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作 者:孔晓丹 张丹[1] KONG Xiao-dan;ZHANG Dan(Glorious Sun School of Business and Management,Donghua University,Shanghai 200051,China)
出 处:《运筹与管理》2020年第10期173-182,共10页Operations Research and Management Science
基 金:国家自科基金重点项目(71832001);上海市科技创新重大项目(2017-01-07-00-03-E00044)。
摘 要:基于合作的集群创新网络知识扩散已经成为企业实现知识创新的重要手段,而集群创新网络知识扩散的动力学过程强烈依赖于异质企业间知识扩散能力的影响,为此,本文综合考虑了企业间不同接触数量、知识吸收和传播能力、知识淘汰率等异质性因素,建立了基于传染病理论的知识扩散模型,验证了由各异质因素构成的知识扩散再生数对知识扩散均衡和扩散效果的影响,并结合仿真实验进一步得出:在知识扩散前期,集群创新网络应发挥hub节点及异质网络的优势加快知识扩散,在中后期应注意企业关系发展的均衡性及企业接触邻居的规模性;相比过于强调知识交流的广泛性,加强企业传播能力和吸收能力的培养对网络知识扩散效果的提升更具意义;随着时间演化,企业知识淘汰率也会影响网络知识扩散的收敛情况。Knowledge diffusion based on cooperative behavior has become the main means for enterprises to realize knowledge innovation and to enhance competitiveness in cluster innovation network.The dynamic process of knowledge diffusion depends strongly on the diffusion ability of heterogeneous enterprises.Therefore,considering the heterogeneity factors such as the quantity of different contacts between enterprises,the ability of knowledge absorption and transmission,and the rate of knowledge elimination,a knowledge diffusion model based on infectious disease theory is established.It is verified that the number of knowledge diffusion regeneration composed of various heterogeneity factors has an impact on the evolution equilibrium of knowledge diffusion.Simulation results show that:In the early stage of knowledge diffusion,cluster innovation network should take full advantages of hub nodes and heterogeneous networks to accelerate knowledge diffusion rate.In the later stage of knowledge diffusion,we should pay attention to the balance of enterprise relationship development and the scale of enterprise contact with neighbors.Compared with over-emphasis on the universality of knowledge exchange,the effectiveness of network knowledge diffusion can be significantly improved by reinforcement of enterprise’s abilities to communicate and absorb.With the evolution of time,the enterprise’s communication willingness and knowledge elimination rate will also affect the convergence of network knowledge diffusion.
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