Relationship between Factors of Construction Resources Affecting Project Cost
Bibliographic record
Abstract
The success of any construction project highly depends on how proper and effective the management of construction resources flow. Studies show that various resources factors affected cost management and have resulted to significant amount of cost overrun worldwide. However, a few investigations had been carried out in Malaysia regarding the effect of resources in construction industry. Hence, this study focuses on identifying significant resource factors causing construction cost overrun and also assessing the relationship between these factors. Data collection was carried out through a structured questionnaire survey consisting of 20 factors identified through a comprehensive literature review. Data was analyzed using statistical software package SPSS. The Cronbach’s alpha of the data was 0.910 which means that the collected data was highly reliable. The factors were ranked through mean rank approach and it was found that 3 most significant factors are “fluctuation of prices of materials”, “cash flow and financial difficulties faced by contractors” and “shortages of materials”. While the least significant factors in causing cost overrun are “insufficient numbers of equipment”, “relationship between management and labour”, and “labour absenteeism”. The result of Spearman test indicates that “cash flow and financial difficulties faced by contractors” with “financial difficulties of owner” correlate strongly at a significant level of 0.752. This identification of factors and relationships will help construction community in controlling resopurce factors for achieving project completion within the budget.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".