Multiattribute Search: Empirical Evidence and Information Design

Pedro M. Gardete () and Megan Hunter ()
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Pedro M. Gardete: Marketing, Nova School of Business and Economics, Universidade Nova de Lisboa, 2775-405 Carcavelos, Portugal
Megan Hunter: Carroll School of Management, Boston College, Boston, Massachusetts 02467

Marketing Science, 2024, vol. 43, issue 5, 1052-1080

Abstract: The search literature has relied on parsimonious models to recover consumer fundamentals and characterize market outcomes. We investigate simple online search patterns that suggest that the dualistic view of fixed sample versus sequential search modes is the likely result of coarse data combined with methodological convenience. In contrast with these paradigms, we find that consumers are selective about the product attributes they inspect, that they revisit items to acquire additional information, and that they often convert without collecting all available data about the selected alternatives. Our substantive motivation is the problem of providing information to consumers in a market with differentiated products. We propose a new model of gradual consumer search based on simulated beliefs and “in-tandem” decision tree and likelihood computation that allows us to characterize the full search problem in contexts with moderate numbers of alternatives. We find that the seller’s incentives to engage in search design activities tend to match the consumers’ incentives. History: Tat Chan served as the senior editor. Funding: This work was funded by Fundação para a Ciência e a Tecnologia [Grants UIDB/00124/2020, UIDP/00124/2020 and Social Sciences DataLab - PINFRA/22209/2016], POR Lisboa and POR Norte [Social Sciences DataLab, PINFRA/22209/2016]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2022.0177 .

Keywords: consumer search; information transmission; differentiated markets; beliefs; online retail; empirical analysis; welfare; incentives (search for similar items in EconPapers)
Date: 2024
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