The Impact of Stakeholders on the Achievement of the Projects Within Malian Firms: Case of SODEMA
Bibliographic record
Abstract
This study highlights the impact of stakeholders on achievement of projects. The recommendations should be used as guideline for Malian projects. The quantitative and qualitative methods, primary data collected by questionnaires and interviews are used. Secondary data are gotten from articles, journals and online resources. The research framework was analyzed using simple regression models. Hypothesis test is adopted to accept or reject the hypotheses formulated in this research. Excel software have been used to perform regression statistics, predicting with the regression equation, Hypothesis test for correlation, ANOVA table and Regression equation plot. The results suggest that stakeholders have significant impact on achievement of projects. Stakeholders have a positive impact on achievement of projects is valid hypothesis. This study makes several contributions to research and theory of key stakeholders and achievement of projects. A greater understanding of stakeholders and achievement of projects provided further investigation of the relationship between of stakeholders and achievement of projects. This model can be used by other project for its achievement. Through the use of this model, project can quickly identify stakeholders requiring special and urgent attention. SODEMA industry needs improvement in communication with stakeholders. The theoretical model developed in this study is applicable in practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".