University technology transfer performance of Dutch Universities : improving the way university technology transfer performance is measured

University Technology Transfer Performance of

Dutch Universities

Improving the way university technology transfer performance is measured

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Claire A. E. Stam – 10858733 // 23 June 2015 – final version // MSc. Business Administration – Entrepreneurship and Innovation // Supervisor: Dr. G. T. Vinig // Second reader: drs. A.C.C. Gruijters

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Statement of originality

This document is written by Student Claire Stam, who declares to take full

responsibility for the contents of this document.

I declare that the text and the work presented in this document is original

and that no sources other than those mentioned in the text and its references

have been used in creating it.

The Faculty of Economics and Business is responsible solely for the

supervision of completion of the work, not for the contents.

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University Technology Transfer Performance of

Dutch Universities

By Claire Stam

Abstract

This study extends the way university technology transfer potential is measured in order to estimate the actual performance. The potential for their performance is assessed based on three factors: research output, amount of researchers and research budget. Assumed is that the university has potential for an innovation for every (1) 1% of scientific publications, (2) 4.65% of researchers, and (3) 3.158.400 Euros. The performance of the technology transfer offices of the Dutch universities were assessed. The annual reports of the universities and reports of independent institutions were evaluated. Where necessary, missing data was obtained by additional inquiries via phone or email. Due to the lack of structure of most TTOs in keeping track of their data, only 43 of the 65 datasets could be completed. The potential performance of the universities was compared with their actual performance. This analysis showed that most of the universities are not realizing their potential performance, and score lower than expected based on the output, researchers, and budget. These results were compared with the Elsevier/ScienceWorks valorization ranking, which validate the findings. Both designate Delft University of Technology, University of Twente, and Eindhoven University of Technology as the most entrepreneurial universities. This resulted in an accurate measurement of the university’s potential and therefore the performance of the universities. The study found that the potential based research budget was the most pressing factor (38%) followed by research output (35%).

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Table of Contents

2.2 The Entrepreneurial University ... 10

2.3 Technology Transfer Offices ... 11

2.4 Technology Transfer Performance ... 12

2.4.1 Technology Transfer Output ... 13

2.4.2 Performance Measures ... 13 2.5 Dutch Universities ... 15 2.5.1 Economic Impact ... 16 2.5.2 Financing ... 16 2.6 Former Research ... 17 3. METHODOLOGY ... 18 3.1 Sample... 18 3.2 Variables ... 19 3.3 Valorization Score ... 20 3.4 Data Collection ... 20 4. RESULTS ... 21 4.1 Results Variables ... 21

4.2 Independent Variables Compared ... 22

4.2.1 Elsevier/ScienceWorks Valorization Ranking ... 24

4.3 Valorization Scores ... 24

4.3.1 Correlation ... 28

4.3.2 Distribution ... 29

5. DISCUSSION ... 31

5.1 Research Performance... 33

5.2 Limitations And Further Research ... 34

6. CONCLUSIONS ... 35

LITERATURE ... 36

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LIST OF TABLES

Table 1. Elsevier/ScienceWorks Ranking Table 2. Average publications per 1M Euros Table 3. Average Euros per publication Table 4. Average output per fte Table 5. Average fte per output Table 6. Average Euros per fte

Table 7. Average fte per 1M Euros budget

Table 8 Elsevier/ScienceWorks – Valorisation Ranking 2013 Table 9. Top 5 – Average Euros per publication

Table 10. Valorization scores – research output (1%)

Table 11. Valorization scores – researchers (9,3% and 4,65% of researchers) Table 12. Valorization scores – research budget (3.158.400 Euros)

Table 13. Valorization scores total

Table 14. Differences in Valorization Scores over the years Table 15. Correlations valorization scores

Table 16. Average percentage valorization scores output, researchers and budget of total Table 17. Valorization scores recalculated

Table 18. Mean valorization scores based on output, researchers and budget Table 19. Research output per 100,000 Euros

Table 20. Research output per fte

LIST OF FIGURES

Figure 1. How technology is transferred from a university to a firm or entrepreneur Figure 2. Gross income Dutch universities, 2013

Figure 3. Graph of correlations

Figure 4. Pie chart of distribution valorization scores Figure 5. Conceptual model

LIST OF APPENDICES

Appendix 1. Total research expenditure, number of licenses and options, and total license income. Appendix 2. Most enterprising universities 2013

Appendix 3. Top-10 Elsevier/ScienceWorks Valorization ranking 2011 Appendix 4. Conceptual model

Appendix 5. Onderzoeksinzet per jaar, in fte (VSNU) Appendix 6. Work and time schedule

Appendix 7. Results: Research output Appendix 8. Results: researchers in fte Appendix 9. Results: research budget

Appendix 10. Results: Commercial output: patents, licenses and spin-offs Appendix 11. Results: Commercial output total

Appendix 12. Results: Output per 1M Euros Appendix 13. Results: Euros per output Appendix 14. Results: Output per researcher Appendix 15. Results: Researchers per output Appendix 16. Results: Euros per researcher Appendix 17. Results: Researchers per 1M Euros

Appendix 18. Expected number of valorization attempts – research output Appendix 19. Expected number of valorization attempts – researchers Appendix 20. Expected number of valorization attempts – research budget

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1. INTRODUCTION

1.1

Technology Transfer

The economies of the Western world are becoming increasingly dependent on knowledge and its producers (Powers, 2003; Vinig & Lips, 2015). Most knowledge is produced by universities and research centers. That is why these knowledge producers are nowadays getting more and more attention in the Western economies (Vinig & Rijsbergen, 2010). The knowledge they produce has to be transferred from the university to the society in order for the economy to benefit from this knowledge. When the university successfully commercializes this knowledge, we speak of a knowledge valorization process, known as technology transfer (Vinig & Lips, 2015; Vinig & Rijsbergen, 2009). Technology transfer is usually considered within or across firms (Siegel, Waldman, Atwater, & Link, 2004). But also a lot of knowledge is originated in universities. University technology transfer can be defined as “the transfer of the results of research from universities to the commercial sector” (Vinig & Lips, 2015).

Technologies that have been transferred from university research to industry in the past, have resulted in some of the most innovative companies of the past decades (for example, Genentech INC and Plastic Logic). Even Google stems from university patents. Therefore, technology transfer is recognized as a very valuable process. It generates revenue for the universities and creates wealth and new technologies for society (Carlsson & Fridh, 2002). It improves local economic development and generally enhances the quality of life (Shane, 2004; Vinig & Lips, 2015). University spin-offs are becoming a significant global phenomenon (Shane, 2004). They are important firms because of their economically powerful subset of high technology start-ups (Shane, 2004). In the Netherlands, few studies have been performed that assess the output of Dutch university valorization activities (Vinig & Lips, 2015).

Right now, Dutch universities have poor performance of technology transfer, although they do create a lot of knowledge. For a long time, universities have operated disconnected from practice and society. In 1980, first entrepreneurial activities were stimulated. Western universities moved from traditional universities to more entrepreneurial universities (Vinig & Rijsbergen, 2009). With this shift, they are becoming network organizations. Since there is a rapid increase in university technology transfer, more academic literature and research is dedicated to this subject. Now, in addition to the traditional goals of education and research, most of the universities in the Western world have incorporated technology transfer in the university objectives (Rasmussen, Moen & Gulbrandsen, 2006). Some universities are more successful in commercializing knowledge than others. In their study, Vinig and Rijsbergen (2009) analyzed the effect of the resources and capabilities possessed by universities and their technology transfer offices (TTOs) on the commercialization output. They see the firm as a collection of capabilities and resources (resource-based view). Since every firm has different resources, different outputs are expected.

7 Since only a small amount of research regarding this subject has been conducted, knowledge about valorization in Dutch universities is still scarce. But recently, this matter has been getting more attention. In recent years, the vast majority of the universities have dedicated a special office that assists and stimulates technology transfer: the technology transfer office (TTO). Little is known about the actual successfulness of TTOs in terms of their performance or output (Lips, 2013). The majority of the technology transfer literature is based on TTOs and universities in the United States. That does not necessarily mean that the reported successes of a number of these TTOs can also be translated to the Dutch TTOs (Lips, 2013). In order to account for this gap in knowledge regarding the performance and output of Dutch TTOs, this research investigates the technology transfer output of the Dutch universities and their TTOs.

1.2

Problem Statement

Currently, technology transfer process’ performance is measured by monetary income generated by the university (Vinig & Lips, 2015). But assessing the income from university technology transfer does not measure the real performance, nor is it based on the potential for technology transfer based on university research (Vinig & Lips, 2015). It is possible that the university has a high dollar income from technology transfer, yet its performance is considered low or mediocre, because the monetary income potential is higher then what is represented by the (high) dollar income. Vinig and Lips (2015) present a novel approach to estimate the actual performance of university technology transfer. Their study provides a comprehensive measure of university technology transfer performance based on the potential of technology transfer measured in terms of research output. In this thesis, I would like to extend their study and extend the way we measure technology transfer by adding two parameters: (1) number of researchers (measured in fte) and (2) university research budget. With this extended model, I will measure the performance of TTOs of Dutch universities. The research question I would like to address is the following:

In what extent can (1) research output, (2) number of researchers (fte) and (3) research budget refine the measure for technology transfer potential and provide more accurate measurement of university’s potential and therefore of the university’s performance?

The objective is to empirically assess the performance of the Dutch university’s technology transfer by extending the existing way we measure technology transfer with two parameters: (1) number of researcher and (2) research budgets. So the potential for technology transfer will be considered, based on three parameters: research output, number of researchers and research budget. Assumed is that these are indicators for university’s potential for technology transfer and they provide a good proxy of the potential of valorization.

8 In the next section, I will elaborate on and present the most important literature and studies done in this field. The concept technology transfer, the entrepreneurial university, TTOs and valorization performance will be discussed. In section three, I will explain the methodology of the study, how the research is conducted and the planning of the project. Section four gives an overview of the findings and results of this study. Finally, I will present my results and draw a conclusion from my findings.

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2. LITERATURE REVIEW

2.1

Technology Transfer

Many different definitions for technology transfer exist. The exact definition depends on the discipline (Bozeman, 2000). Technology transfer is commonly defined as the dissemination of university research results to the commercial sector (Vinig & Lips, 2015). The technology transfer process begins with a scientific discovery of university research (Siegel, Waldman, & Link, 2003). Knowledge generated by these results can be a source of entrepreneurial opportunities and (technological) innovations, when disseminated to firms and entrepreneurs (Friedman & Silberman, 2003).

There is an increasing attention to and importance of university technology transfer. Why is technology transfer so important? The university’s purpose in the technology transfer process is to assist in disseminating research results for the public good (Carlsson & Fridh, 2002). The main reason for universities to engage in technology transfer is the amount of money that can be generated with disseminating their research results, for example licensing and royalty income. Technology transfer can create a lot of revenue for the university (Carlsson & Fridh, 2002; Chapple, Lockett, Siegel, & Wright, 2005; Etzkowitz, Webster, Gebhardt, & Terra, 2000; Friedman, & Silberman, 2003; Link, Siegel, & Bozeman, 2007; Markman, Phan, Balkin, & Gianiodis, 2005). In addition, it can increase training and employment opportunities for students (Lee, 1996). It also helps building relations with external stakeholders and enhances economic growth and development in the local region (Chapple et al., 2005; Etzkowitz et al., 2000; Feldman, Feller, Bercovitz, & Burton, 2002; Lee, 1996; Link et al., 2007). And lastly, university technology transfer also has a positive influence on the curriculum of the university, enhances university prestige, and can function as a marketing tool for students, faculty, and other research support (Feldman et al., 2002; Friedman, & Silberman, 2003). Furthermore, and perhaps most importantly, university technology transfer can be important for the community, since it creates benefits for society as a whole. Studies show that a great amount of money and jobs can be attributed to the results of university licenses (AUTM in Carlsson & Fridh, 2002; BiGGAR Economics, 2011; BiGGAR Economics, 2014a; BiGGAR Economics, 2014b). Results of university research can create wealth, new jobs and solutions to problems in society (Carlsson & Fridh, 2002). Therefore, commercializing research results of universities is beneficial for the society.

Lately, there has been a shift from a traditional research university towards a more entrepreneurial oriented university. The core activities of traditional universities are teaching and engaging in research. Now, universities are increasingly viewed as a source of commercial knowledge (Etzkowitz et al., 2000; Guerrero, Cunningham, & Urbano, 2014; O'Shea, Allen, Chevalier, & Roche, 2005). In addition to teaching and researching, universities are now playing an important role in society by converting new scientific discoveries into entrepreneurial opportunities (O’Shea et al., 2005). This results in an increasing amount of entrepreneurial oriented universities.

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The Entrepreneurial University

The entrepreneurial university extends the traditional research university (Etzkowitz, 2003a). In addition to research and teaching, the entrepreneurial university engages in regional economic development through creation of high-tech spin-off firms (Etzkowitz, 2003b). It takes a proactive approach in disseminating university research results to the public (Etzkowitz, 2013). When an university becomes entrepreneurial, the university itself – as an organization – and their members (employees and students) become entrepreneurial, and contribute to economic development (Etzkowitz, 2013; Röpke, 1998). Etzkowitz et al., (2000) argue in their paper that both the internal development of the university and the external influences on the academic structure, influence the shift from traditional to entrepreneurial. In the entrepreneurial university, the university is transformed into an entrepreneurial source of intellectual property, with the focus on patentable research and commercialization (Garnsey, 2007). Creating and sharing of intellectual property has become main asset for universities (Wright, Birley, & Mosey, 2004). This means that the traditional division between science and industry is slowly breaking down (Garnsey, 2007).

Etzkowitz (2013) analyzes in his paper the evolution and development of the traditional university to an entrepreneurial one. He describes three stages, with each phase building upon the other. In the first phase, the university views its strategic direction and sets its own priorities. They raise their own resources, for example through donations, tuition fees or negotiation with resource providers (Etzkowitz, 2013). In the second phase, the university takes an active role in commercializing university research results (intellectual property). In this phase, the university establishes its own technology transfer capabilities, in-sourcing them from firms to which they may have been contracted (Etzkowitz, 2013). In the third phase, the university takes a more proactive role in improving efficiency and finding solutions for its environment (Etzkowitz, 2013). At this point, they seek opportunities to collaborate with government and industry actors.

Etzkowitz (2013) formulates four propositions or characteristics that shape the ‘entrepreneurial university model’: (1) the close interaction with industry and government, the university is not an ivory-tower university isolated from the rest of society, (2) the independence of the entrepreneurial university, it is not dependent of another institutional sphere, (3) the hybridization, or resolving the tension between interaction and independence leads to a hybrid organizational format to realize both objectives simultaneously, and (4) the reciprocity and ongoing renovation of the internal structure of the entrepreneurial university to changes in (relationship with) industry and government (Etzkowitz, 2013).

Becoming entrepreneurial also entails several challenges. Brinckmann (2006) discusses in his paper the challenges of becoming an entrepreneurial university. He mentions first, that there is a small range of institutional autonomy. Secondly, the ties to the government are very close and this results in a high degree of regulation by the state. Thirdly, the university has academics with little or no entrepreneurial experience. Lastly, there are often few and limited financial opportunities and

11 therefore limited research facilities for the university (Brinckmann, 2006). At the same time the university has to take numerous actions, including finding a balance between independence from state and university, as well as interaction between the two, which Etzkowitz (2013) points out as well.

Universities face a challenging process when trying to become (more) entrepreneurial. Some believe that universities should confine themselves to traditional academic-industrial relationships, such as consultation. These include companies that are concerned about new firms emerging as competitors, they believe the most appropriate role for an university is to publicize their research results (Etzkowitz et al., 2000). However, many scholars and studies emphasize the importance of an entrepreneurial university (Carlsson & Fridh, 2002; Chapple et al., 2005; Etzkowitz et al., 2000; Friedman, & Silberman, 2003; Guerrero et al., 2014; Link et al., 2007; Markman et al., 2005).

2.3

Technology Transfer Offices

As stated before, when the results of research from universities are (successfully) transferred to the commercial sector, we can speak of technology transfer (Vinig & Lips, 2015). Universities have a special office that assists and stimulates this transfer: a technology transfer office (TTO). Universities have established these TTOs to foster interaction with industry and commercialization of university research results (Friedman, & Silberman, 2003). The main purpose of the TTOs is for the university to assist its researchers in commercializing research results (Carlsson & Fridh, 2002). According to Boh, De-Haan, and Strom (2012) TTOs are key institutional mechanisms that influence technology transfer. They provide the necessary resources for commercializing university technologies and they facilitate the process of commercial knowledge transfer from university to industry (Boh et al., 2012; Siegel et al., 2004). They do this through licensing of the universities’ inventions or other forms of intellectual property resulting from research. TTOs are primarily responsible for the protection of inventions, and the management and commercialization of intellectual property (Vinig & Rijsbergen, 2009). The most common commercialization strategy used by TTOs is licensing. A license is an official agreement in which the legal rights to utilize an invention for commercial purposes are sold by the licensor (university) to a licensee (established company) in return for revenues (Vinig & Lips, 2015).

Source: Siegel et al. (2004)

12 In the technology transfer process, three stakeholders are at play: (1) the university scientists, (2) the TTOs, and (3) the firms or entrepreneurs. Figure 1 shows a schematic overview of the transfer from universities to firms or entrepreneurs (Siegel et al., 2004). The process begins with a ‘scientific discovery’ by university research. It is then required by law to file an invention disclosure with the TTO. The university will then decide whether they want to patent the innovation (which is a way to protect the intellectual property). A sufficient justification for filing a patent is when others are interested in the technology. If this is not the case, the TTO itself has to determine whether the technology has commercialization potential (Siegel et al., 2004). Once the patent has been awarded, the TTO can presumably market the technology. In the next stage, the university works with the firms or entrepreneurs to negotiate a licensing agreement. This agreement could include benefits to the university, for example royalties, or an equity stake in a startup. In the last stage, the technology is converted into a commercialized product. Here, the university can continue its involvement with the firm. Or, in the case of startups, faculty members may serve as technical advisors or xxx on boards of directors, and may also have an equity stake in the startup (Siegel et al., 2004). The TTO plays a significant role in this process, since it is the bridge linking the university scientist (the supplier) and the firm or entrepreneur (the consumer).

Universities give more and more attention to their commercialization process. Thursby, Jensen, and Thursby (2001) found in their research that 35% of the studied TTOs of major U.S. universities have been reorganized since 1990. In the past, universities passively licensed their technologies, while today, universities are actively seeking for ways to channel proprietary technology to maximize rents and to spawn new companies (Markman et al, 2005).

Almost every university carries out technology transfer activities. One would expect an equal distribution of successful commercialization among universities. However, this is not the case. The distribution of successful commercialization activities is highly skewed among universities (Vinig & Rijsbergen, 2009). This means that some universities are more successful in commercializing knowledge than others (Vinig & Rijsbergen, 2009). Rasmussen et al. (2006) say that a considerable amount of universities that have established TTOs did not succeed in generating significant amounts of revenues. Rasmussen et al., (2006) argue that only universities with the highest academic track records have received multi-million dollar revenue streams as part of their technology transfer activities. In the Netherlands, so far there is still no multimillion dollar corporation that started out as a university research spin off.

2.4

Technology Transfer Performance

Universities often contribute to economic development through commercialization of research. However, this rarely becomes visible in the form of direct monetary revenues (Rasmussen et al., 2006; Vinig & Lips, 2015; Vinig & Rijsbergen, 2009). The challenge for the university is to measure and to make the extent and results of their activities visible. Lack of agreement of conceptualization of

13 performance of technology transfer makes it hard to study this concept (Rogers, Yin & Hoffman, 2000). Rogers et al., (2000, p. 58) define technology transfer effectiveness as “the degree to which research-based information is moved successfully from one individual or organization to another.”

In this section, first the technology transfer output measures will be described. Second, research done on factors that explain differences in technology transfer performance and thus, provide a measurement of the university’s potential technology transfer performance, will be elaborated.

2.4.1 Technology Transfer Output

Extensive research has already been conducted on the measures of performance and output of university technology transfer, of which several studies focused on the (tangible) output of technology transfer. Licenses and royalties are the most commonly accepted output (Chapple et al., 2005; Friedman & Silberman, 2003; Siegel et al., 2004). As explained before, a license is when a company sells the rights to use a university’s invention in return for revenue in the form of upfront fees at the time of closing the deal, and annual, ongoing royalty payments that are contingent upon the commercial success of the technology in a downstream market (Feldman et al., 2002). For universities, licensing is an important activity, since it can generate a lot of revenue (Siegel et al., 2004). Bremer (1998) argues that this is not a perfect measure. He believes it is about the amount of technology represented in and by the patents of the university. Siegel et al., (2004) state that licensing is by far the most critical output. In their study, the amount of licenses and income generated by these licenses was considered as technology transfer output. Friedman and Silberman (2003) support this approach. Carlsson and Fridh (2002) add start-ups and patents to licenses. They employ in their research the patents, licenses and start-ups of new companies of the universities to measure the performance of technology transfer. They argue that a full evaluation of the output of technology transfer is a complex matter, but they argue that those three factors are (good) indicators of technology transfer performance. Also Anderson, Daim, and Lavoie (2007) find the following three factors tangible outputs of university research: patents, licenses and spin-offs.

Rogers et al., (2000) address in their research the question whether it is possible to develop a measure of technology transfer effectiveness for U.S. universities. They found six indicators of effectiveness of technology transfer: (1) invention disclosures received, (2) total U.S. patent applications filed, (3) licenses and options executed, (4) licenses/options yielding income, (5) number of start-ups, and (6) gross license income received.

2.4.2 Performance Measures

From the above can be derived that spin-offs, patents and licenses are widely adopted as the technology transfer performance output. The question is which factors or measures create a high performance (lots of spinoffs, patents and licenses), and thus are responsible for the difference in technology transfer performance between universities. A lot of research is conducted in this field, focusing on finding the main drivers for technology transfer performance. Carlsson and Fridh (2002)

14 describe the technology transfer process as an input-output model. Inputs are for example, research expenditures and the size of the TTO. The outputs are the results of the technology transfer. They examined twelve different universities in the United States. They found that the U.S. universities that spend the most money on research, were the ones with the largest number of active licenses and patents and the highest royalty income. Appendix 1 shows the research expenditure and the cumulative active licenses for the different universities (ranked in order of total research budget). This means that a correlation exists between research budget and the output licenses (Carlsson & Fridh, 2002). With a regression analysis they show that the budget explains 35 percent of the variation in the number of licenses and options (statistically significant). Carlsson and Fridh (2002) also studied at the correlation between the input variables (research expenditures, number of invention disclosures, number of employees and number of years the TTO exists) and the output variables (the commercializing results in the form of patents, licenses and start-ups of new companies). They found strong correlation links between all the variables. Several studies support the view that the size of the technology transfer office is positively correlated with patenting, licensing and start-up activity (O’Shea et al., 2005; Thursby & Kemp, 2002; Vinig & Rijsbergen, 2009). O’Shea et al (2005) found that the greater the size of the TTO office is, the greater the probability of a university spin-off. Thursby and Kemp (2002) found that the size of the TTO office is important for commercial activity as well. Also the positive correlation of the age of the TTO (years of experience of the TTO) and technology transfer output is confirmed by several studies (O’Shea et al., 2005; Vinig & Rijsbergen, 2009).

O'Shea et al., (2005) conducted research on predictors of (just) university spin-off activity. They found five factors that have a positive effect: (1) previous success in technology transfer; (2) high faculty quality; (3) a strong science and engineering funding base with an orientation in life science, chemistry and computer science disciplines; (4) a relatively high percentage of industry funding; and (5) a strong commercial resource base. An increase in one of these variables will increase the number of spinoff companies generated by a university (O'Shea et al., 2005).

The analysis of Rogers et al., (2000) points out that universities with (1) higher average faculty salaries, (2) larger number of staff for technology licensing, (3) higher value of private gifts, grants, and contracts, and (4) larger R&D expenditures from industry and federal sources, led to higher effectiveness on the aforementioned six-item measure they developed.

Vinig and Rijsbergen (2009) did an extensive research on different factors associated with the previously mentioned technology transfer output measures. They found that (1) the size of the TTO (the number of commercial resources at the university, measured in fte) is positively associated with patenting and spin-off activity, especially in the US. Vinig and Rijsbergen (2009) attribute this outcome to the fact that technology transfer was at that time a new phenomenon in Europe and Australia. (2) Higher TTO experience (measured in age of the TTO) results in a higher patenting activity. (3) A greater research output (stock of technology in a university) leads to higher patenting

15 and spin-off activity. (4) The presence of a business incubator and/or science park is positively associated with increased spin-off activity (although this result contradicts O’Shea et al’s (2005) findings). Lastly, (5) being a private university is associated with higher levels of patenting in contrast to public universities (Vinig and Rijsbergen, 2009). Vinig and Rijsbergen (2009) also found differences in the independent variables that are significantly correlated with commercialization output, between Europe, the US and Australia. They suggest to do further research distinguish different geographical regions.

2.5

Dutch Universities

Dutch universities are increasingly engaging in valorization activities – translating scientific knowledge into social and economic benefits – as well. VSNU (Association of Dutch universities) states that universities give increasing attention to entrepreneurial activities by offering entrepreneurship education (minors and majors). Universities build alliances and offices to support these valorization activities.

ScienceWorks is a research firm in the Netherlands. In collaboration with Elsevier, they conduct a

research every two years on which universities are the ‘most enterprising universities in the Netherlands’. This research comprises a ranking that classifies research universities on a number of metric related to valorization (Elsevier, 2013).Three roles are central in the entrepreneurial university: entrepreneurship, collaboration and communication (Elsevier, 2013). Table 1 below, and appendix 2 show the most enterprising universities of 2013. Appendix 3 shows the top 10 of the Dutch universities of 2011. Elsevier and ScienceWorks aim with this valorization ranking to find out how universities deploy their scientific and technological knowledge for social utility (Elsevier, 2013). According to the Elsevier/ScienceWorks ranking, in 2013, the university of Twente (Universiteit Twente) is assessed as the “most entrepreneurial university” of the Netherlands. They score best on stimulating and facilitating entrepreneurship among their own staff and students.

This ranking is widely accepted and considered highly credible. Therefore, the data obtained in this study will be compared and contrasted with the Elsevier/ScienceWorks ranking. It will function as the main focus and serve as a reference for the results and findings in this study.

Table 1. Elsevier/ScienceWorks Ranking

FY 2013 University

1 University of Twente

2 Delft University of Technology

3 Eindhoven University of Technology

4 Wageningen University

16 2.5.1 Economic Impact

Research bureau BiGGAR Economics assessed different Dutch universities to examine their economic impact. They examined the valorization and commercialization activities of the Dutch University Medical Centers (BiGGAR Economics, 2014a). This research studied technology licensing, spin-outs and start-ups, collaborative research, workforce training, social returns to medical research and the social impacts of staff time as valorization activity. The combined impact of these activities is worth €4,7 billion in gross value added (GVA) and is responsible for 30.436 jobs (in fte) across the country (BiGGAR Economics, 2014a). BiGGAR Economics (2014b; 2011) also studied the commercialization and knowledge transfer activity supported by the University of Groningen and its Medical Centre (UMCG). They have a combined impact which is worth €191,5 million in GVA and responsible for 1.800 jobs (in fte) in Groningen, €245,7 million and 2.100 jobs in The Northern Provinces and €1,1 billion in GVA and 9.100 jobs across the Netherlands (BiGGAR Economics, 2014b). They also looked at the economic impact associated with the outputs of research activity (including benefits from licensing, impact of spin-out and start-up companies) of Leiden University and the Leiden University Medical Centre (LUMC) with an estimated impact around €120 million GVA to the Dutch economy in 2010 and to have supported around 1.700 jobs.

2.5.2 Financing

Besides student fees, universities receive grants from different parties. The income of the universities can be divided in three different flows of funds (Rathenau; Schoutens, 2004; VSNU):

1. First flow of funds: Government grant

This funding comes from the Ministry of Education, Culture and Science. Dutch universities receive a financial contribution so they can carry out their statutory duties in the field of education, research and knowledge transfer (VSNU). This grant is divided in two parts, one for educational purposes and one for research purposes. The amount of the grant is determined by the government and the distribution is based on legislation. Since this grant is in the form of a lump sum, the universities can make their own division about how they distribute the money.

2. Second flow of funds: Grants of other organizations (NOW and KNAW)

This flow of funds encompasses grant from the Dutch organization for scientific research (NOW) and the Royal Netherlands Academy of Arts and Sciences (KNAW). This research funding is distributed among the universities on basis of competition.

3. Third flow of funds: Other revenues

17 Figure 2 below, shows the different revenue sources and its distribution of Dutch universities.

Source: VSNU

Figure 2: Gross income Dutch universities, 2013 2.6

Former Research

Vinig and Lips (2015) assessed in their research the commercial output of TTOs of Dutch universities. They provide a comprehensive measure of university technology transfer performance based on the potential measured in terms of research output. They compared the performance output (number of patents, licenses and spin-offs) to the research output in term of annual number of publications in scientific journals. They did this by calculating for every collected datasets the corresponding valorization score. This will be elaborated later in this report. In the research, they found that the majority of TTOs is not realizing the full innovative potential through their valorization activities. Dutch universities score high on research output as measured by the number of publication, but (except technical universities and medical centers), they fail to translate this into commercial success (Vinig and Lips, 2015).s

This thesis continues on Vinig and Lips’ (2015) research by extending the way technology transfer is measured. Two parameters will be added to the model as predictors of performance: number of researchers and university research budget.

57% 26% 9% 8% Government Grant Contract activities Student fees Other income

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3. METHODOLOGY

3.1

Sample

The sample contains all Dutch universities. There are thirteen universities in the Netherlands and they all own a Technology Transfer Office (TTO). All of these universities with their TTOs are included in this research:

1. EUR – Erasmus University Rotterdam

Technology Transfer Office ErasmusMC (TTO of Erasmus University Medical Center) 2. RU – Radboud University Nijmegen

Radboud Knowledge & Technology Transfer Office - KTTO (TTO of Radboud University) 3. RUG – University of Groningen

Office of the University - Research & Valorisation (TTO of the University of Groningen & University Medical Center Groningen)

4. TUD – Delft University of Technology

Valorisation Centre Delft (TTO of Delft University of Technology) 5. TUE – Eindhoven University of Technology

Innovation Lab (TTO of Eindhoven University of Technology) 6. UL – Leiden University

LURIS (TTO of Leiden University & Leiden University Medical Centre) 7. UT – University of Twente

Technopolis Holding (TTO of the University of Twente) 8. UU – University Utrecht

Utrecht Holdings (TTO of Utrecht University & University Medical Centre Utrecht) 9. WUR – Wageningen University

Werkgroep Kennisvalorisatie Wageningen UR (TTO of Wageningen University) 10. TU – Tilburg University

Het Tilburg Social Innovation Lab (TTO of Tilburg University) 11. UM – Maastricht University (Founded in 2012)

Het Maastricht Valorisation Center (TTO of Universiteit Maastricht) 12. UVA – University of Amsterdam

Technology Transfer Office UvA/AMC (TTO of Academic Medical Center & University of Amsterdam)

13. VU – VU University Amsterdam

Technology Transfer Office VU/VUmc (TTO of Vrije Universiteit & Vrije Universiteit Medical Center)

19 Since November 12, 2014, the technology transfer offices of the UvA/AMC and VU/VUmc have merged to one collective technology transfer office: Innovation Exchange Amsterdam (IXA). IXA is a collaboration of the TTOs of AMC, UvA, HvA, VU and VUmc.

3.2

Variables

In this thesis, I examine the potential for technology transfer for measuring the performance of university technology transfer of Dutch universities, based on (1) research output, (2) number of researchers, and (3) research budget . This leads to the following variables:

Independent variables

- Research output – The amount of articles published by the university.

- Number of researchers (fte) – The amount of researchers at the university, measured in fte. - Research budget – The amount of money consumed (spent) by the university on research, the

proposed budget. Dependent variable

- University’s technology transfer performance – The commercial output of the university.

Appendix 4 shows the conceptual model. As said, this study is based on the study of Vinig and Lips (2015). They have made the first step regarding this specific subject. In their study, they found research output as a predictor of technology transfer performance. This study adds two more variables to this conceptual model. In the model, the three independent variables are used to determine the dependent variable. Assumed is that besides (1) research output, also (2) research budget and (3) number of researchers in terms of fte, provide a good proxy of the potential of valorization.

First, the variables have to be operationalized. The variables used to measure the performance in valorization of the various TTOs in the Netherlands are measured the same as in Vinig and Lips’ (2014) research, by taking (1) patents, (2) license agreements, and (3) university spin-off companies, as university technology transfer output. The patent output is measured by the annual number of new patent applications. The patent output is an indication of the intention of the university to commercialize or valorize the research results. This is not sufficient as the only measurement of the valorization performance of universities, because not all inventions with commercial applications are likely to be patented, and some are not even patentable (Vinig & Lips, 2015). The license output is measured by the number of license agreements a TTO annually makes with external companies in return for royalty income (Vinig & Lips, 2015). Spin-off formation is measured by the annual number of spin-off companies that are founded to exploit a piece of intellectual property created at the related institute (Shane, 2004).

Vinig and Lips (2015) already looked at the research output in their study. For this variable, they assessed the annual number of publications in scientific journals. This study will include an updated

20 version of their research. For the variable research budget, the amount of money the university has available for research is used. This involves the part of the government grant (first flow of funds) that is meant for research purposes and the additional research funding provided by NWO. The third variable is the amount of researchers at the university. This variable is measured in full-time equivalent (fte).

3.3

Valorization Score

For every university, the valorization performance score is calculated. These valorization scores compare the actual commercial output to the expected output based on the potential of the universities (Vinig & Lips, 2015). The actual commercial output is the sum of all the patent applications, license agreements and formed spin-offs. The expected (potential) commercial output is based on the (1) research output, (2) amount of researchers, and (3) research budget. Estimated is that a certain percentage of these three indicators represent an innovation with commercialization potential (Vinig & Lips, 2015). The valorization score (VS) will be measured by dividing the actual number of successful valorization attempts (AVP) by the expected number of valorization attempts (PVP).

VS = AVP / PVP (AVP = actual valorization performance, PVP = potential valorization performance)

If a TTO is effective in transferring the innovations/inventions to the commercial sector, it is expected to have a valorization score of higher than 1.0. This indicates that the actual performance is higher than what is expected, considering its output, budget and researchers. Scores below or equal to 1.0 indicate average or weaker performance than expected (Vinig & Lips, 2015). A valorization score less than 1.0 means less optimal (0,6-0,9) or poor (0-0,5) performance (Vinig & Lips, 2015).

The valorization scores are calculated for each of the three measures separately, and conclusions are drawn from the findings. The valorization scores are compared to and contrasted with each other. The weight of the different measures of the total valorization scores is measured. Finally, a formula is constituted which takes into account all three factors – with their respective weight – in defining the final valorization score.

3.4

Data Collection

Since the sample exists of all universities in the Netherlands and the TTO activities are funded through public money, all the needed data should be publically accessible. However, as also Vinig and Lips (2015) encountered in their study, there exists a lack of structured guidelines on measuring and reporting TTO results in the Netherlands. Some of the data was found in the annual reports of the universities. Associations as VSNU (appendix 5), Rathenau and NWA gave insight in some of the missing data. Additional data was obtained by inquiries (interviews with TTO personnel via email and phone) to complement the dataset. The work and time schedule is included in appendix 6.

21

4. RESULTS

The goal of this study is to advance and improve the measure for technology potential and provide an accurate measurement of the potential and performance of technology transfer of the Dutch universities. Vinig and Lips (2015) already assessed one measure: research output. This study adds research budget and amount of researchers as measures of technology transfer potential to their model. Data is collected via annual reports, data from public institutions and additional inquiries. In this section, the results of the study will be presented and explained. First, the findings of the variables will be discussed. Second, the independent variables will be compared and relations between them will be further examined. Finally, the valorization score for all universities will be calculated and conclusions will be drawn.

4.1

Results Variables

Appendix 7, 8 and 9 show the results of the independent variables. Appendix 10 and 11 show the results of the dependent variable(s). Each variable will be discussed shortly.

Research output is the first measure (appendix 7). This is the amount of scientific publications of the university. The average number of all universities is 5,368 publications for one year over the last five years. In this table is shown that University of Amsterdam (8,965), Utrecht University (8,222) and VU University Amsterdam (6,993) have, by far, the most research output on average in the last five years. Tilburg University has the least amount of scientific publications with on average of only 2,053 publications a year. The table also shows that all universities have either an increasing amount of publications, or an approximately constant amount. The exception to this observation is Delft University of Technology, their amount of publications has decreased over the last five years.

The amount of researchers is the second measure for valorization potential (appendix 8). This is the amount of personnel involved or engaged in research, measured in fte. Utrecht University has the highest amount of researcher (2,455), followed by University of Amsterdam (2,197) and Leiden University (2,115). Tilburg University (455) and Wageningen University (910) have the least researchers employed. The amount of researchers is constant or slightly increasing over the years.

Research budget is the third and last measure. Appendix 9 shows the research budget of the universities in the last five years. This budget exists of two cash flows. The government provides funding to every university for education and research: the government grant. The first cash flow is the research part of the government grant. The NWO (part of the second flow of funds) provides funding for research as well. These two grants together is the budget for research for every university. Utrecht University (276M) has the largest research budget, followed by Delft University of Technology (259M) and University of Amsterdam (225M). Tilburg University (57M) and Maastricht University (96M) have the smallest budgets. Therefore, they have the least money available for research purposes. With some minor fluctuations, these amounts are approximately constant over the past five years.

22 Appendix 10 shows how many patent applications, license agreements and spin-offs each university has delivered in the last five years (2009 – 2013). Not all universities were willing to cooperate in this study and some TTOs had no records (anymore) of the commercial output of their university. For some, one or two of the three factors were not available. Appendix 11 shows the total commercial output per university. This is the total number of these three measures. Eindhoven University of Technology has, with an average of 41 per year, the highest commercial output. Maastricht University has the lowest commercial output with an average of only 11 a year. For two universities, there was no data available at all. These are Wageningen University and Tilburg University. In the end, 43 of the 65 datasets could be completed.

4.2

Independent Variables Compared

Appendix 12, 13, 14, 15, 16, and 17 show the relations between the independent variables. These calculations standardize the results, and the outcomes of the different universities can be compared with each other. Each of these relations will be discussed shortly below.

Table 2. Average publications per 1M Euros Table 3. Average Euros per publication

Appendix 12 shows the research output per 1M Euros per university per year. The last column shows the average over the past five years. Table 2 above shows the average results over de last five years, arranged from the highest to lowest amount. Number 1 has the most publications per 1M Euros and number 13 the least. The table shows that Erasmus University Rotterdam and Maastricht University have the most publications per 1M Euros research budget. Rotterdam has almost 45 scientific publications per 1M Euros research budget, while Delft University of Technology only has 23.4 publications per 1M Euros. That means that the reverse is the case for Euros per output, which is displayed in table 3. One publication at Delft University of Technology “costs” 43,181,- Euros and at the Erasmus University Rotterdam 22,351,- Euros.

# University Output / 1M 1 Rotterdam 44.9 2 Maastricht 43.5 3 VU Amsterdam 42.5 4 UvA Amsterdam 39.9 5 Nijmegen 36.9 6 Tilburg 36 7 Groningen 32.3 8 Utrecht 29.8 9 Leiden 28.7 10 Twente 27.6 11 Wageningen 25.5 12 Eindhoven 24.8 13 Delft 23.4

# University Euros / output

1 Delft € 43,181 2 Eindhoven € 40,355 3 Wageningen € 39,815 4 Twente € 37,232 5 Leiden € 35,191 6 Utrecht € 33,655 7 Groningen € 31,495 8 Tilburg € 28,112 9 Nijmegen € 27,344 10 UvA Amsterdam € 25,181 11 VU Amsterdam € 23,612 12 Maastricht € 23,069 13 Rotterdam € 22,351

23 Table 4. Average output per fte Table 5. Average fte per output

The tables above give (arranged by amount) the amount of scientific publications per researcher (table 4), and the amount of researchers per publication (table 5). Tilburg University has the most publications per researcher (1 fte) on average over the last five years. They have published 4.5 publications per researcher a year. This means that Tilburg University needs 0.22 fte researchers for one publication. For Leiden University, this is only 2.6 publications a year, which means that 0.39 fte researchers is needed for one publication in Leiden.

Table 6. Average Euros per fte Table 7. Average fte per 1M Euros budget

Table 6 and 7 above give the relations of the amount of money available for every researcher, and the amount of researchers at the university for every 1M Euros. Again these results are arranged by amount. At Delft University of Technology € 170,425 is available per researcher. This means that the university has almost 6 researchers per million Euros. Maastricht University has the least money for

# University Output / fte

1 Tilburg 4.5 2 UvA Amsterdam 4.1 3 VU Amsterdam 4.1 4 Rotterdam 4 5 Delft 4 6 Wageningen 3.7 7 Groningen 3.6 8 Utrecht 3.4 9 Eindhoven 3.2 10 Nijmegen 3 11 Maastricht 3 12 Twente 3 13 Leiden 2.6

# University fte / output

1 Leiden 0.39 2 Twente 0.34 3 Maastricht 0.34 4 Nijmegen 0.34 5 Eindhoven 0.31 6 Utrecht 0.3 7 Groningen 0.28 8 Wageningen 0.27 9 Delft 0.25 10 Rotterdam 0.25 11 VU Amsterdam 0.25 12 UvA Amsterdam 0.25 13 Tilburg 0.22

# University Euros / fte

1 Delft € 170,425 2 Wageningen € 145,810 3 Eindhoven € 128,731 4 Tilburg € 126,312 5 Utrecht € 112,562 6 Groningen € 111,705 7 Twente € 110,990 8 UvA Amsterdam € 102,480 9 VU Amsterdam € 95,729 10 Leiden € 90,508 11 Rotterdam € 88,911 12 Nijmegen € 81,584 13 Maastricht € 68,875

# University fte / 1M Euros

1 Maastricht 14.53 2 Nijmegen 12.27 3 Rotterdam 11.28 4 Leiden 11.1 5 VU Amsterdam 10.46 6 UvA Amsterdam 9.79 7 Twente 9.23 8 Groningen 9.07 9 Utrecht 8.9 10 Tilburg 7.96 11 Eindhoven 7.79 12 Wageningen 7 13 Delft 5.88

24 every researcher (€ 68,875). It was to be expected that the more technical universities would have a higher research budget available (standardized, per researcher), since they have more expenditures of laboratories and equipment.

4.2.1 Elsevier/ScienceWorks Valorization Ranking

As discussed in section 2, every two years Elsevier and ScienceWorks assess all Dutch universities to see which one is the most entrepreneurial. When assessing these universities they examine the valorization performance of the different universities. They make a top 5 of the most enterprising universities in the Netherlands. It is remarkable that their top 5 most enterprising universities corresponds with the top 5 universities that have the most money available per publication. The conclusion that can be drawn from this is that the universities who have the most money available per publication, score the best on valorization, according to the Elsevier/ScienceWorks valorization ranking.

FY2013 University

1 University of Twente

2 Delft University of Technology

3 Eindhoven University of Technology

4 Wageningen University

5 Leiden University

Source: ScienceWorks

Table 8. Elsevier/ScienceWorks – Valorisation Ranking 2013 Table 9. Top 5 – Average Euros per publication Table 8 shows the ‘Valorisation Ranking top 5’ of Elsevier and ScienceWorks. Table 9 gives the top 5 universities that have the most money available per publication. Both top 5’s include University of Twente, Delft University of Technology, Eindhoven University of Technology, Wageningen University and Leiden University. Note that the Delft University of Technology and Eindhoven University of Technology (number 1 and 2 in table 9) are technical universities, and generally have more money to spend on equipment and laboratories because of their technology “nature”.

4.3

Valorization Scores

In this section the valorization scores are computed. As explained before, the valorization score is determined by a simple calculation. The potential technology transfer performance is divided by the actual technology transfer performance.

First, the valorization scores with research output as potential are calculated. Vinig and Lips (2015) showed that a certain percentage of all publications represent a potential for commercial output. They concluded that 1% of the research output represents an innovation with potential for commercial application (appendix 18). The number of actual valorization attempts is the total number of patents, licenses and spin-offs (appendix 11). In this study, the same operation is executed for the years 2009 till 2013 and the results are shown in table 10.

# University Euros / output

1 Delft € 43,181

2 Eindhoven € 40,355

3 Wageningen € 39,815

4 Twente € 37,232

25 Table 10. Valorization scores – research output (1%)

Second, the valorization scores with amount of researchers as potential are calculated. The Global Entrepreneurship Monitor (GEM) is a research program and aims to obtain data from different countries on entrepreneurial activity (GEM, 2013). They define a Total early-stage Entrepreneurial Acitivity rate (TEA) as “the percentage of adults between 18 and 64 years of age who are actively trying to start a new business (nascent entrepreneurs) or own and manage a business younger than 3.5 years (young business entrepreneurs)” (GEM, 2013). In the Netherlands, this percentage is 9.3% in 2013. This study assesses two scenarios. First is assumed that if the Netherlands has an overall entrepreneurship percentage of 9.3%, the same will be the case among university researchers. Here is assumed that 9.3% of the researchers represents a potential for commercial output. In the second scenario is assumed that percentage of entrepreneurs in the whole population is higher than the percentage entrepreneurs among researchers at universities. The assumption here is that half of the entrepreneurs in the whole population presents an innovation. The valorization score is calculated by dividing the actual performance of technology transfer by the potential performance (appendix 19). Results are shown in table 11. For the 9,3% assumption, none of the valorization scores is higher than 1.0. When assuming a potential for 4,65%, three scores above 1.0 are found. Thus, only when assuming that 4,65% of the researchers represents an innovation with potential for commercial output valorization scores of 1.0 or higher are found. This means that these universities were able to realize the expected performance based on 4,65% of the researchers presenting commercial output. Results show that again the universities of Delft, Twente and Eindhoven have the highest valorization scores.

Third, the valorizations scores with the research budget as a potential for valorization performance is calculated. Since 1% of the scientific publication is considered to be potential for an innovation that results in commercial output, the mean amount of Euros per 0,01 publication (€3.158.400) is considered as the potential for commercial output based on research budget. Again,

Valorization Scores: Research output

FY2009 FY2010 FY2011 FY2012 FY2013

1% 1% 1% 1% 1% Leiden 1.06 0.57 0.45 0.61 - Nijmegen 0.55 0.65 0.55 0.27 0.33 Utrecht - 0.51 0.39 0.33 0.34 Delft 1.05 0.83 1.22 - 1.42 Groningen 0.49 0.31 0.32 0.45 0.53 Twente 1.32 0.67 0.84 - - Eindhoven 1.14 0.77 1.21 1.02 1.83 Erasmus 0.43 0.46 0.57 0.56 - Adam – UvA 0.24 0.17 0.09 0.14 0.09 Adam – VU 0.42 - 0.53 - - Wageningen - - - - - Tilburg - - - - - Maastricht - - - 0.22 0.31

26 the actual valorization performance is divided by the potential performance (appendix 20). Results are shown in table 12. Only Eindhoven University of Technology has a valorization score higher than 1.0, and this is only the case in 2013. Again, University of Twente, Delft University of Technology and Eindhoven University of Technology have the highest valorization scores based on the research budget potential.

Table 11. Valorization scores – researchers (9,3% and 4,65% of researchers)

Table 12. Valorization scores – research budget (3.158.400 Euros).

Table 13 gives a complete overview of the valorization scores calculated on the potential performance based on the three indicators described in this study: research output, amount of researchers and research budget. The scores of 1.0 or higher are in bold. This table shows that only the universities of Delft, Twente, Eindhoven and Leiden have valorization scores of 1.0 or higher on any of the indicators. These findings correspond with the Elsevier/ScienceWorks valorization ranking of the most entrepreneurial universities. They include the universities of Delft, Twente, Eindhoven, Leiden and Wageningen in their top 5. Unfortunately, data of Wageningen University was not available. It would be interesting to see if they also have a valorization score of 1.0 or higher.

Valorization Scores: Researchers

FY2009 FY2010 FY2011 FY2012 FY2013

9.3% 4.65% 9.3% 4.65% 9.3% 4.65% 9.3% 4.65% 9.3% 4.65% Leiden 0.27 0.55 0.15 0,31 0.12 0.25 0.17 0.34 - - Nijmegen 0.17 0.35 0.21 0.41 0.16 0.32 - 0.19 - 0.23 Utrecht - - 0.18 0.36 0.14 0.27 0.12 0.24 0.13 0.26 Delft _0.49 0.98 0.38 0.76 0.51 1.02 - - 0.54 1.09 Groningen 0.18 0.36 0.12 0.24 0.13 0.25 0.18 0.37 0.19 0.38 Twente 0.43 0.85 0.22 0.44 0.26 0.52 - - - - Eindhoven 0.41 0.82 0.28 0.57 0.38 0.77 0.34 0.68 0.62 1.24 Erasmus _0.17 0.34 0.19 0.37 0.23 0.46 0.27 0.54 - - Adam – UvA 0.10 0.20 0.07 0.14 0.04 0.08 0.06 0.12 0.04 0.08 Adam – VU 0.18 0.35 - - 0.23 0.46 - - - - Wageningen - - - - Tilburg _- - - - - Maastricht - - - 0.08 0.15 0.10 0.21 Valorization Scores: Budget

FY2009 FY2010 FY2011 FY2012 FY2013

Leiden _{0.90 0.46 0.38 0.62 -} Nijmegen _{0.59 0.73 0.58 0.35 0.42} Utrecht _{- 0.48 0.34 0.30 0.35} Delft _{0.89 0.64 0.89 - 0.88} Groningen _{0.40 0.32 0.31 0.53 0.58} Twente _{0.96 0.53 0.64 - -} Eindhoven _{0.89 0.62 0.86 0.84 1.48} Erasmus _{0.60 0.65 0.73 0.87 -} Adam – UvA _{0.28 0.21 0.11 0.17 0.13} Adam – VU _{0.52 - 0.67 - -} Wageningen _{- - - - -} Tilburg _{- - - - -} Maastricht _{- - - 0.33 0.42}

27 Table 13. Valorization scores total

Valorization

Scores FY2009 FY2010 FY2011 FY2012 FY2013

Output Researchers Budget Output Researchers Budget Output Researchers Budget Output Researchers Budget Output Researchers Budget

Leiden 1.06 0.55 0.9 0.57 0.31 0.46 0.45 0.25 0.38 0.61 0.34 0.62 - - - Nijmegen 0.55 0.35 0.59 0.65 0.41 0.73 0.55 0.32 0.58 0.27 0.19 0.35 0.33 0.23 0.42 Utrecht - - - 0.51 0.36 0.48 0.39 0.27 0.34 0.33 0.24 0.3 0.34 0.26 0.35 Delft 1.05 0.98 0.89 0.83 0.76 0.64 1.22 1.02 0.89 - - - 1.42 1.09 0.88 Groningen 0.49 0.36 0.4 0.31 0.24 0.32 0.32 0.25 0.31 0.45 0.37 0.53 0.35 0.38 0.58 Twente 1.32 0.85 0.96 0.67 0.44 0.53 0.84 0.52 0.64 - - - - - - Eindhoven 1.14 0.82 0.89 0.77 0.57 0.62 1.21 0.77 0.86 1.02 0.68 0.84 1.83 1.24 1.48 Erasmus 0.43 0.34 0.6 0.46 0.37 0.65 0.57 0.46 0.73 0.56 0.54 0.87 - - - Adam – UvA 0.24 0.2 0.28 0.17 0.14 0.21 0.09 0.08 0.11 0.14 0.12 0.17 0.09 0.08 0.13 Adam – VU 0.42 0.35 0.52 - - - 0.53 0.46 0.67 - - - - - - Wageningen - - - - - - - - - - - - - - - Tilburg - - - - - - - - - - - - - - - Maastricht - - - - - - - - - 0.22 0.15 0.33 0.31 0.21 0.42

28 4.3.1 Correlation

Table 14 below shows the differences and course of the three measures over the years. The valorization score based on output of Leiden University, for example, almost halved (*0.54) from 2009 till 2010. More or less the same happened to the valorizations scores based on amount of researchers (*0.56) and research budget (*0.51). The table gives an extensive overview of the differences over the years.

Table 14. Differences in Valorization Scores over the years

The correlation between the three measure is checked and is almost equal to 1. This was expected since the three measures are related to each other. The research budget is considered from the output, the amount of researchers has to do with the university’s budget, and output depends in a way on amount of researchers. As shown in the table 15, the correlation between research output and amount of researchers is 0.975, the correlation between researchers and budget is 0.972 and lastly, the correlation between output and budget is 0.944.

Table 15. Correlations valorization scores

Output Researchers Budget

Leiden 2009 – 2010 0.54 0.56 0.51 2010 – 2011 0.79 0.81 0.83 2011 – 2012 1.36 1.36 1.63 Nijmegen 2009 – 2010 1.18 1.17 1.24 2010 – 2011 0.85 0.78 0.79 2011 – 2012 0.49 0.59 0.60 2012 – 2013 1.22 1.21 1.20 Utrecht 2010 – 2011 0.76 0.75 0.71 2011 – 2012 0.85 0.89 0.88 2012 – 2013 1.03 1.08 1.17 Delft 2009 – 2010 0.79 0.78 0.72 2010 – 2011 1.47 1.34 1.39 2013 – 2011 1.16 1.07 0.99 Groningen 2009 – 2010 0.63 0.67 0.80 2010 – 2011 1.03 1.04 0.97 2011 – 2012 1.41 1.48 1.71 2012 – 2013 1.18 1.03 1.09 Twente 2009 – 2010 0.51 0.52 0.55 2010 – 2011 1.25 1.18 1.21 Eindhoven 2009 – 2010 0.68 0.70 0.70 2010 – 2011 1.57 1.35 1.39 2011 – 2012 0.84 0.88 0.98 2012 – 2013 1.79 1.82 1.76 Rotterdam 2009 – 2010 1.07 1.08 1.08 2010 – 2011 1.24 1.12 1.12 2011 – 2012 0.98 1.19 1.19 Adam – UVA 2009 – 2010 0.71 0.70 0.75 2010 – 2011 0.53 0.57 0.52 2011 – 2012 1.56 1.50 1.55 2012 – 2013 0.64 0.67 0.76 Adam – VU 2009 – 2011 1.26 1.31 1.29 Maastricht 2012 – 2013 1.41 1.40 1.27 Correlation Output / Researchers _0.975278 Researcher / Budget _0.971524 Output / Budget _0.943607