Correlates of Co-Sourcing/Outsourcing of Internal Audit Activities
Bibliographic record
Abstract
SUMMARY: I use responses from 1,059 chief audit executives (CAEs) of organizations located in Australia, Canada, New Zealand, South Africa, the U.K./Ireland, and the U.S. to investigate several correlates of co-sourcing and/or outsourcing (hereafter, simply “outsourcing”) of internal audit activities. An important finding of the study is that audit committee involvement is positively and significantly associated with outsourcing of internal audit activities. Interactions of audit committee involvement with organization size and location generally indicate that medium and large international/multinational organizations with audit committee involvement outsource more than medium and large local/national organizations with no audit committee involvement. Analysis of control variables produces significance for an inverse relationship between outsourcing and value-added activities of the internal audit function, and for positive relationships between outsourcing and missing skill set and audit staff vacancies. Other control variables, such as CAE age, college degree (graduate/undergraduate), major (accounting versus others), internal audit certification, and regular meetings with the audit committee do not show significant associations with outsourcing. Also, country of residence (U.S. versus other Anglo-culture countries) is not significant, but for-profit organizations outsource significantly more of their internal audit activities than not-for-profit/governmental organizations. Data Availability: Please contact the Institute of Internal Auditors Research Foundation, which owns the CBOK (2010) database used in this study.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".