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Identifying the Policy Direction of National R&D Programs Based on Data Envelopment Analysis and Diversity Index Approach

Author

Listed:
  • Myoungjae Choi

    (Data & High Performance Computing Science, Korea Institute of Science and Technology Information, University of Science and Technology (UST), Daejeon 34141, Korea)

  • Ohjin Kwon

    (Department of Open Data Convergence Research, Korea Institute of Science and Technology Information, Busan 48059, Korea)

  • Dongkyu Won

    (Division of Data Analysis, Korea Institute of Science and Technology Information, Seoul 02456, Korea)

  • Wooseok Jang

    (Division of Data Analysis, Korea Institute of Science and Technology Information, Seoul 02456, Korea)

Abstract

The Korean government has been continuously conducting diverse national R&D programs to discover new growth engines. The Republic of Korea is one of the countries with the largest investment in national R&D, but its efficiency was relatively low. In response, this study established a framework to identify the characteristics and direction of outstanding R&D programs. In this study, the performance of the R&D programs was identified in the sub-program unit. The efficiency of the national R&D program was analyzed using the data envelopment analysis model through the outputs of the national R&D programs such as papers and patents. However, patent and paper output would take time to be realized. Therefore, this study also calculated the diversity index of R&D programs to identify their potential expected performance. This study applied the suggested framework in the electric vehicle fields, which is one of the core growth engines of South Korea. A list of outstanding programs was identified from the National Institute of Science and Technology Information (NTIS) data. Additionally, this study also discovered the main technology areas and their current issues of outstanding and brand-new R&D programs. These results could contribute to suggesting the policy direction to conduct high-performance national R&D programs.

Suggested Citation

  • Myoungjae Choi & Ohjin Kwon & Dongkyu Won & Wooseok Jang, 2021. "Identifying the Policy Direction of National R&D Programs Based on Data Envelopment Analysis and Diversity Index Approach," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12547-:d:678404
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    as
    1. Mahsa Pishdar & Masoumeh Danesh Shakib & Jurgita Antucheviciene & Arvydas Vilkonis, 2021. "Interval Type-2 Fuzzy Super SBM Network DEA for Assessing Sustainability Performance of Third-Party Logistics Service Providers Considering Circular Economy Strategies in the Era of Industry 4.0," Sustainability, MDPI, vol. 13(11), pages 1-18, June.
    2. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    3. Banker, Rajiv D., 1984. "Estimating most productive scale size using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 17(1), pages 35-44, July.
    4. Andy Stirling, 2007. "A General Framework for Analysing Diversity in Science, Technology and Society," SPRU Working Paper Series 156, SPRU - Science Policy Research Unit, University of Sussex Business School.
    5. María Bordons & Carmen Bravo & Santos Barrigón, 2004. "Time‐tracking of the research profile of a drug using bibliometric tools," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(5), pages 445-461, March.
    6. Kocher, Martin G. & Luptacik, Mikulas & Sutter, Matthias, 2006. "Measuring productivity of research in economics: A cross-country study using DEA," Socio-Economic Planning Sciences, Elsevier, vol. 40(4), pages 314-332, December.
    7. Pengyuan Xu & Meiqing Zhang & Min Gui, 2020. "How R&D Financial Subsidies, Regional R&D Input, and Intellectual Property Protection Affect the Sustainable Patent Output of SMEs: Evidence from China," Sustainability, MDPI, vol. 12(3), pages 1-18, February.
    8. van Rijnsoever, Frank J. & van den Berg, Jesse & Koch, Joost & Hekkert, Marko P., 2015. "Smart innovation policy: How network position and project composition affect the diversity of an emerging technology," Research Policy, Elsevier, vol. 44(5), pages 1094-1107.
    9. Butler, Timothy W. & Li, Ling, 2005. "The utility of returns to scale in DEA programming: An analysis of Michigan rural hospitals," European Journal of Operational Research, Elsevier, vol. 161(2), pages 469-477, March.
    10. Seong Soo Kim & Yo Sop Choi, 2019. "The Innovative Platform Programme in South Korea: Economic Policies in Innovation-Driven Growth," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 13(3), pages 13-22.
    11. Astrid Cullmann & Jens Schmidt-Ehmcke & Petra Zloczysti, 2009. "Innovation, R&D Efficiency and the Impact of the Regulatory Environment: A Two-Stage Semi-Parametric DEA Approach," Discussion Papers of DIW Berlin 883, DIW Berlin, German Institute for Economic Research.
    12. Wang, Eric C. & Huang, Weichiao, 2007. "Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach," Research Policy, Elsevier, vol. 36(2), pages 260-273, March.
    13. Ji Yeon Lee & Richa Kumari & Jae Yun Jeong & Tae-Hyun Kim & Byeong-Hee Lee, 2020. "Knowledge Discovering on Graphene Green Technology by Text Mining in National R&D Projects in South Korea," Sustainability, MDPI, vol. 12(23), pages 1-16, November.
    14. Joseph Drew & Michael Kortt & Brian Dollery, 2015. "What Determines Efficiency in Local Government? A DEA Analysis of NSW Local Government," Economic Papers, The Economic Society of Australia, vol. 34(4), pages 243-256, December.
    15. Yuan Zhou & Fang Dong & Yufei Liu & Zhaofu Li & JunFei Du & Li Zhang, 2020. "Forecasting emerging technologies using data augmentation and deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 1-29, April.
    16. Kim, Gyu-Pan & Kang, Gu Sang & Kim, Jonghyuk & OH, Taehyun & LEE, Hyun Jean & Son, Wonju, 2020. "Innovative Growth Strategy in the US, Europe and Japan," World Economy Brief 20-16, Korea Institute for International Economic Policy.
    17. repec:idn:jimfjn:v:3:y:2018:i:specialissuee:p:1-20 is not listed on IDEAS
    18. Jung Ho Park & Kwangsoo Shin, 2018. "Efficiency of Government-Sponsored R&D Projects: A Metafrontier DEA Approach," Sustainability, MDPI, vol. 10(7), pages 1-17, July.
    19. Jae Yun Jeong & Inje Kang & Ki Seok Choi & Byeong-Hee Lee, 2018. "Network Analysis on Green Technology in National Research and Development Projects in Korea," Sustainability, MDPI, vol. 10(4), pages 1-12, April.
    20. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
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