Marketing and Management of Innovations

ISSN (print) – 2218-4511 

ISSN (online) – 2227-6718

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Identifier in the register: R30-01179 Decision dated August 31, 2023, No. 759

The language of publication is English. 

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Oleksii Lyulyov

Sumy State University | Ukraine

Evaluating Innovation Efficiency in EU Countries: the DEA Approach

Veronika Cabinova 1, *, , , Jana Burgerova 1,  , Peter Gallo 1,  
  1. University of Presov, the Slovak Republic

     * Corresponding author

DOI: https://doi.org/10.21272/mmi.2024.4-02

Pages: 17–30

Received: 16 February 2024

Revised: 19 October 2024

Accepted: 16 December 2024

Abstract

Innovation, science and technology, which are among the most important tools for achieving economic growth, prosperity and competitiveness in the local and global business environments, are increasingly gaining attention. Thus, improving the level of innovation efficiency of countries should be one of the EU priorities. The aim of this paper is to analyse the development of the innovation efficiency of EU member states and to assess the use of resources entering their national innovation systems. To determine the efficiency of the EU countries, basic output-oriented DEA models were applied. The data were processed from databases of the World Bank. First, the development and comparative analysis of input variables (government expenditure on education as a percentage of gross domestic product, research & development expenditure as a percentage of gross domestic product, researchers in research & development per million people) and output variables (patent applications, high-technology exports as a percentage of manufactured exports and scientific and technical journal articles) was performed. The level of efficiency of individual EU countries was subsequently quantified via DEA Solver (LV 8.0) software. Based on the scaling method, 5 groups of countries with similar levels of efficiency were identified and presented in the cartogram (efficient countries, above-average efficient countries, average efficient countries, below-average efficient countries, and inefficient countries). Over the period analysed, a total of 6 countries were identified as efficient – France, Germany, Ireland, Italy, Malta, Romania (and the United Kingdom in 2018–2019). Countries such as Sweden, Denmark, Belgium, Finland and Austria recorded the highest values of the selected inputs, but the efficiency score showed average to below-average results. The findings of this study demonstrated that many of the top-ranked nations in global innovation rankings are misusing and underutilizing the resources that enter their national innovation systems. Makers of policies and strategic plans for the innovation efficiency of EU countries will thus have the opportunity to incorporate the results of the study into real proposals and solutions.

Keywords: data envelopment analysis; inputs; national innovation strategy; outputs; research, development. 

How to Cite: Cabinova, V., Burgerova, J., & Gallo, P. (2024). Evaluating Innovation Efficiency in EU Countries: the DEA Approach. Marketing and Management of Innovations, 15(4), 17–30. https://doi.org/10.21272/mmi.2024.4-02

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References

  1. Abbasi, F., Hajihoseini, H., & Haukka, S. (2011). Use of Virtual Index for Measuring Efficiency of Innovation Systems: A Cross-Country Study. International Journal of Technology Management and Sustainable Development, 9(3), 195–212. [GoogleScholar] [CrossRef]
  2. Afcha, S., & García-Quevedo, J. (2016). The Impact of R&D Subsidies on R&D Employment Composition. Industrial and Corporate Change, 25(6), 955- [GoogleScholar] [CrossRef]
  3. Afzal, M. N. I. (2014). An Empirical Investigation of the National Innovation System (NIS) Using Data Envelopment Analysis (DEA) and the TOBIT Model. International Review of Applied Economics, 28(4), 507–523. [GoogleScholar] [CrossRef]
  4. Alnafrah, I. (2021). Efficiency Evaluation of BRICS’s National Innovation Systems Based on Bias-Corrected Network Data Envelopment Analysis. Journal of Innovation and Entrepreneurship, 10(1), 1–28. [GoogleScholar] [CrossRef]
  5. Andrijauskiene, M., Ioannidis, D., Dumciuviene, D., Dimara, A., Bezas, N., Papaioannou, A., & Krinidis, S. (2023). European Union Innovation Efficiency Assessment Based on Data Envelopment Analysis. Economies, 11(6), Article 163. [GoogleScholar] [CrossRef]
  6. Aytekin, A., Ecer, F., Korucuk, S., & Karamasa, C. (2022). Global Innovation Efficiency Assessment of EU Member and Candidate Countries Via DEA-EATWIOS Multi-Criteria Methodology. Technology in Society, 68(2), 1–11. [GoogleScholar] [CrossRef]
  7. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078–1092. [GoogleScholar] [CrossRef]
  8. Barbero, J., Zabala-Iturriagagoitia, J. M., & Zofío, J. L. (2021). Is More Always Better? On the Relevance of Decreasing Returns to Scale on Innovation. Technovation, 107, Article 102314. [GoogleScholar] [CrossRef]
  9. Bianchini, S., Llerena, P., & Martino, R. (2019). The impact of R&D subsidies under Different Institutional Frameworks. Structural Change and Economic Dynamics, 50, 65- [GoogleScholar] [CrossRef]
  10. Bilan, Y., Lyeonov, S., Lyulyov, O., & Pimonenko, T. (2019). Brand Management and Macroeconomic Stability of the Country. Polish Journal of Management Studies, 19(2), 61–74. [GoogleScholar] [CrossRef]
  11. Bresciani, S., Puertas, R., Ferraris, A., & Santoro, G. (2021). Innovation, Environmental Sustainability and Economic Development: DEA-Bootstrap and Multilevel Analysis to Compare Two Regions. Technological Forecasting & Social Change, 172(2021), 121040. [GoogleScholar][CrossRef]
  12. Cabinova, V., Onuferova, E., Gallo, P. Jr., Gallo, P., & Gallo, J. (2018). A Comparative Analysis of Modern Performance Methods in Economic Practice. Montenegrin Journal of Economics, 14(4), 85–96. [GoogleScholar] [CrossRef]
  13. Cai, Y. (2011). Factors Affecting the Efficiency of the BRICS’ National Innovation Systems: A Comparative Study Based on DEA and Panel Data Analysis. Discussion Papers, 2011(52), 1–24. [GoogleScholar] [CrossRef]
  14. Cano-Kollmann, M., Hamilton, R., & Mudambi, R. (2017). Public Support for Innovation and the Openness of Firms’ Innovation Activities. Industrial and Corporate Change, 26(3), 421- [GoogleScholar] [CrossRef]
  15. Carayannis, E. G., Grigoroudis, E., & Goletsis, Y. (2016). A Multilevel and Multistage Efficiency Evaluation of Innovation Systems: A Multiobjective DEA Approach. Expert Systems with Applications, 62, 63- [GoogleScholar] [CrossRef]
  16. Castillo, V., Garone, L. F., Maffioli, A., Rojo, S., & Stucchi, R. (2020). Knowledge Spillovers Through Labour Mobility: An Employer–Employee Analysis. The Journal of Development Studies, 56(3), 469- [GoogleScholar] [CrossRef]
  17. Cozzens, S. E. (2012). Editor’s Introduction: Distributional Consequences of Emerging Technologies. Technological Forecasting and Social Change, 79(2), 199- [GoogleScholar] [CrossRef]
  18. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 2(6), 429–444. [GoogleScholar] [CrossRef]
  19. Chapman, G., & Hewitt-Dundas, N. (2018). The Effect of Public Support on Senior Manager Attitudes to Innovation. Technovation, 69,28- [GoogleScholar] [CrossRef]
  20. Chen, C. P., Hu, J. L., & Yang, C. H. (2011). An International Comparison of R&D Efficiency of Multiple Innovative Outputs: The Role of the National Innovation System. Innovation: Management, Policy and Practice, 13(3), 341–360. [GoogleScholar] [CrossRef]
  21. Danova, M., Kravcakova Vozarova, I., & Sira, E. (2021). Innovations in Human Resources Management: Impact on Economic Growth. Marketing and Management of Innovations, 3, 53–65. [GoogleScholar] [CrossRef]
  22. Debreu, G. (1951). The Coefficient of Resource Utilization. Econometrica, 19(3), 273–292. [GoogleScholar] [CrossRef]
  23. Dumont, M. (2017). Assessing the Policy Mix of Public Support to Business R&D. Research Policy, 46(10), 1851- [GoogleScholar][CrossRef]
  24. Edquist, Ch., Zabala-Iturriagagoitia, J. M., Barbero, J., & Zofío, J. L. (2018). On the Meaning of Innovation Performance: Is the Synthetic Indicator of the Innovation Union Scoreboard Flawed? Research Evaluation, 27(3), 196–211. [GoogleScholar] [CrossRef]
  25. Emrouznejad, A., & Yang, G. L. (2018). A Survey and Analysis of the First 40 Years of Scholarly Literature in DEA: 1978 – 2016. Socio-Economic Planning Sciences, 2018(6), 4–8. [GoogleScholar] [CrossRef]
  26. Erdin, C., & Çağlar, M. (2023). National Innovation Efficiency: A DEA-Based Measurement of OECD Countries. International Journal of Innovation Science, 15(3), 427–456. [GoogleScholar] [CrossRef]
  27. Eskelinen, J. (2017). Comparison of Variable Selection Techniques for Data Envelopment Analysis in a Retail Bank. European Journal of Operational Research, 259(2), 778–788. [GoogleScholar] [CrossRef]
  28. European Commission (2022). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Region: A New European Innovation Agenda. [Link]
  29. Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A, 120(3), 253- [GoogleScholar] [CrossRef]
  30. Friedman, L., & Sinuany-Stern, Z. (1998). Combining Ranking Scales and Selecting Variables in the DEA Context: The Case of Industrial Branches. Computers & Operations Research, 25(9), 781–791. [GoogleScholar] [CrossRef]
  31. Gavurova, B., Halaskova, M., & Korony, S. (2019). Research and Development Indicators of EU28 Countries from Viewpoint of Super-Efficiency DEA Analysis. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 67(1), 225–242. [GoogleScholar] [CrossRef]
  32. Guan, J., & Chen, K. (2012). Modelling the Relative Efficiency of National Innovation Systems. Research Policy, 41(1), 102−115. [GoogleScholar] [CrossRef]
  33. Guo, D., Guo, Y., & Jiang, K. (2018). Governance and Effects of Public R&D Subsidies: Evidence from China. Technovation, 74–75,18- [GoogleScholar] [CrossRef]
  34. Hauser, C., Siller, M., Schatzer, T., Walde, J., & Tappeiner, G. (2018). Measuring regional innovation: A Critical Inspection of the Ability of Single Indicators to Shape Technological Change. Technological Forecasting and Social Change, 129, 43–55. [GoogleScholar] [CrossRef]
  35. Hewitt-Dundas, N., & Roper, S. (2018). Exploring Market Failures in Open Innovation. International Small Business Journal: Researching Entrepreneurship, 36(1), 1- [GoogleScholar] [CrossRef]
  36. Huergo, E., Trenado, M., & Ubierna, A. (2016). The Impact of Public Support on Firm Propensity to Engage in R&D: Spanish Experience. Technological Forecasting and Social Change, 113, 206- [GoogleScholar] [CrossRef]
  37. Hushko, S., Kulishov, V., Hangoni, T., Puriy, H., Kuzysin, B., & Sip, M. (2021). Global Work Mobility in the Integration Conditions. Quality – Access to Success, 22(183), 72–77. [GoogleScholar] [CrossRef]
  38. Janger, J., Schubert, T., Andries, P., Rammer, C., & Hoskens, M. (2017). The EU 2020 Innovation Indicator: A Step Forward in Measuring Innovation Outputs and Outcomes? Research Policy, 46(1), 30–42. [GoogleScholar] [CrossRef]
  39. Jugend, D., Fiorini, P. D., Armellini, F., & Ferrari, A. G. (2020). Public Support for Innovation: A Systematic Review of the Literature and Implications for Open Innovation. Technological Forecasting and Social Change, 156, [GoogleScholar] [CrossRef]
  40. Jurickova, E., Pilik, M., & Kwarteng, M. A. (2019). Efficiency Measurement of National Innovation Systems of the European Union Countries: DEA Model Application. Journal of International Studies, 12(4), 286–299. [GoogleScholar] [CrossRef]
  41. Kang, K. N., & Park, H. (2012). Influence of Government R&D Support and Inter-Firm Collaborations on Innovation in Korean Biotechnology SMEs. Technovation, 32(1), 68- [GoogleScholar] [CrossRef]
  42. Kontolaimou, A., Giotopoulos, I., & Tsakanikas, A. (2016). A Typology of European Countries Based on Innovation Efficiency and Technology Gaps: The Role of Early-Stage Entrepreneurship. Economic Modelling, 52, 477−484. [GoogleScholar] [CrossRef]
  43. Kwilinski, A., Lyulyov, O., & Pimonenko, T. (2023a). Environmental Sustainability within Attaining Sustainable Development Goals: The Role of Digitalization and the Transport Sector. Sustainability, 15(14), 1–14. [GoogleScholar] [CrossRef]
  44. Kwilinski, A., Lyulyov, & O., Pimonenko, T. (2023b). Unlocking Sustainable Value through Digital Transformation: An Examination of ESG Performance. Information, 14(8), 1–18. [GoogleScholar] [CrossRef]
  45. Levchenko, V., Kobzieva, T., Boiko, A., & Shlapko, T. (2018). Innovations in Assessing the Efficiency of the Instruments for the National Economy De-Shadowing: The State Management Aspect. Marketing and Management of Innovations, 4, 361–371. [GoogleScholar] [CrossRef]
  46. Lipkova, L., & Braga, D. (2016). Measuring Commercialization Success of Innovations in the EU. Marketing and Management of Innovations, 4, 15–30. [GoogleScholar]
  47. Matei, M. M., & Aldea, A. (2012). Ranking National Innovation Systems According to their Technical Efficiency. Procedia – Social and Behavioral Sciences, 62(2012), 968–974. [GoogleScholar] [CrossRef]
  48. Narayanan, E., Ismail, W. R., & Mustafa, Z. (2022). A Data-Envelopment Analysis-Based Systematic Review of the Literature on Innovation Performance. Heliyon, 8(2022), 1–15. [GoogleScholar] [CrossRef]
  49. Omrani, H., Valipour, M., & Mamakani, S. J. (2019). Construct a Composite Indicator Based on Integrating Common Weight Data Envelopment Analysis and Principal Component Analysis Models: An Application for Finding Development Degree of Provinces in Iran. Socio-Economic Planning Sciences, 68, 100618. [GoogleScholar] [CrossRef]
  50. Pan, Т. W., Hung, S. V., & Lu, W. M. (2010). DEA Performance Measurement of the National Innovation System in Asia and Europe. Asia-Pacific Journal of Operational Research, 27(3), 369–392. [GoogleScholar] [CrossRef]
  51. Radicic, D. (2019). Effectiveness of Public Procurement of Innovation versus Supply-Side Innovation Measures in Manufacturing and Service Sectors. Science and Public Policy, 46(5), 732- [GoogleScholar] [CrossRef]
  52. Ratner, S. V., Balashova, S. A., Lychev, A. V. (2022). The Efficiency of National Innovation Systems in Post-Soviet Countries: DEA-Based Approach. Mathematics, 10, 1–23. [GoogleScholar] [CrossRef]
  53. Royston, P. (1995). Remark AS R94: A Remark on Algorithm AS 181: The W-test for Normality. Applied Statistics, 44(4), 547–551. [GoogleScholar] [CrossRef]
  54. Slavinskaite, N., Lapinskiene, G., Hlawiczka, R., & Vasa, L. (2022). Financial Innovation Management: Impact of Fiscal Decentralization on Economic Growth of the Baltic Countries. Marketing and Management of Innovations, 1, 257–271. [GoogleScholar] [CrossRef]
  55. Smoliy, L., Revutska, A., & Novak, I. (2018). Influence of Innovation Factor in Economic Dynamics in Europe. Marketing and Management of Innovations, 1, 247- [GoogleScholar] [CrossRef]
  56. Spanos, Y. E., Vonortas, N. S., & Voudouris, I. (2015). Antecedents of Innovation Impacts in Publicly Funded Collaborative R&D Projects. Technovation, 36-37, 53- [GoogleScholar] [CrossRef]
  57. Sip, M., Kuzysin, B., Sabolik, M, & Valco, M. (2023). Human Dignity in Inpatient Care: Fragments of Religious and Social Grounds. Religions, 14(6), 1–16. [GoogleScholar] [CrossRef]
  58. Toloo, M, Keshavarz, E. & Hatami-Marbini, A. (2021). Selecting Data Envelopment Analysis Models: A Data-Driven Application to EU Countries. Omega, 101(2021), Article 102248. [GoogleScholar] [CrossRef]
  59. Vanino, E., Roper, S., & Becker, B. (2019). Knowledge to Money: assessing the Business Performance Effects of Publicly Funded R&D Grants. Research Policy, 48(7), 1714- [GoogleScholar] [CrossRef]
  60. Vokoun, M. (2016). Innovation Behaviour of Firms in a Small Open Economy: The Case of the Czech Manufacturing Industry. Empirica, 43(1), 111- [GoogleScholar] [CrossRef]
  61. Wang, S., Fan, J., Zhao, D., & Wang, S. (2016). Regional Innovation Environment and Innovation Efficiency: The Chinese Case. Technology Analysis & Strategic Management, 28(4), 396–410. [GoogleScholar] [CrossRef]
  62. Wei, J., & Liu, Y. (2015). Government Support and Firm Innovation Performance: Empirical Analysis of 343 Innovative Enterprises in China. Chinese Management Studies, 9(1), 38- [GoogleScholar] [CrossRef]
  63. World Bank (2022). Indicators. [Link]
  64. Xu, K., Mei, R., Liang, L., & Sun, W. (2023). Regional Convergence Analysis of Sustainable Innovation Efficiency in European Union Countries. Journal of Environmental Management, 325, Article 116636. [GoogleScholar] [CrossRef]

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