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Title: Modelling client satisfaction levels: a comparison of multiple regression and artificial neural network techniques
Authors: Soetanto, Robby
Proverbs, David G.
Keywords: Artificial neural networks
Performance criteria
Multiple regression
Performance assessment
Questionnaire survey
Issue Date: 2001
Publisher: © ARCOM / the authors
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.
Abstract: 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.
Description: This is a conference paper. It is also available at: http://www.arcom.ac.uk/
Version: Published
URI: https://dspace.lboro.ac.uk/2134/16599
Publisher Link: http://www.arcom.ac.uk/-docs/proceedings/ar2001-047-057_Soetanto_and_Proverbs.pdf
ISBN: 095341616X
Appears in Collections:Conference Papers (Civil and Building Engineering)

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