Analysis of project success factors in construction industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A great emphasis has taken place to identify and analyse the factors that have been affecting the success and the failure of construction projects in recent decades. As a project-based industry, construction has heavily invested in such research. Moreover, the construction industry suffers the most to meet deadlines and budgets limits. The objective of this paper is to identify the critical success factors in construction industry. The study focused on Middle East region. In order to achieve this objective, 25 project success factors were identified by reviewing related literature. The factors were assessed for their impact and contribution to the actual performance of the project on three criteria: schedule, cost, and quality. Then a questionnaire was developed and sent to different experts in the construction industry. The collected data of 111 responses was then analysed statistically by using different tools such as: importance index, Spearman’s rank correlation factor and T-test. As a result, company’s technical capacity and scope and work definition were ranked the most important factors. The results of this research may provide a great assistance to professionals and researchers in identifying the critical factors in the construction industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it