To read this content please select one of the options below:

Modelling and evaluating service quality measurement using neural networks

Ravi S. Behara (Department of Information Technology and Operations Management, College of Business, Florida Atlantic University, Boca Raton, Florida, USA)
Warren W. Fisher (Department of Management, Marketing and International Business, Stephen F. Austin State University, Nacogdoches, Texas, USA)
Jos G.A.M. Lemmink (Faculty of Economics and Business Administration, Maastricht University, Maastricht, The Netherlands)

International Journal of Operations & Production Management

ISSN: 0144-3577

Article publication date: 1 October 2002

3261

Abstract

Effective measurement and analysis of service quality are an essential first step in its improvement. This paper discusses the development of neural network models for this purpose. A valid neural network model for service quality is initially developed. Customer data from a SERVQUAL survey at an auto‐dealership network in The Netherlands provide the basis for model development. Different definitions of service quality measurement are modelled using the neural network approach. The perception‐minus‐expectation model of service quality was found not to be as accurate as the perception‐only model in predicting service quality. While this is consistent with the literature, this study also shows that the more intuitively appealing but mathematically less convenient expectation‐minus‐perception model out‐performs all the other service quality measurement models. The study also provides an analytical basis for the importance of expectation in the measurement of service quality. However, the study demonstrates the need for further study before neural network models may be effectively used for sensitivity analyses involving specific dimensions of service quality.

Keywords

Citation

Behara, R.S., Fisher, W.W. and Lemmink, J.G.A.M. (2002), "Modelling and evaluating service quality measurement using neural networks", International Journal of Operations & Production Management, Vol. 22 No. 10, pp. 1162-1185. https://doi.org/10.1108/01443570210446360

Publisher

:

MCB UP Ltd

Copyright © 2002, MCB UP Limited

Related articles