Leveraging activity-based costing and information technology strategic enhancements in decision-making, and competitive advantage in industrial projects
Bibliographic record
Abstract
This study examined the impact of activity-based Costing ABC and information technology IT on decision-making and competitive advantage in Sudanese industrial projects. The experimental results of this study were based on a questionnaire administered to accountants and administrators in 61 industrial projects in Sudan, and the data were analyzed using partial least squares. The study's results indicate that using activity-based costing and information technology methods in determining costs, evaluating company profits, and approximating product costs positively affects managerial decisions while improving the competitive advantage of industrial projects. The use of activity-based Costing and information technology affects decision-making and competitive advantage in industrial projects in Sudan. The study recommended that continuous training helps obtain the benefits of using activity-based Costing and information technology in industrial projects. The results show that the result gained from using activity-based Costing and information technology in determining costs, decision-making, and expanding competitive advantage has been affected by attending training programs, gradually, based on learning the capabilities of the activity-based costing system and the information technology system.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".