Enhancing logistics performance measurement: an effectiveness-based hierarchical data envelopment analysis approach
Bibliographic record
Abstract
The logistics performance index (LPI) is a comprehensive and comparable composite index developed by the World Bank to assess a country’s logistics and trade facilitation environment. However, the existing LPI relies on externally assigned weights. To enhance LPI scores, this study adopts an effectiveness-based hierarchical data envelopment analysis method that internally allocates objective weights based on the dataset. Such endogenous weight information can provide additional valuable insights for countries to prioritize and strategize efforts to enhance their performance in the future. The results of this study indicate that focusing on improving the policy category yields greater benefits than improving the service category in terms of ranking national logistics performance. Furthermore, this study finds that logistics performance is influenced by income levels and geographical area. Income levels impact the regulatory and trade facilitation environment, with varying income levels leading to different priority policy areas. Geographical location also plays a crucial role in regional economic integration and trade facilitation. A favorable geographical location reduces costs and time while enhancing supply chain predictability and reliability. It is hoped that this study serves as a valuable resource for countries in identifying optimization strategies to improve their logistics performance.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| 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) | 0.001 | 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".