Abstract: Online knowledge community is the era of big data organizational learning, collaborative innovation, an important way of knowledge and information exchange and transfer. The analysis of this online knowledge network, prediction and control is large data on economic and social data services and an important part of the decision-making advisory function. Taking CMKT Advisory Group 2 club QQ group, for example, collect all of the group’s analysis of professional exchanges in March 2016, to discuss and share records and dynamic knowledge-based networks construction CMKT these real-time data, analyze the dynamic online knowledge network of individuals knowledge sharing strategic choices, the final validation of the dynamic knowledge network to share individual acts of inertial effects. Based on this inertial effect, in big data technology support, the government, universities or research institutions and large companies can effectively analyze and predict their dynamic evolution trends build cross-organizational learning knowledge network and the timely intervention of choice or control means to direct knowledge networks, play a better role in collaborative innovation, knowledge transfer, and other aspects of cross-organizational learning.

Key words: big data, dynamic knowledge network, knowledge sharing, inertial effect

摘要： 在线知识社群是大数据时代组织学习、协同创新、知识与信息交流与转移的重要方式。针对这种在线知识网络的分析、预测和管控，是大数据对经济社会实现数据服务和决策咨询功能的重要环节。以CMKT咨询俱乐部2群QQ群为例，搜集分析该群在2016年3月份所有的专业交流、讨论和分享记录，并基于这些实时数据建构CMKT动态知识网络，分析了在线动态知识网络中个体知识分享行为的策略性选择，最终验证了动态知识网络中个体分享行为的惯性效应。基于这一惯性效应，在大数据技术的支撑下，政府、高校或科研机构以及大型企业可以对其所构建的跨组织学习知识网络的动态演化趋势进行有效的分析和预测，并适时地选择干预或管控手段以引导知识网络更好地发挥在协同创新、知识转移、跨组织学习等方面的作用。

关键词: 大数据, 动态知识网络, 知识分享, 惯性效应