JOURNAL ARTICLE

On the Benefit of Privatization in a Mixed Duopoly Service System.

  • Published In: Management Science (INFORMS), 2023, v. 69, n. 3. P. 1486 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Zhou, Wenhui; Huang, Weixiang; Hsu, Vernon N.; Guo, Pengfei 3 of 3

Abstract

This article analyzes a mixed duopoly service system comprising one private service provider (SP) focused on profit maximization and one public SP aimed at maximizing social welfare. Customers, heterogeneous in their quality preferences, choose among joining the private queue, the public queue, or balking. The study finds a paradox wherein welfare in a system with a welfare-maximizing public SP can be lower than in a system with two profit-maximizing private SPs, primarily due to workload imbalances causing congestion in the public system and underutilization of the private system. Introducing partial privatization—where the public SP's objective blends welfare and profit maximization—can better balance workloads, reduce congestion, and increase overall social welfare; full nationalization is never optimal, and full privatization may be desirable under certain conditions. These conclusions hold under the assumption that customers can balk (choose an outside option) and are delay sensitive, with caution advised when extending results to systems prioritizing service accessibility or emergency services.

Additional Information

  • Source:Management Science (INFORMS). 2023/03, Vol. 69, Issue 3, p1486
  • Document Type:Article
  • Subject Area:Politics and Government
  • Publication Date:2023
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2022.4424
  • Accession Number:162389403
  • 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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