JOURNAL ARTICLE

Optimal Capacity and Price Designs Under Ex Ante and Ex Post Theft.

  • Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2025, v. 27, n. 6. P. 2016 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Chen, Mingliu; Tian, Feng; Zuo, Ruiting 3 of 3

Abstract

This article addresses the challenge of internal theft in retail firms by modeling it as a principal-agent problem involving moral hazard, where a firm (principal) contracts a retail manager (agent) who may steal either before demand realization (ex ante stealing) or after sales revenue is generated (ex post stealing). The study derives an optimal commission scheme with a threshold structure that deters both types of stealing by providing the agent with incentives aligned to truthful reporting and full capacity deployment. Key findings reveal that optimal pricing and capacity decisions respond nonmonotonically to the severity of ex post stealing, while adjustments to ex ante stealing severity are monotonic beyond a threshold; notably, simply increasing retail prices to offset theft losses is not always optimal and can exacerbate agency problems. The results hold under various demand uncertainty distributions and extend to settings with convex stealing costs, emphasizing the importance of jointly optimizing commission contracts and operational decisions to mitigate internal theft impacts effectively.

Additional Information

  • Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2025/11, Vol. 27, Issue 6, p2016
  • Document Type:Conference Paper/Materials
  • Subject Area:Law
  • Publication Date:2025
  • ISSN:1523-4614
  • DOI:10.1287/msom.2025.0024
  • Accession Number:190748623
  • 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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