When the Client Is a Former Auditor: Auditees' Expert Knowledge and Social Capital as Threats to Staff Auditors' Operational Independence
Bibliographic record
Abstract
ABSTRACT Auditees can play an active role in influencing staff auditors' professional judgment and skepticism. Yet, although it constitutes one of the main threats to auditor independence, very little is known about the means and extent of auditees' power during the audit engagement. To address this knowledge gap, our study focuses on a specific category of auditees, namely, auditees who have worked as auditors in large accounting firms. We interviewed 36 of these auditees and triangulated our findings with 11 interviews conducted with auditors. At the theoretical level, we conceptualize auditees' influence over auditors as intentional and active through the notion of “social power.” Overall, our analysis shows that the efficacy of auditees' power during the engagement materializes through the mobilization of two main power resources developed during their time at the firms: (i) expert knowledge of auditing techniques and (ii) social capital. On the one hand, relying on their cognitive authority, auditees' employ three different power strategies to constrain staff auditors' operational independence: stage‐setting, teaching, and questioning. On the other hand, auditees' social capital can support the use of two additional strategies: attracting and monitoring. Our triangulation analysis confirms our findings and suggests that auditors may be aware of the threats to independence that auditee expertise and social capital pose. By focusing on auditees' agentic capabilities—that is, individuals' capabilities to consciously exert influence over the course of events—we reinterpret the pressures exerted by clients on auditors as the product of strategic actions and discuss substantive consequences for independence risk.
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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 | 0.012 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".