Consumer Online Search with Partially Revealed Information

• 成果类型: Article
• 署名作者: Gu, Chris; Wang, Yike
• 署名单位: University System of Georgia; Georgia Institute of Technology; University of London; London School Economics & Political Science
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.4104
• 发表日期: 2022
• 页码: 4215-4235
• 关键词: online consumer search; cognitive modeling; INFORMATION COMPLEXITY; search intermediaries; platform design

摘要

Modern-day search platforms generally have two layers of information presentation. The outer layer displays the collection of search results with attributes selected by platforms, and consumers click on a product to reveal all its attributes in the inner layer. The information revealed in the outer layer affects the search costs and the probability of finding a match. To address the managerial question of optimal information layout, we create an information complexity measure of the outer layer, namely orderedness entropy, and study the consumer search process for information at the expense of time and cognitive costs. We first conduct online random experiments to show that consumers respond to and actively reduce cognitive cost for which our information complexity measure provides a representation. Then, using a unique and rich panel tracking consumer search behaviors at a large online travel agency (OTA), we specify a novel sequential search model that jointly describes the refinement search and product clicking decisions. We find that cognitive cost is a major component of search cost, while loading time cost has a much smaller share. By varying the information revealed in the outer layer, we propose information layouts that Paretoimprove both revenue and consumer welfare for our OTA.