Success Factors in Project Management in the Financial Sector in Ghana: Screening for Construct Items
Bibliographic record
Abstract
This study screens for individual manifest variables that make up the critical success factors in project management in the financial services sector in Ghana. A descriptive quantitative research technique was employed. A sample of 75 project team members were selected from commercial banks, insurance companies, and non-bank financial institutions in Ghana. Data was analyzed using the Principal Component Analysis. Apart from two manifest variables of culture; thus, beliefs, and general attitudes, all other variables significantly accounted for project success in the financial services sector in Ghana. Among the 9 factors retrieved, project team competence contributes the highest level of variance on project success, with about 21.3% of the variance contributed. Management commitment contributes the second highest variance on project success, with about 16.2% of variance. Culture contributes the least amount of variance to project success, thus 5.6% of the variance contributed. Project management organizations in the financial services sector would need to give priority to considering project team competency and management commitment, with consideration also given to other factors and individual manifest variables.
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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 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".