The influence of industrial attitudes and behaviours in adopting sustainable construction practices
Bibliographic record
Abstract
Abstract Considering the rapid environmental changes, the transitions to sustainable practices in the construction industry is vital now. Though the developed construction industries have already made efforts to switching to more sustainable and environment friendly practices, the developing countries are still lacking it. This research was organised to understand the role of project management practises and integrated methods in the sustainable development in the developing countries, for looking on how such practices can help these construction sectors become sustainable. The moderation effect of industrial attitudes and behaviours on sustainable construction was also conducted to understand the intermediary impacts. A survey based on the snowball sample of 208 construction professionals in Ghana was conducted to determine the impact of project management practises and integrated methods on sustainable construction. Research model was tested by employing bivariate correlation and multiple hierarchical regression analysis techniques, to establish the interrelationships among the project management practices, integrated approaches, industrial attitudes and behaviours, and sustainable construction and to explain these constructs in terms of their common underlying dimensions. The findings highlight that the project management practices and integrated approaches are significantly impacting on the sustainable construction practices in terms of BIM, Digital Twin, LEED, and BREEAM. Whereas the industrial attitudes and behaviours were found to be affecting the project management practises and integrated approaches through moderating role on sustainable construction. The study was concluded by suggesting the importance of sustainable construction practices and shaping industrial attitudes and behaviours towards such practices in the developing construction industries.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".