产业关联与无关性对开发区创新效率的影响——基于上海 21 家开发区的实证分析

Translated title of the contribution: Impact of industry relatedness and unrelatedness on innovation efficiency in industrial parks: Empirical study based on 21 industrial parks in Shanghai

Zihao Cheng, Xiaoyi Liu, Yang Jin, Tianyu Li, Xiang Kong

Research output: Contribution to journalArticlepeer-review

Abstract

Under the guidance of an innovation-driven development objectives, the industrial parks, serving as a complex economic space with the aggregation of various industries, faces the key challenge of stimulating overall innovation vitality through effective industrial policy formulation for its future high-quality development. From the perspective of relatedness, this study focuses on analyzing the impact of inter-industry relatedness on innovation efficiency within industrial parks. Employing a heterogeneous stochastic frontier model and using data from 21 industrial parks in Shanghai from 2013 to 2018, the research findings are as follows: (1) Compared to the initial period, the average innovation efficiency in Shanghai's industrial parks shows an upward trend. While the spatial pattern of“national level > city level”and “urban area > peripheral area”still exists, the inter-group disparities have notably reduced, alleviating polarization to a certain extent. (2) The interrelatedness among industries is significantly correlated with innovation efficiency in industrial parks, with industry relatedness contributing to the enhancement of innovation efficiency, while unrelatedness has the opposite effect. (3) The technological complexity of industries partially mediates the impact pathway of inter-industry relatedness and unrelatedness on innovation efficiency. It reinforces the positive effect of the former while suppressing the negative effect of the latter on innovation efficiency.

Translated title of the contributionImpact of industry relatedness and unrelatedness on innovation efficiency in industrial parks: Empirical study based on 21 industrial parks in Shanghai
Original languageChinese (Traditional)
Pages (from-to)2176-2193
Number of pages18
JournalDili Yanjiu
Volume43
Issue number8
DOIs
StatePublished - 1 Aug 2024

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