{"title":"Selling formats and platform information sharing under manufacturer competition","authors":"Xue Li, Shilu Tong, Xiaoqiang Cai, Jian Chen","doi":"10.1002/nav.22184","DOIUrl":null,"url":null,"abstract":"Online retail platforms have increasingly utilized big data technologies to gather demand information, which is then shared with upstream manufacturers employing various selling modes, including a hybrid format that encompasses both direct and indirect selling. Previous studies have suggested that platforms should refrain from sharing demand information with manufacturers engaged in indirect selling. In this study, we present a game‐theoretic model to examine the factors influencing the online platform's decision to share information with an indirect selling manufacturer and under what conditions. Our initial analysis, considering exogenous selling formats in the base model, reveals that the platform's information sharing behavior is primarily influenced by selling format structures, commission fee rates, and competition intensity. The platform always has an incentive to share information with direct selling manufacturers; however, under a hybrid selling format, information sharing with indirect selling manufacturers may occur, particularly when both the commission fee rate and competition intensity are relatively high. We extend our investigation to explore the platform's optimal format‐dependent information sharing behavior, accounting for manufacturers' endogenous selling format decisions, and demonstrate the robustness of our main findings from the base model. Overall, our research offers valuable insights and guidelines to assist online platforms in making informed decisions about their information sharing practices.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Naval Research Logistics","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1002/nav.22184","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Online retail platforms have increasingly utilized big data technologies to gather demand information, which is then shared with upstream manufacturers employing various selling modes, including a hybrid format that encompasses both direct and indirect selling. Previous studies have suggested that platforms should refrain from sharing demand information with manufacturers engaged in indirect selling. In this study, we present a game‐theoretic model to examine the factors influencing the online platform's decision to share information with an indirect selling manufacturer and under what conditions. Our initial analysis, considering exogenous selling formats in the base model, reveals that the platform's information sharing behavior is primarily influenced by selling format structures, commission fee rates, and competition intensity. The platform always has an incentive to share information with direct selling manufacturers; however, under a hybrid selling format, information sharing with indirect selling manufacturers may occur, particularly when both the commission fee rate and competition intensity are relatively high. We extend our investigation to explore the platform's optimal format‐dependent information sharing behavior, accounting for manufacturers' endogenous selling format decisions, and demonstrate the robustness of our main findings from the base model. Overall, our research offers valuable insights and guidelines to assist online platforms in making informed decisions about their information sharing practices.
期刊介绍:
Submissions that are most appropriate for NRL are papers addressing modeling and analysis of problems motivated by real-world applications; major methodological advances in operations research and applied statistics; and expository or survey pieces of lasting value. Areas represented include (but are not limited to) probability, statistics, simulation, optimization, game theory, quality, scheduling, reliability, maintenance, supply chain, decision analysis, and combat models. Special issues devoted to a single topic are published occasionally, and proposals for special issues are welcomed by the Editorial Board.