 Selling through online marketplaces with consumer profilingXinjie Xing, Hongfu Huang and Carl Philip T. Hedenstierna Journal of Business Research, 2023, vol. 164, issue C Abstract: Retail platforms obtain consumers’ individual preferences by gathering vast amounts of data and can deliver such information to online retailers to support their pricing activities; this is called consumer-profiling services (CPS). We develop a game-theoretic model to study how a retail platform should provide CPS in light of retailers’ competition and consumers’ data-blocking activities. We show that exclusively providing data to high-quality retailers results in a net benefit for the platform and retailers. Low-quality retailers benefit from refusing the CPS provided by the platform to avoid head-to-head competition. In addition, we find that consumers’ data blocking can benefit both the platform and retailers when the data-blocking cost is moderate, which is counterintuitive. We also find that data blocking always hurts consumer surplus and social welfare. To test the robustness of the main model, three extensions are discussed: sequential pricing, asymmetric production costs, and positive service fees. Keywords: Retail platforms; Consumer profiling services; Data blocking; Competition; Pricing (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:164:y:2023:i:c:s0148296323003806 DOI: 10.1016/j.jbusres.2023.114022 Access Statistics for this article Journal of Business Research is currently edited by A. G. Woodside More articles in Journal of Business Research from Elsevier |
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