A Hierarchical Framework of Barriers to Green Supply Chain Management in the Construction Sector
Bibliographic record
Abstract
The research paper presents a hierarchical sustainability framework for evaluating the barriers to the adoption of green supply chain management (GSCM) in the United Arab Emirates (UAE) construction sector. A total of 32 barriers to the adoption of GSCM are identified through extensive literature review and expert interviews with academics and industry professionals. The barriers are grouped on the basis of literature and expert opinion to form 12 criteria. Since the nature of the identified criteria is complex and interdependent; an Interpretive Structural Modeling (ISM) technique is applied to develop a structural model. Driving and dependence power analysis (DDPA) is used to classify and identify the critical barriers. The developed sustainability framework offers a strong and efficient evaluation technique in decision making for policy makers and stakeholders by means of identifying and prioritizing the critical barriers. The barriers identified are also classified as external and internal barriers to the organization and will help policy makers to focus on specific barriers which are important to the adoption of GSCM in the UAE construction sector. The framework has the potential to be applied to other countries across industries.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".