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Territorial patterns of innovation: a taxonomy of innovative regions in Europe

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Abstract

The recent policy approach to innovation calls for thematically/regionally focused innovation policies in line with the place-based approach (EC – Commission of the European Communities, 2010). To achieve this goal, without incurring the unrealistic situation of having one policy action for each European region, a sound taxonomy on innovative European regions is required. The present paper claims that the existing taxonomies are somewhat unsatisfactory, since either they group European regions only on the basis of the intensity of their knowledge production, taking it for granted that knowledge equates to innovation, or they lack a priori on the conceptual links among the variables used, and the territorial conditions behind local innovation modes. The paper presents a territorial taxonomy of innovative regions based on a new conceptual approach which interprets, not one single phase of the innovation process, but the alternative modes of performing the different phases of the innovation process, highlighting the context conditions that accompany each “territorial pattern of innovation.” The paper conceptually derives different territorial patterns of innovation and identifies them empirically for European regions.

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Notes

  1. We accept the idea of a “linear model of innovation”, since we strongly believe that: (1) in many cases scientific advance is a major source of innovation, as the ICT paradigm and trajectory indicate; (2) an alternative model of full complexity, where ‘everything depends on everything else’, does not help in conceptualizing and interpreting the systemic, dynamic and interactive nature of innovation; (3) self-reinforcing feedbacks from innovation to knowledge and from economic growth to innovation and knowledge play an important role in innovation processes.

  2. Patents were assigned to regions according to the respective inventor’s residence address as available in patent documents. Fractional count is applied. The authors are grateful to CRENoS—University of Cagliari (Italy) for granting access to, and use of, their patent database.

  3. We are aware that this measure may be affected by size effects because bigger regions may have larger shares of total EU patents. However, this is not a major concern because the correlation coefficient between the regional share of EU patents and the share of regional patents normalized by the regional population is about 0.8.

  4. Every patent is attributed to one or more technological classes according to the international patent classification (IPC). We reclassified patents according to a 30 technological field classification that aggregates all IPC codes into 30 technological fields, and then into 7 main technological fields. This is a technology-oriented classification, jointly elaborated by Fraunhofer Gesellschaft-ISI (Karlsruhe), Institut National de la Propriété Industrielle (INPI, Paris) and Observatoire des Sciences and des Techniques (OST, Paris). To compute the generality and the originality indexes, we used the 7-class classification.

  5. For an in-depth explanation of the estimation methodology of NUTS2 CIS data and the benchmark exercises implemented as consistency and robustness checks on our estimates, see Capello et al. (2012). Previous exercises implemented for the DG Industry and DG Regio (Hollanders et al. 2009) elaborated and used as well a dedicated estimation strategy to derive regional innovation data. Notwithstanding the use of a different methodology, our results are reasonably consistent with previous estimates.

  6. The availability of financial resources such as venture capital is certainly crucial for engaging in highly risky and costly activities such as research and innovation. Moreover, the availability of financial services such as venture capital shows a prominent tendency to cluster in space and an uneven distribution at the regional level. Unfortunately, the lack of consistent, comparable and detailed data at the NUTS2 level for all EU countries prevented us from including this element in the analysis, although we acknowledge the importance of this aspect when studying innovation processes. As mentioned in the main text, we indirectly controlled for this by means of the dummy variable for agglomerated regions.

  7. See Appendix for the list of variables used and details about the factor analysis.

  8. We opted for the k-means approach because, in the literature, it is preferred to hierarchical approaches (Afifi et al. 2004). The algorithm implemented by k-means cluster analysis assigns a case to the cluster for which its distance to the cluster mean is the smallest. Once the ‘k’ number of expected clusters has been specified, the algorithm starts with an initial set of means and classifies cases based on their distances to the centers. Next, it computes the cluster means again, using the cases assigned to the cluster, and it reclassifies all cases according to the new set of means. This step is repeated until cluster means do not change much between successive steps. Finally, the means of the clusters are calculated once again, and the cases are assigned to their permanent clusters.

  9. The F test was used only for descriptive purposes because the clusters were chosen precisely to maximize the differences among cases in different clusters. The observed significance levels were not corrected for this and therefore cannot be interpreted as tests of the hypothesis that the clusters means are equal.

  10. Lisbon and Attiki’s position in this cluster may be affected by a general overestimation of CIS data at national level encountered for Greece and Portugal, a common risk of all survey data based on respondents’ self-reported evaluation, that can be considered as a limitation of the CIS data collection strategy and of its final national figures.

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Authors and Affiliations

  1. Department of Building, Environment, Science and Technology (BEST), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 , Milan, Italy

    Roberta Capello & Camilla Lenzi

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  1. Roberta Capello
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  2. Camilla Lenzi
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Correspondence to Roberta Capello.

Additional information

This paper is part of the ESPON KIT project, financed within the ESPON program, and led by the authors at the Politecnico of Milan. More on the project can be found at the website: http://www.espon.eu/main/Menu_Projects/Menu_AppliedResearch/kit.html. The authors are grateful to two anonymous referees for their useful suggestions. Usual disclaims apply.

Appendix: Eurobarometer survey

Appendix: Eurobarometer survey

To extract the factor “Strategic thinking on innovation,” we used the following questions from the Eurobarometer Survey 63.4 (Table 6):

Table 6 Factor loadings
Full size table

  • Innovation simplifies everyday life (% of people mentioning this statement), Q396;

  • A company that sells an innovative product or service improves the image of all its products or services (% of people mentioning this statement), Q397;

  • A company which does not innovate is a company that will not survive (% of people mentioning this statement), Q398;

  • Innovation is essential for improving economic growth (% of people mentioning this statement), Q401;

  • Broadband penetration rate (% of households with broadband access) from Eurostat, Qbb.

To extract the factor “Creativity,” we used the following questions from the Eurobarometer Survey 63.4 (Table 6):

  • In general, to what extent are you attracted toward innovative products or services, in other words new or improved products or services? (% of people that are very or fairly attracted to new products), Q398;

  • Compared to your friends and family, would you say that you tend to be more inclined to purchase innovative products or services? (% of people that are more inclined than the average to buy innovative products), Q390;

  • In general, when an innovative product or service is put on the market and can replace a product or service that you already trust and regularly buy, do you quickly try the innovative product or service at least once? (% of people that shift easily consumption patterns toward innovative products), Q392;

  • Innovative products or services are most of the time gadgets (% of people not mentioning this statement), Q394;

  • Innovative products or services are a matter of fashion (% of people not mentioning this statement), Q395;

  • The advantages of innovative products or services are often exaggerated (% of people not mentioning this statement), Q400;

We extracted the two factors by means of principal component analysis and applied a varimax rotation method with Kaiser normalization. The percentage of variance explained is 62.54. In this analysis, within each component, we considered the variables with a factor loading greater than 0.55. Table 6 reports the factor loadings; factor loadings greater than 0.55 are in bold.

 

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Capello, R., Lenzi, C. Territorial patterns of innovation: a taxonomy of innovative regions in Europe. Ann Reg Sci 51, 119–154 (2013). https://doi.org/10.1007/s00168-012-0539-8

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