The Informational Value of Key Audit Matters in the Auditor's Report: Evidence from an Eye-Tracking Study
Bibliographic record
Abstract
SYNOPSIS We examine whether and how the addition of mandatory paragraphs that highlight Key/Critical audit matters (KAMs) in the auditor's report affects users' information acquisition process using eye-tracking technology. We experimentally manipulate the presence of KAMs, their number (one or three KAMs), and their format with the inclusion of an overview of audit procedures performed to address each KAM. We find that KAMs have attention directing impact, in that participants access KAM-related disclosures more rapidly and pay relatively more attention to them when KAMs are communicated in the auditor's report. However, when exposed to an auditor's report with several KAMs, participants devote less attention to the remaining parts of the financial statements. Depending on the relevance of the information for the decision task users are less attentive to, our results have direct policy implications as they underline the potential costs and benefits associated with KAMs.
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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 | 0.008 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".