JOURNAL ARTICLE

Explaining Away Intentional Misstatements: Do Management-Provided Excuses Decrease Auditor Skepticism?

  • Published In: Auditing: A Journal of Practice & Theory, 2024, v. 43, n. 1. P. 151 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Hamilton, Erin L.; Smith, Jason L.; Carlisle, Melissa 3 of 3

Abstract

SUMMARY: When auditors identify a misstatement, they typically engage in client inquiry to understand the misstatement's cause (e.g., whether due to error or fraud). If a misstatement was caused intentionally, client management may attempt to reduce auditor skepticism by making their behavior seem unintentional (an excuse) or appropriate (a justification). Using two experiments, we examine the type of explanation managers use to conceal their fraudulent intent and its effectiveness in reducing auditor skepticism. We predict and find managers use excuses (e.g., "I forgot"), rather than justifications, to "explain away" fraud caused by omission/inaction (omitting information from a source document) as opposed to active misrepresentation (providing misleading information). When these same excuses are provided to auditors, we find they have a skepticism-decreasing effect, particularly when used to explain misstatements resulting from omission. Together, our findings suggest managers' ability to conceal fraud may persist even after the auditors have identified an intentional misstatement. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Auditing: A Journal of Practice & Theory. 2024/02, Vol. 43, Issue 1, p151
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2024
  • ISSN:0278-0380
  • DOI:10.2308/AJPT-2022-084
  • Accession Number:175188892
  • Copyright Statement:Copyright of Auditing: A Journal of Practice & Theory is the property of American Accounting Association 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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