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The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions

机译:排名的力量:量化排名对在线消费者搜索和购买决策的影响

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摘要

Online search intermediaries, such as Amazon or Expedia, use rankings (ordered lists) to present third-party sellers' products to consumers. These rankings decrease consumer search costs and increase the probability of a match with a seller, ultimately increasing consumer welfare. Constructing relevant rankings requires understanding their causal effect on consumer choices. However, this is challenging because rankings are endogenous: consumers pay more attention to highly ranked products, and intermediaries rank the most relevant products at the top. In this paper, I use the first data set with experimental variation in the ranking from a field experiment at Expedia to make three contributions. First, I identify the causal effect of rankings and show that they affect what consumers search, but conditional on search, do not affect purchases. Second, I quantify the effect of rankings using a sequential search model and find an average position effect of $1.92, which is lower than literature estimates obtained without experimental variation. I also use model predictions, data patterns, and a feature of the data set (opaque offers) to show rankings lower search costs, instead of affecting consumer expectations or utility. Finally, I show a utility-based ranking built on this model's estimates benefits consumers and the search intermediary.
机译:诸如Amazon或Expedia之类的在线搜索中介使用排名(有序列表)向消费者展示第三方卖家的产品。这些排名降低了消费者搜索成本,并增加了与卖家匹配的可能性,最终增加了消费者的福利。建立相关排名需要了解其对消费者选择的因果关系。但是,这是具有挑战性的,因为排名是内生的:消费者更加关注排名靠前的产品,而中介机构将最相关的产品排在首位。在本文中,我使用Expedia现场实验排名中具有实验差异的第一个数据集做出了三点贡献。首先,我确定排名的因果关系,并表明它们会影响消费者搜索的内容,但以搜索为条件,不会影响购买。其次,我使用顺序搜索模型量化排名的效果,发现平均排名效果为$ 1.92,这比没有实验差异的文献估算值要低。我还使用模型预测,数据模式和数据集功能(不透明的报价)来显示排名,从而降低搜索成本,而不影响消费者的期望或效用。最后,我展示了一个基于实用程序的排名,该排名基于该模型的估计值而使消费者和搜索中介受益。

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