posted on 2022-02-11, 12:19authored byXiaojing Huang, Lei Ma, Rao Li, Zheng LiuZheng Liu
High-tech zones are an important platform for local governments in China to carry out regional collaborative innovation and an important carrier for the construction of a regional innovation ecosystem. The evolution path of innovation ecosystem in a high-tech zone is divided into three stages: enterprise collection, industrial cluster, and system integration. The innovation subjects form a complex network system that transcends the physical boundary. This paper studies the relationship between innovation input, innovation output, and innovation environment from the perspective of cluster innovation ecosystem structure. Using data mining technology, this paper establishes an index variable system of the innovation ecosystem in a high-tech zone, which includes innovation input, innovation output, and innovation environment. Based on the data of the Nanning National High-tech Zone in China, empirical tests were carried out, using factor analysis and regression analysis to analyze the quantitative relationship between the input, output, and innovation environment of the Nanning High-tech Zone’s innovation ecosystem, and to explain the relationship between each other and the overall innovation of the high-tech zone. This research has certain practical significance for enriching and perfecting the theory of industrial clusters and studying the evolution of the innovation ecosystem of high-tech zones from a micro level. It has important, enlightening significance as a reference for the construction of innovative high-tech zones and the enhancement of high-tech zones’ independent innovation capabilities.
Journal of Open Innovation: Technology, Market, and Complexity
Publisher
MDPI
Version
VoR (Version of Record)
Citation
Huang, X.; Ma, L.; Li, R.; Liu, Z. (2020) 'Determinants of Innovation Ecosystem in Underdeveloped Areas—Take Nanning High-Tech Zone in Western China as an Example', Journal of Open Innovation: Technology, Market, and Complexity 6, 135. https://doi.org/10.3390/joitmc604013