THE DIFFUSION OF AN INTEGRATED ACTIVITY-BASED COSTING (ABC) WITH THE ECONOMIC VALUE ADDED (EVA) NEXT TO TUNISIAN ENTERPRISES
Bibliographic record
Abstract
This article is interested in the success rate of the ABC diffusion method also called the strategic accounting of costs or the activity-based costing when linked with the performance indicator of the economic value added creation (EVA). Its objective consists in showing the extent to which the diffusion of the latter next to Tunisian companies could succeed or fail in the presence of the following contingencies variables: the size of the company, the applied technology, the state of the competitive environment, the culture of these companies as well as the full cost and capital costs distortion. A survey was carried out next to a sample of 60 Tunisian private (small and medium) companies located in different areas and in different economic sectors in Tunisia. It revealed that the main factors affecting the success of the ABC method diffusion linked with the EVA next to these companies are the type of the applied technology, the problem of costs of the products, activities and of the capital distortion and their affectation. Similarly the financial and innovation culture as well as the intensive competition affect the diffusion success of the ABC method linked with the EVA.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".