Determining Critical Success Factors that Contribute to the Delay of Water Infrastructure Construction Projects in the Abu Dhabi Emirtae: A Conceptual Framework
Bibliographic record
Abstract
The purpose of this study is to investigate the relationship between the critical success factors and the critical delays in the context of water infrastructure construction projects (WICPs) in the Abu Dhabi Emirate. In addition, the purpose of this paper is to develop a conceptual model to investigate the potential relationship. The literature concerning the critical success and delay factors and the related models that are available provide a starting point for developing the conceptual model. Based on the comprehensive and thorough literature review, all the dimensions of the variables are identified and discussed in detail.This study attempts to reduce the existing gap in the literature regarding the relationship between the critical success factors and critical delay. It forms a foundation upon which further local research can be conducted. In addition, it attempts to identify and point out the most critical success factors that will minimize the delay claims in water infrastructure construction projects (WICPs), as such delays would lead to some of the most difficult and controversial disputes to resolve. Internationally, it is expected that the findings of this research may help as an evidentiary reference data on which other and further similar comparative researches could be initiated and developed in different environments in terms of cultural, social, contractual, political, and environmental mediums.Finally, the conceptual framework was developed by identifying six (6) variables for project critical success namely Project Management Process (PMP), Project Manager Competency (PMC), Project Team Members’ Competency (PTC), Project Organizational Planning (POP), Project Resources’ Utilization (PRU) and Project Organizational Commitment (POC).
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".