The effect of different types of comparative reviews on product sales

IF 6.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yuzhuo Li , Min Zhang , G. Alan Wang , Ning Zhang
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引用次数: 0

Abstract

Comparative online reviews have evolved into a vital instrument for consumers in decision-making, offering valuable comparisons and available options. Drawing on the insights from the linguistic category model (LCM) and elaboration likelihood model (ELM), we propose that different types (attribute-based and experience-based) of comparative reviews can affect consumers' perceived credibility of online reviews, thus impacting product sales. We analyzed 136,260 reviews on e-commerce platforms to assess these effects and introduced review valence as a boundary condition. Utilizing a combination of pattern discovery, supervised learning techniques, and manual coding, we identified attribute-based and experience-based comparative reviews and subsequently classified them based on positive, neutral, and negative valence. Subsequently, we took the product sales as the dependent variable and applied a two-way fixed effects model. The results indicate that attribute-based comparative reviews exert a more favorable influence on product sales compared to experience-based ones. Additionally, positive comparative reviews, irrespective of their attribute-based or experience-based nature, demonstrate a greater impact than regular positive reviews. However, negative and neutral comparative reviews, only when associated with attribute-based information, exhibit a significant effect. The results highlight the value of different types of comparative reviews and illuminate the moderating role of review valence. Our findings offer new insights and practical guidance for marketers and e-commerce platforms in capitalizing on the important influence of comparative reviews and enhancing the presentation of online reviews.

不同类型的比较性评论对产品销售的影响
在线比较评论已发展成为消费者决策的重要工具,为消费者提供了有价值的比较和可选方案。借鉴语言类别模型(LCM)和阐述可能性模型(ELM)的观点,我们提出不同类型(基于属性和基于经验)的比较性评论会影响消费者对在线评论可信度的感知,从而影响产品销售。为了评估这些影响,我们分析了电子商务平台上的 136260 条评论,并引入了评论价值作为边界条件。利用模式发现、监督学习技术和人工编码相结合的方法,我们识别了基于属性和基于经验的比较评论,并根据正面、中性和负面评价对它们进行了分类。随后,我们将产品销量作为因变量,并应用了双向固定效应模型。结果表明,与基于经验的比较性评论相比,基于属性的比较性评论对产品销售产生了更有利的影响。此外,正面的比较性评论,不管是基于属性的还是基于经验的,都比普通的正面评论有更大的影响。然而,负面和中性比较评论只有在与基于属性的信息相关联时,才会表现出显著的影响。这些结果凸显了不同类型比较性评论的价值,并阐明了评论价值的调节作用。我们的研究结果为市场营销人员和电子商务平台提供了新的见解和实用指导,帮助他们利用比较评论的重要影响并增强在线评论的展示效果。
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
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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