The Effects of Auditors' Accessibility to “Tone at the Top” Knowledge on Audit Judgments
Bibliographic record
Abstract
ABSTRACT: This study examines how external auditors' accessibility to “tone at the top” knowledge impacts subsequent audit judgments. To examine this relationship, a decision aid is investigated that differentially facilitates the auditors' retrieval of “tone at the top” evidence from memory. Results of an experiment indicate that, holding the client's “tone at the top” constant, the structure of a control environment decision aid influences the auditors' mental representation of the “tone at the top.” Further, favorable “tone at the top” mental representations transfer to induce relatively favorable control environment and fraud risk assessments, and greater reliance on management's explanation for variances detected in analytical procedures. Mediation analyses identify the control environment assessment as a mediator between the influenced mental representation and the subsequent fraud risk and analytical procedure judgments. The results of the paper underscore the importance of how auditors develop their “tone at the top” mental representations, the influence of these mental representations on subsequent audit judgments, and the stage in the audit process where interventions can improve audit quality. Data Availability: Contact the author.
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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 | 0.004 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".