多组织知识学习超网络模型及其学习绩效研究——面向复杂产品产业集群

摘要 按照系统科学视角网络建模的研究范式，对复杂产品产业集群知识活动的主体、资源、动力机制等进行了分析，构建形成了具有动态特征的多组织知识学习超网络模型。深入该模型的演化过程，通过仿真实验发现:不同项目团队组建特征会带来不同的产业集群学习绩效，存在着一个能够实现最高绩效的最优项目团队规模;当项目团队所在的组织子网络拓扑结构处于小世界特征下，则多组织知识学习超网络知识水平能够快速上升，学习绩效高启;当项目团队规模小于最优规模时，组织子网络的小世界结构存续时长与项目团队规模成反比等。结合相关结论，提出了一些对策，如要优化网络公共知识与信息平台载体，加强对复杂产品产业集群项目团队规模和结构特征以及多组织知识学习网络的管理等，以提升知识管理绩效。
Abstract：According to the research paradigm of network modeling from the perspective of system science， the main body， resources and dynamic mechanism of the knowledge activities in complex product industrial clusters are analyzed. A super network model of multi-organization knowledge learning with dynamic characteristics is constructed. It is found by going deep into the evolution process of the model through simulation experiments that the organizing characteristics of different project teams will bring different industrial clusters' learning performance and there exists an optimal project team size that can achieve maximum performance. When the topological structure of the organizational sub-network where the project team located is in the small world characteristic， the knowledge level of multi-ganization knowledge learning super network can rise rapidly， and learning performance is high. When the project team size is smaller than the optimal scale， the duration of the small world structure of the organizational sub-network is inversely proportional to the project team scale， etc. There are still some works to improve the learning performance of the complex product industrial cluster， including optimization of the public knowledge and information platform， control of the size and structure characteristics of the project team， and management of the multi-organization learning network， etc.