Logistics sector development potential of world's oil exporters
Notice bibliographique
Résumé
Purpose Oil exporters have been identified as having problems in the friendliness and performance of their logistics sector, particularly in general cargo group. The purpose of this research is to identify, through proposed data envelopment analysis (DEA) models, those oil exporters with most potential to develop (who have resources for this through their economic prosperity). Benchmarks of improvement are not only identified among top oil exporters, but also from larger group of emerging countries. Design/methodology/approach The research uses two different DEA models, and quantitative data available from economic prosperity and general cargo logistics performance. Models are input oriented, and use the most recent data from year 2009. Findings Major oil exporters are not homogeneous group in their performance turning logistics competence on prosperity. Actually, there could be one group of very low performance identified in general cargo logistics performance as compared to the DEA models, and other group with similar or slightly above performance with general cargo handling. Those performing at the lower end, and with most development potential, include such countries as Russia, Venezuela, Algeria, Qatar, Azerbaijan and Turkmenistan (possibly also including Iran, Kuwait and UAE). From the group of major oil exporters, these countries should learn exceptional logistics competence from Malaysia and Canada. Similarly, this research shows that the emerging economies, particularly China, but also India as well as Philippines, Thailand and South Africa are useful benchmarks to develop general cargo logistics performance further (to some extent Korea (South), Taiwan, Czech Republic, Poland, Turkey and Lebanon also could be included). Research limitations/implications The research is not based on longitudinal data, and should be enlarged to take into account earlier logistics performance index ratings from the World Bank studies (year 2007); this not only to verify the results, but also to highlight possible progress in the development of the logistics sector. Also, other global logistics performance ratings (e.g. from infrastructure) should be taken into account, and DEA models be developed further (often infrastructure drives other layers of performance). Originality/value Research work is seminal to study in respect to identifying which countries “lower performing oil producers” include, and which have potential in better developing their logistics sector (general cargo). Among this, research also proposes benchmarks for these countries, not only among oil exporters, but also from other emerging economies. The research findings give a unique and fresh perspective on the logistics performance of oil exporters and emerging economies in general.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».