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European Union Innovation Efficiency Assessment Based on Data Envelopment Analysis

Author

Listed:
  • Meda Andrijauskiene

    (School of Economics and Business, Kaunas University of Technology, LT-44029 Kaunas, Lithuania)

  • Dimosthenis Ioannidis

    (Centre for Research and Technology (CERTH)—Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

  • Daiva Dumciuviene

    (School of Economics and Business, Kaunas University of Technology, LT-44029 Kaunas, Lithuania)

  • Asimina Dimara

    (Centre for Research and Technology (CERTH)—Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
    Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece)

  • Napoleon Bezas

    (Centre for Research and Technology (CERTH)—Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

  • Alexios Papaioannou

    (Centre for Research and Technology (CERTH)—Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

  • Stelios Krinidis

    (Centre for Research and Technology (CERTH)—Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece)

Abstract

Though much attention is dedicated to the development of its research and innovation policy, the European Union constantly struggles to match the level of the strongest innovators in the world. Therefore, there is a necessity to analyze the individual efforts and conditions of the 27 member states that might determine their final innovative performance. The results of a scientific literature review showed that there is a growing interest in the usage of artificial intelligence when seeking to improve decision-making processes. Data envelopment analysis, as a branch of computational intelligence methods, has proved to be a reliable tool for innovation efficiency evaluation. Therefore, this paper aimed to apply DEA for the assessment of the European Union’s innovation efficiency from 2000 to 2020, when innovation was measured by patent, trademark, and design applications. The findings showed that the general EU innovation efficiency situation has improved over time, meaning that each programming period was more successful than the previous one. On the other hand, visible disparities were found across the member states, showing that Luxembourg is an absolute innovation efficiency leader, while Greece and Portugal achieved the lowest average efficiency scores. Both the application of the DEA method and the gathered results may act as viable guidelines on how to improve R&I policies and select future investment directions.

Suggested Citation

  • Meda Andrijauskiene & Dimosthenis Ioannidis & Daiva Dumciuviene & Asimina Dimara & Napoleon Bezas & Alexios Papaioannou & Stelios Krinidis, 2023. "European Union Innovation Efficiency Assessment Based on Data Envelopment Analysis," Economies, MDPI, vol. 11(6), pages 1-19, June.
    • Handle: RePEc:gam:jecomi:v:11:y:2023:i:6:p:163-:d:1167131
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    References listed on IDEAS

    as
    1. Attila Varga & Tamás Sebestyén, 2017. "Does EU Framework Program Participation Affect Regional Innovation? The Differentiating Role of Economic Development," International Regional Science Review, , vol. 40(4), pages 405-439, July.
    2. Aytekin, Ahmet & Ecer, Fatih & Korucuk, Selçuk & Karamaşa, Çağlar, 2022. "Global innovation efficiency assessment of EU member and candidate countries via DEA-EATWIOS multi-criteria methodology," Technology in Society, Elsevier, vol. 68(C).
    3. Ben R. Martin, 2016. "Twenty challenges for innovation studies," Science and Public Policy, Oxford University Press, vol. 43(3), pages 432-450.
    4. Henry Junior Anderson & Jan Stejskal, 2019. "Diffusion Efficiency of Innovation among EU Member States: A Data Envelopment Analysis," Economies, MDPI, vol. 7(2), pages 1-19, April.
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