Comparative innovative logistics performance analysis of G7–BRICS countries using SWARA–MEREC based EDAS methodology
Bibliographic record
Abstract
In today's world, where globalization and digitalization are accelerating, the logistics sector has become a strategic element in determining countries' economic competitiveness. The increasing complexity of logistics and the rapid evolution of trade networks require innovative, adaptive logistics structures. In this process, innovation stands out as a key factor that increases the efficiency and sustainability of logistics systems. In particular, broad innovation capacity and supportive institutional environments significantly shape the development of modern logistics systems. A logistics infrastructure strengthened by innovative approaches both increases operational efficiency and supports environmental sustainability. This study proposes a new index measuring countries' Innovative Logistics Performance (ILP) by integrating data from the Global Innovation Index (GII) and the Logistics Performance Index (LPI). By combining these two widely recognized indices, the study offers a multidimensional perspective on the innovation–logistics nexus. The index provides a systematic tool to assess how innovation dynamics translate into logistics competitiveness at the national level. In this respect, the study introduces a new conceptual framework in the literature and presents a measurable structure for analyzing this relationship. The study's unique feature is its hybrid methodological approach, combining SWARA, MEREC, and EDAS for the first time. This multi-method approach allows for a more comprehensive evaluation compared to traditional single-method analyses. The proposed model integrates both subjective and objective weighting techniques, ensuring balance and reliability in the evaluation process. The findings indicate that the "Institutions" criterion is the most influential determinant of ILP, followed by "Customs" and "International Shipments." The United States, Germany, and Canada stood out as the top-performing countries. Furthermore, a sensitivity analysis was conducted to assess the model's reliability, confirming its robustness and consistency in the evaluation results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".