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Cited by 294

The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.

Auditor Mindsets and Audits of Complex Estimates

Open access
GRIFFITH, EMILY E., HAMMERSLEY, JACQUELINE S., KADOUS, KATHRYN, YOUNG, DONALD·2015·Journal of Accounting Research

Abstract

Auditors experience significant problems auditing complex accounting estimates, and this increasingly puts financial reporting quality at risk. Based on analyses of the specific errors that auditors commit, we propose that auditors need to be able to think more broadly and incorporate information from a variety of sources in order to improve audit quality for these important accounts. We experimentally demonstrate that a deliberative mindset intervention improves auditors’ ability to identify unreasonable estimates by improving their ability to identify and incorporate into their analyses contradictory information from diverse parts of the audit and improving their ability to think critically about the evidence. We perform additional analyses to demonstrate that our intervention improves auditor performance by causing them to think differently rather than simply to work harder. We demonstrate that critical thinking can improve the identification of unreasonable estimates and, in doing so, we provide new directions for addressing audit quality issues.

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Metadata

Authors
GRIFFITH, E. E., HAMMERSLEY, J. S., KADOUS, K., & YOUNG, D.
Venue
Journal of Accounting Research
Publisher
Wiley
Published
2014-12-03
Volume
53
Issue
1
Pages
49-77
ISSN
  • 0021-8456 (ISSN-L)
  • 1475-679X
OpenAlex
W2607441414
Cited by
294
References
60
FWCI
84.01
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Auditor Mindsets and Audits of Complex Estimates. https://app.cochat.ai/papers/10.1111/1475-679x.12066
The paper
GRIFFITH, E. E., HAMMERSLEY, J. S., KADOUS, K., & YOUNG, D. (2015). Auditor Mindsets and Audits of Complex Estimates. Journal of Accounting Research. https://doi.org/10.1111/1475-679x.12066

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.