JOURNAL ARTICLE

On the optimal design of peer monitoring systems in capital budgeting.

  • Published In: Managerial & Decision Economics, 2024, v. 45, n. 7. P. 4590 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Meder, Anthony A.; Nikias, Anthony D.; Schwartz, Steven T.; Young, Richard A. 3 of 3

Abstract

This paper considers peer monitoring in a capital budgeting setting with information asymmetry between principal and agents. The paper demonstrates in a two‐agent, two‐project setting that the principal can have each agent reveal their private information on the other agent's project, at an arbitrarily low cost. Next, an examination of monitoring resource allocations is made. Specifically, a comparison is made of broad monitoring, which spreads monitoring resources equally across agents' projects, and focused monitoring, which applies all monitoring resources to a single agent's project. Focused monitoring is preferred within an intermediate range of profitability, above which broad monitoring is preferred and below which neither is preferred. If project profitability is heterogeneous and focused monitoring is preferred, the more profitable project receives the focus. Both of these results occur because there is a threshold of monitoring resources that must be deployed for monitoring to provide any benefit, and the threshold is higher (more resources must be deployed) for lower profitability projects. That is, even if all monitoring resources are devoted to a low profitability project, they may provide no benefit to the principal. A discussion of how peer monitoring can be engineered by the firm is included. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Managerial & Decision Economics. 2024/10, Vol. 45, Issue 7, p4590
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:0143-6570
  • DOI:10.1002/mde.4286
  • Accession Number:180149342
  • Copyright Statement:Copyright of Managerial & Decision Economics is the property of Wiley-Blackwell 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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