A meta-analysis of critical causes of project delay using Spearman’s rank and relative importance index integrated approach
Bibliographic record
Abstract
This meta-analysis has examined the past ten years’ studies concerning the causes of construction project delay. It aims to update the subject area and investigate critical causes of project delay in three different conditions of the external environment. The data from 50 studies have been analyzed and synthesized to determine the top ten critical causes of delay. The Relative Importance Index (RII) technique was applied to rank the critical causes; subsequently, the Spearman’s rank correlation coefficient was calculated to evaluate the critical causes. The review findings indicate substantial differences between the critical causes of project delay in defined situations. The top ten critical causes of delay in developed countries root in the project’s internal environment. The leading causes of delays in developing countries are from the project’s internal and task environment. While in countries with various constraints and high risk, the general environment has a critical impact alongside the project task and internal environment on time overrun of a project. Moreover, this review summarized and categorized the best available studies to propose a systematic approach in identifying critical causes of delay to bridge the existing knowledge gap.
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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 | 0.045 | 0.121 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.059 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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".