Moderation role of government acts, laws and policies between economic factors and risk management: A case study of Saudi Arabia contractors
Bibliographic record
Abstract
In construction projects, contractors have prioritized risks due to abandonment of operations and events, interruptions, time, and cost overruns. Construction hazards are linked to the ambiguity and unpredictability of the timely delivery of a project, with standard quality and within an allowable budget. The bid process is heavily reliant on economic considerations which include the exchange market, rate of interest and cost inflation for equipment and workforce. Project failure takes place if economic considerations have not complied for effective management of risks in construction. The research framework is founded on Organization Control Theory and focused on the PLS-SEM approach which addresses the effect of economic factors with moderating government regulatory procedures on the management of risks in construction within 303 large (higher than 250 workers) Saudi Arabian contractors. In the PLS-SEM approach, complicated models are effectively analyzed with higher statistical power. The findings show that economic factors and government regulatory procedures have a favorable impact on the management of risks in the Saudi Arabian development industry. Additionally, moderating government regulatory procedures has a favorable correlation to the management of risks in the Saudi Arabian construction sector. By addressing economic considerations, this study enables practitioners, experts and stakeholders involved in construction industries to conduct effective management of risks in the construction sector.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".