Spatial Distributions and Determinants of Regional Innovation in China: Evidence from Chinese Metropolitan Data

Tan, Debao; Cheng, Cheng; Lei, Mujun; Zhao, Yucheng

doi:10.1080/1540496x.2017.1283215

Cited by 12 publications

(11 citation statements)

References 24 publications

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“…Lim (2003) used spatial autocorrelation model to study the US regional innovation spatial correlation effect through patent data and found significant spatial correlation effects among regions in the USA. Inter-regional innovation correlation effects have been examined based on data of different countries using spatial autocorrelation models in a large number of follow-up studies: European countries (Moreno et al , 2004; Lesage et al , 2007; Guastella and Oort, 2015; Kveton and Kadlec, 2018), the USA (Drivas et al , 2014; Nicolosi et al , 2018), Italy (Bertazzon, 2003), Korea (Jang et al , 2017), Spanish (Cabrer-Borrás and Serrano-Domingo, 2007), France (Corsatea and Jayet, 2014) and China (Lei and Jin, 2014; Wang, 2012; Bai and Jiang, 2015; Liu and Xu, 2016; Tan et al , 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Inter-regional innovation correlation effects and influencing factors in China

Pan

Yang

et al. 2019

BPMJ

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Purpose The purpose of this paper is to find out the characteristics and evolutionary trends of China’s inter-regional innovation correlation network, the status and roles of each province in China’s inter-regional innovation correlation network and the influencing factors of China’s inter-regional innovation correlation effect. Design/methodology/approach Based on the patent data of 30 provinces (autonomous regions and municipalities) in China from 1991 to 2017, social network analysis was used to find out the characteristics and evolutionary trends of China’s inter-regional innovation correlation network and the status and roles of each province in China’s inter-regional innovation correlation network. Furthermore, the QAP method was used to find out the influencing factors of China’s inter-regional innovation correlation effect. Findings China’s inter-regional innovation correlation is becoming increasingly close and inter-regional innovation correlation network is becoming increasingly stable. Jiangsu, Zhejiang, Beijing, Shanghai, Guangdong and other eastern coastal provinces are at the core in the inter-regional innovation correlation network, while the western regions are marginal actors. China’s regional innovation development territory can be divided into four blocks, namely, “bidirectional spillover block,” “net spillover block,” “main beneficial block” and “net beneficial block,” and gradient transfer mechanism is obvious between the blocks. The geographical adjacency and similarity in regional industrial structure, urbanization level and government attention degree have significant positive effect on China’s inter-regional innovation correlation effects. Research limitations/implications This paper only uses patent application as a measure of regional innovation level to analyze inter-regional innovation correlation effect. Meanwhile, this paper carries out an empirical study only from the provincial level and not from the city level. Practical implications This paper provides the practical basis for further promoting the coordinated development of regional innovation and promoting the construction of regional innovation systems with different characteristics. Originality/value This paper contributes to understand the status and role of each province in inter-regional innovation correlation network. Meanwhile, this paper also helps to understand the influence of the proximity and external environmental factors on inter-regional innovation correlation effect.

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Section: Literature Reviewmentioning

confidence: 99%

Inter-regional innovation correlation effects and influencing factors in China

Pan

Yang

et al. 2019

BPMJ

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“…If we want to describe and explain the processes of innovation, we must consider all important factors shaping and influencing innovation (Diez, 2009). Chinese regional innovation has an inverse correlation with distance (Tan et al , 2017). Based on this information, and from Niu and Chen (2013), our paper adds factors that account for public infrastructure and the social governance environment to the gravity model.…”

Section: Methodsmentioning

confidence: 99%

Can a region’s network location characteristics affect its innovation capability? Empirical evidence from China

Han

Sun

2020

CMS

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Purpose This study aims to explore the influence of a region’s network location characteristics and indirect connections on its innovation capability. The aim is to assist regions in different network locations to use innovation resources to improve their innovation capabilities more efficiently. Design/methodology/approach This paper represents the Chinese regional innovation network using the gravity model. A theoretical framework is developed to explore the relationships between a region’s innovation capability and its network location. Hypotheses are tested using hierarchical regressions. Findings First, this paper finds that a region’s network centrality can promote its innovation capability. Second, a structural hole can positively adjust the relationship between a region’s centrality and innovation capability. Third, a region’s indirect connections can inhibit its innovation capability while exhibiting a U-shaped relationship in moderating centrality and innovation capability. Originality/value This study uses a multi-index system to construct an innovation network covering 29 regions in China. This network represents the innovation cooperation and overall situation of innovation in China. The paper is one of the first attempts at investigating the relationship between regional network locations and innovation capability. It is also the first attempt at testing the influence of indirect connections on a region’s innovation capability. The findings provide a new perspective on the factors influencing regional innovation capacity and a new way for regions to improve their innovation capability.

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“…Xiao, Fan, and Du (2019) adopted the Theil index to analyze the Chinese regional innovation capability difference and evolution. The second group of methods is concerned with the analysis of the spatial effects, including the exploratory spatial data analysis (Tan, Cheng, Lei, & Zhao, 2017), gravity models (Maggioni, Uberti, & Usai, 2011), kernel density functions (Liu, 2018), and spatial econometric models (Li & Fu, 2015;Peng et al, 2021). For example, Shang, Poon, and Yue (2012) used a spatial autoregressive model to test China's innovation growth's regional knowledge spillover.…”

Section: Spatial Effects Of Innovation Theorymentioning

confidence: 99%

Spatial spillover effects and driving factors of regional green innovation efficiency in china from a network perspective

Zhuang

Lin

Zhong

2022

Front. Environ. Sci.

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The spatial spillover effect of regional green innovation efficiency (GIE) is a heated issue of academic research; however, it has rarely been discussed from a network perspective. It is pretty meaningful to clarify its spatial association network’s evolutionary rules and driving factors. To fill the lack of research, this study measures the regional GIE in China from 2010 to 2019 using an epsilon-based metric (EBM) model that considers undesirable outputs. A modified gravity model and social network analysis (SNA) method are used to analyze the evolutionary rules and spatial spillover effects of the network structure of GIE, and a quadratic allocation process (QAP) was employed to identify its driving factors. The findings reveal that: 1) China’s regional GIE has a geographic correlation network structure with a low network density (peaking at 0.210 in 2018) and an annually increasing slow trend. 2) The network structure is relatively loose and has a certain hierarchical gradient, with “dense in the eastern” and “sparse in the western” characteristics. 3) The eastern provinces are at the relative center position and play a leading role in the network; the central, western, and northeastern regions are relatively inferior and play a fulcrum and conduction role. 4) Spatial adjacency, the differences in infrastructure, urbanization, and economic development level positively affect the spatially correlated regional GIE. In contrast, differences in environmental regulations and differences in science and technology innovation (STI) have negative effects. Finally, from the perspectives of national, regional, block, and driving factors, several recommendations are made to enhance the overall improvement and balanced development of regional GIE in China.

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Spatial Distributions and Determinants of Regional Innovation in China: Evidence from Chinese Metropolitan Data

Cited by 12 publications

References 24 publications

Inter-regional innovation correlation effects and influencing factors in China

Inter-regional innovation correlation effects and influencing factors in China

Can a region’s network location characteristics affect its innovation capability? Empirical evidence from China

Spatial spillover effects and driving factors of regional green innovation efficiency in china from a network perspective

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