence needs a different set of complementary measures to succeed, which include incentives to researchers, changing the legal structure of university incomes to allow academics to earn income from consultancy and the use of public research institutes. These measures compensate for structural differences/deficiencies in national innovation systems. Identifying the appropriate complementary measures is therefore crucial for the success of knowledge transfer policy in both high- and middle-income economies.

As the international convergence of policies in support of university patenting and licensing through the allocation of intellectual property (IP) rights to universities was strongly inspired by US policy, we first revisit the case of the United States of America (U.S.) We then use the six case studies in this book to describe the process of convergence of knowledge transfer policies and the reasons for the substantial differences between the paths followed by the high- and middle-income countries. We highlight the different innovation systems in which these interventions were implemented, the different shapes that these interventions took, and why convergence in policy outcomes and in the overall knowledge transfer systems of these countries has not yet been reached. Finally, we conclude with implications for policy and further research.

11.2 New Policies in Support of Knowledge Transfer from Public Science

11.2.1 The U.S. as a Model for Policy

One of the most visible policy interventions in support of knowledge transfer to industry was the US Federal Government’s implementation of the Bayh-Dole Act in 1980 (Mowery and Sampat 2005). As noted in Chapter 1, although this piece of legislation was not the first attempt by governments to regulate university IP – Israel had introduced university IP policies in the 1960s and the US government had already experimented with giving seventy universities the right to patent federal government-funded inventions since 1968 – the Bayh-Dole Act of 1980 was the most influential. The Act granted universities ownership of the IP emerging from their staff’s federally funded research, which previously used to lie with the US Federal Government. The rationale for this move was to improve the commercialization of research findings by moving the ownership of the IP closer to the researchers and institutions involved and away from distant government offices. Sampat (2009) notes that the most commonly cited justification by proponents of Bayh-Dole-type legislation worldwide is that very few government-owned inventions were commercialized in the U.S. before 1981. For example, in a letter to the prime minister, arguing for an Indian Bayh-Dole act, the National Knowledge Commission noted:

In the United States, before the Bayh-Dole Act was enacted, the country’s federal agencies owned about 28,000 patents, out of which only 5% were licensed to industry to develop commercial products

(Pitroda 2007, as cited in Sampat 2009)

Policymakers were also concerned that unpatented university discoveries at an early stage of development would not be taken up by industry unless a patent provided firms with an incentive to invest in additional research for their commercialization (Berman 2008; Kenney and Patton 2009). Hence, Bayh-Dole was designed to encourage commercialization, particularly through permitting exclusive licensing and preferential access to public science for SMEs (Schacht 2005). However, the requirement to give preference to small businesses does not seem to have had much of an effect and may have been revised at a later date.

The years following the implementation of the Bayh-Dole Act saw the emergence of several blockbuster patents bringing very high economic returns to the institutions that owned them. One of these was the Cohen-Boyer patent (1980–97) for recombinant DNA, which during its seventeen-year term earned Stanford University USD 254 million (90 percent of which came from royalties on product sales), was licensed to 468 companies and used in 2,400 products (Feldman et al. 2005). Another example was the Axel patent for rDNA in mammalian cells, which earned Columbia University and its inventors USD 790 million (Colaianni and Cook Degan 2009). Yet another was the exclusive license for the drug Taxol given by the National Institutes of Health (NIH) and Florida State University to Bristol-Myers-Squibb (BMS). Florida State University earned more than USD 200 million in royalties from BMS (Powers 2006).

It is very likely that these very high-profile examples, combined with the general perception of the US national innovation system as being particularly successful in the development of advanced technology, inspired policymakers in other countries to implement similar policies. Graff (2007) notes that several countries, including India, Brazil, South Africa, Malaysia, and Jordan, debated or passed legislation modeled on the US Bayh-Dole Act.

The success of the Bayh-Dole legislation in the U.S. masks the fact that Bayh-Dole did not happen in isolation but was nested in a broader policy mix that aimed to transfer knowledge from the science base to industry. Block (2008) argues that the U.S. has been engaged since the 1970s in the creation of a “developmental network state” whose aim is to facilitate the translation of fundamental research into cutting-edge technologies. It has done so through the deployment of a broad range of interventions supporting the transfer of knowledge between university and industry, both on the “supply side” and also, very importantly, on the “demand side” (see also Bozeman 1994). Supply-side policies encouraged federal laboratories to engage with state and local government, universities, and private industry (Stevenson-Wydler Technology Innovation Act, 1980; Federal Technology Transfer Act, 1988). These policies also supported the formation of university research centers focused on translational research (Engineering Research Centers, 1985) as well as centers diffusing technologies developed by the Department of Defense to small firms (Defense Industrial and Technology Base Initiative, 1991). Complementary demand-side policies, by contrast, provided matching grants to firms investing in the commercialization of new technologies (Advanced Technology Program, 1988), earmarked a share of the budget of federal laboratories to support the research efforts of small firms (Small Business Innovation Development Act, 1982), encouraged collaborations between small firms and universities (Small Business Research and Development Enhancement Act, 1982), and incentivized firms’ adoption of advanced technologies (Manufacturing Extension Program, 1988). Nevertheless, these additional interventions have not figured prominently in the mainstream policy discourse (Block 2008).

Equally important to note is the lack of any consensus on what created successful knowledge transfer in areas such as Silicon Valley. Along with the importance of particular universities, attention has also focused on other aspects of the innovation system, namely, the presence of superstar scientists who drew firms into their regions (Zucker et al. 1998), the presence of knowledge networks and conducive regional systems of innovation (Storper and Walker 1983; Saxenian 1994), and the role of diasporic labor and their transnational links (Saxenian 2007) that allowed nascent small-scale technological experiments in multiple locations and the smooth scale-up of successful innovations without running into labor and material shortages. These factors lurk in the background of the explanations of US Bayh-Dole successes, but are notable by their absence in other parts of the world.

11.2.2 Convergence of Knowledge Transfer Policies

Bayh-Dole-inspired legislation has been progressively implemented around the world since the early 1990s and particularly during the 2000s. In continental Europe – where there was a diversity of ownership arrangements for public sector science – most countries switched to university ownership between the mid-1990s and 2010 (Geuna and Rossi 2011). Early adopters of the university ownership system, such as Switzerland, the United Kingdom, France, and Spain, began to enforce it stringently from the 1990s. In other countries – Germany, Austria, and most of Scandinavia – the switch in university ownership was from a previous system of “professor’s privilege,” where academics owned the IP rights to their inventions and were able to dispose of them freely. Cambridge University, which had maintained a professor’s privilege system, finally switched to university ownership in 2005. Currently, in Europe, only a few cases of inventor ownership systems remain – Sweden being the clearest example. Italy has a system combining aspects of both. Countries in the former Eastern bloc – Hungary, the Czech Republic, Slovakia, Slovenia, Poland – also switched to university ownership from a previous system of government ownership. The Republic of Korea, having broken into the group of high-income countries in the 1990s, has implemented Bayh-Dole-type policies since 2000.

Despite the overarching convergence to Bayh-Dole, university ownership systems have been implemented in different ways. For example, there are differences in the vesting of IP rights in the university: in some countries, the university is the first owner of the IP, while, in others, the IP is owned by the inventor, and the university has the right to claim it if it is not used within a certain period, or vice versa, the university has a time limit within which it has the right to claim ownership of IP, after which it reverts to the inventor. The scope of the policy also differs: in some countries, all inventions produced by academics fall under university ownership, while, in others, there are distinctions depending on whether the invention was developed in the course of their normal employment or outside it (see DLA Piper 2007, for a detailed analysis of the different systems).

The three middle-income countries considered in this book also followed the pattern of convergence to Bayh-Dole popular in Europe. However, as shown in Table 11.1, policy changes were implemented beyond the vesting of IP ownership rights in the university. Laws were passed to allow universities to license IP and to profit from it by allowing them to receive incomes from royalties. Universities were also obliged to compensate the inventor with a share of royalties. Brazil, China, and South Africa, which had previously limited the extent to which universities and public research institutes were permitted to engage with industry, began to relax these rules to allow universities much greater freedom of action. Laws were also passed so that public universities were given greater freedom to contract with industry, to establish spinoffs, and to allow academics to take leave of absence in order to engage in commercialization activities in their own or another firm.

Table 11.1 Convergence of knowledge transfer policies

	University can own IP	University has right to license IP	University can retain royalties from IP	University must compensate inventor for IP	Universities can contract with industry	University can establish spinoffs	Academics can take leave of absence to work in spinoffs
UK	Yes (1948)	Yes	Yes	Yes	Yes	Yes	Yes
Germany	Yes (2000)	Yes	Yes	Yes	Yes	Yes	Yes
Republic of Korea	Yes (2000)	Yes (2000)	Yes (2000)	Yes (2000)	Yes	Yes (2003)	Yes
China	Yes (2002)	Yes (2015)	Yes (2015)	Yes (2002)	Yes	Yes	Yes (2016)
Brazil	Yes (1996)	Yes (1996)	Yes (2004)	Yes (1996)	Yes (2004)	Yes	Yes
South Africa	Yes (2008)	Yes (2008)	Yes (2008)	Yes (2008)	Yes	Yes	Yes

Source: AuthorsNote: In parenthesis, we report the year in which the law allowing the activity above was enacted (in cases without a date, this means that there was no previous prohibition against the activity).

More detail on the regulations that underpinned these changes in other middle-income countries is presented in WIPO (2011: Chapter 4) and in Zuniga (2011). This shows that the Bayh-Dole-inspired legislative reforms implemented in each country were usually a distinct package (e.g., in terms of the specific rules on the scope of university patenting, invention disclosure, incentives for researchers, and whether certain safeguards were instituted to counteract the potentially negative effects of patenting). Therefore, as argued in Chapter 1, what we term convergence to Bayh-Dole was never a process of countries making simple binary choices with respect to institutional or individual patent ownership, but one with significant differences in the features of the whole package of policies.

Yet, in all cases, this new policy framework was justified by a “lack of commercialization of public research” argument: the idea that national universities in all countries produced good-quality research, which stayed locked up in ivory towers and which they failed to commercialize sufficiently. In other words, the problem was framed as one of lack of interaction between university and industry, which needed to be corrected by implementing changes in legal ownership rights and in incentives to encourage interaction between the actors in the system. As Arundel points out in Chapter 10, an absence of interactions between university and industry could also be caused by the failure of industry to “demand” knowledge from universities.

11.3 Different Innovation Systems and Different Policy Mixes

Chapter 2 detailed six different types of knowledge transfer channel from the public science base to industry. These ranged through research publications; conferences seminars and meetings with industry; education and training of students/researchers recruited by the private sector; consultancies and contract innovation research (including university–industry joint research projects, joint research centers, and PhD projects); creation of IP for licensing; and creation of spinoff companies. A striking feature of knowledge transfer from the science base in middle-income countries lies in the fact that the last two forms of knowledge transfer are negligible and occur very rarely. This is clear from the country studies. One could, of course, argue that IP licensing and spinoff companies account for very small shares of overall university incomes even in high-income economies (see, for example, Chapter 4 and the evidence in WIPO 2011). This suggests in turn that the proportion of formal to informal knowledge transfer may not be the main indicator that sets high- and middle-income countries apart.

It is, of course, apparent that the institutional frameworks within the six country case studies (where Bayh-Dole-type measures were implemented) were very different. Further, none of these contexts (and set of packages) was similar to that of the U.S., the model that they appeared to be inspired by and aspired to. Some of the key differences between the six countries’ innovation systems are summarized in Table 11.2, based on the country chapters and data in Chapter 1. The first key difference is that firms’ R&D engagement is higher in high-income countries than in middle-income countries, as noted in Chapter 10. Interactions between public research and industry, and universities’ research intensity, are also higher in high-income countries than in middle-income countries. The importance of public research institutes versus universities is variable. The Republic of Korea is a fascinating case as it presents some features that are intermediate between the two groups. Here, collaborative R&D between public research institutes and private firms has been the most important and effective form of breaking into the higher-end segment of the industry (Lee et al. 2005; Lee 2013), whereas interactions between universities and industry have been low, although this has been changing since the 1990s.

Table 11.2 Differences between the national systems of innovation of six high- and middle-income countries

	Firms’ R&D engagement	Interactions between public research and industry	Importance of research from public research institutes vs universities	Universities’ research intensity
UK	High	High	Low	High
Germany	High	High	Medium	High
Republic of Korea	High	High	High	Medium
China	Low	Low	High	Low
Brazil	Low	Low	Medium	Low
South Africa	Low	Low	High	Low

Source: Authors

Note: The table builds on information provided in Chapter 1 and in the six country studies presented in this book and refers to the period considered in them. In most cases, firms’ R&D engagement is measured in terms of R&D expenditure or R&D and patenting intensity of domestic firms. Interactions between public research and industry are measured in terms of firms’ licensing of university patents and business funding of university research. Publications per academic are the most commonly used indicator of a university’s research intensity.

The detailed case studies in this book, situated in a historical perspective, help us to appreciate the often overlooked point that what sets high- and middle-income countries apart are that their innovation systems, and, more particularly, the system of production and transfer of knowledge between university and industry are quite different. In well-functioning high-income economies there are two-way flows of ideas and people between the university and industrial sectors. In middle-income economies, there is a healthy flow of people between universities and industry, but channels to establish a flow of ideas are very underdeveloped, and public research institutes are prominent as they bridge the gap between frontier science and its application to domestic industrial conditions. Thus, the success of Bayh-Dole-type policies in these economies needs to be judged not only by the vector of knowledge transfer outputs but also by the impetus that the legislation provided for establishing channels through which new ideas, emerging in universities, could find direct application in the industrial sector, without necessarily involving public research institutes in an intermediate role.

The stress in this chapter on links for people and ideas is thus complementary to the discussion of formal and informal channels of knowledge exchange outlined in Chapter 2. Formal methods of knowledge transfer and commercialization identified in that chapter are likely to require the institution of legal arrangements that favor the movement of ideas, while the movement of people is likely to favor more informal methods of knowledge exchange.

11.3.1 High-Income Countries

As noted in Chapter 1, high-income countries are characterized by high private expenditure in R&D, with numerous large firms that employ R&D staff and possess a high absorptive capacity for new technological knowledge and thus are able to interact with universities. In these countries, the university system is also highly developed, with many research-intensive universities. Although public research institutes are more (e.g., Germany, Republic of Korea) or less (e.g., United Kingdom) present in the system, in all cases, they are not the only source of research in the country: universities also play a prominent role. The Republic of Korea is slightly different from Germany, as public research institutes, although highly research-intensive, deal exclusively with large domestic firms and have struggled to establish links with smaller companies.

When the knowledge ecosystem is well developed we see two-way links between public research institutes, universities, and firms, as shown in Figure 11.1. The first link is through the movement of people, shown by the solid lines. Students may move to placements in firms and continue to collaborate with their former professors. Equally, managers of firms may draw on expertise in their alma mater to solve technical problems in the firms they are employed in. Similarly, public research institutes may invite secondments, allow the use of their R&D labs, and develop joint R&D projects. These people links, based on both institutionalized and interpersonal links, are distinct from arms’ length transactions in technology and ideas through formal knowledge transfer.

Figure 11.1 The knowledge ecosystem in high-income economies

The formal links, shown by the dashed lines in Figure 11.1, are likely to be based on the issue of patents, technology contracts, or equity investments. Patents are likely to be preferred in the case of mature, codifiable technologies, where licensing is a viable option because buyers can understand the technology quite readily (although evidence suggests that very often scientists who develop patented inventions continue to collaborate with licensing firms through consulting contracts, in order to support the implementation of the licensed technology; Thursby et al. 2001). Equity investment in spinoffs may make sense in the context of early-stage technologies, which cannot be easily codified and may need joint development with firms.

In high-income economies, due to the presence of R&D-intensive domestic firms and multinational corporations (MNCs) and of research-intensive universities and public research institutes (see also Chapter 10), both people and ideas circulate relatively frequently, through both institutionalized channels and well-established interpersonal relations. Indeed, for these countries, the assumption that a lack of interactions required new institutions was probably misleading. Certainly in Germany (see Chapter 5), there was not a lot of university patenting, and universities were not generating income from IP licensing, but a lot of knowledge transfer was happening without requiring university patents. Professors often collaborated directly with industry, either informally or through research contracts, and ceded their IP rights to their collaborating firms, which then patented the resulting inventions. Hence, although German universities owned few patents, professors often figured as inventors of industrially owned patents, a pattern that was present in most of continental Europe (Lissoni et al. 2008; Geuna and Rossi 2011).

The United Kingdom was an intermediate case, since it never had the professor’s privilege and the IP rights to academic inventions belonged to the university, which initially ceded them to a central agency tasked with research commercialization, the British Technology Group. In spite of some successes (e.g., successful commercialization of the technology behind hovercrafts and magnetic resonance imaging), this approach did not lead to a large amount of commercialization: outside the medical sector, academics’ interactions with industry were mostly either informal or focused on research contracting. Cambridge, which maintained a system of professor’s privilege until 2005, saw intense involvement of professors in the development of spinoff companies, some of which spawned very large firms, particularly in ICT, which eventually generated a local high-technology cluster (Athreye 2004).

In the Republic of Korea, large firms were strong investors in R&D and had a very strong relationship with the government (chaebol system). Public research institutes played a greater role than universities in public R&D and knowledge transfer in the process of catch-up.

In these countries, characterized by preexisting strong interactions between universities, public research institutes, and firms, the introduction of Bayh-Dole-type legislation could disrupt as well as enhance interactions. In German-speaking and Scandinavian countries, as well as in Cambridge pre-2005, patents were owned by inventors, so the introduction of Bayh-Dole-type legislation brought the patenting process further away from inventors; exactly the opposite of what had happened in the U.S., where patents were already owned by the Federal Government so the change brought the patenting process closer to inventors (Mowery and Sampat 2005). As a result, this process could disrupt existing relationships – which had developed in harmony with a system where inventors held IP rights – rather than enhance them. Indeed, Chapter 5 argues that this is what happened in Germany, where, up to 2008, the change in policy reduced the number of patents by university academics by 17 percent and had no effect on the number of startups.

In the United Kingdom, which already had a system of university ownership for many universities, universities’ obligation to commercialize their patents through the British Technology Group was removed in 1985. Since then, most universities have set up internal knowledge transfer offices (KTOs) dealing with commercialization activities. Given that in the United Kingdom individual academics could not dispose of their IP freely, most collaborations with industry already occurred with some involvement of the university institution, and there is little evidence of displacement effects. However, there is evidence that the increase in university patenting did not lead to the expected increase in licensing income, with licensing income concentrated in a few universities (see Chapter 4).¹ There are signs of a decline in university patenting in recent years as universities are becoming more selective in which patents they pursue (Tang et al. 2010).

11.3.2 Middle-Income Countries

In contrast to high-income countries, middle-income countries are characterized by a low number of R&D-intensive firms, with the majority of domestic companies performing very little research and possessing low absorptive capacity, as discussed in Chapter 1. The few companies that perform R&D tend to be MNCs, or companies that are partly government owned (e.g., the Brazil country study noted that 80 percent of patents generated in the country have nonresident applicants). These countries also tend to have a strong division of labor in knowledge production, with universities concentrating on basic research and the training of students, while public research institutes are tasked with adapting frontier research to the needs of industry and government. The data in Chapter 1 also show that in middle-income countries most research is performed in public research institutes funded by and responding to the government.

Figure 11.2 sketches the knowledge ecosystem in middle-income countries. The people links work well, and there are institutionalized links between public research institutes and firms. However, public research institutes tend to be strongly specialized by sector (very often, agriculture, engineering, and health), therefore interactions with firms are concentrated in particular industries and involve large firms. Furthermore, as the country studies of China, Brazil, and South Africa note, the links between public research institutes and industry are still limited outside of particular sectors or technology areas. One reason for this limitation may be the small size of many public research laboratories, which, with a few exceptions, do not produce world-leading research. This limited interaction may also be due to contractual laws that limit the employment of university researchers by other employers: for example, public research institutes in Brazil have only been allowed to sign knowledge transfer contracts with companies since 2004. The cultural chasm between scientists working in labs and industry staff may also be a factor. In many countries, pursuit of science and learning may be seen as a “pure” goal and one that should not be contaminated by commercial considerations.²

Figure 11.2 The public research ecosystem in middle-income economies

Our case studies suggest that in middle-income countries, the main difference when compared to Figure 11.1 for high-income countries is the presence of only weak linkages (and sometimes their complete absence) for transferring research ideas, knowledge, and technology from the university science base to industry. Universities in middle-income countries mainly engage in training and provide the human capital for both industry and public research institutes. These training links have resulted in informal people-mediated channels of knowledge transfer, but formal channels are largely limited – where present, they usually take the form of contract research and consulting. Interestingly, the Republic of Korea shows these features as well, despite being a high-income country.

The absence of linkages between university and industry poses two challenges for the implementation of knowledge transfer policies. As already noted in Chapter 10, universities have not yet developed a way to bridge the gap between the basic scientific research they produce and the prototype level of development of an innovative idea. Many firms require the latter to readily absorb and use to scale up production. The second challenge comes from the legal and contractual obligations surrounding university researchers, which are often not conducive to engagement with industry on research issues. Our case studies provide evidence of the full range of such challenges.

In all the middle-income countries studied in this book, the implementation of Bayh-Dole-inspired knowledge transfer policies encountered problems created by the missing (ideas) links between universities and industry and further rounds of policy changes were required to overcome the problems. Thus each of our country chapters also outlines a policy-induced process of adaptation of institutional frameworks that share some important similarities, but that also differ in several details. While the general policy discourse around the implementation of Bayh-Dole-type legislation seemed to suggest that these changes in formal rules would, in themselves, be sufficient to achieve the hoped-for increases in commercialization activities, in practice, most countries have also implemented a range of supporting measures aimed at stimulating the creation of the infrastructures and competences that are required to connect university actors to industry. This includes rules to support exploiting the new IP rights framework, as well as measures to stimulate firms’ demand for university IP (e.g., funds for joint research projects).

11.3.3 Policy Mixes Adopted

Analysis of the policy interventions enacted in the six country case studies can be subsumed under two major categories and related subcategories. The first category of complementary interventions aims to introduce supply-side incentives for universities to supply technology more readily and frequently to industry. These include institutional-level incentives for universities to patent and license IP and to interact with industry in different ways, for example, by establishing KTOs and other intermediaries encouraging interactions between universities and industry. These intermediaries manage research contracts, the creation of spinoffs, the establishment of joint research centers, and other related activities. In some cases, KTOs were established at local, regional or national levels, with or without the involvement of universities, and several universities “shared” KTO services.

As technology transactions are plagued by asymmetric information about the nature of technology and potential applications, individual researchers are in the best position to alleviate these concerns. In addition, the interpersonal networks of researchers can often be used to kick-start links with industry when none exists. Therefore, in all cases, supply-side measures also included incentives for individual academics to engage in knowledge transfer. Examples include monetary incentives that allow academics to receive income from royalties and consulting activities in addition to their salaries; career development incentives that include knowledge transfer performance as a criterion for academic promotion; incentives for academics to engage in entrepreneurial and other business activities, including permissions to take leave of absence in order to work for university spinoffs or for other companies, and to earn income from these activities.

The second category of interventions consists of demand-side incentives for firms to engage with industry. These incentives range from general incentives for firms to invest in R&D (such as R&D tax credits, grants, innovation vouchers) and more specific incentives for firms to collaborate with universities and public research institutes (such as joint project grants, subsidies for the establishment of joint research centers, and mandatory investment in university research). All these incentives can be created purely through legal requirements, or they can involve the use of government funds, either in the form of government subsidies for certain activities or in the form of rewards for universities’ good performance in certain activities.

Table 11.3 summarizes the extent to which the six countries implemented supply-side and demand-side incentives of the kinds just described. As can be seen, most countries implemented a combination of incentives. All of them focused on supply-side incentives, consistent with the argument that they saw the problem primarily as one of getting universities to reach out to industry.

Table 11.3 Differences in range of supporting policies

	Supply-side incentives				Demand-side incentives
	Incentives for formation of KTOs	Incentives for universities to patent and license IP	Incentives for universities to engage with industry through contracts, spinoffs etc.	Incentives for individual academics to engage with industry	Incentives for industry to invest in R&D	Incentives for industry to engage with universities
UK	M	E	M / E	L	M	M
Germany	M	L	M	M	M	M
Republic of Korea	L / M	L	L	L	M	M
China	L / M	L / E	L / E	L		M
Brazil	L / M	L	L	L	L / M	L / M
South Africa	M	L		L	M	M

Source: Authors.Note: L = legal requirement to implement the activity; E = the activity is part of performance evaluation; M = direct government support provided to the activity, including tax breaks.

Middle-income countries have mainly issued legal requirements for universities to engage with industry (although individual universities can enact their own internal policies providing academics with monetary incentives, for example, to develop spinoff companies or engage in consulting activities), while high-income countries have also dedicated government funds to supporting universities in these activities. All countries have invested public funds in the setup of KTOs (either within universities, or at local, regional, or national levels). In Brazil, while every public university and public research institute in the country is required to have a KTO, the Innovation Act allowed institutions to share a KTO, so that several of them serve more than one research institution.

On the demand side, all countries enacted specific monetary incentives for companies to interact with public research, mainly in the form of R&D tax breaks and opportunities to bid for joint R&D projects. In Brazil, companies in some sectors such as energy are required to invest a share of their revenue in R&D in partnership with universities and public research institutes. This has been successful in stimulating university–industry interactions. However, this requirement applies to companies that are large, often multinationals headquartered outside Brazil, and benefits only a few research-intensive universities and public research institutes. The majority of public research institutes in Brazil continue to be too small and insufficiently research-intensive to benefit from these incentives.

In addition, the case studies provide interesting insights into the timing and sequence of the policy changes. Almost all countries started by implementing legal changes but had to follow them up with a mixture of carrot (monetary incentives) and stick (performance evaluation) policies, to encourage take-up of these activities.

In Brazil, policy interventions implemented in 1996 allowed universities to own and license IP as well as compensating individual inventors. In 2004, the policy changed to allow universities to retain some income from licensing and to contract with industry. Universities and public research institutes were also required to have a KTO or use the services of a shared KTO, and were provided with some funding for this.³

Despite some increases in university patenting and licensing activities in Brazil, these supply-side incentives have had limited effectiveness in stimulating universities’ interactions with industry, due to several problems, including lack of clarity, insufficient financial incentives for individual researchers, and limited resources and competences of KTOs, whose staff consist entirely of public servants. KTOs suffer from high staff turnover and a dearth of qualified or specialized staff, because their link to government institutions obliges them to rely on public tenders in hiring new staff and they do not offer competitive salaries. According to the MCTIC (2017), more than 50 percent of KTO staff had no previous experience in the private sector.

China started in 2002 by enacting laws that allowed university ownership of IP and compensation of inventors for their IP. More than a decade later (2015–16), it enacted a raft of measures allowing universities to profit from their IP (retain royalties, set up spinoff firms), and permitting academics to work for companies by taking leave of absence. One way of interpreting this lag is that the early experiments with researchers initiating links were ready to be institutionalized only a decade later. A remarkable feature of the Chinese case is the combination of monetary incentives and performance monitoring to achieve the objective of knowledge exchange. In this respect, the country’s overall policy is similar to the case of the United Kingdom.

South Africa is the most recent country in our sample to have enacted knowledge transfer policies, and, in contrast to the other countries, they immediately allowed both individuals and universities to retain profits from their IP. In South Africa, the Council for Scientific and Industrial Research (CSIR) dominates the public research institute landscape and has highly specialized labs in the area of petrochemicals. CSIR also has extensive links with universities. The government has not provided direct funding to support universities’ engagement with industry, but it has enacted some demand-side measures in the form of monetary incentives, which include the provision of funding for companies’ R&D, often in partnership with universities or public research institutes.

In the Republic of Korea, the government’s promotion efforts were focused on encouraging interactions between domestic public research institutes and universities and firms, particularly SMEs, whose role in the economy the government intended to strengthen. They did so through incentives for commercialization but particularly through strengthening the KTO system through numerous measures. So far, efforts have not been as successful as was hoped (see Chapter 6). Large firms do interact with universities by establishing R&D centers on the campuses of major universities,⁴ although they have tended to invite and hire star professors (mostly Korean) from universities located abroad. SMEs lack absorptive capacity and consequently need technologies to be provided at a higher level of technological readiness. This has been stymied by a research funding model that provides funds on a project basis, with funding often terminated before a discovery is developed sufficiently.

11.4 An Ideal Policy Mix?

If one form of legislation cannot fit all circumstances, how should policymakers decide which polices to adopt? Clearly the objectives of policies to support knowledge transfer from universities will be different in high- and middle-income countries and may even differ among universities within the country.

In high-income countries, the main challenge facing knowledge transfer policies is the need to avoid displacement. As patent-mediated commercialization processes can produce very high financial rewards, it may be important to ensure that financial constraints do not push universities to prefer any one kind of commercialization. Thus, first, funding to universities should be increased so that different channels of commercialization are not substituted for one another. Second, universities should try to promote a broad range of interactions outside formal channels, such as through the involvement of alumni active in research − often in other countries. In recent years, the United Kingdom has sought to redress the imbalance in perceptions of value by inclusion of research impact as an important evaluation criterion on a par with patents and publications in research-active universities. Last, the policy focus on rewarding IP ownership should subtly shift to encourage and reward IP use by universities or firms rather than ownership per se. This could take the form of recognizing impact (as in United Kingdom universities) or take the form of a subsidy that could be used by the department or inventor as income for further research.

In middle-income countries, interactions between university and industry face greater challenges and may need more policy intervention. In these countries, the knowledge ecosystem is relatively immature and research interactions between universities and industries are generally lacking (except for a few interactions involving large, multinational firms and a few highly research-intensive universities and public research institutes) due to weaknesses on both sides.

A first problem confronting middle-income country governments is the overall limited research intensity of their universities due to a traditional focus on teaching. Several additional factors reinforce this low research intensity. Universities have traditional incentive structures that reward teaching over commercialization and industry engagement. Additionally, universities find it difficult to retain their brightest and their best. Policies to correct these problems include allowing the university and inventor to profit from knowledge transfer and research, in part, by ensuring that extra profits are not taxed away as higher income – that is, ensuring that there is a financial incentive to use scientific knowledge and plow back the investments from it.

A second problem confronting policymakers in middle-income countries is the small volume of (or nonexistent) industry interactions. When such interactions are nonexistent because the contractual obligations of university researchers do not allow self-employment or employment by others (e.g., through research contracts), then legal changes may be needed to permit such interactions to take place. If there are no legal barriers, then establishing contacts may require the active involvement of the researcher and direction by knowledge transfer specialists with knowledge of industry needs and an understanding of university researcher contexts. In high-income economies such roles are usually played by KTOs, which is warranted when there is a large volume of technology transactions. In middle-income economies, the small volume of transactions may not warrant a specialist centralized intermediary to aid and advise the university. In the short term, universities may also gain by interacting more closely with public research institutes, which were historically set up to translate frontier technology into applicable technology for local industries. There is some evidence that South African and Indian CSIR laboratories are doing this for particular sectors, and utilizing existing public research institutes may offer a more resource-efficient method of knowledge exchange than establishing new KTOs.

The third problem facing middle-income countries is the lack of a culture of interaction with industry and lack of awareness of commercialization possibilities. KTO staff are usually career civil servants, governed by civil service rules and promotion policies. Staffing KTOs with scientists familiar with industrial R&D is extremely important to changing the culture of interaction between university and industry. Policies that target the recruitment into management positions of scientists who had some training abroad could also help to change the research culture in universities. This has been done extremely successfully in China and Singapore.

Chapter 10 has offered a number of suggestions for improving firms’ uptake of technology produced in universities and we will not repeat them here except to note that offering joint funds for exploitation with university partners may both alleviate the low research intensity of existing firms and encourage them to search for the best university partners and so make it mutually beneficial for universities and firms to establish links around research and the commercialization of research.

We summarize our arguments in Figure 11.3, which outlines five questions that governments and universities must ask themselves before deciding on the appropriate policy mix.

Figure 11.3 Five questions to guide policy toward knowledge exchange from universities

11.5 Summary

Our concern in this chapter has been to look more closely at the policy-induced convergence of the knowledge ecosystem that the Bayh-Dole-type legislation in various countries attempted, drawing on the extensive material of the country case studies. Our analysis suggests that in high-income countries, such as the United Kingdom and Germany, where the knowledge ecosystems were already well developed and mature, the adoption of Bayh-Dole-type legislation while simultaneously cutting back on government funding of research in universities and public research institutes created the risk of “displacement effects.” By displacement effects, we mean research expected to produce patents being incentivized and preferred (due to its higher expected value) over other types of commercialization, such as more informal and risky codeveloped research (in spinoff firms or with domestic firms). Despite this, the data show that non-IP methods such as contract research are a much bigger income earner, even in the United Kingdom, than research-producing patents for licensing, while overall income from knowledge transfer has remained steady as a proportion of university incomes. This could be because patent use was harder to achieve than patent ownership and/or in many research fields, contract research, or the development of applications in spinoff firms were simply better avenues of commercialization.

In middle-income economies, where knowledge ecosystems were less mature, with missing links in the knowledge ecosystem, the Bayh-Dole legislation kick-started a process of institutional reform. Middle-income countries often needed to adopt a complementary set of policies (in stages) in addition to permitting university ownership of IP. In most countries in our study, the requirement that universities undertake research that benefits industry was supported by a generous allocation of financial resources to enable such a transformation. The role that policy played in plugging institutional gaps is interesting (although it differs from country to country) and may, in time, deliver the desired outcome of an increase in the value of university research for the innovation activities of national firms.

The chapter concludes by noting that there cannot be a one-size-fits-all policy and enumerates a number of university-level factors that should be taken account of in middle- and low-income economies in order to deliver an effective knowledge transfer policy.

Comment 11.1

Antenor Cesar Vanderlei Corrêa and Fernanda Magalhães

The chapter put into perspective the implementation of policies to support the transfer of knowledge from public research organizations to industry in high- and middle-income countries, drawing on the experience of six countries (Germany, Republic of Korea, the United Kingdom, Brazil, China, and South Africa). Some common traits are identified, although it is made clear that each country requires an individualized analysis of what is needed to build effective knowledge transfer channels between universities and public research institutes and industry.

Our experience in dealing with these issues in Brazil suggests that a set of measures aimed at increasing the “two-way flow” of ideas and people, as identified by the authors, between universities and public research institutes and industry should address three different layers of the problem: cultural, institutional, and financial.

The cultural layer is the most crucial because it deals with the foundation of the system. The limited interaction between the aforementioned actors in middle-income countries is somewhat difficult to overcome. Publishing papers and contributing to the dissemination of knowledge are the main tasks that have been historically associated with universities. Commercial considerations are often seen as a deviation from the purpose of academia. In this context, the relationship with industry is often neglected and sometimes seen as undesirable. One of the problems of not being used to dealing with the industry is related to the protection of knowledge. It is not uncommon for professors involved in applied research to be so keen to show the results of their work that they end up revealing secrets that should be protected. Another element that can add difficulty is the financial incentives normally associated with industry collaboration. Sectors of the university that do not benefit from these incentives in a situation of scarce public funding can produce an environment that is not conducive to commercial dealings. Timing and excessive bureaucracy are other issues often mentioned by industry when identifying difficulties in dealing with public research organizations.

The second layer, institutional, is addressed in depth by the authors when they mention the impact of the Bayh-Dole-type legislation on worldwide policies to support the transfer of knowledge from public science. Indeed, the Bayh-Dole Act strongly influenced Brazilian legislation on the matter. The Brazilian Innovation Act from 2004 was the first step toward establishing institutional and legal frameworks to facilitate the process of change in the interplay between universities and public research institutes and industry. Some of the key aspects of this initiative were: (i) it consolidated the legislation in order to provide a more coherent and unambiguous basis on which this interaction could happen, thus helping to positively affect the first layer mentioned in the previous paragraph; (ii) the ownership rights attributed to universities and public research institutes placed greater emphasis on the results of research conducted within these institutions; and (iii) the incentives provided to the institution and researchers involved in projects with industry were important in assisting the establishment of a new pattern of relationships. The Innovation Act has been fully revised and a revamped version was approved in 2016. In fact, there was a thorough and overall revision of all aspects of the national science, technology, and innovation policy in the country, even involving changes in the Brazilian National Constitution, which is referred to as the New Science, Technology, and Innovation Legal Framework. The key aspect of the new legislation in regard to the process of knowledge transfer discussed here is the mandatory requirement for all public research organizations to establish their own innovation policies, whose main focus is exactly knowledge transfer to industry.

The third layer, financial, refers to the lack of funding, not only by the government but mainly by the private sector, to support engagement of universities and public research institutes with industry in R&D projects. This layer is considerably influenced by the previous ones, particularly in middle-income countries where the national innovation systems are less mature. For example, in Brazil, most of the investment in R&D is made by the public sector (56 percent, according to data from the Brazilian Ministry of Science, Technology, Innovations, and Communications), which emphasizes the need for stronger cooperation between public research organizations and industry in order to accrue more economic benefits for society as whole from this type of investment. Due to the lack of tradition of investing in high-risk technology-based ventures, such as startup and spinoff companies, there is a low flow of financial resources between industries and universities and public research institutes. It is also worth mentioning that regulations related to the attribution of responsibilities in case of business failure are harsh in relation to all parties involved, including the investors, which is not particularly encouraging for venture capitalists.

These factors, added to budget constraints, do not create a motivating environment for strong partnerships between industry and public research organizations. One measure that could be taken to foster collaboration would be the provision of additional monetary incentives for companies to interact with public research organizations on top of those that provide general incentives for firms to invest in R&D.

During the analysis, the authors explore the policy interventions adopted by countries to promote an effective knowledge exchange policy. They divide policy interventions into two categories: the supply side, which includes incentives for universities to provide technology to industry, and the demand side, which includes incentives for firms to engage with industry. Regarding these incentives, it is interesting to note in Table 11.3, which shows the types of supporting policy among the selected countries, a trend in high-income countries to provide direct government support and a tendency in middle-income countries to enact legal requirements to implement the activity. In Brazil, both legal measures and governmental funding were used to support the interaction.

Although the current institutional framework in Brazil has evolved significantly in order to bridge the gap between public science and industry, there are still considerable challenges to be overcome. The report produced by the Brazilian government with data from universities and research institutes on the implementation of the Innovation Act shows that some progress has been achieved. Over the years, there has been an increase in knowledge transfer offices, protection of intellectual assets, contracts with industry, and revenue from these contracts. Nevertheless, it is difficult to say that it is a harmonized movement, because the country is vast and full of particularities. Most contracts and revenues from these contracts are concentrated within a small number of institutions. Some factors can be mentioned that favor these institutions: location, type of research developed, tradition in the relationship with industry, good laboratory infrastructure, and open-minded researchers to relate to industry.

From the institutional point of view, we believe that Brazil has established a comprehensive legal framework on innovation policy. But is that enough? As we have tried to point out, there are barriers that can only be overcome with continuous assessment and adjustment of the policies in place. In this context, the authors provide a good guide to assist in these tasks.

Comment 11.2

Si Kyong Sung

In 2000, Korea established the Technology Transfer and Commercialization Promotion Act, a Korean Bayh-Dole Act, and the ownership of public research results became able to be transferred to universities and public research institutes. The next year, the ownership of public research results was transferred to universities and public research institutes in compliance with the new regulation on the management of government-funded R&D projects.

In the last five years, there have been three major blockbuster public knowledge transfers in Korea. In this chapter, these cases are introduced, along with the detailed concerns of the Korean Intellectual Property Office.

The first was the case in which a startup established by a public research organization grew successfully and a large amount of inventor compensation (more than USD 10 million) was awarded to each of two researchers. The Korean Bayh-Dole Act and the regulation on the management of government-funded R&D projects both stipulate that public researchers must be compensated for more than 50 percent of the royalty income of their research products. This case gave rise to the argument to alleviate the compensation gap between researchers. As a result, the government introduced a regulation to reduce the percentage of inventor compensation for the royalty gradually if the compensation exceeds USD 2 million a year.

The second case is one in which a university gave up filing a patent application for an invention (Bulk fin-FET), where the professor filed a patent application in United States of America (U.S.) in his own name and with his own money, and, ten to fifteen years later, received USD 10 million and USD 400 million royalty from foreign and Korean companies, respectively. In Korea, the market size is about one-tenth smaller than that of the U.S., and the compensation for patent infringement is as low as one-hundredth of the U.S compensation. Therefore, inventions that only have a Korean patent cannot make a good license contract. Unlike the Bayh-Dole Act and the German employee invention law, the Korean Act did not stipulate the return of ownership to the inventor when the university gave up filing or sustaining a patent application. Therefore, academic inventions are often left idle unless the university applies for overseas patents. For reference, Korean universities file patent applications in foreign countries for only 4.5 percent of their domestic patent applications.

The third case is where a professor transferred his invention (CRISPR genetic scissors) to his own startup three days after disclosing it to his university, using a cheap upfront payment (about USD 20,000) and appropriate running royalty rate, and the company grew to about USD $800 million in value last year. Since public technology in Korea can be transferred exclusively only when there is no demand for a nonexclusive license, the professor and the KTO are being accused of concealing the valuable patent and selling it off to the company concerned at a low price.

The reason for these problems is that ownership of public research results has been transferred institutionally to universities and public enterprises, but they are still regarded as public goods rather than private property. The purpose of ownership transfer in the Bayh-Dole Act is to ensure that the patents are widely used while public research organizations maximize their own profits. By the same token, the Korean Bayh-Dole Act aims for public research results to be widely used but disapproves of a public research organization maximizing its profits. During the last fifty years of rapid economic growth, the purpose of public research in Korea was to help domestic companies to catch up quickly, and the research results were to be shared rather than be owned exclusively. Even after the establishment of the Korean Bayh-Dole Act, this perception has not changed, and Koreans still consider it unfair for someone to monopolize the profits from the results of tax-based research. It is clear that they do not want the invention to be returned to the inventor even if it is not going to be filed, and they consider it to be monopolization for a patent to be assigned to a startup and make a success.

We would like to add a few more details to the institutional incentives suggested by the authors of this chapter.

First, a patent should be recognized as the private property of the university and the public research institute. This means that the public research institute should be able to decide autonomously on the selection criteria for the company to transfer its results, the license type (exclusive/nonexclusive), and the royalty distribution so that the public research institute can actively maximize its profits. When knowledge transfer can make money, universities and public research institutes, as well as researchers, will make efforts to get valuable patents and transfer technologies.

Second, the purpose of the knowledge transfer policy should be that the invention is used more rather than making the invention used by more companies. The ultimate goal of the patent system is to make more utilization of inventions in the long run, even if transitory monopolies are allowed. In fact, this is consistent with the public interest of public research policy, and is consistent with why researchers prefer knowledge sharing rather than knowledge transfer.

According to the second objective, it is better to choose a company that has the ability and willingness to commercialize the patent rather than the company that pays the highest upfront lump sum fee. Unfortunately, the running royalty income of Korean universities was only 8 percent of their total royalty incomes in 2017, while that of the U.S. was as high as 55 percent. If universities and public research institutes are pushed to raise the immediate financial achievements with R&D, they cannot but license their knowledge focusing on the upfront fee, regardless of commercialization.

There’s no need to worry that SMEs and startups may have no opportunities if technologies are transferred institutionally to the companies that will utilize them more. Technologies transferred to large companies often go to the warehouse and lie dormant until a patent dispute arises. Rather, SMEs and startups are often eager to commercialize the patent for their survival. In particular, a company founded by the inventor him(her)self is very competitive in terms of technical expertise. If full authority over the profits is given to universities and public research institutes, they will make reasonable choices.

Although the purpose of public research policy may vary from country to country, the purpose behind granting ownership of research results to public research organizations must be the promotion of utilization of the results. For patent utilization, public research organizations pursuing profits are much better than bureaucracy. Policies that are applied to the knowledge transfer process should limit the private property rights of institutes only if they are clearly against the public interest. In the future, Korea intends to improve the system in this direction.

12 Toward a Comprehensive Set of Metrics for Knowledge Transfer

Anthony Arundel and Nordine Es-Sadki

12.1 Introduction

The commercialization of knowledge produced by public research organizations, consisting of universities and public research institutes, requires the transfer of knowledge to firms, government entities, or nonprofit organizations and the application of this knowledge to products and processes. This transfer process occurs through a number of channels, but, as noted in Chapter 2, most research on knowledge transfer from public research organizations to other organizations uses metrics for IP-mediated forms of knowledge transfer, for instance, licenses for codified intellectual property such as patents. This is partly because research on IP-mediated knowledge transfer is facilitated by the electronic “trail” left by IP. This ensures that the activities and outputs of IP are easier to identify than other methods of knowledge transfer.

Six metrics for IP-mediated knowledge transfer are often collected by private or public sector organizations in high-income countries from surveys of knowledge transfer offices (KTOs): the number of invention disclosures, patent applications, patent grants, licenses, and startups established, plus the total amount of license revenue earned by the public research organization. In addition, many of these surveys also collect data on the number of research agreements with firms, which can result in knowledge transferred by IP or through other channels.

Table 12.1 identifies the collection of data on these knowledge transfer metrics by each of the six case study countries covered in this book. The only metrics that have been collected for a large sample of public research organizations in all six countries are the number of patent applications and patent grants. In most countries, the collection of data for other metrics has been sporadic, with only the United Kingdom collecting these data for all universities over the long term.¹ Additionally, private-sector organizations that represent knowledge transfer professionals such as RedOTRI (Spain), NetVal (Italy), and the umbrella organization ASTP (formerly ASTP-ProTon) collect relevant data for these metrics in Germany and other high-income countries, but with the exception of Spain and Italy, less than 50 percent of universities and public research institutes are covered (Finne et al. 2009). Since these metrics are of high value for benchmarking outcome performance and monitoring the use of IP to transfer knowledge, all countries should ideally collect these metrics on a regular basis for all public research organizations, or at the minimum for research-intensive public research organizations, as done by the Association of University Technology Managers (AUTM) in the United States of America (U.S.) and in Canada (AUTM 2016, 2017).

Table 12.1 Data collected for IP-mediated knowledge transfer plus research agreements at the institutional level (results for six countries)

	China	Brazil	South Africa	UK	Korea	Germany1
Number of invention disclosures			✓	✓	✓
Number of patent applications	✓	✓	✓	✓	✓	✓
Number of patent grants	✓	✓	✓	✓	✓	✓
Number of licenses with firms		✓	✓	✓	✓
Total license income earned		✓	✓	✓	✓
Number of startups using institutional IP			✓	✓	✓	✓
Number of research agreements with firms		✓		✓

Source: National experts responding to a WIPO survey on data collection

¹ Data have been collected by private-sector organizations for all seven metrics for a selected number of leading public research organizations.

A major issue is the international comparability of knowledge transfer metrics. Comparable metrics are of value for benchmarking performance and for policy learning through the use of econometric analysis to evaluate the effects of inputs and outputs on knowledge transfer. For instance, policymakers in one country or region can learn from evaluations of the effects of knowledge transfer activities on outcomes in countries or regions with similar levels of economic development or similar industrial structures. As discussed in Chapter 2, the de facto definitions of IP-mediated knowledge transfer have been set by the AUTM. China collects data on activities such as “knowledge transfer” and university enterprises that are not fully comparable with the AUTM definitions. As some of these metrics are useful for Chinese policy, international comparability would require China to collect additional metrics using the AUTM definitions.

A reliance on metrics for IP-mediated knowledge transfer creates two substantial issues. First, measurement implies that the measured activity is of high value, while unmeasured activities are of low value. Consequently, the act of measuring IP sends a strong signal to university managers (and policymakers) that more university IP is desirable, while other activities to transfer knowledge are erroneously viewed as unimportant. One consequence is that the types of metrics that are collected can affect the distribution of public funding and the ranking of universities. In the United Kingdom this resulted in a dispute over the types of knowledge transfer metrics to be collected between the Russel Group of research universities, which benefited from a narrow focus on IP metrics, and a group of younger universities, established after 1992, that wanted knowledge transfer metrics to cover a broader number of activities (Lockett et al. 2015).

Second, policies and practices to promote knowledge transfer must ensure that all aspects of a knowledge transfer system are functioning. There is a large and diverse variety of channels for transferring knowledge that are not covered by the seven commonly used metrics and which have been identified as important conduits for knowledge transfer in Chapter 11 and other research (Walshok and Shapiro 2014). Bekkers et al. (2008) identify twenty-one channels, ranging from publications to personal contacts. In particular, metrics for IP-mediated knowledge transfer do not capture the transfer of tacit knowledge, which requires direct, personal contact between the provider and the recipient of the knowledge. These personal contacts, for instance, through staff exchanges between firms and public research organizations, play a vital role in the transfer of knowledge for breakthrough discoveries (Bekkers et al. 2008). One concern is that a policy focus on supporting IP-mediated channels can unintentionally interfere with the use of other knowledge transfer channels (Rosli and Rossi 2014; Czarnitzki et al. 2016; Veugelers 2016). The combination of informal and formal channels has been found to have a positive effect on innovation outcomes (Link et al. 2007; Siegel et al. 2003; Grimpe and Hussinger 2013) and could be especially important to the performance of spinoffs (Hayer 2016).

The economic relevance of a broader set of knowledge transfer metrics is well established, with research from both the United Kingdom and China (see Chapters 4 and 8), showing that non-IP-mediated knowledge transfer activities are considerably more important than IP-mediated channels, as proxied by the amount of income earned by public research organizations from IP versus other knowledge transfer methods. For example, in 2015–16 all universities in the United Kingdom combined earned £4.2 billion from all knowledge transfer activities, of which only £176 million (4.2 percent) was due to IP licensing (HEFCE 2017).

These limitations with metrics for IP-mediated knowledge transfer have been recognized for some time (Holi et al. 2008; Jensen et al. 2009; Lockett et al. 2015). They may be particularly important for middle-income countries that have enacted policies to replicate the American Bayh-Dole Act for IP (see Chapter 11), while neglecting policies to support other forms of knowledge transfer. Based on the country case studies and other research, Chapter 10 argues that middle-income countries would benefit from knowledge transfer policies to increase incentives for public research organizations to interact with firms and policies to increase the absorptive capacities of firms to use and apply knowledge produced by public research organizations. Both of these goals can be enhanced by policies that support the full range of knowledge transfer channels, based on evidence showing that the optimal channel varies by firm capabilities and the characteristics of the knowledge to be transferred (Bekkers et al. 2008; Belitski et al. 2019).

In addition to data on IP-mediated knowledge transfer, surveys of KTOs or other administrative units within a public research organization can collect data on other formal channels such as contract and collaborative research with firms or government organizations. However, other knowledge transfer metrics need to be collected from academics and firms in order to overcome a lack of knowledge on the part of KTO staff. Large-scale surveys in Europe show that KTO managers are not always able to report research agreements with firms, as some of these are managed outside KTO administration (Barjak et al. 2015). Furthermore, KTO staff can be unaware of important knowledge transfer activities via publications or through informal channels. Freitas et al. (2013) estimates that approximately 50 percent of knowledge transfer from public research organizations in a province of Northern Italy occurred through personal interactions.

There can also be large differences in the perceptions of KTO managers, academics, and firm managers on the factors that support or act as barriers to knowledge transfer. Siegel et al. (2003) surveyed KTO managers, academics, and firm managers to obtain their opinions on barriers to knowledge transfer. They found large and statistically significant differences among these three groups that were often self-serving. For example, KTO managers did not find university bureaucracy and inflexibility to be important barriers, but both academics and firm managers did. Relying on the perceptions of only one of these three key actors could result in misleading recommendations for how to improve knowledge transfer.

This chapter discusses and identifies data for measuring non-IP-mediated methods of knowledge transfer, as well as metrics for the use of policies and practices to support knowledge transfer. The latter are required to be able to assess policy effectiveness. The purpose is to provide data for all major channels of knowledge transfer in addition to IP-mediated channels, as covered in Chapter 2. Many of the types of data discussed in this chapter also meet statistical requirements to be specific, measurable, reliable, timely, and cost-effective (Jensen et al. 2009). In addition, the chapter discusses a limited number of metrics that can be used to assess the systemic impacts of knowledge transfer, including impacts from IP licensing. Metrics for impacts are, unfortunately, considerably more difficult and costly to obtain than metrics for activities.

In addition to collecting data from KTOs (or university administrations responsible for knowledge transfer), data on knowledge transfer activities need to be collected through surveys of scientists and other academics employed by public research organizations that create knowledge, and the firms that are the intended recipient of knowledge. Surveys of academics and firms are the best method for collecting data on informal knowledge transfer channels (Sigurdson et al. 2015). A fourth method is to use publicly available data, for instance, on patenting or publications or through web-scraping techniques. The types of data that can be collected through each of these methods and their limitations are discussed below. Of note, this chapter follows the Oslo Manual (OECD/Eurostat 2018) by identifying lists of topics to be covered by data collection instead of providing specific questions for surveys, with the exception of questions for policies and practices to promote knowledge transfer.²

12.2 Data from KTOs and University Administrations

The AUTM licensing activity surveys have served as the baseline model for data collection from KTOs, but the questions are largely limited to IP-mediated knowledge transfer outcomes and activities.³ The British Higher Education-Business and Community Interaction (HE-BCI) survey, sent to KTOs or other responsible administrative units within British universities, covers a broader range of formal knowledge transfer activities that are not always part of IP licensing, although some of these activities can contribute to IP licensing (Holi et al. 2008; Rossi and Rosli 2015). Part A of the survey collects data on policies and practices for knowledge transfer, including the strategic goals for these activities, priorities by region, staff incentives, in-house capabilities for managing IP, and practices for supporting spinoffs and startups. Part B of the survey collects financial data on income earned by universities for five formal knowledge transfer activities: collaborative research, contract research, consultancies, facilities and equipment-related services, and professional development and continuing education.⁴ Income data are obtained by the source of funding: government, businesses, and third-sector organizations. In addition, business funding is separated into SMEs and large businesses for all activities other than collaborative research.⁵ KTO data are useful for benchmarking and monitoring formal knowledge transfer outcomes such as different forms of income earned by universities and public research institutes and for policy evaluation.

The HE-BCI survey is a useful model for collecting data on a full range of formal knowledge activities for all countries and was proposed for implementation by Australia (Jensen et al. 2009). To succeed, public research organizations need to invest in accounting systems to collect financial data for specific income sources. As this can be costly, a governmental authority may need to provide an incentive to compel universities to collect these data. The United Kingdom is able to collect these data for almost all universities because compliance with the HE-BCI reporting requirements is necessary for eligibility for one of the UK government’s funding programs. A similar approach could be useful in other countries that provide publicly funded research grants to public research organizations.

12.2.1 Measuring the Benefits of Knowledge Transfer to Public Research Organizations

The financial benefit to public research organizations is usually measured through license revenue from IP licensing and income from industry-funded research, including contracts, research collaboration, consultancy, renting of equipment and facilities, and professional development and education programs (HEFCE 2017). Other benefits that are difficult to measure in financial terms have not been measured on a consistent basis, although they have been examined in the academic literature (Perkman et al. 2011). They include knowledge flows from firms to universities, information on interesting opportunities for research, including research of value for commercial applications, and job placements for graduates and PhD candidates. Potential costs include the costs of funding KTO activities, such as marketing and managing IP, evaluating the commercial potential of inventions, and patenting and other legal costs. Nonfinancial costs include disruptions to the research function of universities, such as delays in publication, a decline in academic involvement in basic research, or the diversion of academic time to patenting and licensing activities (Thursby and Thursby 2007).

12.2.2 Data on Policies and Practices to Support Knowledge Transfer

Data on the policies and practices that public research organizations use to support knowledge transfer can be used to evaluate and identify the factors that support or hamper knowledge transfer activities. These include both policies at the national level and policies and practices that are implemented at the institutional level for each university or public research institute. Useful data on policies and practices implemented by national or regional governments or the institution itself can be obtained from surveying KTOs. International comparability in data for policies and practices is required for multi-country analyses of the factors that influence knowledge transfer performance.

Many countries have introduced legislation on the ownership of IP produced by public research organizations, the establishment of KTOs, whether or not researchers should be provided financial incentives if a discovery is licensed, and, in some countries, the percentage of license income that researchers should receive; and whether or not academics at universities must file invention disclosure reports. For Europe, Geuna and Rossi (2011) found that it is difficult to disentangle the effects of changes in IP ownership on academic patenting activities from the effects of concurrent transformations in the institutional, cultural, and organization landscape surrounding knowledge transfer. National policies that are directed toward businesses can also encourage knowledge transfer, such as subsidies for firms to collaborate on innovation with university or public research institute partners, or government reimbursement “vouchers” that firms can give to researchers in return for assistance with practical problems. Information on national policies is valuable for understanding the factors that shape national knowledge transfer activities.

Practices are often based on written regulations or guidelines, but are either not legally required (in the case of a guideline), or, if based on regulation, not enforced. For example, policies on the ownership of IP are usually established at the national level and universities are legally required to follow them. In contrast, a national or institutional regulation requiring academics to file an invention disclosure report for a discovery with potential commercial value is often closer to a practice, with few or any penalties for academics that fail to file.

Data collection for policies and practices is less developed than for knowledge transfer activities, as shown in Table 12.2 for the six case study countries. China only provides data on national regulations on the assumption that these are implemented by all public research organizations. Neither Brazil nor Germany collects data on policies and practices for large samples of public research organizations.

Table 12.2 Data collected for IP policies at the national (✓) or institutional (✓✓) level (results for six countries)

	China	Brazil	South Africa	UK	Korea	Germany1
Incentives for academics to disclose inventions to support knowledge transfer	✓			✓✓	✓✓
Promotion of knowledge transfer opportunities to the business sector	✓
Written rules or guidelines for knowledge transfer	✓		✓		✓✓
Written rules or guidelines made publicly available	✓		✓
Rules for publication delays to support IP licensing			✓✓
Academics permitted to take leave to work at a firm or startup	✓			✓✓
Goals of KTOs for knowledge transfer	✓		✓✓	✓✓	✓✓

Source: National experts responding to a WIPO survey on data collection

¹ Some data have been collected on a sporadic basis by private-sector organizations or academics for a selected number of leading public research organizations.

Most of the research on policies and practices has primarily focused on IP-mediated knowledge transfer, although some has also evaluated research contracts and consulting. Knowledge transfer via licensing is influenced by IP regulations (Baldini et al. 2006), rules for exclusive licensing, licensing practices (Okamuro and Nishimura 2013; Shen 2016), the involvement of academics in contract or consulting research (Weckowska 2015), and financial and nonfinancial incentives (Chatterjee and Sankaran 2015) for academics to participate in knowledge transfer. Policies that contribute to the establishment of startups include dedicated programs (support for developing business plans, etc.) and facilities (such as an incubator) to support startups (Berbegal-Mirabent et al. 2015; Muscio et al. 2016), and employment conditions that permit academics to take leave to work with startups. The share of license revenue allotted to inventors acts as an alternative source of income for startups and can reduce the interest of academics in participating in them (Markman et al. 2004; Barjak et al. 2015).

Table 12.3 summarizes useful metrics on policies and practices for data collection at the level of the university or public research institute. The main purpose of these metrics is for monitoring and policy evaluation. Some of the data need only be collected at the nominal level (yes or no), whereas other data can be collected on an interval level (percent of royalties provided to inventors or length of academic leave to work with a firm) or ordinal level (importance of different goals for knowledge transfer). The table is divided into key and supplementary metrics, based on the importance, as identified in the literature, of each policy or practice. Sample questions for measuring many of these policies, derived from a 2016 WIPO survey, are provided in the Technical Annex.

Table 12.3 Metrics at the institutional level for policies and practices to support knowledge transfer

Metric	Measurement level
	Nominal	Ordinal/interval
Key policy metrics
Importance of goals for knowledge transfer (earn income, support regional development, marketing university capabilities, etc.)		✓
Ownership rules for IP developed by public research organizations, including ownership of IP resulting from public research organization–firm research agreements	✓
Financial incentives for researchers to support knowledge transfer: (incentives for invention disclosure, share of revenue from licenses, research contracts, etc.)	✓	✓
Rules for consulting (time limits on consulting, how income is distributed between the academic, research group, etc.)	✓	✓
Nonfinancial incentives for researchers for different types of knowledge transfer (reputation, job promotion, etc.)	✓
Researcher permitted to temporarily work with a licensee/spinoff, firm involved in collaborative research (including maximum length)	✓	✓
Presence and amount of supporting infrastructure for startups and spinoffs (incubator, science park, etc.)	✓	✓
Presence of different types of financial support (funding for KTOs, seed funding, etc.)	✓	✓
Supplementary policy metrics
Requirement or incentives for researchers to assist commercialization (i.e., work with a licensee, research contract partner)	✓
Requirement for researchers to report invention disclosures	✓
Presence of written rules or guidelines for licensing, including publicly available model contracts	✓
Presence of flexible rules for licensing	✓
Presence of written rules for the conditions for an exclusive or nonexclusive license	✓
Policy for publication delays (including maximum length) to support patenting, licensing, or collaborative research	✓	✓
KTO or other public research organization activities to promote IP or staff capabilities to the business sector	✓

Source: Authors

12.3 Surveys of Academics (Researchers) at Public Research Organizations

Surveys of academics can provide several types of data that cannot be obtained through surveys of KTOs or university administrations: the use and importance of informal knowledge channels compared to other channels and the influence on knowledge transfer activities and outcomes of the personal characteristics of academics and organizational factors at the departmental or research group level. The main purpose of collecting data from academics is for monitoring and policy evaluation.

Compared to research using data obtained from KTOs, there are considerably fewer empirical studies on the engagement of academics in activities to transfer knowledge to firms. A 2013 systematic literature review of studies on academic engagement published between 1980 and 2011 identified twenty-five separate surveys of academics in thirteen countries: ten surveys in the U.S., four in the United Kingdom, two surveys in each of the Netherlands and Germany, and one survey in each of Spain, Chile, South Africa, Italy, Norway, Ireland, Sweden, Belgium, and Japan (Perkman et al. 2013). In addition, the study reported on two studies with over thirty interviews. The studies focused on engagement through contractual, collaborative, and consulting agreements and collected data on four types of factor, as summarized in Table 12.4. The first three factors influence knowledge transfer activities while the fourth measures the effects of knowledge transfer.

Table 12.4 Data collected in previous surveys of academic engagement

Factor	Data
Characteristics of academics	Gender, age, seniority, previous commercialization experience, government grants awarded, and scientific productivity
Organizational factors	Quality of the university or department, organizational support, incentives, organizational experience with commercialization, peer effects
Institutional factors	Discipline or field, national regulations/policies
Outcomes	Scientific productivity (publications, patents), commercial productivity, shift to applied research, secrecy, collaborative behavior, teaching

Source: Based on Perkman et al. 2013

Relevant data to collect in surveys of academics include the number or percentage of different types of academic staff involved in knowledge transfer through informal, contractual, and IP-mediated channels; barriers to interactions, including “cost” factors such as secrecy and concern over academic freedom (see Table 12.5); and the goals for participation in each type of channel. In addition, academic surveys can provide relevant information on the types of partner, such as firms, government organizations, and nonprofits. An example of good practice is the large 2008–9 survey of 22,556 UK academics active in teaching or research (Abreu and Grenevich 2013; Abreu et al. 2016; Zhang et al. 2016).⁶ Due to the use of a representative sample, this study was able to determine that more academics interact with government organizations (53 percent) than with firms (41 percent) (Hughes and Kitson 2012), that academics in regional areas are more intensively involved in university–industry linkages than academics in the metropolitan regions, and that teaching-oriented universities are also very active in these linkages (Zhang et al. 2016).

Table 12.5 Knowledge transfer metrics from surveys of academics and firms

	Academics	Firms
Incentives for participation in knowledge transfer
Financial	✓	✓
Promotion	✓
Previous experience with knowledge transfer
Informal (personal contacts, conferences/meetings)	✓	✓
Training	✓	✓
Use of advanced equipment/facilities	✓	✓
Research contracts or consultancy	✓	✓
Collaborative research	✓	✓
Licensing IP	✓	✓
Barriers/reasons not to participate
University rules for knowledge transfer	✓	✓
Lack of time (teaching responsibilities etc.)	✓
Research of little interest	✓	✓
Concern over publication/delays	✓	✓
Underdeveloped technology	✓	✓
Difficulties in find right licensee	✓
Costs to evaluate commercial potential	✓	✓
Costs to prepare legal matters involving IP rights	✓	✓
Potential loss of technological/competitive edge		✓
Prices charged by licensor too high		✓
Goals for participation
Acquire leading-edge research results	✓	✓
Freedom-to-operate		✓
Close technological gaps		✓
Funding from businesses for research, PhD candidates etc.	✓
Better insight to commercialization opportunities	✓
Economic effects
New knowledge from public research organizations incorporated in products and processes		✓
Sales share/imputed savings due to knowledge from public research organizations		✓

Source: Authors

The main challenge for surveying academics is to reduce the costs of surveying. A common method is to construct a sample that excludes academics who are unlikely to develop knowledge with commercial potential and consequently have little or no experience with knowledge transfer. A solution is to focus surveys on academics in applied science departments, or on research-intensive universities who are likely to have experience in developing commercially valuable knowledge (Perkmann et al. 2013), or by selecting the departmental heads for technology disciplines, principal investigators on research projects, the heads of research groups (Van Dierdonck et al. 1990), academics who have been granted a patent, or academics who have founded a firm (Agiar-Díaz et al. 2016; Czarnitzki et al. 2016).

The disadvantage of these methods for selecting academics for surveys on knowledge transfer is that they can undervalue the opportunities for knowledge transfer from teaching-oriented universities or from the social sciences and result in inaccurate measures of the disadvantages of different types of knowledge transfer activity. For example, the possible disadvantages of close university–industry linkages include a loss of academic freedom (ability to choose to conduct basic research or research of low commercial interest) and restrictions or delays on publication due to the interest in commercial partners in secrecy (Van Looy et al. 2004; Tartari and Breschi 2012; Muscio and Pozzali 2013). The importance of publication delays is likely to be greatest for early career researchers such as PhD candidates and post-doctorates that need to rapidly build up a list of publications. Yet this possible effect will be missed entirely in studies that focus on the heads of research groups or departments. This could be one reason why a study of departmental heads finds that publication delays are given a low importance ranking as a barrier to collaboration with industry, whereas impacts on the choice of research is given a much higher importance ranking (Muscio and Pozzali 2013).

12.4 Surveys of Firms

Surveys of firms can complement surveys of academics. Both types of survey can include similar questions and thereby identify differences in the perspectives of academics and firm managers on knowledge transfer activities. Data from firms can be used for benchmarking performance (if data are collected on economic effects), monitoring and policy evaluation.

Survey research on firms consistently points to the importance of open science methods of knowledge transfer in high-, medium-, and low-income countries, although in middle-income countries in Asia contractual methods are often more commonly cited than open science (Siegel et al. 2003; De Fuentes and Dutrénit 2012; Frietas et al. 2013; Grimpe and Hussinger 2013; Okamuro and Nishimura 2013; Dutrénit and Arza 2015; Kafouros et al. 2015; Kruss et al. 2015; Schiller and Lee 2015). A possible explanation is the importance of contractual relationships to building innovative capacity and problem-solving abilities among firms.

Firms in low- and middle-income countries in Africa, Asia, and Latin America were surveyed on their use of knowledge channels in an internationally coordinated study that used the same questionnaire (Albuquerque et al. 2015). In two low-income countries (Uganda and Nigeria) informal methods dominate (Kruss et al. 2015), whereas in middle-income countries in Asia (Malaysia, Thailand, and China) the most common methods are consultancy and research contracts (Schiller and Lee 2015). In four middle-income Latin American countries (Argentina, Brazil, Mexico, and Costa Rica) both contracts and informal methods are more frequently used than IP-mediated methods (Dutrénit and Arza 2015). These results indicate that surveys of firms are of value to identifying the relative importance of different knowledge channels.

Surveys of firms face similar issues to those of surveys of academics: to reduce costs, the 80–90 percent of firms that are unlikely to source knowledge from public research organizations in a defined time period are usually excluded. Targeting can be improved by limiting surveys to firms in specific sectors where the use of knowledge produced by public research organizations is more likely (Bekkers et al. 2008), such as life science firms, or excluding firms with few employees.

Surveys that follow the Oslo Manual guidelines (OECD/Eurostat 2018) for measuring innovation in the business sector, such as the European Community Innovation Survey (CIS), often collect relevant data on university–firm linkages. For example, the CIS includes a question on the importance of information obtained from universities to the firm’s innovation activities and a question on collaboration with universities. These surveys consistently find that universities are an important source of information to less than 10 percent of firms, but their importance is higher for large firms and for firms in sectors such as pharmaceuticals that draw extensively on science. R&D surveys, although limited to R&D-performing firms that account for less than half of innovative firms, can also include relevant questions, such as business expenditures for R&D that is contracted out to universities or government laboratories.

Table 12.5 identifies useful indicators that can be obtained from surveys of academics and firms. Many of the indicators are applicable to both academics and firms, although some of the questions may need to be adapted to the type of respondent. For instance, questions on financial incentives for firms to source knowledge from public research organizations could list specific policy instruments, such as vouchers, subsidies for collaboration, etc. To improve recall quality, these surveys need to be limited to a defined period of time of between one and three years (OECD/Eurostat 2018) or refer to specific research outputs or inventions.

The benefits of knowledge transfer for firms consist of solutions for known problems (mostly relevant to contractual or collaborative research) (Perkman et al. 2011), improvements in innovative capabilities (De Fuentes and Dutrénit 2012), innovative products and processes, and earned income or cost savings from these innovations. Knowledge transfer activities can increase costs for firms when licenses are required for types of knowledge that were previously available at no cost or as part of open science. Otherwise, most of the costs incurred by firms are opportunity costs.

Several additional details to the questions listed in Table 12.5 for economic effects would assist research on the economic benefits for businesses. Relevant questions include (1) whether new knowledge obtained through public research organization research contracts, public research organization licensing, or informal public research organization contacts was implemented in products and processes, (2) the total sales revenue from these products and the imputed savings from these processes, (3) the fraction of sales revenue/cost savings attributed to knowledge obtained from public research organizations, (4) the sector of application for products and processes, (5) expectations for the next two years for a change in sales/cost savings for these products and processes, and (6) total sales revenues from all products (required to estimate the share of sales from products containing knowledge obtained from public research organizations).

12.5 Publicly Available (Big) Data

Big data are collected automatically and available in electronic form. Patent records, Google citations, and administrative data collected by governments for taxation and other purposes are all examples of big data. Another form that is attracting increasing attention is the use of Internet data, such as web-scraping to identify innovation activities within firms or university startups (NESTA 2018).

Big data such as patent databases can be used to directly produce knowledge transfer metrics or combined with data from KTOs or academic surveys. For example, patent data can be used to identify the share of patents produced by academics that are owned by firms or by universities (Geuna and Rossi 2011). Big data on publications, patents, or administrative data can also be linked to university-level data on a range of knowledge transfer activities and outcomes (Van Looy et al. 2011; Berbegal-Mirabent and Sabate 2015). An example is to evaluate the relationship between regional firm capabilities and knowledge transfer activities. Firm-level capabilities can be estimated from regional administrative data on R&D expenditures, business sector R&D intensities, and industrial structure (for instance, the share of private-sector output in low-, medium-, and high-technology sectors).

The use of web-scraping to produce metrics for innovation, including knowledge transfer, is in its infancy, but experimentation in this area is expected to produce useful results in the future. Woltmann (2018) used web-scraping methods to try to identify knowledge transfer from the Technical University of Denmark to firms via publications and university-owned patents. Text mining was used to identify similarities in the text of business websites and university patents and publications. The assumption is that firms that benefit from these two types of university output will replicate relevant text on their corporate webpages. The method identified a small number of matches with business websites, with matching better for publications than for patents.

12.6 Metrics for the Systemic Benefits of Knowledge Transfer

The main policy goal for knowledge transfer is to support the systemic economic and social benefits of knowledge transferred to firms, individuals, and governments and the subsequent effects at the municipal (Felsenstein 1996), regional, or national level (Cheah 2016). A review of academic research on the economic contribution of publicly funded basic research concludes that it is positive and substantial (Salter and Martin 2001), but it is a challenge to link specific knowledge transfer channels to systemic outcomes. Research using patent citation data has found positive benefits from academic research on the number of corporate patents in technology-based sectors (Verspagen 1999), which could result in an increase in innovative products and processes, but, in other sectors, knowledge transfer via patents is likely to be less important since the majority of innovations are not patented (Arundel and Kabla 1998). For a region or country, the greatest contributor to systemic benefits could be via non-IP-mediated channels such as research contracts, open science, and the employment of individuals with university qualifications (Roessner et al. 2013).

Collaboration between government, academia, and industry is considered to be of critical importance in enhancing regional economic and social development (Etzkowitz and Leydesdorff 2000; Klofsten et al. 2010; Urbano and Guerrero 2013). The effectiveness of tripartite collaboration has, however, been questioned, as many regions have failed to obtain expected benefits from knowledge transfer in terms of innovation, GDP, and employment (Asheim and Coenen 2005; McAdam et al. 2012). In order to address this challenge, recent policy initiatives identify the need for a more open science approach that includes social innovators involved at various stages throughout the knowledge transfer process (Wilson 2012). The inclusion of social innovators (Leydesdorff 2011) reflects the increasing importance placed on knowledge transfer to meet societal needs (Bozeman et al. 2015). It also emphasizes that knowledge transfer occurs not only between public research organizations and firms but also between public research organizations, governments, and nonprofit organizations.

Estimates of systemic financial benefits require data from surveys of firms, nonprofits, and government organizations on the uptake, application, and economic value of knowledge produced by public research organizations. This is very difficult to estimate because many innovations are built on multiple sources of knowledge. For all knowledge transfer channels, estimates need to obtain data from surveys of managers, but managers are unlikely to be able to estimate the diffuse effects of open science on their organization and often may not know or recognize the role of open science on key products (Mazzucato 2015).

A more feasible approach is to focus on formal knowledge transfer channels. Data on the economic impacts of knowledge transfer on government organizations or firms (using the data described in Section 12.5) from a random sample could be extrapolated to specific sectors. For contracts, this would require data on which contracts led to commercialized products or processes, the sector of application, and the sales revenue earned by the firm (or the value of services provided by governments) for products or imputed savings from processes. The reliability of this approach depends on the willingness of managers to volunteer information that could be commercially confidential and their ability to provide accurate retrospective information over a number of years.

Estimates of financial benefits are perhaps easiest to obtain for public research organization spinoffs on the heroic assumption that all future sales derive from the initial development of knowledge obtained from the public research organization at the time of establishment (Vincett 2010).

Other researchers have estimated the effect of public research organization research on GDP by combining data on running royalties (percentage of total sales) from licensed IP with estimates of the running royalty rate and the value-added components of sales from sectoral input–output models. For example, a study for the U.S. uses AUTM licensing data to estimate output from 1996 to 2010 for assumed royalty rates of 2, 5, and 10 percent. The estimated contribution to GDP in 2009 from licensing varied from USD 70.4 billion at a 2 percent royalty rate to USD 16.4 billion for a 10 percent royalty rate (Roessner et al. 2013). Although the former estimate exceeds total university R&D expenditures in 2009 of USD 55 billion, it is important to note that the estimated contribution is based on IP developed over multiple years before 2009. The disadvantage of this method is that it is only likely to account for a small percentage of the benefits from all knowledge transfer channels.

A regular survey aimed at universities and public research organizations conducted by the State Intellectual Property Office (SIPO) of the People’s Republic of China asks patent applicants about the knowledge transfer process and commercialization method and, for patented products, the total income earned from product sales.⁷ This information is potentially of great interest, but patent applicants may not always know the answers to questions on commercialization or income earned from product sales.

Nonfinancial systemic benefits are diverse and include improved quality of life from new therapeutic treatments for diseases, new business opportunities, and social benefits such as new educational and entertainment platforms on the Internet. These types of benefit are rarely measured, in part due to the difficulty in attaching a financial value to social outcomes. The default is to use case studies to highlight the social benefits of university research (Kearnes and Wienroth 2011). The AUTM, as part of its “better world project,” includes case-study examples in its annual licensing reports of the social and economic impacts of licensed university inventions. In many cases, this is a practical solution to illustrating the range of different types of both financial and nonfinancial benefit for specific inventions based on knowledge produced by public research organizations.

Systemic costs are difficult to identify and estimate since they are based on “what if” situations involving unmeasurable counterfactuals. For example, a theoretical social cost would occur if academics neglect basic research with high benefits over the long term in order to pursue applied research that meets short-term industry needs.

12.7 Conclusions

Knowledge transfer metrics are required for benchmarking changes in performance over time and for econometric analysis to evaluate the effectiveness of policies and practices. In both high- and middle-income countries most of the existing metrics focus on IP-mediated knowledge transfer, such as the number of patents produced by universities and the amount of license income earned. The premise of this chapter is that this is insufficient – both because it sends an erroneous signal to policymakers and administrators in universities and public research institutes that IP-mediated knowledge transfer is the optimum form, resulting in distortions in incentives, and also because it is not fit for purpose, with most knowledge transferred by means of other formal and informal channels. Consequently, a comprehensive set of knowledge transfer metrics to guide policy requires collecting metrics for a diverse range of knowledge transfer channels.

In addition to the basic metrics for IP-mediated knowledge transfer (see Chapter 2), this chapter recommends collecting metrics for other formal channels (collaboration, contracts, consultancy, etc.) from universities and public research institutes (for instance, by surveying KTOs) and metrics for informal knowledge transfer methods from surveys of academics and firms. Such surveys as these can also collect useful data on the goals of academics and firms in participating in knowledge transfer and the barriers that they face. Surveys of firms in middle-income countries should also include metrics to identify differences in the use of and need for knowledge transfer by firm capabilities (see also Chapter 11) and the types of financial incentive that they receive from government, such as vouchers. The main topics to be covered through data collection are identified through several tables in this chapter.

Another feature of a comprehensive set of metrics is the need to collect institutional data on policies and practices for use in policy evaluation and monitoring. This is essential for determining which factors best promote knowledge transfer and support the absorptive capacity of firms under different conditions. For example, the set of factors that promote knowledge transfer are likely to differ depending on the outcome (startup establishments versus adoption by existing firms), interactions with other policies, firm capabilities, and the industrial structure of a country or region.

Data for all types of formal knowledge channel should be collected on an annual basis from universities and public research institutes in order to encourage them to establish rigorous administrative records for these types of knowledge transfer activity. The marginal cost of annual data collection is also likely to be very low compared to the cost of biennial or less frequent surveys. In contrast, surveys of academics and firms are expensive and consequently these surveys only need to be conducted every three to five years, possibly by contracting out surveys to academics with expertise in knowledge transfer. Data on policies and practices tend to change slowly and therefore could be collected every three to five years from KTO surveys, although an open question could be included in annual surveys of KTOs to identify recent changes.

Comment 12.1

Philippe Kuhutama Mawoko

Africa has become an investment magnet thanks to its general economic performance as measured by a continuous improvement of its gross domestic product (GDP) and its population dynamics. Africa’s GDP increased by 3.5 percent in 2018, about the same as in 2017 and up from 2.1 percent in 2016. This growth is projected to accelerate to 4.0 percent by the end of 2019 and 4.1 percent in 2020.¹ Projections of population growth predict that the African population is likely to reach 1.7 billion by 2030.² These prospects have created an environment in which foreign firms and development partners are bound to interact with local firms and institutions. This has led to a complex and crowded policy environment that, if properly managed, will lead to good business outlooks, employment, and well-being for the African population.

In this space, technology and knowledge transfer will undoubtedly play a pivotal role in sustaining these undertakings. Consequently, relevant metrics will be required to produce evidence on which better policy can be made. Metrics will also facilitate better understanding and management of the complex patterns and interrelationships that will likely emerge. So, knowledge needs to be acquired, stored, created, disseminated, and improved on or added into existing knowledge. To that end, we welcome the chapter by Anthony Arundel and Nordine Es-Sadki, on which we are pleased to comment from an African perspective.

STISA’s Call for Knowledge Transfer Metrics

The African Union adopted its current Strategy for Science, Technology, and Innovation (STISA-24) in 2014. Its implementation is going through a Monitoring and Evaluation (M&E) phase which requires, among other things, an inclusive set of metrics that gauge the transfer of knowledge between various actors in order to reinforce the M&E relevance for facilitating evidence-based, transparent, and accountable decision making (Chux et al. 2018). This chapter discusses themes that are undoubtedly of use to the STISA M&E processes. These are the basic metrics for non-IP-mediated knowledge transfer, metrics for policy and practices related to knowledge transfer activities, and metrics to gauge the costs and benefits of knowledge transfer.

However, STISA has embraced a broader concept of knowledge that includes formal or codified tacit knowledge as well as traditional or indigenous knowledge. Tacit knowledge needs to be understood well especially for service industries, which are becoming a vital source of income and employment in Africa. Yes, tacit knowledge is difficult to write down, visualize, or transfer from one person to another but it underpins several innovations in the service sector, as shown by the Community Innovation Survey (CIS)³ conducted in about thirty African countries. The compilation and sharing of lessons learnt and other experiences and stories could be used alongside the CIS questionnaire to capture tacit knowledge. However, traditional knowledge transfer will need its own family of metrics to measure its dynamics.

STISA was designed in a way that responds to the demand of STI from socioeconomic sectors by embedding STI in those sectors. It outlines the key priorities that countries in Africa should collectively address through a series of innovative programs and projects. In that manner, the strategy aims to position STI to contribute toward Africa’s transition to a knowledge-based economy. This strategy requires buy-in from, and collaboration between, state and nonstate actors at various level of implementation. These include continental, regional, and national public institutions, the private sector, research institutions, and actors operating in the formal and informal sectors, as well as a significant cluster of multinational and development partners operating on the continent. In this complex environment, the set of metrics for knowledge transfer highlighted in the Arundel and Es-Sadki chapter will play a critical role if STISA seeks societal-led knowledge transfer in extending Africa’s development toward a knowledge-based economy.

The metrics for non-IP-mediated knowledge transfer need to be expanded and adapted to collate relevant data that will feed the analysis of the critical factors that underpin tacit knowledge. The CIS referred to above indicates that innovation is a connected activity. Innovative firms in Africa collaborate and their first choice of collaborator is the client or customer. Thus, knowledge transfer happens and needs to be measured. This is a link that needs to be expanded for additional data collection regarding tacit knowledge. Finally, there is a need to establish a consensus on a framework to connect the informal economy, innovation, and intellectual property to round up the measurement agenda for STISA as far as “moving the continent towards a knowledge-based economy” is concerned. As a matter of fact, the informal sector plays a major role in the national economies, as measured by the share of its contribution to GDP. This is estimated to be between 25 and 45 percent, and its contribution to employment ranges from 3 percent to 90 percent. In this context, both African Innovation Outlook series pointed to several areas that need further research, including the definition of comparable indicators for policy purposes, understanding how innovation takes place in the informal economy, and how knowledge is passed between generations, and the barriers, incentives, and linkages between the informal and formal sector dynamics.

Measurement Challenges for New Data Dynamics in Africa

New technologies can help African countries harness new sources of data and indicators by exploring knowledge transfer mechanisms between socioeconomic actors operating at the time of the Fourth Industrial Revolution (4IR).

The advent of the African Continental Free Trade Agreement (AfCFTA) would, in its optimal operational phase, transform the continent into a single market of a billion people with a combined GDP estimated to reach USD 2.5 trillion.⁴ The AfCFTA was signed in 2018 by the African countries. In this context, public policies, especially those related to the digital divide, trade regulations, and tariffs, ought to be amended or transformed to support free movement of capital and to sustain the single continental market for goods and services.

Other interesting advances to note concern the regional and continental dialogues that are taking place in the areas related to the 4IR. For instance, at their meeting in September 2017, ministers responsible for information and communication technology (ICT) of the Southern African Development Community (SADC) noted that their region is on the brink of a technological revolution that will fundamentally alter the way people live, work, and relate to one another, and, in this regard, they signed the Declaration on the Fourth Industrial Revolution to guide the development of regional programs and projects.⁵ The Declaration is a commitment to preparing SADC for the Fourth Industrial Revolution through the use of ICT. The Declaration also calls for harmonization of enabling digital policies and universal access to critical broadband infrastructure.

The continent has also been home to several innovations, including the digital payment system (M-PESA), made in Kenya – which is gradually boosting many services ranging from e-commerce to healthcare and transportation. Blockchain technology has been trialed in areas of micro-lending. The Africa blockchain conference, which was due to be held in March 2020, offers an opportunity for African researchers to explore how this technology might simplify and streamline systems and processes across various industries. The question that begs for an answer remains: How to measure? What are the appropriate and relevant metrics to measure knowledge transfer?

Measuring knowledge transfer in this new era will require defining new sources of data. As pointed out by Arundel and Es-Sadki, big data and web-scraping would be the technologies for continental institutions like AOSTI to explore and invest in by continental institutions like the African Observatory for Science, Technology, and Innovation (AOSTI).

Conclusion

The production of the African Innovation Outlook Series, including the bibliometrics series produced by AOSTI, shows the importance of better understanding the transfer and application of knowledge between firms, policymaking institutions, research institutions, and the public. Yet the African measurement community needs to invest in metrics related to knowledge activities, especially knowledge transfer. The chapter by Arundel and Es-Sadki is an important step that needs to be extended by collecting more African examples. It is also important to gain knowledge on big data, blockchain, artificial intelligence, and the Internet of Things through case studies across impact sectors in the African contexts highlightedhere.

Comment 12.2

Giancarlo Caratti¹

A consensus exists that the EU’s excellence in the realm of scientific research does not translate into a correspondingly high level of performance in terms of technological innovation. The perceived failure of European countries to turn scientific advances into marketable innovations is often termed the “European paradox.” The innovation landscape is undergoing profound changes due to the accelerating pace of technological development, the globalization of markets, and the shortening economic life of products and processes. Hence, support frameworks for innovation and knowledge transfer are foreseen to play a highly significant role in the forthcoming EU Multiannual Financial Framework (MFF).

Although some scholars doubt the validity of this paradox, claiming that it is also a question of lower scientific quality and weak industry (Dosi et al. 2006), it is undisputable that Europe is lagging behind in terms of exploitation of its research and this is also matter of culture, for example, risk aversion, inertia, and resistance to change in universities, limited financial availability connected to an incomplete internal market, and delays in enacting legislation encouraging the exploitation of R&D such as the Bayh-Dole Act of 1980 in the United States of America (U.S.).

Knowledge transfer offices (KTOs) play a strategic role in innovation in Europe. The adoption of standard metrics and standardized performance measurements is crucial to monitor and measure the KTOs’ annual activities, and to compare and combine their results so as to get a global view of the European situation.

Many KTOs have established specialized staff and services for assessing knowledge transfer in terms of disclosed inventions, patenting, research agreements, licensing and developing, and funding spinoffs and startups. The European Commission recognizes the need for comparable and consistent metrics across Europe regarding knowledge transfer (KT) activities in public research organizations.

A European Commission Expert Group on KT Metrics was established in 2008 in order “to identify indicators used in several existing recurrent surveys and nominate a small selection of these as core indicators, and agree on a harmonised set of definitions for them” (European Commission 2009).

Over time, the need for coherent KT metrics at the European level is still felt, and the results of the 2008 Expert Group need to be updated to take into account the specific evolving priorities, such as artificial intelligence, the Internet of Things, climate change, and the greater attention consumers are paying to the social and environmental impacts of industrial products.

In this context, a new Expert Group on Metrics for Knowledge Transfer was set up in 2019 by the European Commission’s Competence Centre on Technology Transfer (CC TT), in partnership with ASTP (pan-European association for professionals involved in knowledge transfer between universities and industry) and its network of National Associations Advisory Committee (NAAC), in order to review the past work toward a key set of harmonized KT indicators that would be accepted by most in Europe. Therefore, the input provided from the authors in this chapter is both timely and useful.

The CC TT is a new service of the Joint Research Centre, which was established in 2018, and its core mission is to provide expert services to European Commission Directorates-General, regional and local authorities, and relevant stakeholders in three key areas: knowledge transfer operational support, financial instrument conception and design, and support for innovation ecosystems and clusters.

The new expert group on metrics for knowledge transfer will take into account the indicators from the European Commission report “Metrics for Knowledge Transfer from Public Research Organisations in Europe” (European Commission 2009) recent literature, current transnational and national surveys, and interviews and recommendations from national KT associations, gathered in the ASTP NAAC.

The expert group will adopt a broad concept in which knowledge transfer incorporates all functions that can lead to improved use of knowledge developed and held in the research sector for the benefit of society and its individuals. The main objectives of this expert group are the implementation of a core set of harmonized indicators, including identified risk mitigation, and the setting up of recommendations on IT infrastructures able to manage pan-European KT metrics data (database, security). The deliverables will be published in 2020. The expert group gathers experienced practitioners of technology and knowledge transfer with experience in KT activities and output measurement at a regional, national, or transnational level.

Anthony Arundel and Nordine Es-Sadki’s chapter correctly points out that the indicators collected by KTOs are not capturing a significant part of knowledge transfer, which is transferred via tacit channels and, increasingly, via open science. Therefore putting emphasis only on codified knowledge may provide a distorted analysis of the ability of a research organization to transfer its knowledge. The authors propose additional indicators using specific surveys of academics or firms to complement the data from KTOs. In my view, this is an interesting proposal from a theoretical point of view but it is also very challenging to put into practice. Besides the high organizational costs of consulting a large number of academics and industries (and their survey fatigue), ASTP already finds it challenging to consult the existing KTOs in Europe, with the result that their statistical data have a skewed geographic coverage. One of the reasons is that the current set of indicators is probably too large, and many small KTOs cannot regularly monitor all of them. One of our recommendations to the experts undertaking the review of the KT metrics would therefore be to develop as simple as possible a system of indicators and to specify other potentially important factors, in most cases nonmeasurable, that contribute to the success of the knowledge transfer process. Some expert readers will be aware of “Goodhart’s Law”: “when a measure becomes a target, it ceases to be a good measure.”

Comment 12.3

Amit Shovon Ray

This chapter makes an important contribution to the literature on knowledge transfer from public research organizations by expanding its scope well beyond the conventional IP-driven channel. After the enactment of the Bayh-Dole Act of 1980 in the United States of America (U.S.), academic and policy attention centered on streamlining the “clumsy” IPR frameworks prevalent in public research organizations across the world. Enthused by the US legislation, many countries, both developed and emerging (France, Denmark, Japan, Brazil, China, and South Africa, among others), started enacting their own Bayh-Dole-type legislations from the late 1990s onward. There prevailed a sense of faith in such legislation as though it would act as a magic formula to energize public-funded research for knowledge transfer in different countries. However, the subsequent academic literature on the US post-Bayh-Dole experience suggests that the evidence in this regard is far from unambiguous (Ray and Saha 2011). This has not only raised questions about the effectiveness of IP as a vehicle of knowledge transfer from public research organizations but also redirected policy focus in many countries toward other (perhaps more) important channels of knowledge transfer, hitherto underemphasized.

The need to expand the scope of knowledge transfer from public research organizations to other formal and informal channels, like collaborations, contracts, consultancies, use of public research organization facilities and infrastructure, training, student placements, and so on, is now fairly well established in academic and policy circles, and many of these channels are now frequently used for knowledge transfer in both developed and emerging nations. However, there is still a lack of comprehensive information metrics for the non-IP channels, the United Kingdom being a noted exception in this regard, as highlighted by the authors in the chapter. The British Higher Education–Business and Community Interaction (HE-BCI) survey collects data on knowledge transfer activities of British universities through multiple channels. In fact, using these data, Sengupta and Ray (2017) showed that among the various channels of knowledge transfer in British universities, it is only the academic exchange channel (contracts and collaborations) that brings about a virtuous cycle. They showed that a large research base leads to greater knowledge transfer through this channel, some of which, in turn, further augments the research base, thus completing the virtuous cycle. Studies on other countries have relied primarily on sample surveys or case studies of knowledge transfer offices (KTO) and academic researchers (in some cases). One of the few studies for India on the subject is by Ray and Saha (2012) – a study commissioned by the Department of Science and Technology, Government of India. It highlighted the importance of non-IP channels for selected Indian public-funded institutions. Based on case studies of six public research organizations in India, Ray and Saha (2012) found that while success stories of effective knowledge transfer through the IP-driven channel in many of these institutions are limited in number, many of these Indian public research organizations do engage in knowledge transfers significantly and effectively through various non-IP channels.

Despite such compelling evidence on the importance of non-IP channels of knowledge transfer in different countries, there has unfortunately been very little attempt until now to construct comprehensive metrics of knowledge transfer activities, including the various facets of non-IP channels. The chapter by Arundel and Es-Sadki fills this very important gap in the knowledge transfer literature. Without such a comprehensive database, knowledge transfer activities can never be fully captured and understood for appropriate policy interventions. Informational bias toward the IP-mediated channels may lead to distortion of policy prioritization, resulting in suboptimal knowledge transfer through the other important channels. In drawing up these comprehensive metrics of knowledge transfer activities, the authors have correctly distinguished between three different methods of data collection from three distinct sources: (1) KTOs and the public research organization administration, (2) surveys of academics and researchers at public research organizations, and (3) firm-level surveys. The importance of combining all three sources stems from the fact that the conventional source, that is, the KTOs, may not have information on all channels of knowledge transfer, particularly the informal ones. Moreover, perceptions about the relative importance of different channels of knowledge transfer and their determinants and barriers may diverge widely among the three sets of stakeholders. The paper contains an elaborate and useful discussion of the types of data that can be collected through each of these modes of data collection and their limitations.

While reiterating the importance of collecting data through multiple modes to create comprehensive metrics, I would like to add a word of caution and a suggestion. First, if one ends up collecting information on the same variable from all three sources, there is a possibility of ending up with data discrepancies. For instance, the number of cases of knowledge transfer through licensing reported by the KTO may or may not exactly tally with the total number of these cases reported in the survey of academics. One must, therefore, have a well-designed strategy to tackle such data discrepancies. Second, academics are often survey fatigued, as they are regularly bombarded with questionnaires asking for the same factual information along with some questions on their perceptions. As a result, academics are often reluctant to respond to survey questionnaires. This is highly avoidable if the public research organization administration mandates that all researchers submit a comprehensive annual report of their academic activities undertaken in the preceding academic year, including an extensive set of information pertaining to their knowledge transfer activities through multiple channels. This could form a database of factual information that would be compiled by the public research organization and made available in the public domain. Such databases could be used for numerous purposes by multiple agencies and stakeholders. The public research organization will use this information to prepare its annual reports. Funding agencies (government and nongovernment) may use this information to assess the performance and accountability of public research organizations. National and international ranking agencies may use it for ranking and accreditation purposes. And, most importantly, this database would go a long way in constructing comprehensive metrics for knowledge transfer activities. The survey of academics could then be restricted to a much smaller set of questions only about their perceptions of the knowledge transfer policies and practices. A smaller questionnaire would allow a larger sample to be surveyed with little or no escalation of the survey costs, a challenge highlighted by the authors. Enlarging the sample size could potentially mitigate the sample selection bias of small sample surveys that tend to ignore academics and departments with little or no knowledge transfer experience.

The final section of the chapter highlights another very important aspect of knowledge transfer, namely, the metrics of costs and benefits. This is a complex issue as both costs and benefits include a large number of elements that cannot be adequately captured purely in financial terms. The authors present a detailed discussion of the systemic benefits and costs as well as the benefits and costs to the public research organizations and to the firms, focusing on both financial and nonfinancial elements. The authors do acknowledge that nonfinancial benefits and costs are more difficult to measure, especially in a format that is amenable to comparisons over time, both nationally and internationally. But, unfortunately, we fail to find much in the chapter by way of clear directions in this regard. Likewise, the authors also highlight the difficulties of identifying and estimating the systemic benefits and costs. But again, we do not find any concrete guidelines here to overcome this difficulty in order to come up with comparable measures of systemic costs and benefits. Of course, neither of these limitations has a simple solution. It could be a matter of another extensive research study just to explore possible solutions to the highlighted problems of measuring the costs and benefits of knowledge transfer. Nevertheless, one must acknowledge that the chapter makes a good beginning by flagging the issues and concerns pertaining to the creation of comparable metrics of benefits and costs.

Overall, the chapter makes a significant value addition to the scholarship by putting forward a concrete pathway for generating comprehensive metrics of knowledge transfer activities – facts, policies, and practices. If the framework proposed by the authors, along with the suggestions given here, were to be implemented judiciously, it could go a long way in providing incisive insights on various facets of knowledge transfer activities and their determinants and obstacles in different contexts, regions and time periods.