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

Eun Sun Kim, Yunjeong Choi and Jeongeun Byun
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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

Sustainability, 2019, vol. 12, issue 1, 1-14

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.

Keywords: big data; decision tree; government; national R&D project; small and medium-sized enterprises; commercialization performance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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