Auditors’ judgment subordination and the theory of planned behavior
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine circumstances under which auditors subordinate their judgment. More specifically, the authors investigate factors associated with auditors’ propensity to accept client-preferred accounting methods that conform to accounting standards but do not faithfully represent the entity’s financial position, financial performance and cash flows. Design/methodology/approach Based on the theory of planned behavior (TPB), the authors developed a survey that was sent to auditors at a non-Big 4 audit firm. Findings Main results suggest that auditors tend to agree with a client’s preferred accounting method when they anticipate little fallout from this decision, they believe they can easily justify the method, and they perceive that colleagues, shareholders and creditors would also agree with the decision. Practical implications Results benefit auditing standard setters and regulators and are relevant for accounting institutes and audit firms because practitioners can learn about circumstances under which auditors subordinate their judgment. Originality/value This study contributes to the audit literature by using the TPB to identify factors associated with auditors’ judgment subordination. In addition, it applies the TPB in a context where a client-preferred accounting method is considered acceptable but is not the most appropriate in light of the audited entity’s specific circumstances.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".