Risk Analysis in International Construction Projects: A Look at the Prefabricated Wood Construction Sector in the Province of Quebec
Bibliographic record
Abstract
Construction projects that are completed abroad involve not only the typical risks that are faced at home but also various complex risks that are specific to international transactions. However, little research has been conducted on the risks that exist in prefabricated construction projects, but they need to be discussed. This paper aims to analyze the operational and financial risks associated with the internationalization of small and medium-sized enterprises (SMEs) in the province of Quebec operating in the prefabricated wood construction sector in the American market. Firstly, a literature review was carried out on operational and financial risks in overseas construction projects. This research identified 36 risks, including 21 operational and 15 financial. Next, the risks identified were divided into eight categories: design, standards, coordination, resources, internal to the alliance, partner, customer, and market. Professionals from different types of wood prefabrication companies were then asked to identify the probability of occurrence and magnitude of the impact of each identified risk. This information was used to calculate the criticality of each risk using Monte Carlo simulation to generate scenarios for use as a decision-making tool in risk assessment. The results show that highly critical operational risks are concentrated in the operational risk categories of coordination and resources. It should be noted that the most critical risk is that of ineffective communication and coordination, which is linked to project governance. On the other hand, financial risks with high criticality are spread across the four financial risk categories. A comparison of the criticality of the operational and financial risks identified revealed that the financial risks were the most critical.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".