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A fuzzy set theory approach to national composite S&T indices

Author

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  • Hye-Seon Moon

    (Korea Institute of Science and Technology Evaluation and Planning)

  • Jeong-Dong Lee

    (Techno-Economics and Policy Program, Seoul National University)

Abstract

Summary Composite science and technology (S&T) indices are essential to overall understanding and evaluation of national S&T status, and to formulation of S&T policy. However, only a few studies on making these indices have been conducted so far since a number of complications and uncertainties are involved in the work. Therefore, this study proposes a new approach to employ fuzzy set theory and to make composite S&T indices, and applies it. The approach appears to successfully integrate various S&T indicators into three indices: R&D input, R&D output, and economic output. We also compare Korea’s S&T indices with those of five developed countries (France, Germany, Japan, the United Kingdom, and the United States) to obtain some implications of the results for Korea’s S&T.

Suggested Citation

  • Hye-Seon Moon & Jeong-Dong Lee, 2005. "A fuzzy set theory approach to national composite S&T indices," Scientometrics, Springer;Akadémiai Kiadó, vol. 64(1), pages 67-83, July.
    • Handle: RePEc:spr:scient:v:64:y:2005:i:1:d:10.1007_s11192-005-0238-7
    DOI: 10.1007/s11192-005-0238-7
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    Cited by:

    1. Lee, Jun Gon & Park, Min Jae, 2020. "Evaluation of technological competence and operations efficiency in the defense industry: The strategic planning of South Korea," Evaluation and Program Planning, Elsevier, vol. 79(C).
    2. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    3. Chen Kaihua & Kou Mingting, 2014. "Staged efficiency and its determinants of regional innovation systems: a two-step analytical procedure," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(2), pages 627-657, March.
    4. Park, Han Woo & Leydesdorff, Loet, 2010. "Longitudinal trends in networks of university-industry-government relations in South Korea: The role of programmatic incentives," Research Policy, Elsevier, vol. 39(5), pages 640-649, June.
    5. 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.
    6. Wang, Ya & Pan, Jiao-feng & Pei, Rui-min & Yi, Bo-Wen & Yang, Guo-liang, 2020. "Assessing the technological innovation efficiency of China's high-tech industries with a two-stage network DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    7. Wang, Qunwei & Hang, Ye & Sun, Licheng & Zhao, Zengyao, 2016. "Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 254-261.
    8. Daniele Archibugi & Francesco Crespi & Mario Denni & Andrea Filippetti, 2009. "The Technological Capabilities of Nations: A Survey of Composite Indicators," QA - Rivista dell'Associazione Rossi-Doria, Associazione Rossi Doria, issue 2, May.
    9. Han, Yoo-Jin, 2007. "Measuring industrial knowledge stocks with patents and papers," Journal of Informetrics, Elsevier, vol. 1(4), pages 269-276.
    10. Adnan Aktepe & Süleyman Ersöz & Bilal Toklu, 2019. "A multi-stage satisfaction index estimation model integrating structural equation modeling and mathematical programming," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2945-2964, December.
    11. Kairui Zuo & Jiancheng Guan, 2017. "Measuring the R&D efficiency of regions by a parallel DEA game model," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 175-194, July.
    12. Grupp, Hariolf & Schubert, Torben, 2010. "Review and new evidence on composite innovation indicators for evaluating national performance," Research Policy, Elsevier, vol. 39(1), pages 67-78, February.
    13. László Molnár, 2011. "Research and Development Activity Matrix," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, vol. 7(02), pages 29-37.
    14. Jiancheng Guan & Kairui Zuo, 2014. "A cross-country comparison of innovation efficiency," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 541-575, August.
    15. Makkonen, Teemu, 2013. "Government science and technology budgets in times of crisis," Research Policy, Elsevier, vol. 42(3), pages 817-822.
    16. Teemu Makkonen & Robert P. Have, 2013. "Benchmarking regional innovative performance: composite measures and direct innovation counts," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 247-262, January.
    17. Li, Hongkuan & He, Haiyan & Shan, Jiefei & Cai, Jingjing, 2019. "Innovation efficiency of semiconductor industry in China: A new framework based on generalized three-stage DEA analysis," Socio-Economic Planning Sciences, Elsevier, vol. 66(C), pages 136-148.
    18. Siran Fang & Xiaoshan Xue & Ge Yin & Hong Fang & Jialin Li & Yongnian Zhang, 2020. "Evaluation and Improvement of Technological Innovation Efficiency of New Energy Vehicle Enterprises in China Based on DEA-Tobit Model," Sustainability, MDPI, vol. 12(18), pages 1-22, September.
    19. Proksch, Dorian & Busch-Casler, Julia & Haberstroh, Marcus Max & Pinkwart, Andreas, 2019. "National health innovation systems: Clustering the OECD countries by innovative output in healthcare using a multi indicator approach," Research Policy, Elsevier, vol. 48(1), pages 169-179.

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