Investigating Critical Factors for Project Success and the Impact of Certification/Training
Bibliographic record
Abstract
This paper investigates via a survey methodology, project critical success factors (CSFs) of a UN organization as perceived by computer and information technology trained and certified professionals. James Wan and Raafat Saade adopt their CSFs from three seminal studies done at different times. They provide a critical analysis of those factors for the 21st century United Nations context facing today an increasing need for agility in a fast-changing global environment. The authors investigate project CSFs in this study with two goals in mind: Firstly, to test the applicability of well-studied CSFs in the United Nations context, and secondly, to assess the influence of certification/training on these factors. Results show that 5 out of 13 factors differ in the United Nation's context and that certification is not perceived as important while training is. Results are discussed bringing forth insights into the nature of UN-type organization project management. Results have shown that close to 40% of the CSFs previously studied do not apply to the United Nations context. At the same time, correlation analysis shows that training in project management knowledge areas are more important that actual certification.
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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 | 0.012 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".