New Strategic Thinking in Mitigating the Challenges in Implementing Key Performance Indicators (KPIs) and Increasing Efficiency in Corporate Performance Management in MENA Region
Bibliographic record
Abstract
The implementation of key performance indicators (KPIs) is a challenging task for many businesses. Yet, effective implementation of KPIs is among the major determinants of performance and success of an organization. This study explored the new strategic thinking in mitigating the challenges in implementing key performance indicators (KPIs) and increasing efficiency in corporate performance management in the Middle East & North Africa (MENA) region. The study sought to test three hypotheses: (i) there is a significant relationship between having enough training and awareness sessions before implementation and effective implementation of KPIs; (ii) there is a significant relationship between having KPI professionals and specialists and effective implementation of KPIs; and (iii) there is a significant relationship between having clear KPI goals and objectives, on one hand, and the effective implementation of KPIs on the other. Hypotheses 2 and 3 were proved to be true while results for hypothesis 1 were inconclusive. A total of 1007 participants from across the MENA region were involved in the study. The findings demonstrate the importance of having clear KPI goals and objectives and KPI professionals or specialists to oversee the KPI selection and implementation process. Further research should be conducted to establish whether there is a significant relationship between having enough training and awareness sessions before implementation and effective implementation of KPIs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".