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Intelligent models for predicting levels of client satisfaction

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journal contribution
posted on 14.01.2015, 14:47 by Robby SoetantoRobby Soetanto, David G. Proverbs
Presents the development of artificial neural network models for predicting client satisfaction levels arising from the performance of contractors, based on data from a UK wide questionnaire survey of clients. Important independent variables identified by the models indicate that long-term relationships may encourage higher satisfaction levels. Moreover, the performance of contractors was found to only partly contribute to determining levels of client satisfaction. Attributes of the assessor (i.e. client) were also found to be of importance, confirming that subjectivity is to some extent prevalent in performance assessment. The models demonstrate accurate and consistent predictive performance for ‘unseen’ independent data. It is recommended that the models be used as a platform to develop an expert system aimed at advising project coalition (PC) participants on how to improve performance and enhance satisfaction levels. The use of this tool will ultimately help to create a performance-enhancing environment, leading to harmonious working relationships between PC participants.

History

School

  • Architecture, Building and Civil Engineering

Published in

Journal of Construction Research

Volume

5

Issue

2

Pages

1 - 21 (21)

Citation

SOETANTO, R. and PROVERBS, D.G., 2004. Intelligent models for predicting levels of client satisfaction. Journal of Construction Research, 5 (2), pp. 233-253.

Publisher

© World Scientific Publishing

Version

AM (Accepted Manuscript)

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

2004

Notes

This is the electronic version of an article published in Journal of Construction Research, Volume 5, Issue 2, 2005, pp. 233-253, DOI: 10.1142/S1609945104000164 © World Scientific Publishing Company. The Journal is available at: http://www.worldscientific.com/worldscinet/jcr

ISSN

1793-687X

Language

en