Do Audit Teams Affect Audit Production and Quality? Evidence from Audit Teams' Industry Knowledge*
Bibliographic record
Abstract
ABSTRACT We examine how the extent and distribution of industry knowledge within an audit team affect audit outcomes. While prior research examining the role of auditors' industry knowledge focuses mainly on audit firms, audit offices, and audit partners, audits are conducted by audit teams. Using an audit framework and proprietary data from a Big 4 firm that includes audit hours for each team member, we find that Big 4 audit teams with higher average industry knowledge are associated with more audit effort. In contrast, we find mixed evidence on the relation between the average hourly internal cost rate and team knowledge. Furthermore, we find that balanced teams, which have at least one team member who qualifies as an industry specialist at both the senior rank and junior rank, produce higher‐quality audits than teams that have no specialists. In contrast, the audit quality of unbalanced teams, which have a specialist at the senior rank but not the junior rank or vice versa, is not statistically different than teams with no specialists. Overall, our evidence suggests that both the extent and distribution of industry knowledge within a team matter for audit production and that industry knowledge is utilized more effectively when it is spread throughout the team. The findings have useful implications for audit firms and regulators regarding how team composition and industry knowledge affect audit outcomes.
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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 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".