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Cross-country analysis of science, technology and innovation policies: non-covid-19 related and Covid-19 specific STI policies in OECD countries

Author

Listed:
  • Margherita Russo

    (Università di Modena e Reggio Emilia)

  • Pasquale Pavone

    (Università di Modena e Reggio Emilia)

  • Dirk Meissner

    (National Research University Higher School of Economics, Russian Federation)

  • Fabrizio Alboni

    (Università di Bologna)

Abstract

In OECD countries, Science, Technology and Innovation (STI) policies were seen as key aspects of coping with the Covid-19 pandemic. Now that the pandemic is over, identifying which policy mix portfolios characterised countries in terms of their non-Covid-19 related and Covid-19 specific STI policies fills a knowledge gap on changes in STI policies induced by exogenous shocks. The descriptive nature of this exercise sheds light on the emergency phase, which was addressed in different ways by countries with similar STI policy portfolios in the last decade before the pandemic. Using information on STI policy initiatives in OECD countries, this paper proposes a multidimensional analysis to classify policy initiatives based on both codes (of innovation policy themes, policy instruments and target beneficiaries) and free text policies’ descriptions. Based on text mining and clustering techniques, the multidimensional analysis highlights semantic similarities between the combinations of codes and terms, making it possible to identify policy mixes that characterise non-Covid-19 related and Covid-19 specific STI policies. The cross-country comparison draws attention to the specific policy mix portfolios implemented by countries during the pandemic. The paper contributes to the literature on innovation policy mix in terms of research methods and results in identifying STI policy portfolios and groups of countries with similar structural composition of their innovation policy portfolios, implementing a range of STI strategies in tackling the pandemic. Policy implications of the findings are discussed, with a forward-looking perspective for the analysis of post-pandemic STI policies.

Suggested Citation

  • Margherita Russo & Pasquale Pavone & Dirk Meissner & Fabrizio Alboni, 2025. "Cross-country analysis of science, technology and innovation policies: non-covid-19 related and Covid-19 specific STI policies in OECD countries," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(1), pages 343-367, February.
    • Handle: RePEc:spr:qualqt:v:59:y:2025:i:1:d:10.1007_s11135-024-01982-y
    DOI: 10.1007/s11135-024-01982-y
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    References listed on IDEAS

    as
    1. Dirk Meissner & Sandrine Kergroach, 2021. "Innovation policy mix: mapping and measurement," The Journal of Technology Transfer, Springer, vol. 46(1), pages 197-222, February.
    2. Fabrizio Alboni & Pasquale Pavone & Margherita Russo, 2023. "The search for topics related to electric mobility: a comparative analysis of some of the most widely used methods in the literature," METRON, Springer;Sapienza Università di Roma, vol. 81(3), pages 367-391, December.
    3. Dirk Meissner & Sandrine Kergroach, 2021. "Correction to: Innovation policy mix: mapping and measurement," The Journal of Technology Transfer, Springer, vol. 46(5), pages 1705-1705, October.
    4. Russo, Margherita & Pavone, Pasquale, 2021. "Evidence-based portfolios of innovation policy mixes: A cross-country analysis," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    5. Howoldt, David, 2024. "Characterising innovation policy mixes in innovation systems," Research Policy, Elsevier, vol. 53(2).
    6. Barry Bozeman, 2022. "Use of science in public policy: Lessons from the COVID-19 pandemic efforts to ‘Follow the Science’ [Health-protective Behaviour, Social Media Usage and Conspiracy Belief during the COVID-19 Public," Science and Public Policy, Oxford University Press, vol. 49(5), pages 806-817.
    7. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
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