Analysis of the impact of craft labour availability on North American construction project productivity and schedule performance
Bibliographic record
Abstract
The North American construction industry has experienced periods of craft shortages for decades. While this problem has received significant attention from researchers, less attention has been given to quantifying the impact of availability of craft labour on project performance. The primary contribution of the current work to the body of knowledge is the quantification of the relationship between craft labour availability and project performance, as measured by project productivity and schedule. Data from 97 construction projects completed in the U.S. and Canada between 2001 and 2014 were collected from two industry databases. The primary analysis shows that projects that experienced craft shortages underwent substantial and statistically lower productivity compared to projects that did not. The analysis also shows a significant growth in schedule overrun due to the craft labour shortages among the same population of projects. Further exploration by means of several regression analyses shows a statistically significant correlation between increased craft recruiting difficulty and lower project productivity and also higher schedule overruns in both project databases. The results are confirmed across both databases and serve as informative models that provide valuable insight for project management teams to perceive the risk that lack of skills poses on project productivity and time performance. Understanding the level of impact that craft shortages are having through robust statistical analyses is a first step in developing the motivation for industry leaders, communities and construction stakeholders to address this challenge.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".