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• W4377012310 startingPage "114022" @default.
• W4377012310 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." @default.
• W4377012310 created "2023-05-19" @default.
• W4377012310 creator A5010668763 @default.
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• W4377012310 date "2023-09-01" @default.
• W4377012310 modified "2023-10-14" @default.
• W4377012310 title "Selling through online marketplaces with consumer profiling" @default.
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• W4377012310 doi "https://doi.org/10.1016/j.jbusres.2023.114022" @default.
• W4377012310 hasPublicationYear "2023" @default.
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