Consumer Online Search with Partially Revealed Information

Chris Gu () and Yike Wang ()
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Chris Gu: Scheller College of Business, Georgia Institute of Technology, Atlanta, Georgia 30308
Yike Wang: Department of Economics, London School of Economics and Political Science, London WC2A 2AE, United Kingdom

Management Science, 2022, vol. 68, issue 6, 4215-4235

Abstract: 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 Pareto-improve both revenue and consumer welfare for our OTA.

Keywords: online consumer search; cognitive modeling; information complexity; search intermediaries; platform design (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:68:y:2022:i:6:p:4215-4235

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