This paper analyses the impact of strategic sub-components of the Global Competitiveness Index (GCI) on the Logistics Performance Index (LPI). As a hypothesis, it is assumed that there is a relationship between the LPI and selected factors in GCI, which were grouped into three clusters: infrastructure, human factor, and institutions. The purpose is to investigate which of those groups has the most significant impact on the LPI - an interactive comparative analysis tool created by the World Bank that addresses logistics issues in a broad context against world regions' development or countries' economies. For this purpose, the LPI was used as the dependent variable, while a linear regression model measured some GCI components' influence. The study was conducted for Africa, Asia, and the EU, employing the ANOVA method. The paper finds the three clusters are related to higher efficiency. While the new method shows these clusters are essential for improving the logistics performance index, an extensive range of factors might affect logistics sector performance in both geography and stage of development. In Europe, human factor is far more critical for progressively improving the LPI, while necessary infrastructure remains crucial in Asia. All three factors are central to Africa's logistics development.

Competitiveness and the Logistics Performance Index: The ANOVA method application for Africa, Asia, and the EU regions

Sergi B. S.
Primo
Membro del Collaboration Group
;
D'Aleo V.;Ioppolo G.
2021

Abstract

This paper analyses the impact of strategic sub-components of the Global Competitiveness Index (GCI) on the Logistics Performance Index (LPI). As a hypothesis, it is assumed that there is a relationship between the LPI and selected factors in GCI, which were grouped into three clusters: infrastructure, human factor, and institutions. The purpose is to investigate which of those groups has the most significant impact on the LPI - an interactive comparative analysis tool created by the World Bank that addresses logistics issues in a broad context against world regions' development or countries' economies. For this purpose, the LPI was used as the dependent variable, while a linear regression model measured some GCI components' influence. The study was conducted for Africa, Asia, and the EU, employing the ANOVA method. The paper finds the three clusters are related to higher efficiency. While the new method shows these clusters are essential for improving the logistics performance index, an extensive range of factors might affect logistics sector performance in both geography and stage of development. In Europe, human factor is far more critical for progressively improving the LPI, while necessary infrastructure remains crucial in Asia. All three factors are central to Africa's logistics development.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3203678
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