Critical success factors affecting project success in construction projects: A contemporary Indian perspective
Bibliographic record
Abstract
The government of India has increased its focus on investment in infrastructure, allocating US$ 130.57 billion in 2022-23 for the sector. Effective project management is crucial for success. However, despite a huge body of knowledge on project success, project delays persist, with 33% of projects delayed by an average of 47 months as of August 2021. This study aims to identify Critical Success Factors for contemporary construction projects in India, offering guidance for project stakeholders. Forty-five attributes of project success were collated from literature and expert discussions and a questionnaire survey was conducted to solicit the views of experts on the critical impact of these attributes on overall project success. The research posits that these attributes have underlying constructs that cause them. Factor analysis was employed to extract the underlying constructs. Six critical success factors (CSF) were extracted. To comprehend the relative importance of the factors, RII was employed on summated factor scores that were then ranked in order of their importance. ANOVA showed consistent assessments of the CSFs across professional roles and geographies. The findings are expected to aid project professionals in prioritizing key factors for optimal project management and successful outcomes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".