IDEAS home Printed from https://ideas.repec.org/a/gam/jecomi/v13y2025i4p110-d1635448.html
   My bibliography  Save this article

A Quest for Innovation Drivers with Autometrics: Do These Differ Before and After the COVID-19 Pandemic for European Economies?

Author

Listed:
  • Jorge Marques

    (Business Sciences Department, University of Maia, Avenida Carlos de Oliveira Campos—Castelo da Maia, 4475-690 Maia, Portugal)

  • Carlos Santos

    (Business Sciences Department, University of Maia, Avenida Carlos de Oliveira Campos—Castelo da Maia, 4475-690 Maia, Portugal
    NECE-UBI—Research Centre for Business Sciences, Universidade da Beira Interior, Estrada do Sineiro, s/n, 6200-209 Covilhã, Portugal
    CeBER, Faculty of Economics, University of Coimbra, Av Dias da Silva 165, 3004-512 Coimbra, Portugal)

  • Maria Alberta Oliveira

    (Business Sciences Department, University of Maia, Avenida Carlos de Oliveira Campos—Castelo da Maia, 4475-690 Maia, Portugal
    NECE-UBI—Research Centre for Business Sciences, Universidade da Beira Interior, Estrada do Sineiro, s/n, 6200-209 Covilhã, Portugal)

Abstract

The literature regarding innovation drivers is usually based on variables taken from some theoretical approach and validated within a methodology. Some authors have included COVID-19 as a driver for innovations. In this paper, we address the pandemic from a different viewpoint: trying to find if innovation drivers for European countries are the same in pre- and post-pandemic years. The automated general-to-specific model selection algorithm—Autometrics—is used. The main potentially relevant drivers for which data were available for both years and two proxies of innovation (patents and the Summary Innovation Index) were considered. The final models provided by Autometrics allow for valid inference on retained innovation drivers since they have passed a plethora of diagnostic tests, ensuring congruency. The attractiveness of the research system is the most impactful driver on the index in both years but other drivers indeed differ. SMEs’ business process innovation and their cooperation networks matter only in 2022. We found crowding-out effects of public funding of R&D (in both years, for the index). Sustainability was a driver in both periods. The ranking of common drivers also changes. Non-R&D innovation expenditures, the second most relevant before COVID-19, concedes to digitalization. Surprisingly, when patents are the proxy, digitalization is retained before COVID-19, with the attractiveness of the research system replacing it afterwards. Explanations for our findings are suggested. The main implications of our findings for innovation policy seem to be the facilitating role that the government should have in fostering linkages between stakeholders and the capacity the government might have to improve the attractiveness of the research system. Policies based on the public funding of R&D appear ineffective for European countries.

Suggested Citation

  • Jorge Marques & Carlos Santos & Maria Alberta Oliveira, 2025. "A Quest for Innovation Drivers with Autometrics: Do These Differ Before and After the COVID-19 Pandemic for European Economies?," Economies, MDPI, vol. 13(4), pages 1-41, April.
  • Handle: RePEc:gam:jecomi:v:13:y:2025:i:4:p:110-:d:1635448
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7099/13/4/110/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7099/13/4/110/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Faurel, Lucile & Li, Qin & Shanthikumar, Devin & Teoh, Siew H., 2024. "Bringing Innovation to Fruition: Insights From New Trademarks," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 59(2), pages 474-520, March.
    2. Basberg, Bjorn L., 1987. "Patents and the measurement of technological change: A survey of the literature," Research Policy, Elsevier, vol. 16(2-4), pages 131-141, August.
    3. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Felix Pretis, 2015. "Detecting Location Shifts during Model Selection by Step-Indicator Saturation," Econometrics, MDPI, vol. 3(2), pages 1-25, April.
    4. Jurgen A. Doornik & David F. Hendry, 2016. "Outliers and Model Selection: Discussion of the Paper by Søren Johansen and Bent Nielsen," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 360-365, June.
    5. Viviana Costa & Maria Alberta Oliveira & Carlos Santos, 2024. "Assessing the Pandemic Aviation Crisis: Speculative Behavior, Government Bail Outs, and Accommodative Monetary Policy," Economies, MDPI, vol. 12(10), pages 1-24, September.
    6. Fontana, Roberto & Nuvolari, Alessandro & Shimizu, Hiroshi & Vezzulli, Andrea, 2013. "Reassessing patent propensity: Evidence from a dataset of R&D awards, 1977–2004," Research Policy, Elsevier, vol. 42(10), pages 1780-1792.
    7. Ganau, Roberto & Grandinetti, Roberto, 2021. "Disentangling regional innovation capability: what really matters?," LSE Research Online Documents on Economics 114921, London School of Economics and Political Science, LSE Library.
    8. Beynon, Malcolm J. & Jones, Paul & Pickernell, David, 2023. "Evaluating EU-Region level innovation readiness: A longitudinal analysis using principal component analysis and a constellation graph index approach," Journal of Business Research, Elsevier, vol. 159(C).
    9. Roberto Ganau & Roberto Grandinetti, 2021. "Disentangling regional innovation capability: what really matters?," Industry and Innovation, Taylor & Francis Journals, vol. 28(6), pages 749-772, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:osf:socarx:nftv3_v1 is not listed on IDEAS
    2. Weilong Wang & Jianlong Wang & Shaersaikai Wulaer & Bing Chen & Xiaodong Yang, 2021. "The Effect of Innovative Entrepreneurial Vitality on Economic Resilience Based on a Spatial Perspective: Economic Policy Uncertainty as a Moderating Variable," Sustainability, MDPI, vol. 13(19), pages 1-23, September.
    3. Espasa, Antoni & Senra, Eva, 2017. "22 Years of inflation assessment and forecasting experience at the bulletin of EU & US inflation and macroeconomic analysis," DES - Working Papers. Statistics and Econometrics. WS 24678, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Nnaemeka Vincent Emodi & Girish Panchakshara Murthy & Chinenye Comfort Emodi & Adaeze Saratu Augusta Emodi, 2017. "A Literature Review on the Factors Influencing Patent Propensity," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 14(03), pages 1-30, June.
    5. Keyan Zheng & Fagang Hu & Yaliu Yang, 2023. "Data-Driven Evaluation and Recommendations for Regional Synergy Innovation Capability," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    6. Angelo Leogrande & Carlo Drago & Giulio Mallardi & Alberto Costantiello & Nicola Magaletti, 2024. "Patenting Propensity in Italy: A Machine Learning Approach to Regional Clustering," Working Papers hal-04854759, HAL.
    7. Viviana Celli & Augusto Cerqua & Guido Pellegrini, 2024. "Does R&D Expenditure Boost Economic Growth in Lagging Regions?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 173(1), pages 249-268, May.
    8. Antoni Espasa & Eva Senra, 2017. "Twenty-Two Years of Inflation Assessment and Forecasting Experience at the Bulletin of EU & US Inflation and Macroeconomic Analysis," Econometrics, MDPI, vol. 5(4), pages 1-28, October.
    9. Rosalia Castellano & Gaetano Musella & Gennaro Punzo, 2023. "Does context matter? Exploring the effects of productive structures on the relationship between innovation and workforce skills’ complementarity," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 1991-2011, June.
    10. Gaetano Musella & Rosalia Castellano & Emma Bruno, 2023. "Evaluating the spatial heterogeneity of innovation drivers: a comparison between GWR and GWPR," METRON, Springer;Sapienza Università di Roma, vol. 81(3), pages 343-365, December.
    11. Manajit Chakraborty & Maksym Byshkin & Fabio Crestani, 2020. "Patent citation network analysis: A perspective from descriptive statistics and ERGMs," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-28, December.
    12. Guodong Yan & Lin Zou, 2024. "Cluster heterogeneity and efficiency of innovation network—Evidence from Shanghai and Taizhou in China," Growth and Change, Wiley Blackwell, vol. 55(3), September.
    13. Ericsson, Neil R., 2016. "Eliciting GDP forecasts from the FOMC’s minutes around the financial crisis," International Journal of Forecasting, Elsevier, vol. 32(2), pages 571-583.
    14. Fontana, Roberto & Nuvolari, Alessandro & Shimizu, Hiroshi & Vezzulli, Andrea, 2013. "Reassessing patent propensity: Evidence from a dataset of R&D awards, 1977–2004," Research Policy, Elsevier, vol. 42(10), pages 1780-1792.
    15. Luo, Lianfa & Cheng, Zhiming & Ye, Qingqing & Cheng, Yanjun & Smyth, Russell & Yang, Zhiqing & Zhang, Le, 2024. "Nonmonetary awards and innovation: Evidence from winning China's Top Brand Contest," China Economic Review, Elsevier, vol. 86(C).
    16. Dima, Bogdan & Dima, Ştefana Maria & Ioan, Roxana, 2025. "The short-run impact of investor expectations’ past volatility on current predictions: The case of VIX," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 98(C).
    17. Felix Pretis & Michael Mann & Robert Kaufmann, 2015. "Testing competing models of the temperature hiatus: assessing the effects of conditioning variables and temporal uncertainties through sample-wide break detection," Climatic Change, Springer, vol. 131(4), pages 705-718, August.
    18. Bedford, Anna & Ma, Le & Ma, Nelson & Vojvoda, Kristina, 2022. "Australian innovation: Patent database construction and first evidence," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    19. Éric Archambault, 2002. "Methods for using patents in cross-country comparisons," Scientometrics, Springer;Akadémiai Kiadó, vol. 54(1), pages 15-30, April.
    20. Kaihuang Zhang & Qinglan Qian & Yijing Zhao, 2020. "Evolution of Guangzhou Biomedical Industry Innovation Network Structure and Its Proximity Mechanism," Sustainability, MDPI, vol. 12(6), pages 1-20, March.
    21. Karsten Kohler & Engelbert Stockhammer, 2023. "Flexible exchange rates in emerging markets: shock absorbers or drivers of endogenous cycles?," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 32(2), pages 551-572.

    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:gam:jecomi:v:13:y:2025:i:4:p:110-:d:1635448. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.