Sequential Search with Refinement: Model and Application with Click-Stream Data
提出了消费者在不确定产品属性时进行序贯搜索的结构模型，利用在线点击流数据识别搜索成本，发现细化工具（如排序和筛选）使搜索量增加33%、购买效用提升17%，但若消费者不了解默认排序规则，总搜索成本可能超过效用提升，导致消费者剩余下降。
We propose a structural model of consumer sequential search under uncertainty about attribute levels of products. Our identification of the search model relies on exclusion restriction variables that separate consumer utility and search cost. Because such exclusion restrictions are often available in online click-stream data, the identification and corresponding estimation strategy is generalizable for many online shopping websites where such data can be easily collected. Furthermore, one important feature of online search technology is that it gives consumers the ability to refine search results using tools such as sorting and filtering based on product attributes. The proposed model can integrate consumers’ decisions of search and refinement. The model is instantiated using consumer click-stream data of online hotel bookings provided by a travel website. The results show that refinement tools have significant effects on consumer behavior and market structure. We find that the refinement tools encourage 33% more searches and enhance the utility of purchased products by 17%. Most websites by default rank search results according to their popularity, quality, or relevance to consumers (e.g., Google). When consumers are unaware of such default ranking rules, they may engage in disproportionately more searches using refinement tools. Consequently, overall consumer surplus may deteriorate when total search cost outweighs the enhanced utility. In contrast, if the website simply informs consumers that the default ranking already reflects product popularity, quality, or relevance, consumers search less and their surplus improves. We also find that refinement tools lead to a less concentrated market structure. This paper was accepted by J. Miguel Villas-Boas, marketing.