Problemy Transportu i Logistyki

Previously: Zeszyty Naukowe Uniwersytetu Szczecińskiego. Problemy Transportu i Logistyki

ISSN: 1644-275X     eISSN: 2353-3005    OAI    DOI: 10.18276/ptl.2016.35-08
CC BY-SA   Open Access 

Issue archive / nr 3 (35) 2016
Wykorzystanie analizy skupień w procesach poznawczych stratyfikacji przedsiębiorstw transportowych
(CLUSTER ANALYSIS APPLICATION IN COGNITIVE PROCESSES OF TRANSPORT ENTERPRISE STRATIFICATION)

Authors: Andrzej Letkiewicz
Uniwersytet Gdański

Beata Majecka
Uniwersytet Gdański

Michał Suchanek
Uniwersytet Gdański
Keywords: cluster analysis economic stratification transport enterprises
Year of publication:2016
Page range:8 (83-90)
Klasyfikacja JEL: L91
Cited-by (Crossref) ?:

Abstract

The research on transport may concern many issues, including the entity structures of the transport market, which is filled with diversified behavior of different entities, both on the supply and on the demand side of the market. On each of the sides one can observe layers of entities characterized by similar behavior – in the stratification systems of the market, individual entities in one layer show a similarity of the behavior, which is different between the layers. The goal of the article is to prove the usefulness of the cluster analysis in the processes of stratification arrangement of the transport enterprises. The analysis, by allowing the detection of particular structures in the transport market may become a starting point to further research on the explanation of stratification processes in that market. A ranking of transport and haulage enterprises, in which they were grouped according to the revenues in the year 2014 was used in the article. The procedure of k-means clustering and a further ANOVA were used to obtain the results which allow to conclude that there are in fact clusters on the market, which are different when it comes to crucial economic data. This leads to a conclusion that the use of presented research methods allows to show the processes on the transport market in a new way, thus proving the practical use of cluster analysis.
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