网络社群学习效应：理论机制与实证检验

摘要/Abstract

摘要： 互联网环境下，消费者从传统个体决策逐步转向群体共享协作，网络社群俨然已成为其观察性学习的重要信息来源。首先，考虑其渐进性和阶段性特征，本文从“群体认可”“成员参与”和“观点分布”三个层面构建网络社群学习理论框架。其次，使用网络爬虫抓取在线社群互动数据，并采用机器学习算法对非结构化数据进行分析处理。最后，基于系统矩估计方法建立动态面板计量模型，实证检验了本文理论假设。研究发现：网络社群中“群体认可”“成员参与”和“观点分布”均对社群学习效应具有正向影响，而“成员参与”和“观点分布”对上述效应具有递进的正向调节作用。本文研究试图打开社群学习机理“黑箱”，为方兴未艾的社群经济发展提供微观理论支撑。

关键词: 网络社群, 社群学习, 学习效应, 文本挖掘, 社群经济

Abstract: Under the Internet environment, consumers gradually shift from traditional individual decision-making to group sharing and collaboration, and the online community has become an important source of information for their observational learning. Considering its gradual and phased characteristics, this paper constructs the theoretical framework of network community learning from three levels: “group recognition”, “member participation” and “viewpoint distribution”. Secondly, web crawlers are used to capture interactive data of online communities, and machine learning algorithms are used to analyze and process unstructured data. Finally, the dynamic panel econometric model is established based on the system moment estimation method. It is found that “group recognition”, “member participation” and “viewpoint distribution” all have positive effects on the community learning effect in the network community, while “member participation” and “viewpoint distribution” have progressive positive moderating effects on the above effects. This paper attempts to open the “black box” of community learning mechanism and provide micro theoretical support for the burgeoning community economic development.

Key words: network community, community learning, learning effect, text mining, community economy

刘征驰, 李文静, 黄雅文. 网络社群学习效应：理论机制与实证检验[J]. 管理评论, 2022, 34(4): 131-139,161.

Liu Zhengchi, Li Wenjing, Huang Yawen. Network Community Learning Effect: Theoretical Mechanism and Empirical Test[J]. Management Review, 2022, 34(4): 131-139,161.

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