Network Analysis of Audit Partner Rotation<sup>†</sup>
Bibliographic record
Abstract
ABSTRACT Focusing on mandatory partner rotations, we examine the importance of within‐firm network connections to the selection of successor partners and the impact of those connections on post‐rotation audit performance. Using data from China, we track partners' history and identify incumbent‐successor connections stemming from jointly conducted prior engagements. Although these connections can enhance incumbent‐successor information transfers and thus post‐rotation audit performance, they may also pose a threat to quality by compromising the successor's independence. Among the pool of replacement candidates, we find that individuals with stronger connections with the incumbent are more likely to be appointed as successors. This finding is more pronounced when the audit engagement is more complex, client‐specific knowledge is not readily available to the succeeding partner, and the engagement is more valuable to the audit firm. We also document that successor‐incumbent connections are associated with equal or better post‐rotation audit quality and fewer client defections. These results suggest that the benefits of network‐based successor selection may outweigh its costs. By enriching our understanding of the partner transition process, this study contributes to the public policy discourse on partner rotation.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".