Factors Affecting Auditors' Assessments of Planning Materiality
Bibliographic record
Abstract
This study investigates the planning materiality values used by auditors in The Netherlands for a sample of engagements performed by Big 5 and non-Big 5 firms in 1998–99. We find that, consistent with archival evidence from KPMG in Elliott (1983), planning materiality is not a constant percentage of a base, but increases at a decreasing rate with client size. In addition, we find that planning materiality values increase with the quality of the client's control environment and the magnitude of the client's rate of return on assets, while decreasing with the complexity of the client. We also find that Big 5 firms use lower planning materiality values than non-Big 5 firms, ceteris paribus, which is consistent with the production of relatively higher audit quality levels by the Big 5. Finally, we find that auditors use lower materiality values in situations where earnings might be managed to show a small profit or a small loss.
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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 | 0.012 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".