科研管理 ›› 2011, Vol. 32 ›› Issue (9): 74-82.

• 论文 • 上一篇    下一篇

组织间知识溢出吸收模型与仿真研究

刘满凤1, 唐厚兴2   

  1. 1. 江西财经大学产业集群与企业发展研究中心,江西 南昌 330013;
    2. 南昌工程学院工商管理学院,江西 南昌 330099
  • 收稿日期:2009-12-08 修回日期:2010-09-16 出版日期:2011-09-27 发布日期:2011-09-22
  • 作者简介:刘满凤(1964-),女(汉),江西吉安人,教授,博士生导师,主要研究方向为企业决策优化、绩效评估、技术创新管理。唐厚兴(1982-),男(汉),安徽滁州人,讲师,主要研究方向为知识管理、经济管理决策分析。
  • 基金资助:

    国家自然基金项目"高技术产业集群内的知识溢出机制与溢出效应研究"(批准号:70961002);江西省教育厅科技项目"区域创新系统中的知识扩散机制与扩散效应研究"(项目编号:GJJ09291)资助。

The model and simulaton for absorption of knowledge spillovers among organizations

Liu Manfeng1, Tang Houxing2   

  1. 1. Office of Scientific Research, Jiangxi University of Finance and Economics, Nanchang 330013, China;
    2. College of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China
  • Received:2009-12-08 Revised:2010-09-16 Online:2011-09-27 Published:2011-09-22

摘要: 本文主要通过对组织间知识溢出吸收过程的分析来研究知识溢出对组织间知识状态的影响。本文首先分析了知识吸收过程中知识交互的动机、基础和结构,分别提出了知识交互的"效用准则"、交互阈值条件和内生的交互网络结构,并据此构建了改进的知识累积模型。通过仿真分析表明:知识溢出对组织间知识分布的影响并不是单纯的趋同或者趋异,而是受到组织初始知识存量和交互阈值条件这两个关键因素的限制,而这两个因素代表着不同发展阶段组织间在知识学习能力、吸收能力上的差异,因此有的集群组织间表现为同化,有的表现为异化,而有的则表现为先同化后异化。这些分析为学术界关于知识溢出会使不同企业技术水平趋于相同还是走向分化的争论提供了有益的参考。

关键词: 知识溢出, 社会网络模型, 仿真, 同化, 异化

Abstract: The effect of knowledge spillovers on the evolution of organizations is focused on from the perspective of absorption in knowledge spillovers. Firstly, the motivation, precondition, and structure of knowledge interaction are analyzed. And then "utility rule", "threshold value rule", and "endogenous network" of knowledge interaction are proposed. Based on these rules, a new knowledge accumulation model is constructed. The simulation indicates that the effect of knowledge spillovers on the evolution of organizations is neither convergence nor divergence. However, the results are affected by initial knowledge stock of organizations and threshold value rule of knowledge interaction. The two factors reflect the differences on learning ability and absorption capacity for different organizations. Therefore, the organizations in some clusters tend to convergence, but others tend to divergence and moreover some of them tend to convergence firstly and turn into divergence eventually. All these results are useful for understanding the controversy that whether the knowledge spillovers prompt the convergence of organizations or not.

Key words: knowledge spillover, social network model, simulation, convergence, divergence

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