Knowledge patterns and spatial dynamics of industrial districts in knowledge cities: Hsinchu, Taiwan
•The paper argues the strategic role of knowledge or industrial districts in increasing the competitiveness of knowledge cities.•This paper analyzes the correlation between the spatial dynamics of knowledge in major industries and innovation.•The paper puts Hsinchu region under the knowledge pattern...
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Veröffentlicht in: | Expert systems with applications 2014-09, Vol.41 (12), p.5587-5596 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •The paper argues the strategic role of knowledge or industrial districts in increasing the competitiveness of knowledge cities.•This paper analyzes the correlation between the spatial dynamics of knowledge in major industries and innovation.•The paper puts Hsinchu region under the knowledge pattern and spatial dynamics microscope.
In the era of a global knowledge economy, urban regions that seek to increase their competitive edge and become destinations for talent and investment have little chance of achieving these goals without forming effective knowledge-based urban development strategies. Hence, the development of clusters of knowledge-based corporations has become an important strategic factor in increasing the competitiveness of knowledge cities. Whereas previous studies have tended to focus on the characteristics of local clusters and the causes of their success, empirical studies of the long-term development of local knowledge-based industries are few. Accordingly, this investigation takes the knowledge city region-Hsinchu as its subject, and quantitatively analyzes the correlation between the spatial dynamics of knowledge in major industries and innovation based on empirical data. This finding shows that steadily developing industries in the Hsinchu region have continued to strengthen their new knowledge of product development and innovation. An overview of innovative activities of firms also revealed that their knowledge patterns have been changing from patterns of internal dependency to a locality-based, broader networking and agglomeration pattern. |
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ISSN: | 0957-4174 1873-6793 |
DOI: | 10.1016/j.eswa.2014.02.009 |