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• W4295169242 abstract "Over the past decade, the developed and emerging markets have witnessed an exponential growth in online selling strategies that leverage social interaction among customers and enable sellers to offer discounts or rewards on the basis of the size of the buyer pool. This article classifies these diverse strategies into two categories—referral reward (e.g., Uber) and collective buying (e.g., GroupGets)—with associated subtypes. The authors employ an analytical model in which the seller faces customers with heterogeneity in their knowledge and/or intrinsic valuation of a product. Informed customers may inform and increase their less-informed peers’ valuation of the product. The study's richer behavioral model and consideration of a broader strategy space, relative to the existing analytical models, provide new insights into when and how specific strategies are optimal. Referral reward and collective buying encourage information sharing with less-informed potential customers and are typically superior to the individual selling strategy (under which the seller does not incentivize information sharing among customers), except when information sharing is significantly difficult. The authors conduct model refinements and robustness checks and identify clear qualitative managerial implications that can aid strategic decisions under different product-market characteristics. The authors conclude by suggesting future research opportunities to build on this article and add new theoretical insights and managerial guidance." @default.
• W4295169242 created "2022-09-11" @default.
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• W4295169242 date "2022-09-08" @default.
• W4295169242 modified "2023-09-23" @default.
• W4295169242 title "Leveraging Social Interaction Among Customers: Referral Reward Versus Collective Buying" @default.
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• W4295169242 doi "https://doi.org/10.1177/10949968221112624" @default.
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