Regional R&D Efficiency in Korea from Static and Dynamic Perspectives

Abstract

R&D efficiency has gained great attention in regional innovation. This study examines the R&D efficiency patterns of Korean regions for 2005-2009 from static and dynamic perspectives. This study employs data envelopment analysis to identify the regions' R&D performances relative to the best practices from the static perspective, and the Malmquist Productivity Index to evaluate their dynamic changes within a given timeframe.The results classify Korean regions into the deteriorating, lagging, and improving groups and indicate that most regions suffer from declining R&D productivity over time because of the inability of catching up with the best practices. To improve the catch-up effect, this study suggests (1) implementing exploitative strategies including direct technical imports and complementary R&D, (2) growing the number of government research institutes on a regional scale, and (3) increasing the ratio of industrial R&D organisations relative to industrial R&D expenditures are recommended.

The Impact of Government Competition on Regional R&D Efficiency: Does Legal Environment Matter in China’s Innovation System?

“…Government investment in R&D are public goods, and it is still a question that whether government competition can improve R&D efficiency. In addition, The topic of R&D efficiency has received increasing academic interest in recent years all over the world [2][3][4][5][6][7][8][9]. Most of the previous studies on this topic have attempted to measure R&D efficiency or productivity at the firm or industry levels [10][11][12][13][14][15][16], as well as country or national level [17][18][19].…”

Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs

“…Chen and Guan [19] measured the efficiency of China's regional innovation system through a two-stage network DEA model and found that one-fifth of China's provinces could be classified as best-practice province, leaving the other four-fifths of provinces at the other side of the line. Han, Asmild, and Kunc [6] evaluated the R&D efficiency patterns of 15 Korean regions and classified the regions into deteriorating, lagging, and improving groups. Carayannis et al [100] integrated an assessment and classification framework for national and regional innovation efficiency based on a set of 23 European countries and their 185 corresponding regions, and discovered large innovation efficiency differences.…”

“…A country's technological innovation activities always have obvious regional characteristics [1][2][3][4], and the efficiency of regional innovation can differ among regions [5][6][7][8]. Technological innovation can be seen as a main driver of regional development and economic growth [9], but at the same time can also bring sincere damage to the natural environment when it is unsustainable [10].…”

Dynamic analysis of the efficiency of research and development systems of South-European countries

“…Generally, two types of studies dedicated to this issue can be distinguished: the first type of studies analyzes R/D efficiency determinants, while the second type measures R/D efficiency. The techniques used in the second study group are the Malmquist Productivity Index [38], [20]; DEA [42], [25], [9], [16], [28], [2], [26]; Stochastic Frontier Analysis [42], [21] and Regression Analysis [26]. DEA is the most often used method to measure R/D efficiency, because it shows how to increase efficiency of inefficient decision-making units [10].…”