JOURNAL ARTICLE

On-Demand, Long-Term, or Hybrid? An Economic Analysis of Optimal Rental Models on Sharing Platforms.

  • Published In: Information Systems Research (INFORMS), 2025, v. 36, n. 1. P. 307 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Chen, Jianqing; Feng, Nan; Guo, Zhiling; Zhang, Wenyi 3 of 3

Abstract

This study analyzes three rental models in the sharing economy—long-term, on-demand, and hybrid—and their effects on social welfare, consumer surplus, and platform profit. It finds that consumers' setup and transaction costs critically influence which rental models are viable and optimal for platforms, with high total costs limiting the sustainability of on-demand and hybrid models. The platform's optimal rental model depends on the relative setup and transaction costs between owners and renters: the on-demand model is favored when owners' setup costs are significantly higher, the long-term model when owners' setup costs are significantly lower and renters' costs are low, and the hybrid model when setup costs are comparable or renters' costs are high. While the hybrid and long-term models often maximize social welfare and consumer surplus, the on-demand model is never socially optimal. The study suggests policymakers should consider subsidies or regulations to balance participation incentives and promote socially beneficial outcomes in sharing platforms.

Additional Information

  • Source:Information Systems Research (INFORMS). 2025/03, Vol. 36, Issue 1, p307
  • Document Type:Article
  • Subject Area:Economics
  • Publication Date:2025
  • ISSN:1047-7047
  • DOI:10.1287/isre.2022.0441
  • Accession Number:184136950
  • Copyright Statement:Copyright of Information Systems Research (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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