APPLICATION OF MULTI-CRITERIA DECISION MAKING PROCESS TO DETERMINE CRITICAL SUCCESS FACTORS FOR PROCUREMENT OF CAPITAL PROJECTS UNDER PUBLIC-PRIVATE PARTNERSHIPS
Bibliographic record
Abstract
Investigation about project success has attracted the interest of many researches and practitioners. Determining the critical success factors for procurement of capital projects is a contemporary phenomenon. This paper presents the outcome of an investigation into the critical success factors in Public-Private-Partnerships (P-P-P) for procurement of capital projects using the multi-criteria decision making process. Drawing from the results of responses to a survey of 705 experts involved in P-P-P projects worldwide, the paper presents the critical success factors (CSF) from a list of 47 factors, identified as contributing to the successful delivery of capital projects. The study revealed that owner satisfaction with the delivered project, adherence to schedules/budget/quality/ safety/environmental controls, and appropriate funding mechanisms were predictable factors while lack of legal encumbrances, clearly defined project mission and adequate planning and control techniques were less commonly expected factors. http://dx.doi.org/10.13033/ijahp.v3i2.121
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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 | 0.037 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".