Internal Audit Sourcing Arrangement and the External Auditor's Reliance Decision*
Bibliographic record
Abstract
This paper examines the effects of internal audit sourcing arrangement on the external auditor's reliance decision in the presence of different levels of inherent risk and task subjectivity. We posit that external auditors will rely more on outsourced than in-house internal audit functions (IAFs) and that this difference will be more pronounced when inherent risk is high or when the work performed by internal auditors relates to a subjective task. Participants in the study were 127 external auditors from a Big 4 firm who completed an experimental case in which we manipulated internal audit sourcing, inherent risk, and task subjectivity. Results from the experiment indicate an interaction between sourcing and inherent risk such that external auditors rely more on outsourced than in-house internal auditors when the level of inherent risk is high but do not differentiate based on sourcing arrangement when inherent risk is low. While we find that reliance is lower for subjective than for objective tasks, we do not find an interaction between sourcing arrangement and the subjectivity of the work performed. We do find a marginally significant interaction between inherent risk and subjectivity of work performed: external auditors are less willing to rely on subjective work performed by internal auditors when inherent risk is high but do not differentiate based on task subjectivity when inherent risk is low. Additional analyses indicate that sourcing arrangement affects the reliance decision both directly and indirectly. The indirect effect appears to be mediated through external auditors' perceptions of internal auditor objectivity.
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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 | 0.011 | 0.031 |
| 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 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".