Delay causes of the site-clearance process for public infrastructure projects in Vietnam: Differences across roles and project sizes
Bibliographic record
Abstract
Site clearance remains one of the most persistent causes of delay in Vietnam’s public infrastructure delivery, yet systematic evidence on how different stakeholders and project sizes perceive its underlying drivers is limited. This study investigates variations in stakeholder perceptions and project-scale effects on site clearance delays through an integrated analytical framework combining ranking, concordance, correlation, and variance analyses. Data were collected from key actors involved in land acquisition and clearance, including project owners, project management units, consultants, contractors, and land development centers, covering small, medium, and large public projects. Findings show that stakeholders share a general awareness of major delay causes but differ in prioritization based on institutional roles and responsibilities. Project owners, management units, and contractors emphasize coordination and staffing constraints, whereas consultants focus on technical and procedural issues, and land development centers view administrative and community challenges as routine. Across project sizes, perceptions diverge more strongly: small projects face capacity and resource shortages, large projects experience bureaucratic fragmentation and complex multi-agency coordination, and medium projects operate at a relatively balanced scale. These results indicate that project complexity increases nonlinearly with size, amplifying administrative and procedural burdens at both extremes. The study contributes by clarifying how institutional role and project scale shape perceptions of delay in Vietnam’s infrastructure sector. It underscores the need for scale-sensitive and role-specific management strategies to enhance the timeliness and effectiveness of site clearance processes.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".