Abstract
This paper investigates the technological orientation of firms and universities and their propensity to have knowledge and technology transfer (KTT) activities. This study looks at the technological potential for KTT and how it is used, emphasizing differences between smaller and larger firms. To this end we collected information about the technology activities of firms (patent statistics) and the technology activities of universities. Furthermore, we used survey data on technology transfer activities. We combined the three datasets and found—especially for smaller firms—that great technology proximity fosters transfer activities with different universities (case 1). The same is true if proximity is low and expertise is considerable at universities in the respective technology field (case 2). In both cases additional transfer potential exists. In the second case firms engage in transfer activities in order to update and modify their knowledge base and as a consequence improve “competitiveness” in certain technology fields. Furthermore, firms show a tendency to diversify their contacts with universities in order to avoid knowledge lock-in.
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Notes
Definition technology proximity for the purpose of this paper: Technology proximity between two entities (e.g., university and enterprise) is given, if they are working in the same patent class (technology field). Technology proximity of two entities is not given, if they are working in different patent classes (technology fields). Thus technology proximity has two expressions, i.e. 1 if they work in the same patent class and 0 if they do not work in the same patent class.
Technology proximity might be a kind of enabling factor for KTT (Knowledge and Technology Transfer) and thus relevant for transfer policy making. Policy makers should know about technology affinities between the private and public research sector, since it would be rather unwise to “force” collaborations (e.g., through funding schemes) without some knowledge about the technology potential. It would also be unwise to “force” universities into more applied fields of technology just to approximate their research to firm needs. One should be aware of and respect the two different goal setting mechanisms of applied (mostly private) research and basic (mostly public) research and their different purposes from a public point of view (see Hall 2001; Beise et al. 1995 for different goal dimensions). One should also be aware that intensified interactions lead to goal harmonization between the actors; that could be caused by mutual adaptation (see Beise et al. 1995) or through an improved absorptive capacity of private enterprises (see Izushi 2002). As a consequence the characters of universities are changing (see Gibbons et al. 1994).
For the explanation the different dimensions of proximity see Boschma (2005). Geographical proximity could be of less importance for transfer activities in Switzerland, since Switzerland is a very small country and universities are well distributed across regions. However, we know from investigations in other countries that geographical proximity plays an important role (see Santoro and Gopalakrishnan 2001; for the USA).
Broad definition of transfer activities: Knowledge and technology transfer between academic institutions and the business sector is understood in this study as any activities aimed at transferring knowledge or technology that may help either the company or the academic institute—depending on the direction of transfer—to further pursue its activities.
In order to capture the technology orientation of firms and universities we refer to the international patent classification (see http://www.wipo.int/classifications/ipc/en/). Patents can be assigned to more than one sub-class. Sub-classes are aggregated to more than 100 classes and 8 sections. We assigned technology fields only to firms that filed patent(s). In case of universities we assigned technology fields according to their research activities presented on their websites (see section on data).
Since it is the purpose of this study to investigate the meaning of technology knowledge for transfer activities between enterprises and universities, it is important if a firm has knowledge in a certain technology field, independently of the firm’s knowledge in other fields. Therefore the absolute number of patent field inscription is used as a proxy for technology knowledge and not the relative number of technology inscriptions.
Switzerland’s main funding institution for more applied research, CTI (Innovation Promotion Agency), mainly promotes R&D collaborations between firms and public research institutions. It is inevitable to have a collaboration partner at a public research institution in order to be considered for public support. The rule is that at least 50% of the total project costs have to be covered by the private partner. The costs on part of the public partner(s) are funded by the CTI in case the project is promoted. Thus, the private partner does not receive any direct financial aid. Promotion of private innovation activities takes place indirectly through funding the public partner in a collaboration project between public and private partners.
“hits” shows us the number of websites related to technology fields found on the servers of the respective university/science institution. We only searched servers related to science institutes (economics, humanities, or law have been excluded).
For a similar reasoning in terms of technology adoption and organizational change see Battisti et al. (2007) or Bresnahan et al. (2002), Hollenstein (2004), Hempell et al. (2004). We can not test endogeneity econometrically due to data limitations. We would need at least 104 valid instruments for the technology fields identified (see Wooldridge 2003; for valid instruments).
We also looked at the 10 and 30 most (least) important technology fields. The results are showing the following trend: Looking at a smaller group (e.g., the 10 most (least) important fields) makes the results clearer. For instance we would find only positive significant signs in the field of high potentials. Looking at a greater group (e.g., the 30 most (least) important fields) makes the results less clearer. For instance, some technology fields found in ‘not used potentials’ or ‘lone stars’ are now found in the category ‘high potentials’. Basically we see that the larger the group, the more heterogeneous are the results and the smaller the group the more homogeneous are the results. After some trials it turned out that 20 fields is the largest possible group in order to get rather homogenous results.
With the different size groups we mainly want to distinguish between small and large firms. Firms with more than 500 employees are seen as large firms in Switzerland. The group of firms with less than 300 employees can be seen as a kind of sensitivity test of the ‘500 employees’ frontier. Sample size does not permit an investigation of much smaller size groups.
Technology orientation of a firm is measured in absolute terms. I also calculated a relative measure (number of technology field inscriptions in a certain technology field related to all field inscriptions of a firm), indicating a type of technology specialization. Some preliminary estimations with the relative measure shows similar patterns, e.g. ‘high potentials’ with positive signs, ‘low potentials’ with negative signs etc. However, technology fields with significant signs are different. That is not surprising, since this way we look whether firms specialized in a technology field are more likely to have transfer contacts with different universities. We see, for instance, that competences in b23 are not sufficient to have transfer contacts; but if a firm is specialized in b23, transfer activities with different universities are very likely.
For a similar reasoning of choosing instruments in a zero-inflated negative binomial estimation see Kahn (2005, p. 276).
NetBreeze is an ETH spin-off that developed an internet search engine (http://www.netbreeze.ch).
Patents are not a perfect indicator (see Griliches 1990). However, most of the criticism refers to patents as an innovation output measure or as an economic indicator. In the study at hand we use the patent statistic as an indicator for the knowledge base of a firm. Thus most of the criticism does not apply.
For the patent classification please refer to http://depatisnet.dpma.de/ipc/ipc.do.
Based on the developed software we searched the espacenet.com website for the name of the firm and related patent information and saved the assigned patent classifications. For more information please see also http://www.netbreeze.ch on open source software.
Sections: human necessities; performing operations, transporting; chemistry, metallurgy; textiles paper; fixed constructions; mechanical engineering, lighting, heating, weapons, blasting; physics; electricity. For the class level please refer to the Table 11.
We looked at the patent activities of a firm across its whole life span. The earliest patent of a sample firm we found in 1904. However, considerable patent activities of our panel firms (more than 1,000 annually) could have been detected from 1990 upwards. How is the possibility for changing fields accounted for? Since we are looking at the number of patent field inscriptions a change in the knowledge base of a firm is indicated through a greater number of patent field inscriptions in, let say a01 instead of b06. The longer time period make sense, since many firms file patents irregularly. Hence, unless we do not have more recent information, we assume that the patents granted so far indicate the knowledge base of the firm, even if patent activities lie back 10 years.
It is likely that one patent is assigned to several patent fields. For example, if patent 1 is classified into, say both a01 and c08, the firm holding this patent would be recorded as having two “inscriptions”, one in a01 and one in c08.
Patents mainly mirror research activities with short/middle-term market perspectives.
Technological fields are assigned based on a binary classificatory that follows a “cascade structure”. For a detailed description of the classifier (classification procedures) and tests of robustness see Lang (2008).
Certainly, one could think of other measures for technology knowledge residing within universities, e.g., budgets for technology fields. However, in order to match knowledge of enterprises with knowledge of firms we need to have an indicator available for both universities and enterprises. Budget figures on a technology field level are not available for firms. And also on the university side budgets for research in technology (patent) classes are not available. Since 8.6% of Swiss firms do also have transfer activities with foreign universities it would be interesting to take them into account as well. Unfortunately, we do not know with which universities in which countries firms have transfer activities. Thus, it is beyond our current means to look at the technology profile of universities in Germany, France, United Kingdom, USA, etc. Since 8.6% of Swiss firms do have transfer contacts with foreign universities it is likely that there is a considerable amount of technology spill-in from foreign universities.
However, some questions do not refer to a certain time period, e.g., the question for technology transfer activities with universities refers to two periods “2002–2004” and “before 2002”. If a firm had transfer activities in one of the two periods the firm was identified as transfer active. If a firm was transfer active we asked for the transfer partner. The number of different transfer partners is our measure for transfer intensity (intense; see Table 1).
The following test of robustness has been conducted: I limited the sample to those firms that filed patents between 1988 and 2008 (20 years). I discounted patents (technology fields) filed between 1988 and 1992 with the factor 0.25. Patents filed between 1993 and 1997 were discounted with the factor 0.5. Patents filed between 1998 and 2002 were discounted with the factor 0.75. Patents filed after 2003 were not discounted. Results: first, the number of observation hardly changes [2,099 (after correction), 2,132 (before correction)]. This means that most of the firms that had patent activities before 1988 have had patent activities after 1988 as well. Secondly, the results are very similar. What changes? Low potentials (all firms): c13 moved from significant plus to insignificant. Low potentials (<300 employees): c13 and c14 moved from not significant to significant plus. Lone stars (<300 employees): b65 moved from not significant to significant minus. Thirdly, this indicates a path dependency of knowledge creation within a firm.
For a complete description of the technological fields please refer to Table 11.
NetBreeze is an ETH spin-off that developed an internet search engine (http://www.netbreeze.ch/index.php?id=23).
References
Arundel, A., van de Paal, G., & Soete, L. (1995). Innovation strategies of Europe’s largest industrial firms, results of the PACE survey for information sources, public research, protection of innovations and government programmes, Final Report, MERIT, June.
Arvanitis, S., Kubli, U., Sydow, N., & Woerter, M. (2007). Knowledge and technology transfer (KTT) activities between universities and firms in Switzerland: The main facts. The Icfai Journal of Knowledge Management, V(6), 17–75, November.
Arvanitis, S., Sydow, N., & Woerter, M. (2008). Is there any impact of university-industry knowledge transfer on the performance of private enterprises? An empirical analysis based on Swiss firm data. Review of Industrial Organization, 32, 77–94.
Audretsch, D. B., & Stephan, P. E. (1996). Company-scientist locational links: The case of biotechnology. The American Economic Review, 86(3), 641–652.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 99–120.
Barney, J., Wright, M., & Ketchen, D. J. (2001). The resource-based view of the firm: Ten years after 1991. Journal of Management, 27, 625–641.
Battisti, G., Hollenstein, H., Stoneman, P., & Woerter, M. (2007). Inter and intra firm diffusion of ICT in the United Kingdom (UK) and Switzerland (CH): An internationally comparative study based on firm-level data. Economics of Innovation and New Technology, 16(8), 669–687.
Beise, M., Licht, G., & Spielkamp, A. (1995). Technologietransfer an kleine und mittlere Unternehmen—Analysen und Perspektiven für Baden-Württemberg, Schriftenreihe des ZEW, Nomos Verlagsgesellschaft, Baden-Baden.
Blume, L., & Fromm, O. (2000). Wissenstransfer zwischen Universitäten und regionale Wirtschaft: Eine empirische Untersuchung am Beispiel der Universität Gesamthochschule Kassel. Vierteljahreshefte zur Wirtschaftsforschung, 69(1), 109–123.
Boschma, R. A. (2005). Proximity and innovation: A critical assessment. Regional Studies, 39(1), 61–74.
Boschma, R. A., & ter Wal, A. L. J. (2007). Knowledge networks and innovative performance in an industrial district: The case of a footwear district in the South of Italy. Industry and Innovation, 14(2), 177–199.
Breschi, S., & Lissoni, F. (2006). Mobility of inventors and the geography of knowledge spillovers. New evidence on US data. CESPRI Working Paper 184.
Bresnahan, T. F., Brynjolfsson, E., & Hitt, L. M. (2002). Information technology, workplace organisation, and the demand for skilled labor: Firm-level evidence. Quarterly Journal of Economics, 117, 339–376.
Broekel, T. (2007). A concordance between industries and technologies matching the technological fields of the patentatlas to the German industry classification. Jena Economic Research Papers (2007-041), pp. 1–21.
Cantner, U., & Meder, A. (2007). Technological proximity and the choice of cooperation partner. Journal of Economic Interaction and Coordination, 2, 45–65. doi:10.1007/s11403-007-0018-y.
Cohen, W., & Levinthal, D. (1989). Innovation and learning: The two faces of R&D. Economic Journal, 99(397), 569–596.
Dosi, G. (1982). Technological paradigms and technological trajectories. Research Policy, 11, 147–162.
Dosi, G. (1988). Sources, procedures, and microeconomic effects of innovation. Journal of Economic Literature, 26, 1120–1171 (September).
Geisler, E., & Rubinstein, A. H. (1989). University-industry relations: A review of major issues. In A. N. Link & G. Tassey (Eds.), Co-operative research and development: The industry-university-government relationship. London: Kluwer.
Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge. London, Thousand Oaks, New Delhi: Sage Publications.
Greenlee, P., & Cassiman, B. (1999). Product market objectives and the formation of research joint ventures. Managerial and Decision Economics, 20, 115–130.
Griliches, Z. (1990). Patent statistics as economic indicators: A survey. Journal of Economic Literature, 28(4), 1661–1707.
Hall, B. H. (2001). University–industry research partnerships and intellectual property. NSF-CISTP Workshop, October.
Hall, B. H. (2004) University–industry research partnerships in the United States. Kansai Conference Paper, February.
Hempell, T., van Leeuwen, G., & van der Wiel, H. (2004). ICT, innovation and business performance in services: Evidence for Germany and the Netherlands. In OECD (Ed.), The economic impact of ICT. Measurement, evidence and implications (pp. 131–152). Paris: OECD.
Hollenstein, H. (2004). Determinants of the adoption of information and communication technologies (ICT)—an empirical analysis based on firm-level data for the Swiss business sector. Structural Change and Economic Dynamics, 15, 315–342.
Izushi, H. (2002). Impact of the length of relationships upon the use of research institutes by SMEs. Research Policy, 32, 1–18.
Jaffe, A. B. (1986). Technological opportunity and spillovers of R&D: Evidence from firms’ patents, profits, and market values. The American Economic Review, 76, 984–1001.
Kahn, M. E. (2005). The death toll from natural disasters: The role of income, geography, and institutions. Review of Economics and Statistics, 87, 271–284.
Kaufmann, A., & Tödling, F. (2001). Science-research interaction in the process of innovation: The importance of boundary-crossing between systems. Research Policy, 30, 791–804.
Kor, Y. Y., & Mahoney, J. T. (2004). Edith Penrose’s (1959) contributions to the resource-based view of strategic management. Journal of Management Studies, 41(1), 183–191.
Lang, J. (2008). Forschungs—und Entwicklungstätigkeiten von Unternehmen und Hochschulen im In—und Ausland. Zürich: Arbeitsbericht.
Laursen, K., & Salter, A. (2004). Searching high and low: What types of firms use universities as a source of innovation? Research Policy, 33, 1201–1215.
Lee, Y. S. (2000). The sustainability of university-industry research collaboration: An empirical assessment. Journal of Technology Transfer, 25, 111–133.
Leibenstein, H. (1989). Organizational or frictional equilibria, X-efficiency, and the rate of innovation. In K. Burton (Ed.), The collected essays of Harvey Leibenstein, volume 2. X-efficiency and micro-micro theory. Aldershot: Edward Elgar Publishing Limited.
Lessmann, G., & Rosner, U. (2004). Aufschwung Ost durch Öffentliche Wissenschaftseinrichtungen—can research institutions make the East German economy prosper? Working Paper Nr. 04004—FEMM (Faculty of Economics and Management Magdeburg), Magdeburg, April.
Malerba, F. (2007). Innovation and the dynamics and evolution of industries: Progress and challenges. International Journal of Industrial Organization, 25, 675–699.
March, J. (1991). Exploration and exploitation in organizational learning. Organization Science, 2, 71–87.
Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Cambridge: Belknap Press of Harvard University Press.
Nooteboom, B., Van Haverbeke, W., Duysters, G., Gilsing, V., & van den Oord, Ad. (2007). Optimal cognitive distance and absorptive capacity. Research Policy, 36, 1016–1034.
OECD. (2002). Benchmarking industry-science relationships. Paris: OECD.
Onida, F., & Malerba, F. (1989). R&D co-operation between industry, universities and research organizations in Europe, background report. Technovation 9, 131–193.
Pavitt, K. (1984). Sectoral patterns of technical change: Towards a taxonomy and a theory. Research Policy, 13, 343–374.
Penrose, E. (1995). The theory of the growth of the firm (3rd ed.). Oxford, New York: Oxford University Press.
Powell, W. W., Koput, K. W., & Smith-Doerr, L. (1996). Interorganizational collaboration and the locus of innovation: Networks of learning in biotechnology. Administrative Science Quarterly, 41(1), 116–145.
Salter, A., D’Este, P., Martin, B., Geuna, A., Scott, A., Pavitt, K., et al. (2000). Talent, not technology: Publicly funded research and innovation in the UK, SPRU (Science and Technology Policy Research). Brighton: University of Sussex.
Santoro, M. D., & Chakrabarti, A. K. (2002). Firm size and technology centrality in industry-university interactions. Research Policy, 31, 1163–1180.
Santoro, M. D., & Gopalakrishnan, S. (2001). Relationship dynamics between university research centers and industrial firms: Their impact on technology transfer activities. Journal of Technology Transfer, 26, 163–171.
Schartinger, D., Gassler, H., & Schibany, A. (2000). Benchmarking industry–science relations, National Report—Austria, Final Report, OEFZS—S-0099, Oesterreichisches Forschungszentrum Seibersdorf, Seibersdorf.
Schartinger, D., Schibany, A., & Gassler, H. (2001). Interactive relations between universities and firms: Empirical evidence for Austria. Journal of Technology Transfer, 26, 255–268.
Schibany, A., Jörg, L., & Polt, W. (1999). Towards realistic expectations. The science system as a contributor to industrial innovation, Tip-report, Vienna.
Schmidt, T. (2008). Absorptive capacity—one size fits all? A firm-level analysis of absorptive capacity for different kinds of knowledge. Managerial and Decision Economics, Forthcoming 2009.
Schmoch, U. (2003). Hochschulforschung und Industrieforschung, Perspektiven und Interaktion, Campus Forschung Band 858, Campus Verlag, Frankfurt, New York.
Schmoch, U., Laville, F., Patel, P., & Frietsch, R. (2003). Linking technology areas to industrial sectors. Final Report to the European Commission, DG Research, Karlsruhe, Paris, Brighton.
Shapiro, C., & Willig, R. (1990). On the antitrust treatment of production joint ventures. Journal of Economic Perspectives, 4, 113–130.
Simon, H. A. (1956). Rational choice and the structure of the environment. In M. Egidi & R. Marris (1992) (Eds.), Economics, bounded rationality and the cognitive revolution, Herbert Simon. Edward Elgar Publishing Limited, Aldershot, Brookfield.
Stephan, P. E. (1996). The economics of science. Journal of Economic Literature, 34(3), 1194–1235.
Sumell, A. J, Stephan, P. E., & Adams, J. D. (2009). Capturing knowledge: The decision of new PhDs working in industry. In R. B. Freeman & D. L. Goroff (Eds.), Science and engineering careers in the United States: An analysis of markets and employment. Chicago: University of Chicago Press, pp. 257–287.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 507–533.
Verspagen, B., van Moergastel, T., & Slabbers, M. (1994). MERIT concordance table: IPC—ISIC (rev. 2). MERIT Research Memorandum 2/94-004, Maastricht.
Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5, 171–180.
Wooldridge, J. M. (2003). Introductory econometrics—a modern approach. Mason OH: Thomson South-Western.
Acknowledgments
I am grateful for insightful comments and discussions on earlier drafts from Spyros Arvanitis. I am also grateful to Joel Lang for the programming of the algorithm to search patent descriptions and to provide descriptive statistics, to François Ruef and Leo Keller for discussions on framing the technology orientation of firms, and to Eric Schwegler for helping me to compile the data. Mistakes are the author’s alone. This study was funded by the ETH Board in Switzerland.
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Appendix
See Table 11.
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Woerter, M. Technology proximity between firms and universities and technology transfer. J Technol Transf 37, 828–866 (2012). https://doi.org/10.1007/s10961-011-9207-x
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DOI: https://doi.org/10.1007/s10961-011-9207-x