Logistics service quality and its effects on customer satisfaction in the manufacturing companies’ supply chains: Empirical evidence from Greece

Abstract

Purpose

The purpose of this paper is to implement a multi-criteria preference disaggregation approach to measure logistics service quality (LSQ) of manufacturing companies’ supply chains.

Design/methodology/approach

A total 216 Greek manufacturing companies took part in a survey with the use of a dedicated questionnaire. They were asked to assess the LSQ of their primary supplier regarding a predefined set of criteria and sub-criteria. The data were analysed with the multi-criteria satisfaction analysis method, which represents an ordinal regression based approach used for customer satisfaction measurement.

Findings

Weak points of the suppliers as well as dimensions that drive satisfaction were identified. Furthermore, the competitive advantages of the suppliers as well as their priorities for improvement were spotted.

Research limitations/implications

The sampling framework, including only the manufacturing companies operating in a specific area of Greece, does not ensure the full generalisation of the results. A larger sample of manufacturing companies from all over Greece would be useful to obtain more reliable results and would enable the comparison of LSQ for different manufacturing sectors.

Practical implications

The method used to assess LSQ of manufacturing companies can be installed as a permanent customer satisfaction barometer to measure, control and improve the LSQ provided to manufacturing companies as well as to other business sectors.

Originality/value

This paper proposes a method to explore the relationships between LSQ and industrial customers’ satisfaction to prioritise strategic plans of companies in the supply chains.

Citation

Politis, Y., Giovanis, A. and Binioris, S. (2014), "Logistics service quality and its effects on customer satisfaction in the manufacturing companies’ supply chains: Empirical evidence from Greece", Journal of Modelling in Management, Vol. 9 No. 2, pp. 215-237. https://doi.org/10.1108/JM2-05-2012-0016