Potential Threats to Audit Firm Independence: Evidence from Italy on Audit Quality
Bibliographic record
Abstract
Purpose – This study aims to investigate the potential threats to the independence of an auditor who provides both auditing and non-audit services (NAS), in terms of credibility of and confidence in audit quality. Design/methodology/approach – In this study, we first replicate the results of Campa and Donnelly (2016) using hand-collected publicly available data for a sample of 91 Italian manufacturing public companies audited by a Big 4 and non-Big 4 audit firm over a longer time horizon (2015˗2019) using the panel data approach, based on three interconnected regression models. Findings – Previous studies in this area did not find a unique interpretation of the association between auditor independence and provision of NAS. Our findings reveal that auditor independence, as measured by the magnitude of discretionary accruals, is compromised by the provision of NAS, especially when unexpected audit fees are lower than expected. Enhanced credibility can lead to greater confidence in audit value. This study’s results should be of interest to European and U.S. legislators, to improve financial reporting quality. Originality/value – In the wake of the global financial crisis and loss of confidence in the role of auditors, this study investigates the supposed threats, to to aim to enhance the credibility of and confidence in audit quality, especially in settings outside the Anglosphere. This study would contribute to the literature to support the more binding approach for audit firms.
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 | 0.017 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".