Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 709

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

Earnings Manipulation Risk, Corporate Governance Risk, and Auditors' Planning and Pricing Decisions

Open access
Bedard, Jean C., Johnstone, Karla M.·2004·The Accounting Review

Abstract

This paper investigates auditors' assessments of earnings manipulation risk and corporate governance risk, and their planning and pricing decisions in the presence of these identified risks. To conduct this investigation, we use engagement partners' assessments of their existing clients made during the participating public accounting firm's client continuance risk assessment process. We find that auditors plan increased effort and billing rates for clients with earnings manipulation risk, and that the positive relationships between earnings manipulation risk and both effort and billing rates are greater for clients that also have heightened corporate governance risk. These findings provide evidence that auditors assess situations involving both an aggressive management and inadequate corporate governance, and that there is a relationship between those assessments and auditors' planning and pricing decisions.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2308/accr.2004.79.2.277

Cites

42 of 42 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Cites

Show all 42 references

Cited by

See all 709

Metadata

Authors
Bedard, J. C., & Johnstone, K. M.
Venue
The Accounting Review
Published
2004-04-01
Volume
79
Issue
2
Pages
277-304
ISSN
  • 0001-4826 (ISSN-L)
  • 1558-7967
OpenAlex
W2148010935
Cited by
709
References
42
FWCI
49.56
Type
journal article
Language
en
Open access
green

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: Earnings Manipulation Risk, Corporate Governance Risk, and Auditors' Planning and Pricing Decisions. https://app.cochat.ai/papers/10.2308/accr.2004.79.2.277
The paper
Bedard, J. C., & Johnstone, K. M. (2004). Earnings Manipulation Risk, Corporate Governance Risk, and Auditors' Planning and Pricing Decisions. The Accounting Review. https://doi.org/10.2308/accr.2004.79.2.277

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.