| 基于产业异质性的关键共性技术合作网络研究 |
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| 引用本文： | 基于产业异质性的关键共性技术合作网络研究.基于产业异质性的关键共性技术合作网络研究[J].科学学研究,2021,39(6):1036-1049. |
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| 作者姓名： | 基于产业异质性的关键共性技术合作网络研究 |
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| 作者单位： | 1. 哈尔滨工程大学经济管理学院;2. 哈尔滨工程大学 经济管理学院;3. ; |
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| 摘 要： | 为探究不同产业关键共性技术合作网络特征及差异，以我国五大产业关键共性技术的合作专利数据为基础构建合作网络，运用社会网络分析法对2000-2018年五大产业关键共性技术合作网络的结构演化特征、研发主体空间特征、产学研合作特征进行比较分析，总结不同产业关键共性技术合作网络的发展规律并提出有益启示。结果表明：五大产业关键共性技术合作网络具有动态演化特征，演化过程中的网络结构存在共性与差异；合作网络中研发主体空间分布呈现不同程度的集聚性，各地区在不同产业区域合作中的影响力不同；企-企合作是五大产业关键共性技术合作网络中的主要合作模式，各产业产学研合作程度有待提升。研究结论对不同产业关键共性技术制定差异化政策、开展各具特色的合作模式具有借鉴意义。
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| 收稿时间： | 2020-05-08 |
A Comparative Study of the Key General Purpose Technologies Cooperation Networks Based on Industrial Heterogeneity |
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| Abstract: | To explore the characteristics and differences of key general purpose technologies (GPTs) cooperation networks in different industries, cooperation networks are constructed based on the cooperation patent data of key GPTs of five industries in China. The social network analysis method is used to compare and analyze the characteristics of network structures evolution, the space characteristics of R&D entities, and the characteristics of industry-university-research cooperation of the key GPTs cooperation networks in five industries from 2000 to 2018. Furthermore, the development laws of key GPTs cooperation networks in different industries are summarized followed by some beneficial inspirations. The present research results show that the key GPTs cooperation networks of five industries have dynamic evolution characteristics, and there are commonalities and differences in network structure during evolution. In the key GPTs cooperation networks of five industries, the spatial distribution of R&D entities shows different degree of clustering. The influence of different regions in different industries area cooperation is difference. Moreover, the enterprise-enterprise cooperation is the main cooperation pattern in the key GPTs cooperation networks of five industries. The degree of industry-university-research cooperation in various industries can be improved. The conclusions are of significance for formulating differentiated policies and developing distinctive cooperation models for key GPTs in different industries. |
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