OECD ÜLKELERİNİN LOJİSTİK PERFORMANS ENDEKSLERİNİN RİDGE REGRESYON ANALİZİ İLE ARAŞTIRILMASI.

EYGÜ, Hakan; KILINÇ, Arife

The purpose of this study is to examine according to their Logistic Performance Index values of OECD countries. Today, the logistics sector has been one of the most effective sectors that has been developing rapidly in the world. Thus, it can be said that the logistics sector is a criterion that distinguishes many countries from others along with the developing world order. Following the developments, it became imperative to develop some performance indicators to measure the logistics activities of countries. The most important of these is the Logistics Performance Index (LPE) developed by the World Bank. Ridge regression analysis was applied by basing on Logistics Performance Index statistics published in 2018 in the study. Ridge regression that used to analyze multivariate data, is one of the biased estimation methods developed as an alternative to the EKK method in the case of multiplexed connections. The functioning of this method is similar to the least squares method. İt also differs from the least squares method by adding of a small bias invariant Ridge parameter (k) only to the diagonal values of the variance and covariance matrix. In the study in which the general LPE score was taken as the dependent variable, customs, infrastructure, international transportation, logistics adequacy and quality, tracking and convenience and timeliness were used as independent variables. As a result of the analysis, it was decided that there is a significant relationship between general LPE score and customs, infrastructure, international transportation, logistics adequacy and quality and timeliness variables. Studies that dealt with OECD countries within the scope of the Logistics Performance Index are available in limited numbers in the literature. Therefore, this study that made will contribute to this gap in the literature. At the same time, the fact that a new method tried in LPE subject is another privileged feature of this study

WORLD Bank; LEAST squares; COVARIANCE matrices; REGRESSION analysis; KEY performance indicators (Management); INTERNATIONAL organization

Trakya University Journal of Social Science, 2020, Vol 22, Issue 2, p899

1305-7766

Academic Journal

10.26468/trakyasobed.688737