Navigating Barriers to the Adoption and Scalability of Modular Construction in Ethiopia
Bibliographic record
Abstract
As a developing economy, there is an increasing demand for infrastructure in Ethiopia that necessitates faster and more scalable construction solutions. Modular construction (MC) offers a viable alternative to conventional methods, but its adoption is hindered by systemic barriers. This study identifies and analyze key barriers influencing the scalability of MC in Ethiopia. Data has been collected through expert surveys involving 34 participants, including policymakers, manufacturers, academia, and construction professionals. The study applies a fuzzy DEMATEL method to quantify interdependencies among six critical barriers. The findings reveal that policy and government support (BR1) acts as a primary driver, influencing downstream barriers such as supply chain resilience (BR2) and process efficiency (BR5). Conversely, financial constraints and fragmented supply chains emerged as high-impact barriers requiring policy intervention. Based on the findings, the study further proposes a strategic framework advocating for public-private partnerships, workforce upskilling, and digital integration to enhance modular construction scalability. By leveraging Fuzzy-DEMATEL analysis, the study bridges the gap between theoretical research and practical implementation, offering actionable insights for policymakers and investors. A limitation of this study is its reliance on a limited pool of expert opinions; however, such studies typically draw insights from five to 20 experts. Further more, this limitation was mitigated through the application of fuzzy logic.
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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 | 0.005 | 0.007 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".