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This study is a starting point to analyze South Korean national innovation systems (KNIS) using big data and provide insights for policy makers regarding how they implement the dynamic process of innovation systems. It examines KNIS that has developed over the past 14 years from 2003 to 2016 during the governments of Roh Moo-hyun, Lee Myung-bak, and Park Geun-hye. The aim of this study is to evaluate the KNIS in three ways. The first way is to analyze the NIS of the three governments based on data of 470,000 national research and development (R&D) projects, following which the second way is to compare innovative outcomes of the three governments. The last way is to figure out the characteristics of the KNIS in innovative performance. Our analysis reveals that the KNIS was developed and evolved from 2003 to 2008, maintained until 2012, and gradually declined, even though national R&D investment increased for 14 years. Empirical evidence highlights that policies implemented for more than a decade do not effectively link to economic outcomes, resulting in an imbalance between innovation input and innovation output. This study further argues that the use of NIS concept in South Korea seems to be skewed towards measuring national performance from a narrower perspective.
Eun Sun Kim; Kuk Jin Bae; Jeongeun Byun. The History and Evolution: A Big Data Analysis of the National Innovation Systems in South Korea. Sustainability 2020, 12, 1266 .
AMA StyleEun Sun Kim, Kuk Jin Bae, Jeongeun Byun. The History and Evolution: A Big Data Analysis of the National Innovation Systems in South Korea. Sustainability. 2020; 12 (3):1266.
Chicago/Turabian StyleEun Sun Kim; Kuk Jin Bae; Jeongeun Byun. 2020. "The History and Evolution: A Big Data Analysis of the National Innovation Systems in South Korea." Sustainability 12, no. 3: 1266.
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.
Eun Sun Kim; Yunjeong Choi; Jeongeun Byun. Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs. Sustainability 2019, 12, 202 .
AMA StyleEun Sun Kim, Yunjeong Choi, Jeongeun Byun. Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs. Sustainability. 2019; 12 (1):202.
Chicago/Turabian StyleEun Sun Kim; Yunjeong Choi; Jeongeun Byun. 2019. "Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs." Sustainability 12, no. 1: 202.