Diversité des pratiques de contrôle budgétaire:approches contingentes et néo-institutionnelles
Bibliographic record
Abstract
(VF)Comment expliquer la diversité des pratiques de contrôle budgétaire? Cette recherche s’appuie sur les cadres contingent et néo-institutionnel pour explorer cette diversité. Des données ont été collectées auprès de 246 contrôleurs de gestion et directeurs administratifs ou financiers. Nous montrons que la prise en compte du contexte institutionnel permet une meilleure explication de la diversité des pratiques de contrôle budgétaire que les facteurs de contingence classiques. Nos résultats suggèrent que le contrôle budgétaire peut ainsi être envisagé comme un dispositif qui aide les organisations à se légitimer dans certains environnements institutionnels.(VA)How can we explain the diversity of budgetary control practices? This research, based on contingency and new institutional theories, explores the reasons that underscore such diversity. Data have been collected from 246 management controllers or CFOs. We show that taking into account institutional factors (belonging to a parent corporation, type of shareholder and functional background of the CEO) allows for a better understanding of budgetary control diversity than traditional contingency factors (environment uncertainty, technology, decentralization and strategy). We suggest that budgetary control can be seen as a device that helps organizations to be legitimated in specific institutional environments.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".