Differences in Auditors' Materiality Assessments When Auditing Financial Statements and Sustainability Reports
Bibliographic record
Abstract
Abstract With increased interest in voluntary sustainability reports from investors and other stakeholders, more companies are having these reports assured. The issue of what is considered material in these assurance engagements is important, and yet research on materiality has focused only on financial statement audits. This article reports the results of an experiment where auditors assess the materiality of audit differences in the same magnitude for both a financial audit and a sustainability (water) assurance engagement. Two factors, the risk of breaching a contract and community impact, are manipulated between‐subjects. We find that auditors assess the materiality of an audit difference significantly higher for a financial case than for a water case. This difference is significantly greater when there is no risk of breaching a contract than when there is a risk of breaching a contract. The risk of breaching a contract has a stronger effect on the difference in auditors' materiality assessments when there is no community impact than when there is a community impact. Overall our findings suggest that qualitative factors have a greater impact on sustainability (water) materiality assessments than on financial statement materiality assessments when an audit difference is between 5 percent and 10 percent of a relevant base. Understanding the factors that impact material judgments in sustainability reports is important as these factors affect the reliability of the reported disclosures.
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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 | 0.041 | 0.255 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".