Journal of the Korean regional development association (한국지역개발학회지)

The Korean Regional Development Association (한국지역개발학회)
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A Study on Establishing Regional Techno-polesand Networking Regional Innovation Projects- A Case Study of the Daegu . Kyongbuk Region

지역기술거점의 형성과 지역혁신네트워크 구축에 관한 연구 - 대구 . 경북지역을 사례로 -

이성근

  • Published : 20020000
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Abstract

This research is a study of the need to establish regional techno-poles and the need to develop networking in regards to regional innovation projects of the Daegu·Kyongbuk region. In order to analyze regional innovation capacity, four variables were selected for analysis. They are source innovation, environmental innovation, activity innovation, and performance innovation. The analytical data used were: 24 units of administrative districts, 5 planning regions, 3 divided regions by population size, and 2 divided regions by degrees of industrial agglomeration between the years 1995-1999. The statistics used were: deprivation index, Kruskal-Wallis test and Wilcoxon rank sum test, and time-series cross-sectional regression. Several characteristics of the analysis and evaluation of regional innovation capacity were revealed from the research. First, the analysis of results of the 24 administrative districts revealed that four cities were much superior to the other regions. The deprivation index for these four cities are as follows: Daegu (0.8677), Kyongsan (0.41265), Kumi (0.41026), and Pohang (0.40981). Second, after analysis of the five planning regions, it was found that four planning regions, - the Daegu metropolitan city (0.68730), the East region (0.47075), the South region (0.46501), and the West (0.38533) Kyongbuk region - had similar indices. But the study showed that the North Kyongbuk region is very weak (0.15217) in comparison. Third, three regions were derived based on population. They are a metropolitan area, a small to medium-sized city a rural area. The study indicated the following indices: metropolitan area (0.86677), small-medium sized city (0.21051), rural area (0.05649). Fourth, the two regions divided by industrial agglomeration indicated these very different results: industrialized region (0.41981) and non industrialized region (0.07513). Fifth, four variables were analysed in terms of time-series cross-sectional regression. The three independent variables positively influenced performance innovation. The breakdown of the positive impact of each independent variable is as follows: source (2.28, prob>0.00), environment (4.89, prob>0.00), and activity (0.90, prob>0.09) innovation. Furthermore, performance innovation was more influenced by regional characteristics (0.44) than by time (0.00).

Keywords

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