| 新能源汽车协同创新网络结构及影响因素研究 |
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| 引用本文： | 新能源汽车协同创新网络结构及影响因素研究.新能源汽车协同创新网络结构及影响因素研究[J].科学学研究,2022,40(6):1128-1142. |
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| 作者姓名： | 新能源汽车协同创新网络结构及影响因素研究 |
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| 作者单位： | 1. 哈尔滨工程大学经济管理学院;2. ; |
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| 摘 要： | 以2012-2020年京津冀地区新能源汽车产业联合申请专利数为样本，构建京津冀地区新能源汽车产业协同创新网络，运用社会网络分析法（SNA）对协同创新网络结构进行分析。为进一步分析产业邻近维度、知识邻近维度和地理邻近维度对协同创新网络演化的作用机理，构建二次指派程序（QAP）回归模型进行实证研究并对系数进行非参数检验。研究表明：9年间京津冀新能源汽车产业协同创新网络快速演化，网络密度与网络中心势表明协同创新网络呈现多中心化趋势，子群之间凝聚力较差；三省市协同创新网络核心节点均为“国家电网公司”，多个核心节点在协同创新网络内部占据重要结构洞位置。京津冀三省市不同阶段的不同邻近效应对协同创新网络产生不同影响：产业邻近与地理邻近始终正向影响协同创新网络发展，不同省市不同阶段知识邻近影响的显著程度不同。
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| 收稿时间： | 2021-02-27 |
Structure and Influencing Factors of Cooperative Innovation Network for New Energy Automobile |
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| Abstract: | Based on the number of joint patent applications for new energy automobile industry in Beijing-Tianjin-Hebei region from 2012 to 2020, the cooperative innovation network of new energy automobile industry in Beijing-Tianjin-Hebei region is constructed, and the cooperative innovation network structure is analyzed by using social network analysis (SNA) method. For further analysis of the mechanism of industrial proximity dimension, knowledge proximity dimension and geographical proximity dimension on the evolution of collaborative innovation network, a quadratic assignment program (QAP) regression model is constructed to carry out empirical research and non-parametric test of coefficients. The research shows that the collaborative innovation network of Beijing-Tianjin-Hebei new energy automobile industry has evolved rapidly in 9 years. The network density and network center potential show that the collaborative innovation network presents a multi-center trend and the cohesion between subgroups is poor. The core nodes of collaborative innovation network are State Grid Company. Different proximity effects in different stages of Beijing, Tianjin and Hebei provinces and cities have different effects on collaborative innovation network: industrial proximity and geographical proximity always positively affect the development of collaborative innovation network, and the significant degree of knowledge proximity influence in different stages of different provinces and cities is different. |
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