转发行为下的电商直播营销影响因素研究——基于观点动力学方法
Research on the Impact Factors of E-Commerce Live Streaming Marketing under the Behavior of Forwarding—Based on Opinion Dynamics Method
摘要: 在当今电子商务的快速发展中，电商直播带货作为一种新兴的互动方式，已经成为产品营销的重要平台。本文基于观点动力学方法，研究了转发行为下的电商直播营销影响因素。通过构建“直播间–转发”HK模型，分析了直播间用户活跃度和原始粉丝数对电商直播营销效果的影响。研究发现，直播间用户活跃度越高，信息传播越快，粉丝增长数越多，但对观众对主播的认可程度影响有限；原始粉丝数增加能显著增强直播营销效果，但当超过一定比例后，效果减弱。本研究为电商直播营销提供了新的理论支持和策略指导，丰富了观点动力学在电商领域的应用。
Abstract: In the rapid development of e-commerce, live-streaming e-commerce has emerged as an important platform for product marketing. This paper investigates the impact factors of e-commerce live-streaming marketing under the behavior of forwarding based on opinion dynamics. By constructing the “live room-retweeting” HK model, it analyzes the influence of live room user activity level and initial follower count on the marketing effect of e-commerce live-streaming. The results show that the higher the live room user activity level, the faster the information spreads and the more the follower increase. However, it has limited impact on the audience’s recognition of the host. The increase in initial follower count can significantly enhance the marketing effect of live-streaming, but the effect weakens when it exceeds a certain proportion. This study provides new theoretical support and strategic guidance for e-commerce live-streaming marketing and enriches the application of opinion dynamics in the field of e-commerce.
文章引用：沈梁. 转发行为下的电商直播营销影响因素研究——基于观点动力学方法[J]. 电子商务评论, 2025, 14(6): 1847-1856. https://doi.org/10.12677/ecl.2025.1461932

参考文献

[1] Morărescu, I.C., Varma, V.S., Buşoniu, L. and Lasaulce, S. (2020) Space-Time Budget Allocation Policy Design for Viral Marketing. Nonlinear Analysis: Hybrid Systems, 37, Article ID: 100899. [Google Scholar] [CrossRef] 
[2] Holley, R.A. and Liggett, T.M. (1975) Ergodic Theorems for Weakly Interacting Infinite Systems and the Voter Model. The Annals of Probability, 3, 643-663. [Google Scholar] [CrossRef] 
[3] Sznajd-Weron, K., Sznajd, J. and Weron, T. (2021) A Review on the Sznajd Model—20 Years After. Physica A: Statistical Mechanics and Its Applications, 565, Article ID: 125537. [Google Scholar] [CrossRef] 
[4] Deffuant, G., Neau, D., Amblard, F. and Weisbuch G., (2001) Mixing Beliefs among Interacting Agents. Advances in Complex Systems, 3, 87-98.
[5] Hegselmann, R. and Krause, U. (2002) Opinion Dynamics and Bounded Confidence, Models, Analysis and Simulation. Journal of Artificial Societies and Social Simulation, 5, 1-33.
[6] 陈一新, 陈馨悦, 吕妍, 等. 基于改进Hegselmann-Krause模型的微博舆论反转研究[J]. 情报理论与实践, 2020, 43(1): 82-89.
[7] 王晗啸, 张楚惠. “双重意见气候”下社交机器人舆论干预影响研究——基于ABM仿真模拟沉默螺旋效应[J]. 新闻大学, 2022(12): 75-90, 124.
[8] Nian, F., Wang, C., Zhang, D. and Dang, Z. (2024) A Study on the Propagation of Online Public Opinion by Internet Water Army. Social Network Analysis and Mining, 14, Article No. 27. [Google Scholar] [CrossRef] 
[9] 胡艳丽. 在线社会网络中的舆论演化关键技术研究[D]: [博士学位论文]. 长沙: 国防科学技术大学, 2014.
[10] 万岩, 张涵. 在线点评模式下的舆论动力学模型研究[J]. 北京邮电大学学报(社会科学版), 2012, 14(4): 9-15.
[11] 何玉梅, 齐佳音, 刘慧丽. 基于微博的个体持续度舆论动力学研究[J]. 情报科学, 2015, 33(12): 117-121.
[12] 黄蓉. 我国电商直播用户行为研究简述[J]. 新媒体研究, 2024, 10(7): 6-9.
[13] 左晶晶, 李盈盈. 电商主播专业性对消费者冲动购买行为的影响及机制[J]. 消费经济, 2023, 39(4): 94-102.
[14] 张淑萍, 冯蛟. 电商直播中消费者冲动购买的机制——基于互动和匹配性假设视角[J]. 哈尔滨工业大学学报: 社会科学版, 2023, 25(3): 138-144.
[15] 闫玉刚, 宫承波. 狂欢化与去狂欢化——基于新冠疫情期间直播带货传播现象的冷思考[J]. 当代电视, 2020(6): 94-97.
[16] 牛小静. 基于内容营销的直播电商创新与启示——以东方甄选直播间为例[J]. 时代经贸, 2023, 20(6): 48-50.
[17] 樊超, 郭进利, 韩筱璞, 等. 人类行为动力学研究综述[J]. 复杂系统与复杂性科学, 2011, 8(2): 1-17.
[18] 哪国人在社交媒体上花费时间最多? [EB/OL]. 2022-05-04.
https://cn.weforum.org/stories/2022/05/na-guo-ren-zai-she-jiao-mei-ti-shang-hua-fei-shi-jian-zui-duo/, 2025-04-21.
[19] Han, W., Feng, Y., Qian, X., Yang, Q. and Huang, C. (2020) Clusters and the Entropy in Opinion Dynamics on Complex Networks. Physica A: Statistical Mechanics and Its Applications, 559, Article ID: 125033. [Google Scholar] [CrossRef] 
[20] 赵奕奕. 突发性群体事件下基于有界信任规则的舆论传播机理研究[D]: [博士学位论文]. 成都: 电子科技大学, 2014.

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