Internal audit: from effectiveness to organizational significance
Bibliographic record
Abstract
Purpose From the perspective of two groups of governance actors, this paper aims to understand how internal audit (IA) achieves and consolidates organizational significance. Design/methodology/approach Interviews were conducted with audit committee chairs and chief audit executives from multinational corporations, and the participating corporations’ registration documents were analyzed. Findings The data indicate that IA achieves and consolidates organizational significance by activating the IA effectiveness “building blocks” (Lenz et al., 2014) all together so as to generate organizational learning and positive change. New IA effectiveness drivers also emerged from the field. Research limitations/implications This research contributes to the IA literature by establishing a connection, through the IA impact on organizational learning, between the constructs of IA effectiveness and organizational significance. It also contributes to the IA literature by identifying new drivers and illustrating the complementarity and interconnections between the IA effectiveness building blocks. Practical implications This paper encourages internal auditors to keep their eyes on the prize (i.e. organizational significance) instead of simply being focused on the mean (i.e IA effectiveness), in order to fight stakeholder disappointment. Originality/value The paper proposes a conceptual model of IA organizational significance and gives key insights for setting up effective IA to stimulate organizational learning and fostering positive change in the whole organization.
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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 | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".