IDEAS home Printed from https://ideas.repec.org/a/taf/riadxx/v10y2020i1p45-66.html
   My bibliography  Save this article

A new comparative model for national innovation systems based on machine learning classification techniques

Author

Listed:
  • Ibrahim Alnafrah
  • Bassel Zeno

Abstract

This study aims to cluster and classify national innovation systems (NISs) dynamically based on analysing the structural differences among NISs’ dimensions. This study provides a tool that will help policymakers monitor the process of building and development NIS.Regarding the methodology, machine learning classification and clustering techniques were used, in which clusters represent three level of development: high, medium and low NISs’ clusters.The empirical study includes 36 indicators from 54 countries over 29 years (1980–2008), which are divided into six groups, that represent the different NISs’ dimensions.The results of clustering show a high level of similarity between clusters and the economic and innovation reality in studied countries. Moreover, the results of classification models indicate a high level of accuracy. These models are considered a good tool for monitoring the development process of NIS and enabling policymakers to improve their innovation strategies to accelerate NIS’s development process.

Suggested Citation

  • Ibrahim Alnafrah & Bassel Zeno, 2020. "A new comparative model for national innovation systems based on machine learning classification techniques," Innovation and Development, Taylor & Francis Journals, vol. 10(1), pages 45-66, January.
    • Handle: RePEc:taf:riadxx:v:10:y:2020:i:1:p:45-66
    DOI: 10.1080/2157930X.2018.1564124
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/2157930X.2018.1564124
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/2157930X.2018.1564124?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Martina Halaskova & Beata Gavurova & Kristina Kocisova, 2020. "Research and Development Efficiency in Public and Private Sectors: An Empirical Analysis of EU Countries by Using DEA Methodology," Sustainability, MDPI, vol. 12(17), pages 1-22, August.
    2. Olga A. Chernova & Lyudmila G. Matveeva & Galina V. Gorelova, 2021. "An ecosystem approach to managing innovative development in industry," Journal of New Economy, Ural State University of Economics, vol. 22(2), pages 44-65, July.
    3. Andrade, Eron Passos & Pereira, Jadiel dos Santos & Rocha, Angela Machado & Nascimento, Marcio Luis Ferreira, 2022. "An exploratory analysis of Brazilian universities in the technological innovation process," Technological Forecasting and Social Change, Elsevier, vol. 182(C).

    More about this item

    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:taf:riadxx:v:10:y:2020:i:1:p:45-66. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/riad20 .

    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.