Engineering Procurement Construction in the Context of Belt and Road Infrastructure Projects in West Asia: A SWOT Analysis
Bibliographic record
Abstract
Engineering procurement and construction (EPC) is a normative practice globally approved since China has been engaging in international cooperation under the Belt and Road Initiative (BRI) infrastructure development. EPC has been adopted in the delivery of BRI infrastructure projects in other countries. Compared to the domestic method of contract, EPC remains at a low level in management practice, such as a lack of coordinating diverse project stakeholders, high cost of information communication, and risk in complex environments in West Asia (WA). However, no research has conducted a strategic analysis of the current situation of EPC for BRI infrastructure projects in West Asian countries. This study aims to understand the current status quo of EPC for BRI projects in WA by performing a strength, weakness, opportunity, and threats (SWOT) analysis and with the support of data collected from the literature review and semi-structured interviews with EPC stakeholders. The study brings awareness along which internally and externally circumstances of the EPC for BRI infrastructure projects can be perceived by major stakeholders participating. The four critical strategies presented based on the SWOTs identified could help EPC firms develop and promote EPC to implement BRI infrastructure projects in WA at the strategic level.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".