Learning by Doing? Partners Audit Experience and the Quality of Audit Services
Bibliographic record
Abstract
A pesar de la evidencia que sugiere que el conocimiento especializado debería resultar más relevante que el genérico para explicar diferentes niveles de calidad de auditoría asociados a los auditores individuales, ningún estudio hasta la fecha ha abordado el posible impacto diferencial de la experiencia genérica y específica en la calidad de los servicios de auditoría. Nuestro estudio investiga esta cuestión en el mercado de auditoría español. Aproximamos la calidad de la auditoría a partir de los ajustes de devengo discrecionales y la opinión del informe de auditoría; diferenciando entre experiencia específica con el propio cliente, experiencia sectorial y experiencia de auditoría genérica. Como se esperaba, los resultados muestran una mayor calidad de auditoría cuando el cliente es auditado por un socio con mayor experiencia en el sector de actividad del cliente. También observamos que ni la experiencia específica con el propio cliente, ni la experiencia genérica de auditoría del socio auditor son determinantes significativos de la calidad de los servicios de auditoría. Por otro lado, mientras que algunos estudios previos señalan que el conocimiento especializado resulta más relevante que el genérico para explicar la calidad de los servicios de auditoría, este trabajo sugiere que el conocimiento especializado es, de hecho, el único tipo de conocimiento que resulta relevante. Estos resultados pueden tener implicaciones interesantes para las firmas de auditoría. Despite evidence suggesting that specialised knowledge should be more relevant than generic knowledge to explain different levels of audit quality across individual auditors, no study to date has addressed the respective impacts of the industry-specific and the generic audit experience of audit partners on the quality of audit services. Our study investigates this issue in the Spanish audit market. We proxy audit quality by discretionary accruals and by the opinion of the audit report, and differentiate among client-specific experience, industry-specific experience and generic audit experience of individual auditors. As expected, our results show significantly higher audit quality when the client is audited by a partner with stronger industry-specific audit experience. Furthermore, we observe that neither client-specific experience nor generic audit experience of audit partners are significant determinants of the quality of audit services provided by these auditors. These results may have some interesting implications for audit firms. Therefore, whereas some prior studies on the related issue of industry specialization point out that specialised knowledge is more relevant than generic knowledge to explain the quality of audit services, our findings suggest that specialised knowledge is, in fact, the only type of knowledge that seems to matter.
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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 | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".