消费者何时会在网络促销期间购买?理论与实证研究

IF 6.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Decision Support Systems Pub Date : 2024-04-28 DOI:10.1016/j.dss.2024.114233
Tao Zhu , Cheng Nie , Zhengrui Jiang , Xiangpei Hu
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引用次数: 0

摘要

越来越多的商家利用网络平台促销产品,然而,消费者在网络促销中的行为方式仍有许多未知之处。本研究探讨了影响消费者在网络促销期间的购买意向和购买行为的因素。我们将消费者分为两类,一类主要受促销时间压力的影响,另一类主要受记忆衰减的影响。然后,我们提出了一个分析模型来捕捉在线促销期间的市场需求。分析结果表明,在线促销期间存在四种需求模式,即 U 型、倒 U 型、单调递增和单调递减。我们随后探讨了影响需求模式类型的因素,如产品类型(非耐用品和耐用品)、促销持续时间、折扣水平和产品类别。我们对来自 B2C 电子商务平台的真实促销和销售数据进行了实证分析,验证了分析结果。需求曲线的类型取决于商品和促销活动的特点。例如,倒 U 型需求曲线只出现在非耐用消费品中,而 U 型曲线只存在于耐用消费品中。最后,在基于所建模型的一系列反事实分析中,我们展示了在线促销期间收入是如何随着促销参数的变化而变化的,并得出了一些有趣的结论。这些发现为在线零售商提供了重要启示,有助于他们更好地了解消费者,优化产品促销策略。
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When do consumers buy during online promotions? A theoretical and empirical investigation

An increasing number of merchants are using online platforms to promote their products; however, much is still unknown about how consumers behave in response to online promotions. This study investigates factors affecting consumers' purchase intentions and purchase behaviors during online promotions. We classify consumers into two categories, one mainly affected by the time pressure of promotion and the other primarily subject to the effect of memory decay. We then propose an analytical model to capture the market demand during an online promotion. Our analytical result indicates that there exist four types of demand patterns during online promotions, i.e., U-shape, inverted U-shape, monotonically increasing, and monotonically decreasing. We subsequently explore factors that can affect the type of demand patterns, such as the product type (nondurable and durable goods), duration of the promotion, discount level, and product category. Our empirical analyses of real-world promotion and sales data from a B2C e-commerce platform validate the analytical results. The type of demand curves depends on the characteristics of the goods and promotions. For instance, the inverted U-shape demand curve appears only for nondurable consumer goods, whereas the U-shape curve exists only for durable consumer goods. Finally, in a series of counterfactual analyses based on the proposed model, we show how revenues change during an online promotion in response to varying parameters of promotions and derive some interesting observations. These findings provide important insights to online retailers and can help them better understand their consumers and optimize their product promotion strategies.

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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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