The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Do practitioner assessments agree with academic proxies for audit quality? Evidence from PCAOB and internal inspections
Abstract
This study investigates the degree of concordance between fifteen measures of audit quality used in academia and two measures of audit process quality determined either by audit firms’ internal inspections or by Public Company Accounting Oversight Board inspections of individual engagements. Using two confidential datasets of these assessments of audit process quality, I find that three of the measures of audit quality used by academics have significant associations with both measures of audit process deficiencies used by auditors and regulators: (i) the propensity to restate financial statements, (ii) the propensity to meet or beat the zero earnings threshold, and (iii) audit fees. Seven academic proxies are significantly associated with only one audit process quality measure, and five have insignificant associations with both practitioner assessments. Overall, the significant associations indicate that practitioners and academics share common ground in identifying low-quality audits. These findings can provide guidance for future studies in selecting audit-quality proxies suitable for different research questions.
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Cites
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Cited by
Is artificial intelligence improving the audit process?
Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022) · Review of Accounting Studies · doi:10.1007/s11142-022-09697-x · Cited by 461
Metadata
- Authors
- Aobdia, D.
- Venue
- Journal of Accounting and Economics
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 67
- Issue
- 1
- Pages
- 144-174
- ISSN
- 0165-4101 (ISSN-L)
- OpenAlex
- W2899883402
- Cited by
- 373
- References
- 51
- FWCI
- 91.97
- Type
- journal article
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.