{"title":"利用顾客之间的社会互动:推荐奖励与集体购买","authors":"Feihong Xia, Rabikar Chatterjee, R. Venkatesh","doi":"10.1177/10949968221112624","DOIUrl":null,"url":null,"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.","PeriodicalId":48260,"journal":{"name":"Journal of Interactive Marketing","volume":"57 1","pages":"583 - 600"},"PeriodicalIF":6.8000,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Leveraging Social Interaction Among Customers: Referral Reward Versus Collective Buying\",\"authors\":\"Feihong Xia, Rabikar Chatterjee, R. Venkatesh\",\"doi\":\"10.1177/10949968221112624\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":48260,\"journal\":{\"name\":\"Journal of Interactive Marketing\",\"volume\":\"57 1\",\"pages\":\"583 - 600\"},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2022-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Interactive Marketing\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1177/10949968221112624\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Interactive Marketing","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1177/10949968221112624","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
Leveraging Social Interaction Among Customers: Referral Reward Versus Collective Buying
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.
期刊介绍:
The Journal of Interactive Marketing aims to explore and discuss issues in the dynamic field of interactive marketing, encompassing both online and offline topics related to analyzing, targeting, and serving individual customers. The journal seeks to publish innovative, high-quality research that presents original results, methodologies, theories, and applications in interactive marketing. Manuscripts should address current or emerging managerial challenges and have the potential to influence both practice and theory in the field. The journal welcomes conceptually rigorous approaches of any type and does not favor or exclude specific methodologies.