| 中国创新合作网络的演进特征及影响因素研究——以SCI论文合作为例 |
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| 引用本文： | 赵康杰,吴亚君,刘星晨.中国创新合作网络的演进特征及影响因素研究——以SCI论文合作为例[J].科研管理,2022,43(7):96-105. |
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| 作者姓名： | 赵康杰 吴亚君 刘星晨 |
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| 作者单位： | 山西财经大学资源型经济转型发展研究院，山西 太原030006 |
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| 基金项目： | 国家社会科学基金一般项目：“多维区域网络视角下经济发展不平衡与资源型地区经济转型研究”(18BJL081,2018.06—2021.12)； |
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| 摘 要： | 创新驱动战略实施背景下，融入创新合作网络是中国各省域提升区域创新能力的重要途径。本文基于2008—2018年中国省际之间在Web of Science核心合集发表的论文合作数，构建中国省际创新合作网络，描绘其时空演化特征，并借助零膨胀负二项回归深入分析影响机制。研究发现：中国省际创新合作网络结构逐渐复杂化、均衡化，省际创新合作网络中网络节点之间的联系不断丰富，在网络中重要节点省份逐渐增多，而且随着时间推移，网络结构不断优化。多维邻近性检验显示网络邻近性、产业邻近性是影响创新合作关系的重要因素，地理邻近性、经济邻近性影响较小，网络邻近性可以通过调节地理邻近性、经济邻近性影响创新合作。
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| 关 键 词： | 创新合作网络 网络结构 中心度 零膨胀负二项回归 邻近性 |
| 收稿时间： | 2020-10-29 |
| 修稿时间： | 2021-04-29 |
Research on the evolution characteristics and influencing factors of China′s innovation cooperation network——A study by taking the cooperation of SCI papers as an example |
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| Zhao Kangjie,Wu Yajun,Liu Xingchen.Research on the evolution characteristics and influencing factors of China′s innovation cooperation network——A study by taking the cooperation of SCI papers as an example[J].Science Research Management,2022,43(7):96-105. |
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| Authors: | Zhao Kangjie Wu Yajun Liu Xingchen |
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| Institution: | Institute of Resource-Based Economy Transformation and Development, Shanxi University of Finance and Economics, Taiyuan 030006, Shanxi, China; |
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| Abstract: | Under the background of implementing the innovation driven strategy and building an innovative country, integrating into the innovation cooperation network is an important way to enhance the regional innovation capacity of China's provinces. Based on the number of papers published in the core collection of web of science between China's provinces from 2008 to 2018, this paper uses the social network analysis method to construct China's provincial innovation cooperation network, and describes its temporal and spatial evolution characteristics from three aspects: network structure characteristics, network density characteristics and agglomerative subgroup analysis, From the perspective of proximity, this paper analyzes the impact mechanism of China's inter provincial innovation cooperation, and tests its robustness.
This paper finds that the network structure of inter provincial innovation cooperation in China is becoming more complex and balanced. As time goes on, the links between the network nodes in China's innovation cooperation network are constantly enriched, and more and more node provinces occupy an important position in the network; The structure of China's innovation cooperation network has been continuously optimized, and the regional characteristics of the point degree centrality are obvious. The point degree centrality of each province has been improved in numerical value, and the order of the point degree centrality of most provinces is relatively stable. The middle centrality of each province is decreasing, and the ranking of middle centrality among provinces is changing greatly. Agglomerative subgroup analysis shows that geographical distance is an important factor affecting inter provincial innovation cooperation, and the close level of economic development between provinces is more conducive to innovation cooperation between the two provinces, and the number of agglomerative subgroups shows a downward trend. On the whole, the network characteristics of inter provincial innovation cooperation in China become more obvious, and the level of inter provincial innovation cooperation is constantly improving.
This paper tests the impact mechanism of China's inter provincial innovation cooperation from the perspective of multi-dimensional proximity. The econometric test shows that improving network, industrial and economic proximity and shortening geographical distance are conducive to promoting China's inter provincial innovation cooperation, but network proximity and industrial proximity have the greatest impact on China's inter provincial innovation cooperation, while geographical proximity and economic proximity have less impact. Network proximity could influence the cooperation by adjusting geographical proximity and economic proximity. No matter random sampling or substitution econometric model, the robustness test results are consistent.
Finally, this paper has some policy implications for the analysis of the characteristics and influence mechanism of China's inter provincial innovation cooperation. Three suggestions are put forward. First, China should focus on creating innovation growth poles in the central and western regions, and promote the balanced development of innovation network pattern. Secondly, each province should put forward ways to optimize innovation cooperation according to its own situation and local conditions. Third, the backward provinces can improve the innovation ecological environment to enhance the status of innovation network. Under the background of implementing the innovation-driven strategy and building an innovative country, integrating into the innovation cooperation network is an important way to enhance the regional innovation capacity of China′s provinces. Based on the number of papers published in the core collection of web of science between China′s provinces from 2008 to 2018, this paper uses the social network analysis method to construct China′s provincial innovation cooperation network, and describes its temporal and spatial evolution characteristics from three aspects: network structure, network density and agglomerative subgroup analysis. From the perspective of proximity, this paper analyzes the impact mechanism of China′s inter-provincial innovation cooperation, and tests its robustness.
This paper finds that the network structure of inter-provincial innovation cooperation in China is becoming more complex and balanced. As time goes on, the links between the network nodes in China′s innovation cooperation network are constantly enriched, and more and more node provinces occupy an important position in the network. The structure of China′s innovation cooperation network has been continuously optimized, and the regional characteristics of the point degree centrality are obvious. The point degree centrality of each province has been improved in numerical value, and the order of the point degree centrality of most provinces is relatively stable. The middle centrality of each province is decreasing, and the ranking of middle centrality among provinces is changing greatly. Agglomerative subgroup analysis shows that geographical distance is an important factor affecting inter-provincial innovation cooperation, and the close level of economic development between provinces is more conducive to innovation cooperation between the two provinces, and the number of agglomerative subgroups shows a downward trend. On the whole, the network characteristics of inter-provincial innovation cooperation in China become more obvious, and the level of inter-provincial innovation cooperation is constantly improving.
This paper tests the impact mechanism of China′s inter-provincial innovation cooperation from the perspective of multi-dimensional proximity. The econometric test shows that improving network, industrial and economic proximity and shortening geographical distance are conducive to promoting China′s inter-provincial innovation cooperation, but network proximity and industrial proximity have the greatest impact on China′s inter-provincial innovation cooperation, while geographical proximity and economic proximity have less impact. Network proximity could influence the cooperation by adjusting geographical proximity and economic proximity. No matter random sampling or substitution econometric model, the robustness test results are consistent.
Finally, this paper has some policy implications for the analysis of the characteristics and influence mechanism of China′s inter-provincial innovation cooperation. Three suggestions are put forward. First, China should focus on creating innovation growth poles in the central and western regions, and promote the balanced development of innovation network pattern. Secondly, each province should put forward ways to optimize innovation cooperation according to its own situation and local conditions. Third, the backward provinces can improve the innovation ecological environment to enhance the status of innovation network. |
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| Keywords: | innovation cooperation network network structure centrality zero-inflated negative binomial regression proximity |
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