Assessment of the impact of disregarding influencing factors on artisans performance in building construction projects in Tanzania
Bibliographic record
Abstract
The success of building construction projects in developing countries heavily relies on the specialized skills of artisans who are responsible for executing physical construction activities. However, the performance of these artisans depends on various influencing factors (IFs) that significantly affect their productivity and workmanship. This study aims to assess the impact of disregarding IFs on the performance of artisans in building construction projects in Tanzania. Using the individual performance theory, the study identifies the core IFs that influence artisans' performance and develops a structural equation modelling (SEM) to understand the inter-relationship between these IFs. The study collects data from 289 building construction projects through a non-probability technique and analyses it using SPSS-25 and AMOS-20. The study finds that the enforcement of IFs at construction sites by stakeholders in the construction industry is weak, which undermines the performance of artisans. Therefore, the study recommends that employers and supervisors should consider IFs during the construction process to achieve better results in terms of time, cost, and quality. The findings of this study can guide employers and supervisors in the construction industry to enhance the overall performance of building construction projects by improving the performance of artisans through ensuring that IFs are taken into consideration during the recruitment and construction process.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".