JOURNAL ARTICLE

Balancing Supply with Demand on Ride-Hailing Platforms in Markets with Price Regulations: An Operational Approach.

  • Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2025, v. 27, n. 5. P. 1515 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Zhou, Qin; Wang, Jingqi; Jiao, Yifan; Du, Jinzhao 3 of 3

Abstract

The article focuses on addressing the supply-demand imbalance in ride-hailing platforms through operational incentive schemes as alternatives to surge pricing, particularly in markets with regulated and inflexible prices. It proposes two schemes: the qualification scheme, which restricts off-peak work to drivers meeting a peak-period work target, and the prioritization scheme, which prioritizes such drivers for off-peak requests but allows others to serve as well. Analysis shows that in closed systems (only full-time drivers), the qualification scheme yields higher total matching volume but reduces driver welfare more than the prioritization scheme; conversely, in open systems (including abundant part-time drivers), the prioritization scheme outperforms the qualification scheme in matching volume and can improve both platform performance and full-time driver welfare. The study also compares these schemes with surge pricing, finding that incentive schemes can achieve similar or better outcomes without requiring price increases, offering practical insights for platforms and policymakers in regulated markets.

Additional Information

  • Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2025/09, Vol. 27, Issue 5, p1515
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:1523-4614
  • DOI:10.1287/msom.2022.0216
  • Accession Number:188158211
  • Copyright Statement:Copyright of Manufacturing & Service Operations Management (M&SOM) (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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