JOURNAL ARTICLE

Managing Multirooming: Why Uniform Price Can Be Optimal for a Monopoly Retailer and Can Be Uniformly Lower.

  • Published In: Management Science (INFORMS), 2024, v. 70, n. 5. P. 3102 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Chen, Yuxin; Dai, Yue; Zhang, Zhe; Zhang, Kun 3 of 3

Abstract

This article develops an analytical model to analyze the pricing strategy of a monopoly retailer managing both online and offline channels, focusing on the choice between uniform pricing (same price online and offline) and dual pricing (different prices across channels). The model incorporates consumer multirooming behavior—where consumers browse and purchase across channels—and product return costs, considering consumers’ uncertainty about product attributes and differing shopping costs. Key findings reveal that uniform pricing can be optimal and more profitable than dual pricing even when the uniform price is lower than both online and offline prices under dual pricing. This outcome arises because uniform pricing removes consumers’ uncertainty about offline prices, encouraging in-store search for nondigital product attributes, reducing product returns, and allowing the retailer to set lower prices while maintaining profitability. Extensions of the model confirm robustness of these results under heterogeneous online shopping costs and price-dependent return decisions. The research highlights the strategic importance of considering multirooming and product returns in multichannel pricing decisions and challenges conventional wisdom that dual pricing always yields higher profits.

Additional Information

  • Source:Management Science (INFORMS). 2024/05, Vol. 70, Issue 5, p3102
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2024
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2023.4849
  • Accession Number:177188263
  • Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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