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Improving logistics performance by reforming the pillars of Global Competitiveness Index

  • Şule Önsel Ekici
  • , Özgür Kabak
  • , Füsun Ülengin*
  • *Corresponding author for this work
  • Dogus University
  • Sabanci University

Research output: Contribution to journalArticlepeer-review

102 Citations (Scopus)

Abstract

The logistics performance of a country is crucial to national and international trade, and therefore has a direct effect on economic development. Owing to limited resources, policymakers need a guide for specifying the factors that need to be focused upon to bring about immediate and significant improvements in the logistics performance of their countries. This study aims to propose a methodology to develop a roadmap for policymakers in improving the logistics performance of their countries. For this purpose, we analyze the effect of the competitiveness pillars of the Global Competitiveness Index (GCI) on logistics performance (as measured by the Logistics Performance Index (LPI)), using a three-stage integrative methodology based on a tree-augmented naive Bayesian network, partial least square path model, and importance-performance map analysis. An empirical study is conducted using the GCI pillars of the World Economic Forum and the LPI of the World Bank. The results indicate that governments should focus on technological readiness, higher education and training, innovation, market size, and infrastructure to facilitate improvement in the logistics performance of their countries.

Original languageEnglish
Pages (from-to)197-207
Number of pages11
JournalTransport Policy
Volume81
DOIs
Publication statusPublished - Sept 2019

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Ltd

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 8 - Decent Work and Economic Growth
    SDG 8 Decent Work and Economic Growth

Keywords

  • Global competitiveness index
  • Importance-performance analysis
  • Logistics performance index
  • Partial least square path model
  • Tree augmented naive bayesian network

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