The Disposition of Audit‐Detected Misstatements: An Examination of Risk and Reward Factors and Aggregation Effects*
Bibliographic record
Abstract
Abstract While professional standards indicate that auditors have the responsibility to both detect and report material errors, empirical evidence shows that auditors waive approximately 50 percent of material errors (Wright and Wright 1997). Unlike prior research that has examined factors that may affect auditors' decisions to waive single material misstatements, the current study examines auditors' propensity to waive proposed adjusting journal entries (henceforth PAJEs) that exceed materiality, either individually or in aggregate, under several different aggregation contexts. These contexts are represented in the form of different cases that vary in terms of the materiality and income direction of the individual and aggregate PAJEs. The current paper posits that auditors will be more likely to waive PAJEs in excess of materiality (i.e., make a “non‐GAAS” decision) when there is potential reward for doing so or when there is little litigation risk from doing so. The case decisions of 155 audit partners and managers indicate that they are not affected by potential reward (Client's Relative Fees), but are affected by potential risk (the Client's Financial Health, the PAJE's Subjectivity, and the PAJEs' Aggregate Directional Effect on Income). However, these factors are not equally influential across all aggregation contexts. Additionally, auditors are more likely to make non‐GAAS decisions when they are evaluating immaterial PAJEs that aggregate to a material level than when they are evaluating a single material PAJE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".