Analyzing vehicle-sharing challenges in disaster relief operations using the fuzzy DEMATEL method
Bibliographic record
Abstract
Purpose Humanitarian organizations (HOs) continue to lag in adopting the benefits of vehicle-sharing during relief operations. A detailed understanding of how different vehicle-sharing challenges interact and affect each other is crucial for identifying key leverage points, and prioritizing actions. Design/methodology/approach This study employs the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method by contacting officials from HOs. The DEMATEL method provides a visual representation of causal relationships among selected vehicle-sharing challenges. This study focuses on Bangladesh, a nation that faces hazards, rendering it one of the most disaster-prone countries globally. Findings The study uncovers intricate interconnections among key challenges related to vehicle sharing. Notably, it identifies government incentives as the most influential factor impacting other challenges. For example, enhancing government support can diminish top management’s reluctance by highlighting leadership-building programs, thereby fostering a more collaborative environment. This implies that addressing certain challenges can lead to improvements in others. In another instance, compliance standards cannot be flexible until issues of corruption and unethical behavior are addressed. Research limitations/implications By uncovering these interrelationships among vehicle-sharing challenges, the study provides a framework for prioritizing efforts towards fostering interorganizational collaboration. Practical implications By addressing these interconnected challenges, the findings aim to create a more robust vehicle-sharing system in Bangladesh. Solving one challenge often leads to progress in other areas, showing the importance of a holistic and integrated approach to policy-making decisions. Originality/value This study constitutes the second empirical exploration within the sparse literature on vehicle-sharing during relief operations. Sharing of assets is becoming a key concern among HOs.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".