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Modelling client satisfaction levels: a comparison of multiple regression and artificial neural network techniques

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conference contribution
posted on 14.01.2015 by Robby Soetanto, David G. Proverbs
The performance of contractors is known to be a key determinant of client satisfaction. Here, clients' satisfaction is defined in several dimensions identified using factor analysis techniques. Based on clients’ assessment of contractor performance, a number of satisfaction models are presented, developed using multiple regression (MR) and artificial neural network (ANN) techniques. The MR models identified that various attributes of the contractor, project and client were found to significantly influence satisfaction levels. Results of the ANN modelling were similar, however the importance of independent variables was found to be different. The models demonstrate accurate and reliable predictive power as confirmed by validation tests. While the MR models tend to be more accurate for specific dimensions of client satisfaction, the ANN models were found to be superior for models of average satisfaction and overall satisfaction. The MR models suggest that contractors have more effect on client satisfaction than the ANN models. Contractors could use the models to help improve their performance leading to more satisfied clients. This will also promote the development of harmonious working relationships within the construction project coalition.

History

School

  • Architecture, Building and Civil Engineering

Published in

ARCOM

Volume

1

Pages

47 - 57 (11)

Citation

SOTANTO, R. and PROVERBS, D.G., 2001. Modelling client satisfaction levels: a comparison of multiple regression and artificial neural network techniques. IN: Akintoye, A. (ed.) Proceedings of the 17th Annual ARCOM Conference, 5-7 September 2001, University of Salford. Association of Researchers in Construction Management, Vol. 1, pp. 47-57.

Publisher

© ARCOM / the authors

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2001

Notes

This is a conference paper. It is also available at: http://www.arcom.ac.uk/

ISBN

095341616X

Language

en

Location

Salford

Exports