Internal audit: from effectiveness to organizational significance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 it