Analysis of Critical Success Factors Influence on Critical Delays for Water Infrastructure Construction Projects in the Abu Dhabi emirate Using PLS-SEM Method
Bibliographic record
Abstract
The objective of this study is to investigate the significance impact of critical success factors on critical delays in the field of water infrastructure construction projects (WICPs) in the Abu Dhabi emirate in particular. Investigation was conducted utilizing quantitative approach by means of questionnaire survey to examine the understanding of professionals engaged in water infrastructure construction towards several critical success factors influencing critical delays. A total of 323 completed responses from owners, consultants and contractors representatives were gathered against 450 distributed questionnaires.The gathered questionnaires were analysed using an advanced multivariate statistical method of Partial Least Square Structural Equation Modelling (PLS-SEM). Data analysis was conducted in two major phases. The first phase involved a preliminary analysis of the data, to ensure that the data adequately meet the basic assumptions in using SEM. The second phase applied the two stages of SEM. The first stage included the establishment of measurement models for the latent constructs in the research. After confirming the uni-dimensionality, reliability and validity of the constructs in the first stage, the second stage developed to test the research hypotheses through developing the structural models. The results indicated that Project Management Process (PMP), Project Manager’s Competency (PMC), Project Team’s member Competency (PTC), Project Organizational Planning (POP), Project Resources’ Utilization (PRU) and Project Organizational Commitment (POC) had significant positive effects on Critical Delay Factor Evaluation (CDFS). From the results of moderation analysis revealed that Project Benchmark Characteristics (PBC) is positively moderate the effects of Project Management Process (PMP), Project Manager’s Competency (PMC) and Project Team’s member Competency (PTC) and Project Organizational Planning (POP) on Critical Delay Factor Evaluation (CDFS).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".