Bibliographic record
Abstract
Abstract There has been growing recognition in recent years of the importance of corporate governance in ensuring sound financial reporting and deterring fraud. The audit serves as a monitoring device and is thus part of the corporate governance mosaic. The objective of this paper is to examine the impact of various corporate governance factors, such as the board of directors and the audit committee, on the audit process. Importantly, there is little professional guidance on how auditors should consider such factors when formulating an appropriate audit strategy, and there has been only one prior study on this issue (Cohen and Hanno 2000). Because there are no current specific auditing standards that relate to the effect of corporate governance on the audit process, we conducted a semi‐structured interview with 36 auditors on current audit practices in considering corporate governance in the audit process. Reflecting on client experiences, auditors indicate a range of views with regard to the elements included in the rubric of “corporate governance”. Most significantly, auditors view management as the primary driver of corporate governance. The inclusion of top management in the “corporate governance mosaic” is inconsistent with agency theory's prescription of the board and other mechanisms serving as a means to independently oversee management's actions to protect stakeholders. Auditors consider corporate governance factors to be especially important in the client acceptance phase and in an international context. Further, despite the attention placed on the audit committee in the academic literature, in the business community, and by regulators in different countries (e.g., Canada, United States, Australia), several respondents indicated that their experiences with their clients suggest that audit committees are typically ineffective and lack sufficient power to be a strong governance mechanism. Implications for research and practice are presented.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".