Mandatory Disclosure of Engagement Partner Identity: Insights from Practice*
Bibliographic record
Abstract
ABSTRACT This study uses semistructured interviews to gain insights from 19 practicing Canadian audit partners into the practical implications of the engagement partner identity mandate requiring firms to disclose the identity of the engagement partner(s) auditing Canadian publicly traded companies. Building on prior literature that suggests accountability can reach a ceiling, we explore whether audit partners perceive incremental increases in accountability pressures to be effective in increasing audit quality. Based on the existing literature, we propose a nonlinear relation between accountability and performance (audit quality, in the current context), reflecting this ceiling effect. We find partners believe they are reaching, or are at, a ceiling level of accountability and that further initiatives to increase their accountability are ineffective in eliciting procedural changes in the audit or the audit's outcome. Contrary to regulators' motives for the disclosure, our interviewed partners do not believe the transparency of publicly disclosing their names will further increase their level of accountability or overall audit quality. We document that one possible reason for the disconnect is that partners are less concerned with managing external reputation than with managing internal reputation, which they believe has a more direct impact on their careers. We also discuss partners' perceptions of the required disclosure's impact on individual reputations, client risk choices, personal safety, and partner recruitment. We offer suggestions for future research building on the partners' insights.
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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 | 0.042 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".