摘要
目的:探究上海生物医药创新合作网络的特征。方法:采用社会网络分析方法,对上海市2013~2021年间由生物医药企业主导基于临床试验构建的创新合作网络加以刻画,并分别对创新合作网络的整体网络特征及演化、重要节点识别及演化、“强关系”子网络演化作了分析。结果:上海生物医药创新合作网络规模较大、密度较低、结构较为松散,有明显的“小世界”现象;在创新网络的重要节点上,企业日益占据主导性,医疗机构的重要性逐渐被弱化;在“强关系”子网络中,以企业为核心节点形成了多个密集子群,数量先增后降,次级子群的数量逐渐增多,零散的小型子群数量扩张较为迅猛,但核心节点在减少。结论:针对上海生物医药创新网络运行情况,需要优化创新资源配置、鼓励医疗机构积极参与合作、培育更多强势主导性机构,从而提高创新网络紧密度、医疗机构协同创新能力、网络主体创新能力。
Objective: To investigate the characteristics of biopharmaceutical innovation collaboration networks in Shanghai. Methods: Social network analysis was used to characterize the innovation collaboration network based on clinical trials led by biopharmaceutical companies in Shanghai from 2013 to 2021, and the overall network characteristics and evolution, important node identification and evolution, and “strong relationship” sub-network evolution of the innovation collaboration network were analyzed. The analysis was conducted on the overall network characteristics and evolution, the identification and evolution of important nodes, and the evolution of “strong relationship” sub-networks. The results: Shanghai biomedical innovation cooperation network is large in scale, low in density, and loose in structure, with an obvious “small world” phenomenon;in the important nodes of the innovation network, enterprises are increasingly dominant, and the importance of medical institutions is gradually weakened;in the “strong relationship” sub-network, several dense subgroups are formed with enterprises as the core nodes, and the number increases first and then decreases;the number of secondary subgroups gradually increases, and the number of scattered small subgroups expands more rapidly, but the core nodes are decreasing. Conclusion: In view of the operation of Shanghai biomedical innovation network, it is necessary to optimize the allocation of innovation resources, encourage medical institutions to actively participate in cooperation, and cultivate more strong dominant institutions, so as to improve the tightness of innovation network, the collaborative innovation capacity of medical institutions, and the innovation capacity of network subjects.
出处
《运筹与模糊学》
2023年第1期145-156,共12页
Operations Research and Fuzziology
