广东工业大学学报 ›› 2023, Vol. 40 ›› Issue (02): 111-119.doi: 10.12052/gdutxb.220093

• 综合研究 • 上一篇    下一篇

基于在线用户行为的产品非对称竞争市场结构研究

莫赞, 范梦婷, 刘洪伟, 严杨帆   

  1. 广东工业大学 管理学院，广东 广州 510520
  • 收稿日期:2022-05-31 出版日期:2023-03-25 发布日期:2023-04-07
  • 作者简介:莫赞(1962-),男,教授,博士,主要研究方向为数据挖掘与神经科学,E-mail:mozan@126.com
  • 基金资助:
    国家自然科学基金资助项目(71671048)；广东省哲学社会科学规划项目(GD19CGL09)

Market Structure of Product Asymmetric Competition Based on Online User Behavior

Mo Zan, Fan Meng-ting, Liu Hong-wei, Yan Yang-fan   

  1. School of Management, Guangdong University of Technology, Guangzhou 510520, China
  • Received:2022-05-31 Online:2023-03-25 Published:2023-04-07

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摘要： 消费者对产品的卷入度研究成为热点问题，然而从产品竞争市场结构中获得竞争情报一直被忽略。为了更全面了解产品的竞争市场结构，本文以在线用户行为数据为对象从卷入理论视角构建产品考虑集，利用复杂网络聚类可视化产品的非对称竞争市场。结果发现，由消费者对产品的关注卷入度构成的考虑集对映射产品非对称竞争市场是最具有代表性的；复杂网络聚类方法映射的产品非对称竞争市场结构图简洁清晰，不仅识别了焦点产品的竞争对手，还表征了与焦点产品的竞争对手之间的非对称竞争程度、不同的子市场受欢迎程度、子市场内部产品竞争程度高而外部之间产品竞争程度低等；证明了本研究模型比传统模型更具有可解释性。本文为电商企业理解焦点产品的非对称竞争市场提供了更好的理论依据及方法。

关键词: 在线消费者行为, 竞争市场结构, 非对称竞争, 复杂网络聚类, 卷入理论

Abstract: The study of consumer involvement in products has become a hot issue, yet obtaining competitive intelligence from the structure of the product competitive market has been neglected. To gain a more comprehensive understanding of the competitive market structure of products, product consideration sets are constructed from an involvement theory perspective using online user behavior data as the object. Complex network clustering method is used to visualize the asymmetric competitive market of the product. The results show that the consideration set consisting of consumers' attention involvement of products is the most representative for mapping the product asymmetric competition market. The structure map of the product asymmetric competition market mapped by the complex network clustering method is concise and clear. This market structure map not only identifies the competitors with the focal product, but also maps the degree of asymmetric competition with the competitors of the focal product. In addition, it shows insights such as the popularity of different submarkets, the high level of product competition within the submarkets and the low level of product competition between the external ones. This study demonstrates that this research model is more interpretable than traditional models.

Key words: online consumer behavior, competitive market structure, asymmetric competition, complex network clustering, involvement theory

中图分类号: 

  • F713.55
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