The utilization of project risk monitoring and control practices and their relationship with project success in construction projects
Bibliographic record
Abstract
Risk monitoring and control is often poorly implemented in construction projects because of a failure to monitor and manage identified risks. Construction companies experience significant losses due to project managers' lack of project risk monitoring and control in construction projects. Most studies have concentrated on risk identification, risk assessment, and risk analysis processes while neglecting crucial risk management processes of risk control, risk monitoring, and risk response. The lack of research on these three crucial processes highlights a gap in the literature concerning how these processes can increase the delivery of successful projects. The purpose of this study was to examine whether the utilization of project risk monitoring and control practices was related to project success in construction projects in the United States. An electronic survey instrument was used to collect data from a sample of 50 construction project managers in the Dallas-Fort Worth area in the state of Texas, in the United States. Spearman rho correlation analysis was used to examine the relationship between project risk monitoring and control practices and project success. The results of this study indicated that all project risk monitoring and control practices, including risk reassessment, risk audits, contingency reserves analysis, and risk status meetings, were significantly and positively related to project success in construction projects. One of the recommendations presented in this study was that future research should conduct the same study in developing countries to see if the study’s findings remain the same and generalizable. The study concluded that construction organizations should regularly consider the importance and usage of project risk monitoring and control practices and apply them to improve the success rate of a project.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".