The DOI matches its OpenAlex record.
Reliance on Decision Aids: An Examination of Auditors' Assessment of Management Fraud.
Abstract
Abstract The assessment of management fraud risk is a complex decision process, but one with which few auditors have had experience. As a result, decision aids have been suggested to support this process. Unfortunately, many times users do not rely on decision aids even when doing so would improve the quality of the decision. This paper reports on an experiment that examines the use of an expert system decision aid created to enhance the engagement of the user and Increase reliance on the aid. Auditors using the expert system exhibited the ability to better discriminate between situations with varying levels of management fraud risk and selected more consistent subsequent decisions regarding appropriate audit actions than did users of a decision aid that provided only a suggested assessment, a logit statistical model. The logit model users did discriminate better than both checklist users and unaided decision makers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.08.006 · Cited by 1,361
A systematic review of algorithm aversion in augmented decision making
Burton, J. W., Stein, M., & Jensen, T. B. (2020) · Journal of Behavioral Decision Making · doi:10.1002/bdm.2155 · Cited by 814
Data Mining techniques for the detection of fraudulent financial statements
KIRKOS, E., SPATHIS, C., & MANOLOPOULOS, Y. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.02.016 · Cited by 722
A Decision Aid for Assessing the Likelihood of Fraudulent Financial Reporting
Bell, T. B., & Carcello, J. V. (2000) · AUDITING: A Journal of Practice & Theory · doi:10.2308/aud.2000.19.1.169 · Cited by 437
Brown-Liburd, H., Issa, H., & Lombardi, D. (2015) · Accounting Horizons · doi:10.2308/acch-51023 · Cited by 434
Metadata
- Authors
- Eining, M. M., & Jones, D. R.
- Venue
- Auditing A Journal of Practice & Theory
- Publisher
- American Accounting Association
- Published
- 1997-09-01
- Volume
- 16
- Issue
- 2
- Pages
- 1-19
- ISSN
- 0278-0380 (ISSN-L)
- OpenAlex
- W2994020124
- Cited by
- 195
- References
- 0
- FWCI
- 14.63
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Reliance on Decision Aids: An Examination of Auditors' Assessment of Management Fraud. https://app.cochat.ai/papers/10.2308/ajpt-1302504
- The paper
- Eining, M. M., & Jones, D. R. (1997). Reliance on Decision Aids: An Examination of Auditors' Assessment of Management Fraud. Auditing A Journal of Practice & Theory. https://doi.org/10.2308/ajpt-1302504
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.
