开放创新搜索、知识转移与创新绩效——基于无标度加权网络的仿真研究

Open innovation search,knowledge transfer and innovation performance——Simulation analysis based on weighted scale-free network

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。

The existing researches on innovation search are mostly built on the un-weighted static network,and assume that the innovation search and knowledge transfer are two independent processes. In order to reproduce the overall behavior mechanism of innovative search,knowledge transfer and knowledge innovation more realistically,this paper adopts the method of simulation modeling based on the scale-free weighted dynamic network,to reveal the evolution of the dynamic growth of knowledge,explore the intrinsic relationship between innovative search,innovation network and innovation performance. The study found： the innovation network evolution is basically the same,and the final network evolution will reach equilibrium; the relationship between network connection density and average knowledge stock is inverted U-shaped type,and network tie strength and network average knowledge stock is positively correlated,finally the innovation search strategy has a significant impact on the average knowledge stock and the level of knowledge innovation.