Quantitative Analysis of Critical Success Factors in the Development of Public-Private Partnership (PPP) Project Briefs in the United Arab Emirates
Bibliographic record
Abstract
This paper presents the research findings on Public-Private Partnerships (PPP) brief development in the United Arab Emirates. A questionnaire survey was conducted to assess and rank the relative importance of the Critical Success Factors (CSFs) identified in PPP brief development in the UAE. A quantitative analysis was then conducted on the data gathered from the survey, and the results of the analysis are described. The processes of purifying and computing the measurement instruments are also explored using Cronbach’s alpha to assess the reliability of scale measurements. The statistical analysis focuses on the importance and ranking of the identified thirty-eight (38) CSFs and their Sub-Success Factors (SSFs). The overall assessment of these factors highlights their importance in a brief development process. Accordingly, these factors are grouped into seven categories, and the developed CSF framework is presented. The categories, listed in descending order, are Regulatory and Legal Factors; Finance and Economic Factors; Risk-Related Factors; Public Sector Capacity-Related Factors; Procurement-Related Factors; Stakeholder-Related Factors; and Social, Cultural, and Ethical Factors. The research findings offer a comprehensive framework of CSFs for brief development tailored to the unique PPP environment of the UAE to ensure project success.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".