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

Eun Sun Kim,Jeongeun Byun,Yunjeong Choi

doi:10.3390/su12010202

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

Eun Sun Kim, Jeongeun Byun + Show 1 more

Open Access

https://doi.org/10.3390/su12010202

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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.

Highlights

The concept of Big Data analytics (BDA) pertains to accumulating, combining, analyzing, and using large-scale data for various purposes and of various types
Based on data on national research and development (R&D) projects in Korea, we conducted cluster and decision tree analyses to identify the determinants of commercialization performance
These analyses showed a low success rate of commercialization for national R&D projects

Summary

Introduction

The concept of Big Data analytics (BDA) pertains to accumulating, combining, analyzing, and using large-scale data for various purposes and of various types. Governments globally have announced comprehensive strategies for using Big Data at the national level. They first focused on the construction of infrastructure to open access to data and promote its utilization. Thereafter, they supported legal and institutional improvements to empower the private sector to use public data and create added value (indirect role) as well as used Big Data for policymaking (direct role) [4]

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