JOURNAL ARTICLE
Trust-and-Evaluate: A Dynamic Nonmonetary Mechanism for Internal Capital Allocation.
Published In: Management Science (INFORMS), 2024, v. 70, n. 11. P. 7811 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Gupta, Shivam; Bansal, Saurabh; Dawande, Milind; Janakiraman, Ganesh 3 of 3
Abstract
This article addresses the challenge of internal capital allocation in large firms, where managers submit cost and benefit estimates for in-house projects competing for limited funding, often leading to strategic misreporting such as understating costs to secure funding. Motivated by the internal capital allocation program (ICAP) at Dow AgroSciences (DAS), the authors develop a dynamic, nonmonetary mechanism called the trust-and-evaluate (TE) mechanism. This mechanism assigns and updates trust scores for managers based on their reported estimates and actual project outcomes, determining their eligibility for funding in subsequent periods. The TE mechanism is proven to induce near-truthful reporting as an ϵ-Bayesian Nash equilibrium and ensures the principal’s expected utility is within ϵ of the first-best scenario where true costs and benefits are known. Numerical experiments using real DAS data demonstrate that the TE mechanism achieves principal utility within 5% of the first-best and significantly outperforms naïve mechanisms, making it a practical approach for improving internal capital allocation without monetary incentives.
Additional Information
- Source:Management Science (INFORMS). 2024/11, Vol. 70, Issue 11, p7811
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:0025-1909
- DOI:10.1287/mnsc.2022.01121
- Accession Number:180699493
- 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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