<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Countries can check the performance of their logistics’ activities to determine their competitiveness in trade logistics. One way to check these performances is to analyze the country’s LPI value in detail which is released by the WB every two years. When calculating the LPI, six indicators (criteria) are taken into account. The weights (importance level) of these criteria are important for countries which would like to focus more on the most important criteria and move their ranking up in the LPI list. However the WB takes into account indicators (criteria) weights equally when calculating LPI values. In order to overcome this problem some studies have used subjective weighting methods and others have used objective weighting methods. Both methods have advantages and disadvantages. The aim of this study is to integrate two weighting methods (subjective (SWARA) and objective (CRITIC)) in determining the weights of criteria in order to balance the two weighting methods. Unlike other studies in the literature this study combines two weighting methods. Additionally the PIV method, which is seldom used to address any MCDM problem, is used in this study and a new integrated MCDM model is introduced to literature. In this respect this study contributes to the literature.</p></abstract>

Countries can check the performance of their logistics’ activities to determine their competitiveness in trade logistics. One way to check these performances is to analyze the country’s LPI value in detail which is released by the WB every two years. When calculating the LPI, six indicators (criteria) are taken into account. The weights (importance level) of these criteria are important for countries which would like to focus more on the most important criteria and move their ranking up in the LPI list. However the WB takes into account indicators (criteria) weights equally when calculating LPI values. In order to overcome this problem some studies have used subjective weighting methods and others have used objective weighting methods. Both methods have advantages and disadvantages. The aim of this study is to integrate two weighting methods (subjective (SWARA) and objective (CRITIC)) in determining the weights of criteria in order to balance the two weighting methods. Unlike other studies in the literature this study combines two weighting methods. Additionally the PIV method, which is seldom used to address any MCDM problem, is used in this study and a new integrated MCDM model is introduced to literature. In this respect this study contributes to the literature.

An analysis of the logistics performance index of EU countries with an integrated MCDM model

Faculty of Economics and Administrative Sciences, Department of International Trade and Logistics

Economics and Business Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Countries can check the performance of their logistics’ activities to determine their competitiveness in trade logistics. One way to check these performances...

An analysis of the logistics performance index of EU countries with an integrated MCDM model

Pubblicato online: 30 dic 2019

Pagine: 49 - 69

Ricevuto: 28 ago 2019

Accettato: 14 nov 2019

DOI: https://doi.org/10.18559/ebr.2019.4.3

Parole chiave
CRITIC, SWARA, PIV, MCDM, LPI, logistics, performance

© 2019 Alptekin Ulutaş et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

An analysis of the logistics performance index of EU countries with an integrated MCDM model

Pubblicato online: 30 dic 2019

Pagine: 49 - 69

Ricevuto: 28 ago 2019

Accettato: 14 nov 2019

DOI: https://doi.org/10.18559/ebr.2019.4.3

Parole chiaveCRITIC, SWARA, PIV, MCDM, LPI, logistics, performance

© 2019 Alptekin Ulutaş et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
CRITIC, SWARA, PIV, MCDM, LPI, logistics, performance