Analysis of Cost Overrun Factors for Small Scale Construction Projects in Malaysia Using PLS-SEM Method
Bibliographic record
Abstract
This study investigated the effect of various factors affecting cost performance in achieving project success. Investigation was carried out with quantitative approach of questionnaire survey to understand the perception of practitioners involved in construction industry towards various factors in causing cost overrun. The targeted respondents were client, contractor, and consultant representative involved in handling small scale projects in Malaysia. A total of 54 completed responses were collected against 100 sets of questionnaire distributed. Collected questionnaires were analyzed with advance multivariate statistical approach of Partial Least Square Structural Equation Modeling (PLS-SEM). It modeled the relationship of various factors and their relative effects to cost overrun. Structural Model analysis results showed that the identified factors have overall substantial impact on cost overrun. This was assessed with convergent and discriminant validity test where R2 value for the model is 0.71 which means that 71% variance extraction is resulted from investigated factors. Further, GoF value of the model achieved is 0.70 which shows that developed structural model has substantial power in explaining the factors of cost overrun in small scale projects of Malaysia. Amongst all the factors, contractor’s site management related factors are found as most significant factors. This indicated that for achieving better cost performance in small projects, contractors are required to improve their management related to the identified factors. Beside that, these findings will benefit parties involved in managinging cost performance of small scale construction projects.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".