Modeling the evolution of collective overreaction in dynamic online product diffusion networks

IF 6.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Decision Support Systems Pub Date : 2024-04-24 DOI:10.1016/j.dss.2024.114232
Xiaochao Wei , Yanfei Zhang , Xin (Robert) Luo
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Abstract

With the development of e-commerce, collective overreactions such as buying frenzy have become prominent. However, studies have rarely investigated the mechanism of irrational consumer behavior at the group level. To investigate the evolution of collective overreaction in dynamic online product diffusion networks, we employed a sequential multiple-methods approach. A conceptual model is constructed to capture the influence of social network dynamic evolution on individual irrationality. An agent-based model (ABM) under different network dynamic growth mechanisms is implemented and verified. The findings revealed the following. In external dynamic growth mechanisms, key opinion consumer (KOC) connection can lead to positive collective overreaction (i.e., the adoption rate of consumer groups spikes). This effect fades as the probability of KOC connection increases and stabilizes as the node change rate decreases. Random connection is prone to negative collective overreaction (i.e., a sudden and sharp decline in the adoption rate of consumer groups), and key opinion leader (KOL) connection exhibits both positive and negative collective overreaction. Increasing the edge change rate increases the frequency of negative collective overreaction in KOL connections. In internal dynamic growth mechanisms, KOL and KOC connections are prone to negative collective overreaction; increasing the edge change rate can reduce the frequency of negative collective overreaction in KOL overreaction, and an appropriate edge change rate can inhibit the emergence of negative collective overreaction in KOC connection. This research contributes to the area of internet product marketing and provides a new basic framework through which to combine psychology and the ABM.

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动态在线产品传播网络中集体过度反应的演变建模
随着电子商务的发展,购买狂潮等集体过度反应已变得十分突出。然而,很少有研究从群体层面探讨消费者非理性行为的机理。为了研究动态在线产品扩散网络中集体过度反应的演变,我们采用了一种连续的多种方法。我们构建了一个概念模型,以捕捉社会网络动态演化对个体非理性行为的影响。建立并验证了不同网络动态增长机制下的基于代理的模型(ABM)。研究结果如下。在外部动态增长机制中,关键意见消费者(KOC)联系会导致积极的集体过度反应(即消费者群体的采纳率激增)。这种效应会随着 KOC 连接概率的增加而减弱,并随着节点变化率的降低而趋于稳定。随机连接容易出现消极的集体过度反应(即消费者群体的采用率突然急剧下降),而关键意见领袖(KOL)连接则同时表现出积极和消极的集体过度反应。提高边缘变化率会增加 KOL 联系中负面集体过度反应的频率。在内部动态增长机制中,KOL 和 KOC 连接容易出现负面集体过度反应;提高边缘变化率可以降低 KOL 过度反应中负面集体过度反应的频率,而适当的边缘变化率可以抑制 KOC 连接中负面集体过度反应的出现。这项研究为互联网产品营销领域做出了贡献,并提供了一个新的基本框架,通过这个框架可以将心理学与 ABM 结合起来。
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来源期刊
Decision Support Systems
Decision Support Systems 工程技术-计算机:人工智能
CiteScore
14.70
自引率
6.70%
发文量
119
审稿时长
13 months
期刊介绍: The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).
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