The effect of the product categories diversity recommended on cross-buying in electronic commerce platforms: The moderating role of user navigation heterogeneity

IF 4.4 3区 管理学 Q2 BUSINESS
Xiaosong Dong, Baofeng Li, Kai Xie, Xing Zhao, Ruyi Xiao
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Abstract

E-commerce platforms encourage consumers' cross-buying behavior to boost user traffic and trading volume. However, balancing the precision and richness of recommended information with the mechanisms influencing consumer cross-buying remains unclear, which also poses challenges to the specific operation of these platforms. Therefore, this study leverages extensive online behavioral data from an e-commerce platform, encompassing detailed records of 26,034 consumers. By employing unsupervised machine learning algorithms to distinguish the heterogeneity of consumer browsing, we apply clickstream data to explore the mechanisms of the impact of information diversity, specifically the breadth and depth of recommended product categories, on cross-buying. This study reveals that the effect of diverse recommendations on online decision-making undergoes marginal variations due to alterations in both the breadth and depth of these recommendations. Specifically, the depth and breadth of recommended product categories exhibit an inverted U-shaped relationship with cross-buying, initially promoting, and subsequently suppressing it. Additionally, consumers with different browsing behavior characteristics respond differently to recommendation diversity. Confused visitors and hedonic visitors positively moderate the depth of information and negatively moderate the breadth of recommendations with the inverted U-shaped effect on cross-buying. Search-oriented visitors positively adjust the inverted U-shaped relationship between the depth of information and the breadth of recommendations on consumer cross-buying. This study enriches research on cross-buying in the domain of recommendations on e-commerce platforms from the perspective of information diversity, providing valuable insights for optimizing recommendation strategies, promoting cross-buying behavior, and enhancing consumer loyalty.

电子商务平台推荐产品类别多样性对交叉购买的影响:用户导航异质性的调节作用
电子商务平台鼓励消费者的交叉购买行为,以增加用户流量和交易量。然而,如何平衡推荐信息的准确性和丰富性与影响消费者交叉购买的机制之间的关系仍不明确,这也给这些平台的具体运营带来了挑战。因此,本研究利用了来自电子商务平台的广泛在线行为数据,包括26,034名消费者的详细记录。通过采用无监督机器学习算法来区分消费者浏览的异质性,我们应用点击流数据来探索信息多样性(特别是推荐产品类别的广度和深度)对交叉购买的影响机制。本研究表明,由于这些建议的广度和深度的改变,不同的建议对在线决策的影响经历了边际变化。具体而言,推荐产品类别的深度和广度与交叉购买呈倒u型关系,最初促进,随后抑制。此外,不同浏览行为特征的消费者对推荐多样性的反应也不同。困惑型访问者和享乐型访问者对信息深度有正向调节作用,对推荐广度有负向调节作用,呈倒u型交叉购买效应。以搜索为导向的访问者在消费者交叉购买中积极调整信息深度与推荐广度的倒u型关系。本研究从信息多样性的角度丰富了电商平台推荐领域的交叉购买研究,为优化推荐策略、促进交叉购买行为、提升消费者忠诚度提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.30
自引率
11.60%
发文量
99
期刊介绍: The Journal of Consumer Behaviour aims to promote the understanding of consumer behaviour, consumer research and consumption through the publication of double-blind peer-reviewed, top quality theoretical and empirical research. An international academic journal with a foundation in the social sciences, the JCB has a diverse and multidisciplinary outlook which seeks to showcase innovative, alternative and contested representations of consumer behaviour alongside the latest developments in established traditions of consumer research.
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