Cathy Lecocq Dimetic session, Pecs, July 2007

The role of science - industry interactions within emerging fields: An analysis of technological performance on the level of regions and firms

Cathy LecocqDimetic session, Pecs, July 2007

The role of science - industry interactions within emerging fields

PhD Framework:The role of science – industry interactions for the technological performance of regions and

firms in new emerging fields

The role of (national/regional) policies aimed at stimulating the collaboration between academia and industry, and their distinctive impact on inter-organisation collaboration at the level of the firm and the region.

SCIENCE –

TECHNOLOGY INTERACTIONS 2. Technological performance of

FIRMS

1. Technological performance of REGIONS

3. POLICIES aimed at stimulating science – industry interactions

The role of science - industry interactions within emerging fields

Using formal R&D collaborations, based on co-publication and co-patenting data => hereby exploring the relevancy of this set of indicators as comprehensive measures for the

amount and nature of scientific and technological collaboration on the level of regions and firms.

Focusing in a first phase on biotechnology, which can be considered as an emergent and growing field of technological and economical activity over the last decades

In a next step, this pilot will be extended towards other technology fields in order to check whether and to what extent the role of science – industry interactions is really distinctive within emerging, knowledge intensive technologies

Collaboration between Academia and Industry and the technological Performance of European Regions: the Case of Biotechnology

Catherine LecocqBart Van Looy

Managerial Economics, Strategy and InnovationFaculty of Economics and Applied Economics

K.U.Leuven

Science-Industry interaction and the technological performance of regions

System approach of innovation: interaction between multiple actors

Innovation Systems (National) Innovation Systems (Lundvall, 1992; Freeman, 1987; Nelson, 1993) Regional dynamics (Acs, 2000; Blind and Grupp, 1999; Cooke, 2002; Florida and

Cohen, 1999; Keeble and Wilkinson, 2000; Saxenian, 1994) Triple Helix model (Leydesdorff and Etzkowitz, 1996; 1998)

Firms Suppliers and customers (Shaw, 1994; Von Hippel, 1988) Potential lead users (Quinn, 1985; Von Hippel et al., 1999) Universities and public research centres (Gerwin et al., 1992, Santoro, 2000; Tidd

et al., 2002, Veugelers and Cassiman, 2005) Future or existing competitors (Hamel, 1991; Dodgson, 1993)

Open Innovation paradigm (Chesbrough, 2003)


Recent research on R&D collaboration of firms differentiates between different types of alliances based on March’s (1991) exploration vs exploitation framework and indicates differentiated relationships with innovative performance (multi-dimensional):

Integrated product development path (Rothaermel and Deeds, 2004; 2006): exploration alliances -> products in development -> exploitation alliances -> new products on the market

Firms engaging more in collaboration with universities and knowledge generating institutes perform better in terms of the development of new technologies and products (Belderbos et al., 2004; Faems et al.,2005)

Firms engaging in exploitative collaborations with other firms perform better in terms of obtaining turnover from improved products (Faems et al., 2005) or show a significant impact on labour productivity growth (Belderbos et al., 2004).


For knowledge creation and diffusion processes involving a substantial amount of tacit knowledge proximity matters (Malmberg and Maskell, 1997; 1999 Jaffe, Trajtenberg, and Henderson, 1993, Anselin, Varga and Acs, 1997)

Universities and research labs contribute to the technological and innovative performance of their regions (Jaffe, 1989; 1993; Mansfield, 1995; Acs et al. 1991; 2002; Anselin et al. 1997; Varga, 2002; Fischer et al. 2003)

But seems more pronounced within certain (broad) technological fields than across all fields (Jaffe, 1989; Acs, et al., 1991; Anselin et al. 2000)

Results in increasing attention for regional innovation dynamics/clusters: unit of analysis within this study


Technologies progress along a Technology Life Cycle (Utterback and Abernathy 1975; Roussel, 1984; Foster 1986; Anderson and Tushman, 1997; Andersen 2001)

Different stages of technology coincide with different characteristics of the technologies with respect to technical and market uncertainty, technical performance, levels of R&D investments, etc. (Roussel, 1984; Foster 1986)

The development path of technologies typically follows an S-shaped growth path (Andersen 2001)


We hypothesize that the nature and impact of university – industry collaborations for regional development vary as technologies and industries progress along the technology/product life cycle.

And more specifically :

1) More R&D collaborations between companies and universities/ research centres will lead to better technological performance of regions (within emerging, knowledge intensive, fields) during the first, more explorative, phases of the technology life cycle.

2) More R&D collaboration between companies will lead to better technological performance of the regions during next, more exploitation oriented phases of the technology life cycle.

Patent Technology

class (IPC code)


Assignee(s):Name(s)

Addresse(s)

Inventor(s):Name(s)

Adresses(s)

DATA: EPO patents -> consistent, field specific and comparable data for a large number of regions and over longer time periods


EPO patents within the domain of Biotechnology (appl years 1978-2001)Result of a prior effort to map the field of biotechnology (Glänzel et al., 2003)

Assignment of assignee type : University, public research centre, company, hospital, private personBased on the sector assignment methodology developed by the Policy Research Centre for R&D Statistics (Leuven, Belgium, see Van Looy, Du Plessis & Magerman, Eurostat WP, 2006)

Allocation of addresses to regions: nuts 3 level Using the 3-level hierarchical classification of regions established by Eurostat: the

Nomenclature of Territorial Units for Statistics (NUTS)

Selection of nuts level: nuts1/2Nuts1 for smaller European countries, nuts 2 for other countries Criterion: average population on the region level > 1 mio


Overview of selected nuts level, number of regions and average population per country ( EU-15 + Switzerland)

Country Nuts level Number of regions Average population (in ‘000)

Austria Nuts1 3 2.681 Belgium Nuts1 3 3.429 Denmark Nuts2 1 5.355 Finland Nuts2 5 1.038 France Nuts2 26 2.351 Germany Nuts2 41 2.008 Greece Nuts1 4 2.738 Ireland Nuts2 2 1.920 Italy Nuts2 21 2.713 Luxembourg Nuts1 1 442 Netherlands Nuts2 12 1.337 Portugal Nuts2 7 1.470 Spain Nuts2 19 2.143 Sweden Nuts2 8 1.112 Switzerland Nuts2 7 1.032 United Kingdom Nuts2 37 1.598 Total 197 1.964


Indicators of technological performance of regions (based on inventor addresses)

Collaboration indicators(based on co-assigneeship, allocated to regions based on assignee addresses)

KGI – I collaboration Co-patenting between at least one knowledge generating institute (university or public research institute) and one or more industrial partners

I – I collaboration Co-patenting between 2 or more industrial partners

Number of patents Patent count per region per year

Patents per population Patent count per million inhabitants of the region


Panel dataset with 4.728 observations pertaining to 197 regions in EU-15 and Switzerland, over the time period 1978-2001 (24 years)

Descriptive statistics (per region and year, period 1978-2001)

Min Max Mean Std. Dev Number of patents 0 156 4,69 10,55 Patents per population (by million inhabitants)

,00 43,32 2,15 4,14

Population (thousands) 26 11.118 1.963,61 1.607,55 Collaborations KGI-I 0 20 ,16 ,834 Collaboration I-I 0 19 ,14 ,879


Clustering of biotech activities in EU-15 and Switzerland (1978-2001)

1/3 of patents is concentrated within 10 regions

17 regions (8,6%) have no biotech patents; 27 (13,7%) regions have no more than 5 patents

Country Region Patents Cum % France Île de France 1.356 6%

Germany Oberbayern 1.013 11%

Germany Darmstadt 879 15%

Denmark Danmark 846 18%

United Kingdom Berkshire, Buckinghamshire & Oxfordshire 693 22%

United Kingdom East Anglia 652 25%

Netherlands Zuid-Holland 579 27%

Belgium Vlaams gewest 563 30%

Germany Karlsruhe 498 32%

Germany Köln 496 34%

… EU-15 + Switzerland (197 regions) 22.190 Cumulative amount of EPO patents (1978-2001), full count of inventor addresses


Top 10 regions in EU-15 + CH

Île de France (FR)

Oberbayern (DE)

Darmstadt (DE)

Denmark (DK)

Berkshire, Buckinghamshire and Oxfordshire(UK) East Anglia (UK)

Zuid-Holland (NL)

Vlaams gewest (BE)

Köln (DE)

Karlsruhe (DE)

GeoDa


Collaboration within biotechnology

15.015 patents with at least 1 assignee in EU-15 and Switzerland (1978-2001)

=> 1.843 (12,3%) with 2 or more assignees 536 KGI – industry collaboration (3,6%) 409 Industry – industry collaboration (2,7%)

Correlations

Number of patents

Patents per population

Collaboration KGI-I

Collaboration I-I

Number of patents 1 Patents per population ,763(**) 1 Collaborations KGI-I ,615(**) ,384(**) 1 Collaborations I-I ,278(**) ,318(**) ,188(**) 1 ** Correlation is significant at the 0.01 level (2-tailed).


Evolution of patenting in the field of biotechnology

Biotech patents: applications

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Patents

EPO Patents 1978-2001, worldwide

Period 1978-1990: steady linear increase of the patent stockPeriod 1991-1999: exponential growth of the number of patents


MODEL: What is the nature and impact of university – industry collaborations for regional development as technologies and industries progress along the technology/product life cycle?

Period 1978-1990: First, explorative phase of the TLC

Period 1991-1999: Next, more exploitation orientated phase of the TLC

Collaboration in year t-> technological performance of the region in year t+2

Fixed Effect Negative Binomial regression model-> controls for unobserved between region - differences such as BERD and HERD

Biotech patents: applications

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Patents

EPO Patents 1978-2001, worldwide


RESULTS (1):

Number of patents per Region (t+2) Coef. Std. Err. P>|z|

Period 1978 – 1990 Collaboration: KGI – Industry 0,1364 0,026 0,000 Industry – Industry 0,1205 0,017 0,000 _constant 0,8655 0,072 0,000

Number of observations 2158 Number of groups 166

Period 1991 – 1999

Collaboration: KGI – Industry 0,0528 0,0167 0,002 Industry – Industry 0,0008 0,0171 0,964 _constant 1,8605 0,0970 0,000



RESULTS (2):

Number of patents per population per Region (t+2) Coef. Std. Err. P>|z|

Period 1978 – 1990

Collaboration: KGI – Industry 0,1261 0,0276 0,000 Industry – Industry 0,1080 0,0172 0,000 _constant 1,3200 0,1142 0,000


Period 1991 - 1999

Collaboration: KGI - Industry 0,0524 0,9187 0,005 Industry - Industry -0,0042 0,0191 0,824 _constant 2,0985 0,1375 0,000



CONCLUSIONS:

During the first explorative phases of the technology life cycle: – science – industry interaction leads to a better technological performance of the

region – collaboration between industrial partners contributes to the technological

performance of regions

During the more exploitative phases of the technology life cycle:– science – industry interaction leads to a better technological performance of the

region, suggesting that even during the later phases of the technology life cycle, exploratory research activities remain present;

– but collaboration between industrial partners does not lead to better technological performance of regions: Reduced importance of collaboration between firms during later stages of the life cycle? (<> open innovation system rhetoric)


FURTHER RESEARCH will be focused on the introduction of

- the geographical distribution of co-patenting (local, national, international),

- characteristics of the regional economical texture (number and size of the firms),

- the specific role of scientific capabilities,

- and extending indicators signaling collaboration (co-publication) in order to further qualify the relationships identified so far

Documents

Cathy Lecocq Dimetic session, Pecs, July 2007