Audit Quality Indicators Across Jurisdictions: Regulatory Diversity, ESG Integration, and Implications for Public Trust
Bibliographic record
Abstract
Audit Quality Indicators (AQIs) have become vital tools for assessing and improving audit performance. This study examines how AQIs are defined, implemented, and interpreted across jurisdictions, with a particular focus on their integration into ESG (Environmental, Social, and Governance) assurance. Through a cross-jurisdictional comparison covering the EU, UK, US, and Canada, we analyze regulatory diversity and explore how different oversight models influence AQI adoption. Our findings reveal that AQIs play an increasingly important role not only in enhancing audit transparency but also in reinforcing the credibility of sustainability reporting under evolving frameworks like the EU’s Corporate Sustainability Reporting Directive (CSRD). However, implementation remains fragmented, and standardization challenges persist. The study offers empirical insights from a large-scale survey of audit professionals, highlighting how perceptions of audit quality vary by country, experience, and organizational context. We conclude that the harmonization of AQI frameworks, especially with ESG-focused metrics, is essential to foster public trust and ensure the accountability of sustainability assurance.
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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 | 0.078 | 0.206 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| 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".