The contextual nature of the association between managerial ability and audit fees
Bibliographic record
Abstract
Purpose This study examines whether auditors’ pricing decisions on managerial ability are affected by auditor litigation risk (financial distress or financial crisis), auditor’s familiarity with their client or regulatory changes in the post-Sarbanes–Oxley Act of 2002 (SOX) era. Design/methodology/approach Building on the extant audit fee literature, this study constructs an audit fee determinants model to examine how context affects auditors’ pricing of managerial ability. Findings Auditors offer a larger fee discount to more able client management teams when auditors face lower litigation risks or are more familiar with the client. Furthermore, managerial ability has a more pronounced effect on audit fees in the post-SOX era when managers are mandated to play more active roles in financial reporting (i.e. certification of financial statements required by SOX 302). Research limitations/implications Based on the audit risk model (Simunic, 1980), Krishnan and Wang (2015) show that the managerial ability of an audit client is relevant and important to auditors’ pricing decisions. This study demonstrates that managerial ability exhibits a non-linear relationship with audit fees and contextual factors, such as litigation risk, and that auditors’ familiarity with managers can alter the negative association between audit fees and managerial ability. This study extends Krishnan and Wang’s study by offering additional insights into auditors’ use of soft information such as managerial ability. Furthermore, the findings add to the literature on the impact of SOX on audit fees by suggesting that SOX has not only increased overall audit fees (Ghosh and Pawlewicz, 2009; Huanget al., 2009), it has also increased auditors’ price sensitivity to soft information (e.g. managerial ability). Practical implications This study provides insights for audit firms and client companies who are interested in understanding audit fee-pricing decisions. The findings also suggest that auditors need to be sensitive and responsive to various contextual factors when making pricing decisions. Originality/value Previous studies have not addressed the non-linear relationship between audit fees and soft information about managerial ability.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".