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现有关于网络环境下的创新搜索研究大都构建在无权静态网络的基础上,且假设创新搜索与知识转移是两个相互独立的行为过程。为更加真实的再现创新网络中的创新搜索、知识转移、知识创新的整体行为机制,现基于无标度加权动态网络,采用仿真建模的方法,以揭示创新网络中知识动态增长的演化规律,探究创新搜索与创新网络、创新绩效之间的内在关系。研究发现:无论基于何种搜索策略,创新网络演化的路径基本相同,且最终网络演化将达到均衡;网络联结密度与网络平均知识存量存在倒U型关系,而网络关系强度与网络平均知识存量呈正相关关系,同时创新搜索策略对网络平均知识存量以及知识创新的水平均具有显著影响关系。
At present, most of the research on innovation search in the network environment is based on the non-quasi-static network, and it is assumed that the innovation search and knowledge transfer are two independent behavioral processes. In order to reveal the evolution law of dynamic growth of knowledge in innovation network, this paper uses simulation-based modeling as the whole behavior mechanism of innovative search, knowledge transfer and knowledge innovation in a more realistic representation network. Explore the relationship between innovation search and innovation networks and innovation performance. The research shows that the path of innovation network evolution is basically the same regardless of the search strategy and eventually the evolution of the network will reach an equilibrium. There is an inverted U-shaped relationship between the network link density and the network average knowledge stock, while the network relationship strength and the network average knowledge stock are positive Correlation, meanwhile innovative search strategy has a significant impact on the average stock of knowledge and the level of knowledge innovation.