摘要
在创意产业集群知识网络中,核心企业的诱致性战略一方面能够带来集群企业行动的一致性,提升知识创造与流动的效率,另一方面也可能导致知识网络趋于僵化,甚至进入锁定状态,因而形成了核心企业悖论。为从知识的流动性视角探讨打破这一悖论的可能路径,文章基于集群内核心企业与跟随企业间的知识流动行为,构建了创意产业集群知识网络知识流动的系统动力学模型,并运用Vensim PLE软件进行模型检验和灵敏度分析。仿真结果表明:核心企业具有控制力和知识刚性,前者正向影响核心企业知识存量,而后者恰好相反;跟随企业创新意愿促进企业知识创新能力的提升并为其赶超核心企业提供了可能;集群内存在知识协同创新效应,该效应受集群网络外部知识资源与创新协同情境共同作用。
In the knowledge network of creative industry cluster,the induced strategy of the core enterprise can bring the consistency of cluster enterprises’ actions,improve the efficiency of knowledge creation and flow,on the other hand,it may also lead to the rigidity of knowledge network or even into the lock-in state,thus forming the paradox of the core enterprise. In order to explore the possible ways to break this paradox from the perspective of knowledge mobility,based on the knowledge flow between core enterprise and followers,we construct a system dynamics model of knowledge flow in creative industry cluster knowledge network and use Vensim PLE software to test the model and analyze the sensitivity.The simulation results show that the core enterprise has control power and knowledge rigidity. The former is positively affecting the knowledge stock of the core enterprise while the latter is just the opposite. It is possible for enterprises to improve their knowledge innovation ability and catch up with the core enterprises by following their innovation intention.There is knowledge synergistic innovation effect in cluster,which is affected by the external knowledge resources and innovation synergistic situation of the cluster network.
作者
李林
杨承川
何建洪
LI Lin;YANG Chengchuan;HE Jianhong(Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
出处
《重庆邮电大学学报（社会科学版）》
2020年第1期81-91,共11页
Journal of Chongqing University of Posts and Telecommunications(Social Science Edition)
基金
重庆市社会科学规划项目:文化嵌入情景下创意产业集群知识网络知识溢出、流动与关系治理研究(2018YBGL057)
重庆市教育委员会人文社会科学项目:重庆创新创业集群知识网络体系构建思路研究(18SKGH036)
重庆市研究生科研创新项目:网络视角下创意产业集群知识溢出及流动研究(CYS18254).
关键词
创意产业集群
知识网络
知识流动
系统动力学
creative industry cluster
knowledge network
knowledge flow
system dynamics
