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Regional collaborations and indigenous innovation capabilities in China: A multivariate method for the analysis of regional innovation systems

Zhao, S.L., Cacciolatti, L., Lee, Soo Hee, Song, W. (2014) Regional collaborations and indigenous innovation capabilities in China: A multivariate method for the analysis of regional innovation systems. Technological Forecasting and Social Change, 94 . pp. 202-220. ISSN 0040-1625. (doi:10.1016/j.techfore.2014.09.014) (KAR id:61573)

Abstract

In this study we analyse the emerging patterns of regional collaboration for innovation projects in China, using official government statistics of 30 Chinese regions. We propose the use of Ordinal Multidimensional Scaling and Cluster analysis as a robust method to study regional innovation systems. Our results show that regional collaborations amongst organisations can be categorised by means of eight dimensions: public versus private organisational mindset; public versus private resources; innovation capacity versus available infrastructures; innovation input (allocated resources) versus innovation output; knowledge production versus knowledge dissemination; and collaborative capacity versus collaboration output. Collaborations which are aimed to generate innovation fell into 4 categories, those related to highly specialised public research institutions, public universities, private firms and governmental intervention. By comparing the representative cases of regions in terms of these four innovation actors, we propose policy measures for improving regional innovation collaboration within China.

Item Type: Article
DOI/Identification number: 10.1016/j.techfore.2014.09.014
Uncontrolled keywords: Multidimensional scaling; Collaboration; Indigenous innovation capability; Regional innovation system; Institutions
Divisions: Divisions > Kent Business School - Division > Department of Leadership and Management
Depositing User: Soo Hee Lee
Date Deposited: 27 Apr 2017 10:22 UTC
Last Modified: 08 Oct 2021 14:11 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/61573 (The current URI for this page, for reference purposes)

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