JOURNAL ARTICLE

Internal Capital Allocation, Voluntary Disclosure, and Investment Efficiency.

  • Published In: Management Science (INFORMS), 2025, v. 71, n. 12. P. 10088 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Hwang, Hyun 3 of 3

Abstract

This article develops an analytical model to investigate how disclosure costs influence a founder’s choice between organizing two investment projects as a single multidivisional firm or as two stand-alone firms. In the model, managers may truthfully disclose project profitability at a cost to raise capital, while a multidivisional firm can internally allocate capital without incurring disclosure costs, potentially avoiding inefficient cross-subsidization seen in stand-alone firms. The findings indicate that a multidivisional structure is optimal when disclosure costs are high relative to project payoffs, enabling efficient internal capital allocation despite less disclosure, whereas stand-alone firms are preferred when disclosure costs are low and project payoffs are high to prevent cross-subsidization. Extensions of the model consider project heterogeneity, correlated cash flows, and alternative disclosure frictions, confirming the robustness of the main insights. The results suggest that disclosure frictions significantly shape organizational structure decisions and may explain why multidivisional firms often trade at a discount despite potential investment efficiency advantages.

Additional Information

  • Source:Management Science (INFORMS). 2025/12, Vol. 71, Issue 12, p10088
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2022.01295
  • Accession Number:189795878
  • 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.