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Territorial innovation models: to be or not to be, that’s the question

Abstract

Industrial agglomerations are key in explaining the development paths followed by territories, particularly at sub-national levels. This field of research has received increasing attention in the last decades, what has led to the emergence of a variety of models intended to characterize innovation at the regional level. Moulaert and Sekia (Reg Stud 37:289–302, 2003) introduced the concept of ‘Territorial Innovation Models’ (TIMs) as a generic name that embraced these conceptual models of regional innovation in the literature. However, the literature does not help to assess the extent to which convergence or divergence is found across TIMs. In this paper we aim to clarify if there are clear boundaries across TIMs, so each TIM has particular characteristics that make it conceptually different from others, and hence, justify its introduction in the literature. Based on natural language processing methodologies, we extract the key terms of a large volume of academic papers published in peer review journals indexed in the Web of Science for the following TIMS: industrial districts, innovative milieu, learning regions, clusters, regional innovation systems, local production systems and new industrial spaces. We resort to Rapid Automatic Keyword Extraction to identify the associations between the topics extracted from the previous corpus. Finally, a configuration to visualise the results of the methodology followed is also proposed. Our results evidence that the previous models do not have a unique flavour but are rather similar in their taste. We evidence that there is quite little that is truly new in the different TIMs in terms of theory-building and the concepts being used in each model.

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Notes

  1. 1.

    Similar categorizations have also other been provided by scholars such as O’Gorman and Kautonen (2004), Moulaert and Nussbaumer (2005) or Cainelli (2008) to mention a few.

  2. 2.

    We used both the English 'innovative milieu' and the French 'milieux innovateur' as search keywords.

  3. 3.

    In her analysis, Porto Gómez (2014) also included the competitive poles (i.e. Pôles de compétitivité) as a TIM.

  4. 4.

    The details of the procedure followed in each of these algorithms are provided in “Topic extraction”, “Topic aggregation” and “Topic association” sections below.

  5. 5.

    The choice of the Web of Science as the main source of data is due to the quality and peer review process of the journals included in it (Archambault et al. 2009; Bergman 2012; Amara and Landry 2012). Since 2004, Google Scholar or Scopus have also emerged as relevant sources of scientific information. However, since many of the articles in the TIMs literature date back to the 1980s and 1990s, we have resorted to the Web of Science.

  6. 6.

    This tool was developed for the European Project DANTE. DANTE (Detecting and analysing terrorist-related online contents and financing activities) aims to deliver more effective, efficient, automated data mining and analytics solutions, and an integrated system to detect, retrieve, collect and analyse huge amount of heterogeneous and complex multimedia and multi-language terrorist-related contents, from both the Surface and the Deep Web, including Dark nets. See: http://www.h2020-dante.eu/.

  7. 7.

    “Appendix 1” provides the details for the keywords of each TIM.

  8. 8.

    The Tables showing how the original keywords extracted from the text of the pdfs for each TIM correspond to the meta-keywords identified in Table 1 are available upon request.

  9. 9.

    See https://www.r-project.org/.

  10. 10.

    The graphical representations of the keywords associated to each TIM are available upon request.

  11. 11.

    The meta-keywords highlighted in bold below represent those identified as key distinctive features of TIMs by Moulaert and Sekia (2003). The remaining are the meta-keywords we have added to complement their analysis.

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Funding

The funding was provided by Eusko Jaurlaritza (Grand No. IT885-16) and H2020 Societal Challenges (Grand No. H2020-700367).

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Correspondence to Igone Porto Gómez.

Appendix

Appendix

See Table 3.Footnote 11

Table 3 Relative weight of the meta-keywords in each of the TIMs considered.

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Doloreux, D., Gaviria de la Puerta, J., Pastor-López, I. et al. Territorial innovation models: to be or not to be, that’s the question. Scientometrics 120, 1163–1191 (2019). https://doi.org/10.1007/s11192-019-03181-1

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Keywords

  • Territorial innovation models
  • Bibliometric analysis
  • Natural language processing
  • Regional development