Analysis of Effectiveness Measures of Construction Project Success in Malaysia
Bibliographic record
Abstract
Project effectiveness measures are normally used by most researchers and practitioners to judge project performance and project success. This paper provides an empirical analysis of measures of success in terms of effectiveness performance in the development of construction projects in Malaysia. A survey was conducted in Malaysia among the four project stakeholders: the Government, private clients, consultants, and contractors. In total 93 respondents completed the questionnaire. Lists of effectiveness of success measures were identified for the respondents to identify their level of success criticality to the Malaysian construction projects. The data were analysed by means of statistical analysis i.e. ranking of variables based on the mean values, Analysis of Variance (ANOVA) and factor analysis techniques. The first finding revealed that the level of success criticality with regards to project efficiency performance in the development of construction projects in Malaysia is according to the specific requirements and priorities of different project stakeholders. The second finding shows that effectiveness measures are related to the project ‘results’ achieved in the development of construction project. These are represented by the five principal factors namely: Learning and Exploitation; Client Satisfaction; Stakeholder Objectives; Operational Assurance and User Satisfaction. It is anticipated that the findings reported in this paper could be important for future strategies and guidelines for the development of projects in Malaysia.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| 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) | 0.001 | 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".