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Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs

Author

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  • Eun Sun Kim

    (Data Analysis Division, Korea Institute of Science and Technology Information, 66 Hoegi-ro, Dongdaemun-gu, Seoul 02456, Korea)

  • Yunjeong Choi

    (Technology Commercialization Center, Korea Institute of Science and Technology Information, 66 Hoegi-ro, Dongdaemun-gu, Seoul 02456, Korea)

  • Jeongeun Byun

    (Technology Commercialization Center, Korea Institute of Science and Technology Information, 66 Hoegi-ro, Dongdaemun-gu, Seoul 02456, Korea)

Abstract

To expand the field of governmental applications of Big Data analytics, this study presents a case of data-driven decision-making using information on research and development (R&D) projects in Korea. The Korean government has continuously expanded the proportion of its R&D investment in small and medium-size enterprises to improve the commercialization performance of national R&D projects. However, the government has struggled with the so-called “Korea R&D Paradox”, which refers to how performance has lagged despite the high level of investment in R&D. Using data from 48,309 national R&D projects carried out by enterprises from 2013 to 2017, we perform a cluster analysis and decision tree analysis to derive the determinants of their commercialization performance. This study provides government entities with insights into how they might adjust their approach to Big Data analytics to improve the efficiency of R&D investment in small- and medium-sized enterprises.

Suggested Citation

  • Eun Sun Kim & Yunjeong Choi & Jeongeun Byun, 2019. "Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs," Sustainability, MDPI, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:202-:d:301964
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    References listed on IDEAS

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    1. Mangiameli, Paul & Chen, Shaw K. & West, David, 1996. "A comparison of SOM neural network and hierarchical clustering methods," European Journal of Operational Research, Elsevier, vol. 93(2), pages 402-417, September.
    2. Hongbo Jiang & Qigan Shao & James J.H. Liou & Ting Shao & Xiaosheng Shi, 2019. "Improving the Sustainability of Open Government Data," Sustainability, MDPI, vol. 11(8), pages 1-27, April.
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    1. Zhu, Minglei & Huang, Haiyan & Ma, Weiwen, 2023. "Transformation of natural resource use: Moving towards sustainability through ICT-based improvements in green total factor energy efficiency," Resources Policy, Elsevier, vol. 80(C).

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