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Science, Technology and Innovation Policy Indicators and Comparisons of Countries through a Hybrid Model of Data Mining and MCDM Methods

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  • Gokhan Ozkaya

    (Department of Business Administration, Faculty of Economics and Administrative Sciences, Yildiz Technical University, 34220 Istanbul, Turkey
    Institute of Social Sciences, Quantitative Methods, Istanbul University, 34119 Istanbul, Turkey)

  • Mehpare Timor

    (School of Business, Quantitative Methods, Istanbul University, 34320 Istanbul, Turkey)

  • Ceren Erdin

    (Department of Business Administration, Faculty of Economics and Administrative Sciences, Yildiz Technical University, 34220 Istanbul, Turkey)

Abstract

Science, technology and innovation (STI) policies are of great importance for countries to reach their sustainable development goals. Numerous global databases have many indicators that measure and compare the performance of STI policies of countries. However, many problems arise regarding how to identify, classify and systematically analyze these indicators in order to measure, monitor and improve the performance of STI. The study includes a literature review on global problems and new trends in STI policies, while mentioning the necessity of an internationally comparable STI indicator set, current STI indicator studies and efforts, and studies for each continent. In light of these, all the indicators selected are introduced in detail. The strengths and weaknesses of the countries in the study in terms of evaluation indicator values are indicated. After determining the indicator weights objectively with the entropy method, 40 countries are compared with TOPSIS, VIKOR, PROMETHEE I-II, ARAS, COPRAS, MULTIMOORA, ELECTRE, SAW and MAUT methods. In addition, countries that show similarities with each other are evaluated by cluster analysis, which is one of the data mining classification methods. This study offers a new and original approach with MCDM methods on this subject. Considering all the results obtained in the study together, these rankings are compared among themselves and with the rankings specified in the Global Innovation (2019) and Global Competitiveness (2019) indices, and it is seen that the results are consistent. In addition, it is possible to update and publish this study every year with updated data.

Suggested Citation

  • Gokhan Ozkaya & Mehpare Timor & Ceren Erdin, 2021. "Science, Technology and Innovation Policy Indicators and Comparisons of Countries through a Hybrid Model of Data Mining and MCDM Methods," Sustainability, MDPI, vol. 13(2), pages 1-49, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:694-:d:479270
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    Cited by:

    1. Alvarez, Pavel Anselmo & Valdez, Cuitláhuac & Dutta, Bapi, 2022. "Analysis of the innovation capacity of Mexican regions with the multiple criteria hierarchy process," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    2. Ai Zhang, 2021. "Influence of data mining technology in information analysis of human resource management on macroscopic economic management," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-12, May.
    3. Samira Vakilipour & Abolghasem Sadeghi-Niaraki & Mostafa Ghodousi & Soo-Mi Choi, 2021. "Comparison between Multi-Criteria Decision-Making Methods and Evaluating the Quality of Life at Different Spatial Levels," Sustainability, MDPI, vol. 13(7), pages 1-36, April.
    4. Gokhan Ozkaya & Ayse Demirhan, 2022. "Multi-Criteria Analysis of Sustainable Travel and Tourism Competitiveness in Europe and Eurasia," Sustainability, MDPI, vol. 14(22), pages 1-26, November.

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