IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2409.13316.html
   My bibliography  Save this paper

Why do we need to complement the European Union Regional Innovation Scoreboard with a cluster-neural network tool for what-if policy analysis?

Author

Listed:
  • Vincenzo Lanzetta
  • Cristina Ponsiglione

Abstract

The European Union Regional Innovation Scoreboard (EURIS) is currently and broadly used for the definition of regional innovation policies by European policymakers; it is a regional innovation measuring tool for the analysis of each specific innovation indicator, from which it is possible to analyze the overtime evolution of each regional innovation indicator; according to the importance of the European Union Regional Innovation Scoreboard for innovation policy purposes, we state that European regional policymakers need integrative and synergistic methodological tools, with respect to the EURIS one, for innovation policy purposes. Thus, we highlight the need to integrate the current methodology of the European Regional Innovation Scoreboard with a Factorial K-means (FKM) tool for clustering purposes, and with a neural network (NN) tool for performing what-if policy analyses. We claim that our proposed FKM-NN tool could be used, by regional innovation policymakers, as a very effective synergistic instrument of the European Union Regional Innovation Scoreboard.

Suggested Citation

  • Vincenzo Lanzetta & Cristina Ponsiglione, 2024. "Why do we need to complement the European Union Regional Innovation Scoreboard with a cluster-neural network tool for what-if policy analysis?," Papers 2409.13316, arXiv.org.
  • Handle: RePEc:arx:papers:2409.13316
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2409.13316
    File Function: Latest version
    Download Restriction: no
    ---><---

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2409.13316. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.