Enhancing Humanitarian Supply Chain Resilience Through AI: The Moderating Role of Artificial Intelligence in Achieving Sustainable Development Goals (SDGs) in Jordan
Bibliographic record
Abstract
AI has transformed crisis management throughout humanitarian supply chains by making organizations capable of attaining SDGs with efficiency.This study investigates how AI brings benefits to supply chain agility alongside integration as well as sustainable logistics and readiness against disasters in humanitarian operations across Jordan.The research used Structural Equation Modeling technique to evaluate data obtained from 321 humanitarian supply chain professionals.The research results demonstrate AI serves as a positive moderator that enhances the connection between supply chain agility (β = 0.21, p < 0.001) and supply chain integration (β = 0.25, p < 0.001), sustainable logistics (β = 0.27, p < 0.001), and disaster preparedness (β = 0.22, p < 0.001) which helps improve humanitarian supply chain performance.This study establishes predictive decision-making and real-time tracking along with optimized resource distribution to be fundamental features enabled by AI-driven analytics.The integration of AI improves coordination between non-government organizations and governmental agencies and logistics providers which results in better crisis response capabilities.The research delivers theoretical value to AI-driven supply chain literature and provides useful recommendations to policy makers together with logistics managers and aid organizations who aim to maximize their emergency relief performance.AI technologies require sustained funding to enhance humanitarian operations by making them more efficient and transparent as well as sustainable.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".