JOURNAL ARTICLE
Inexperienced Component Auditors and the Internal Information Asymmetry of Multinational Corporations.
Published In: Journal of Accounting, Auditing & Finance, 2024, v. 39, n. 4. P. 1200 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Adams, Tom; Zhou, Ying 3 of 3
Abstract
This article investigates how the involvement of inexperienced component auditors—defined as auditors who participate in group audits of multinational corporations (MNCs) but lack experience issuing audit opinions for U.S. publicly traded companies—affects the internal information environment and capital allocation efficiency within MNCs. Using a hand-collected sample from Public Company Accounting Oversight Board (PCAOB) Form 2 filings, the study finds that Securities and Exchange Commission (SEC) issuers with such inexperienced component auditors exhibit higher internal information asymmetry (IIA), measured by differences in trading profits between top executives and divisional managers, and lower internal capital allocation efficiency (ICAE), assessed via segment-level capital expenditure deviations. These associations are economically significant and primarily driven by inexperienced auditors performing subsidiary audits. Additional analyses reveal that firms with inexperienced component auditors also experience lower firm value and accounting performance, suggesting that audit quality at the subsidiary level influences not only external reporting but also internal managerial decision-making in geographically dispersed MNCs.
Additional Information
- Source:Journal of Accounting, Auditing & Finance. 2024/10, Vol. 39, Issue 4, p1200
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:0148-558X
- DOI:10.1177/0148558X221116537
- Accession Number:179362268
- Copyright Statement:Copyright of Journal of Accounting, Auditing & Finance is the property of Sage Publications Inc. 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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