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An analysis of science–industry collaborative patterns in a large European University

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Abstract

Our paper analyses the modalities according to which a large European university collaborates with firms by exploring its relational portfolio. We address this issue by exploiting a database that lists more than 1,000 firms having collaborated with the University Louis Pasteur (ULP) between 1990 and 2002. We first observe the relative importance of four collaborative channels (private contracts, European contracts, co-publications, co-inventions) across the whole population of firms. Second, using a multi-correspondence analysis, we derive a typology of collaborative patterns which underline the discriminating features of the frequency of interactions, of the exclusive versus open character of the relationships and of the nature of the collaborative channels. Four coherent classes emerge from particular combinations of these relational characteristics. Finally, using multinomial logit estimation, we show how this diversity of partnerships is connected to some individual attributes of the firms: size, status, sector and location.

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Notes

  1. Although it would have been interesting to include nationally or regionally subsidized cooperative R&D, the latter are not considered in the present contribution.

  2. We decided to retain four classes of partners, because intra-class and inter-class variances are clearly improved compared to the 3-class case (in the latter situation they equal respectively 31.8 and 68.2). In the case of five classes, intra-class variance is 11.9 and inter-class variance is 88.1, but we only divide the smallest class of 105 firms into two groups of 32 and 73 firms.

  3. In fact Alsatian firms have privileged relations with only one research lab or even with only a few academic researchers of ULP. They have intense and repetitive co-publication activities with them.

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Acknowledgements

This work is part of a larger project of a team of researchers at BETA co-ordinated by P. Llerena. We are grateful to all the members of this team. Acknowledgements should be extended to the administrative departments of ULP, the technology transfer offices of ULP and of local delegation of CNRS, and to the French patent office (INPI). The paper also benefited from the highly relevant and interesting comments of Natalia Zinovyeva, Maria Theresa Larsen, Réjean Landry, and two anonymous referees.

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Authors and Affiliations

  1. BETA, CNRS, Université Louis Pasteur (Strasbourg 1), 61, avenue de la Forêt Noire, Strasbourg Cedex, 67085, France

    Rachel Levy, Pascale Roux & Sandrine Wolff

  2. ADIS, Faculté Jean Monnet, Université Paris Sud-Orsay (Paris 11), 54 Bd Desgranges, Sceaux, 92331, France

    Pascale Roux

Authors
  1. Rachel Levy
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  2. Pascale Roux
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  3. Sandrine Wolff
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Correspondence to Rachel Levy.

Appendices

Appendix A: Description of ULP database

Data concerning ULP’s composition and scientific production are gathered in a set of tables constituting a relational database. Information is organized and centralized based on the lists of the 1805 permanent researchers and 105 research units present at the university in 1996 and 2000. These two lists originate from official documents, called “contrats quadriennaux’, which have to be produced by each lab in the specific French context of 4-year contractual affiliation rounds. Each research unit provides a document summarizing its research activity and its composition for the past 4 years, as well as a forecast of its research activity for the next 4 years.

Concerning the outputs of research, the database contains information about all the publications cited in the Science Citation Index or Social Science Citation Index (bibliographic databases produced by ISI, the US Institute for Scientific Information) published between 1990 and 2002 with at least one author mentioned in the “permanent researchers’ table. A query was run for each individual researcher, leading to a total of 43,241 publications, that is to say 23,215 different articles after deleting redundant papers. In parallel, all 841 patents (French, European or American) invented by at least one researcher mentioned in the same researcher table were identified. Data were completed by collecting all the 4,495 research contracts signed between 1990 and 2002 by one of the ULP laboratories, thanks to the technology transfer offices of ULP and CNRS. This “contract table” provides information about the contracts themselves (topic, duration, finance,...), the names of the different partners (firms and public institutions). It includes the contracts executed in the framework of European Research Programmes.

Appendix B: MCA and AHC methodologies

The MCA is designed to analyze the relations between more than two categorical variables that can be presented in multi-way contingency tables. It allows us to identify the relationships between the variables retained for the analysis. More precisely, the total variation of the data matrix (the inertia) is computed by the usual Chi2-statistics which measures the distance separating the original distribution from the one assuring the independence of the variables. Three main criteria can be used to retain the more discriminating axes of the analysis: the percentage of the inertia explained; the marginal contribution of the axes to the inertia explained; and the general meaning of the axis which will constitute the new synthetic variables (Benzécri 1992). Since the more the number of axes retained for the AHC, the more variance intra-class of the resulting classes, researchers often only keep two or three axes.

The AHC algorithm proceeds as follows: at each step pairs are formed by merging the closest clusters in order to minimize the within-types variance and to maximize the between-types one. The comparison of these two values (intra versus inter-class variances) is the criterion used for choosing the number of classes to be retained. Finally, in order to highlight the main characteristics of the individuals by class, the co-ordinates of the class centers are represented on the axes determined in the MCA.

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Levy, R., Roux, P. & Wolff, S. An analysis of science–industry collaborative patterns in a large European University. J Technol Transf 34, 1–23 (2009). https://doi.org/10.1007/s10961-007-9044-0

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