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基于区域创新系统的知识转移模型研究
引用本文:王向阳,郗玉娟,谢静思.基于区域创新系统的知识转移模型研究[J].图书情报工作,2017,61(17):13-20.
作者姓名:王向阳  郗玉娟  谢静思
作者单位:吉林大学管理学院 长春 130022
基金项目:本文系国家自然科学基金青年项目"科技资源整合对企业创新能力提升的作用机制研究——基于极化效应和扩散效应的视角"(项目编号:71403100)和国家自然科学基金青年项目"组织再学习、组织忘记对跨国并购知识转移的影响研究"(项目编号:71302038)研究成果之一。
摘    要:目的/意义]以组织学习理论和知识转移理论为基础,研究基于区域创新系统的知识转移模型,解析区域创新系统中知识库的知识如何转移到企业层面并被企业整合与利用。方法/过程]通过对组织学习和知识转移相关理论回顾,以区域知识库为起点,根据知识流动决策过程和组织学习过程,构建基于区域系统的知识转移模型,并提出保障措施。结果/结论]研究结果表明,基于区域创新系统的知识转移是一项复杂过程。该过程的顺利实施以区域知识库为知识源,即实现知识资源的聚集。同时,通过组织学习促进知识流动,实现知识从区域知识库层面到企业层面的转移。

关 键 词:区域创新系统  组织学习  知识流动  知识转移  
收稿时间:2017-04-07

Research on the Knowledge Transfer Model Based on Regional Innovation System
Wang Xiangyang,Xi Yujuan,Xie Jingsi.Research on the Knowledge Transfer Model Based on Regional Innovation System[J].Library and Information Service,2017,61(17):13-20.
Authors:Wang Xiangyang  Xi Yujuan  Xie Jingsi
Institution:School of Management, Jilin University, Changchun 130022
Abstract:Purpose/significance] According to the theory of organizational learning and knowledge transfer, this paper studied the knowledge transfer model of the regional innovation system and analyzed how the knowledge regional innovation system level was transferred into enterprise, and made an analysis of integration and utilization. Method/process] Through the review of organizational learning and knowledge transfer, as the regional knowledge base to the starting point, according to the mechanism of knowledge flowing and the organizational learning, this paper constructed a model of knowledge transfer in the regional system, and put forward measures. Result/conclusion] The results show that knowledge transfer based regional innovation system is a complex process. In order to implement process smoothly, it must be based on regional knowledge base to gather knowledge resources. At the same time, we must be guided by organizational learning and improve the flowing of knowledge, to achieve the knowledge transfer which means from one level to another.
Keywords:regional innovation system  organizational learning  knowledge flowing  knowledge transfer  
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