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Using multiobjective mathematical programming to link national competitiveness, productivity, and innovation

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  • Elias G. Carayannis

    (The George Washington University)

  • Evangelos Grigoroudis

    (Technical University of Crete)

Abstract

Innovation-driven competitiveness is critical for a country’s long run economic performance in today’s knowledge-based global economy. Although several alternative measures of innovation, productivity, and competitiveness have been proposed, these concepts are inherently linked and this justifies the necessity of studying them in an integrated way, giving emphasis on their potential interrelations. This paper proposes a methodological measurement framework based on multiobjective mathematical programming in order to study the linkage among national innovation, productivity, and competitiveness and discover potential performance patterns. The model is applied in a set of European countries for the period 1998–2008. The empirical results reveal important gaps and show that innovativeness, income, and geographic area significantly affect national performances.

Suggested Citation

  • Elias G. Carayannis & Evangelos Grigoroudis, 2016. "Using multiobjective mathematical programming to link national competitiveness, productivity, and innovation," Annals of Operations Research, Springer, vol. 247(2), pages 635-655, December.
    • Handle: RePEc:spr:annopr:v:247:y:2016:i:2:d:10.1007_s10479-015-1873-x
    DOI: 10.1007/s10479-015-1873-x
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    References listed on IDEAS

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    Cited by:

    1. Dorgyles C.M. Kouakou, 2022. "Separating innovation short-run and long-run technical efficiencies: Evidence from the Economic Community of West African States (ECOWAS)," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 19(1), pages 103-141, June.
    2. Zabala-Iturriagagoitia, Jon Mikel & Aparicio, Juan & Ortiz, Lidia & Carayannis, Elias G. & Grigoroudis, Evangelos, 2021. "The productivity of national innovation systems in Europe: Catching up or falling behind?," Technovation, Elsevier, vol. 102(C).
    3. Arif, Asma & An, Pengli & Qi, Yajie & Li, Huajiao & An, Haizhong & Hussain, Mujahid & Wang, Yanli, 2021. "The influence factors of the national roles in the FDI network: A combined methods of complex networks and Panel Data Analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    4. G. Martinidis & N. Komninos & E. Carayannis, 2022. "Taking into Account the Human Factor in Regional Innovation Systems and Policies," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(2), pages 849-879, June.
    5. Yu, Tiffany Hui-Kuang & Huarng, Kun-Huang & Huang, Duen-Huang, 2021. "Causal complexity analysis of the Global Innovation Index," Journal of Business Research, Elsevier, vol. 137(C), pages 39-45.
    6. Ioannis Sitaridis & Fotis Kitsios, 2020. "Competitiveness analysis and evaluation of entrepreneurial ecosystems: a multi-criteria approach," Annals of Operations Research, Springer, vol. 294(1), pages 377-399, November.
    7. Carayannis, Elias G. & Grigoroudis, Evangelos & Wurth, Bernd, 2022. "OR for entrepreneurial ecosystems: A problem-oriented review and agenda," European Journal of Operational Research, Elsevier, vol. 300(3), pages 791-808.
    8. Carayannis, Elias G. & Goletsis, Yorgos & Grigoroudis, Evangelos, 2018. "Composite innovation metrics: MCDA and the Quadruple Innovation Helix framework," Technological Forecasting and Social Change, Elsevier, vol. 131(C), pages 4-17.
    9. Dorgyles C.M. Kouakou, 2022. "Separating innovation short-run and long-run technical efficiencies: Evidence from the Economic Community of West African States (ECOWAS)," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 19(1), pages 103-141, June.

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