Using the Balanced Scorecard in Private Sector Organizations: A Case Study of Private Telecommunication Companies in Sudan
Bibliographic record
Abstract
The improvement of organizations and employee performance is ongoing journey. Previous literature and official document of managerial practices have confirmed that the balanced scorecard (BSC) is one of the latest innovations in management assist organizations to rationalize vision and strategy with business activities and measure areal organizational performance versus predetermined goals. The balanced scorecard also is applied to measure financial processes, customer relation, internal business processes and learning & growth characters of an organization. the main aims of this research is to identify the role of balanced scorecard (BSC) represent in better performance of organizations and to shed light on the impacts of balanced scorecard on organizational performance. The research has been performed with quantitive method and the organizations in questions are Telecommunication companies in Sudan for the purpose, the research supported by a questionnaire identifies that balanced scorecard is well implemented in the private sector organization in Sudan. Furthermore, currently there is luck of published research on BSC within private sector, so this has motivated me to highlight gaps in this research and to outline some ideas for future research. The finding indicate that positive relationship exists between the four perspectives in the BCS model and organization performance in Sudanese private sector companies.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".