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 (Reference Ordanini, Parasuraman and RuberaOrdanini 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 (Reference Gibbons, Limoges, Nowotny, Schwartzman, Scott and TrowGibbons 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:

The “mode 2” model (Reference Gibbons, Limoges, Nowotny, Schwartzman, Scott and TrowGibbons 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” (Reference HederHeder 2017; Reference Munari, Sobrero and ToschiMunari 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. Reference Carayannis and CampbellCaryannis and Campbell (2009) and Reference Miller, McAdam and McAdamMiller 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, Reference KaplanKaplan (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” (Reference KahnKahn 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. Reference O’Shea, Chugh and AllenO’Shea et al. (2008) note that there are large differences among academic researchers in their interest in engaging with a variety of stakeholders, while Reference Arque-Castells, Cartaxo, Garcia-Quevdo and GodinhoArque-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 (Reference Hallam, Wurth and ManchaHallam 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. Reference Miller, McAdam and McAdamMiller 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 (Reference LundvallLundvall 1992; Reference Hallam, Wurth and ManchaHallam 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 (Reference GarengoGarengo 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” (Reference ChesbroughChesbrough 2003; Reference Miller, McAdam and McAdamMiller et al. 2016), where researchers from firms and public research organizations jointly develop inventions, often through collaborative research projects.

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.

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.

10.4 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).

10.5 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.

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

11.3 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 (Reference Lee, Chaisung and SongLee et al. 2005; Reference Stiglitz and LinLee 2013), whereas interactions between universities and industry have been low, although this has been changing since the 1990s.

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; Reference Thursby, Jensen and ThursbyThursby 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 (Reference Lissoni, Llerena, McKelvey and SanditovLissoni et al. 2008; Reference Geuna and RossiGeuna 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 (Reference Athreye, Breshanan and GambardellaAthreye 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 (Reference Mowery and SampatMowery 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).Footnote ¹ There are signs of a decline in university patenting in recent years as universities are becoming more selective in which patents they pursue (Reference Tang, Weckowska, Campos and HobdayTang 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.Footnote 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).

11.4 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.

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.Footnote 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 (Reference Finne, Arundel, Balling, Brisson and ErseliusFinne 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).

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 (Reference Lockett, Wright and WildLockett 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 (Reference Walshok and ShapiroWalshok and Shapiro 2014). Reference Bekkers and Bodas-FreitasBekkers 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 (Reference Bekkers and Bodas-FreitasBekkers 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 (Reference Rosli and RossiRosli and Rossi 2014; Reference Czarnitzki, Doherr, Hussinger, Schliessler and TooleCzarnitzki et al. 2016; Reference VeugelersVeugelers 2016). The combination of informal and formal channels has been found to have a positive effect on innovation outcomes (Reference Link, Siegel and BozemanLink et al. 2007; Reference Siegel, Waldman and LinkSiegel et al. 2003; Reference Grimpe and HussingerGrimpe and Hussinger 2013) and could be especially important to the performance of spinoffs (Reference HayerHayer 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 (Reference Holi, Wickramasinghe and van LeeuwenHoli et al. 2008; Reference Jensen, Palangkaraya and WebsterJensen et al. 2009; Reference Lockett, Wright and WildLockett 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 (Reference Bekkers and Bodas-FreitasBekkers et al. 2008; Reference Belitski, Aginskaja and MarozauBelitski 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 (Reference Barjak, Es-Sadki and ArundelBarjak et al. 2015). Furthermore, KTO staff can be unaware of important knowledge transfer activities via publications or through informal channels. Reference Freitas, Geuna and RossiFreitas 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. Reference Siegel, Waldman and LinkSiegel 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 (Reference Jensen, Palangkaraya and WebsterJensen 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 (Reference Sigurdson, Sa and KretzSigurdson 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.Footnote 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.Footnote 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 (Reference Holi, Wickramasinghe and van LeeuwenHoli et al. 2008; Reference Rossi and RosliRossi 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.Footnote 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.Footnote 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 (Reference Jensen, Palangkaraya and WebsterJensen 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.

12.3 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 (Reference Perkman, Tartari and McKelveyPerkman 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.

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 (Reference Abreu and GrenevichAbreu and Grenevich 2013; Reference Abreu, Demirel, Grenevich and Karatas-OzkanAbreu et al. 2016; Reference Zhang, MacKenzie, Jones-Evans and HugginsZhang et al. 2016).Footnote 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) (Reference Hughes and KitsonHughes 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 (Reference Zhang, MacKenzie, Jones-Evans and HugginsZhang et al. 2016).

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 (Reference Perkman, Tartari and McKelveyPerkmann et al. 2013), or by selecting the departmental heads for technology disciplines, principal investigators on research projects, the heads of research groups (Reference Van Dierdonck, Debackere and EngelenVan Dierdonck et al. 1990), academics who have been granted a patent, or academics who have founded a firm (Reference Agiar-Díaz, Díaz-Díaz, Ballesteros-Rodríguez and De Sáa-PérezAgiar-Díaz et al. 2016; Reference Czarnitzki, Doherr, Hussinger, Schliessler and TooleCzarnitzki 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 (Reference Van Looy, Ranga, Callaert, Debackere and ZimmermannVan Looy et al. 2004; Reference Tartari and BreschiTartari and Breschi 2012; Reference Muscio and PozzaliMuscio 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 (Reference Muscio and PozzaliMuscio 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 (Reference Siegel, Waldman and LinkSiegel et al. 2003; Reference De Fuentes and DutrénitDe Fuentes and Dutrénit 2012; Reference Freitas, Geuna and RossiFrietas et al. 2013; Reference Grimpe and HussingerGrimpe and Hussinger 2013; Reference Okamuro and NishimuraOkamuro and Nishimura 2013; Reference Dutrénit, Arza, Albuquerque, Suzigan, Kruss and LeeDutrénit and Arza 2015; Reference Kafouros, Wang, Piperopoulis and ZhangKafouros et al. 2015; Reference Kruss, Adeoti, Nabudere, Albuquerque, Suzigan, Kruss and LeeKruss et al. 2015; Reference Schiller, Lee, Albuquerque, Suzigan, Kruss and LeeSchiller 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 (Reference Albuquerque, Suzigan, Kruss and LeeAlbuquerque et al. 2015). In two low-income countries (Uganda and Nigeria) informal methods dominate (Reference Kruss, Adeoti, Nabudere, Albuquerque, Suzigan, Kruss and LeeKruss et al. 2015), whereas in middle-income countries in Asia (Malaysia, Thailand, and China) the most common methods are consultancy and research contracts (Reference Schiller, Lee, Albuquerque, Suzigan, Kruss and LeeSchiller 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 (Reference Dutrénit, Arza, Albuquerque, Suzigan, Kruss and LeeDutrénit 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 (Reference Bekkers and Bodas-FreitasBekkers 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) (Reference Perkman, Neely and WalshPerkman et al. 2011), improvements in innovative capabilities (Reference De Fuentes and DutrénitDe 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 (Reference Geuna and RossiGeuna 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 (Reference Van Looy, Landoni, Callaert, Van Pottelsberghe, Sapsalis and DebackereVan Looy et al. 2011; Reference Berbegal-Mirabent and SabateBerbegal-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. Reference WoltmannWoltmann (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 (Reference FelsensteinFelsenstein 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 (Reference Salter and MartinSalter 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 (Reference VerspagenVerspagen 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 (Reference Arundel and KablaArundel 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 (Reference Roessner, Bond, Okubo and PlantingRoessner et al. 2013).

Collaboration between government, academia, and industry is considered to be of critical importance in enhancing regional economic and social development (Reference Etzkowitz and LeydesdorffEtzkowitz and Leydesdorff 2000; Reference Klofsten, Heydebreck and Jones-EvansKlofsten et al. 2010; Reference Urbano and GuerreroUrbano 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 (Reference Asheim and CoenenAsheim and Coenen 2005; Reference McAdam, Miller, McAdam and TeagueMcAdam 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 (Reference WilsonWilson 2012). The inclusion of social innovators (Reference LeydesdorffLeydesdorff 2011) reflects the increasing importance placed on knowledge transfer to meet societal needs (Reference Bozeman, Rimes and YoutieBozeman 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 (Reference MazzucatoMazzucato 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 (Reference VincettVincett 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 (Reference Roessner, Bond, Okubo and PlantingRoessner 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.Footnote 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 (Reference Kearnes and WienrothKearnes 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.

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 (Reference Chux, Mawoko and KonteChux 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)Footnote 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.Footnote 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.Footnote 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.