Imported Project Management Practices in Developing Countries: The Problem of Insufficient Adaptation to Local Project Governance Systems in the Construction Sector
Bibliographic record
Abstract
Few professionals in developing countries want to “miss the boat.” There is a growing call in the Global South for standardized project management “Bodies of knowledge” (BoK), Building Information Modelling (BIM) practices, green building certifications, and other imported methods and tools. There is a constant rush to rapidly integrate procedures and practices that come from the North (or the West), without questioning their validity and pertinence in local contexts. It is clear that imported practices contribute to consolidating a consistent language and to project protocols, but quite often, they fail to respond to the fragmented and dynamic character of the construction industry and are a poor fit when it comes to improving project quality.In this chapter, we focus on the characteristics of project governance in the construction industry in the global North and South. We argue that there are several mismatches between governance approaches in the Global South and the importation of project management BoK, BIM practices, and green certification methods. We argue that the challenges related to implementing project management practices in developed countries are exacerbated when methods are adopted in developing countries without sufficient adaptation and customization. We conclude that there is a need to highlight project management practices that truly respond to the characteristics of construction project governance in the Global South. There is a need to improve practices in low-income countries, but these should not be based on the introduction of methods from the North or the West. Instead, they must emerge from culturally relevant best practices and be rooted in local project governance conditions and the characteristics of the local building sector.
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.
How this classification was reachedexpand
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 | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".