详细信息

产业集群网络中知识转移行为仿真分析——企业知识刚性视角    

Simulation of knowledge transfers in industrial cluster networks: A firm's knowledge rigidity perspective

文献类型:期刊文献

中文题名:产业集群网络中知识转移行为仿真分析——企业知识刚性视角

英文题名:Simulation of knowledge transfers in industrial cluster networks: A firm's knowledge rigidity perspective

作者:周钟[1];陈智高[1]

机构:[1]华东理工大学商学院,上海200237

年份:2015

卷号:18

期号:1

起止页码:41

中文期刊名:管理科学学报

外文期刊名:Journal of Management Sciences in China

收录:CSTPCD;;北大核心:【北大核心2014】;CSSCI:【CSSCI2014_2016】;

基金:国家自然科学基金资助项目(71001037);教育部人文社会科学基金资助项目(09YJC630070)

语种:中文

中文关键词:知识刚性;产业集群;知识网络;知识转移;小世界网络

外文关键词:knowledge rigidity; industrial cluster; knowledge network; knowledge transfer; small-world network

摘要:知识刚性使得企业固守现有知识并阻碍外部新知识的流入,从知识刚性视角分析产业集群企业间的知识转移,有助于研究造成集群知识锁定和路径依赖的原因.通过设定企业知识刚性和知识吸收能力等状态变量,设计知识刚性影响规则、最短路径规则和知识转移发生规则,针对具有小世界网络特征的产业集群网络中的知识转移行为进行了仿真.仿真数据和分析结果表明,企业知识刚性对集群内部知识转移有显著影响,控制知识刚性,建立多渠道多样化的相互学习和合作关系,进而扩大知识转移范围,提高知识转移成效,是缓解产业集群知识锁定和路径依赖的有效途径.
Knowledge rigidities in firms make them stick to existing knowledge and impede the inflow of new external knowledge. This paper explores knowledge transfer behaviors among the cluster firms from a firm's knowledge rigidity perspective,which helps investigate the factors inducing knowledge lock-in and path dependency in industrial clusters. To conduct a simulation of knowledge transfer behaviors in cluster networks with small-world characteristics,state variables,such as firm's knowledge rigidity,knowledge absorptive capacity and so on,are defined. Knowledge rigidity impact rule,shortest path rule and knowledge transfer rule are also designed. The simulation data and analysis results show that the firms' knowledge rigidities influence the knowledge transfers in clusters significantly. Controlling cluster firms' knowledge rigidities and building diversified learning and cooperation relationships in different channels can expand the scope of the knowledge transfer and improve its effectiveness. These are effective solutions to ease the knowledge lock-in and path dependence in industry clusters.

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