intelligent models for predicting levels of client satisfaction.pdf (80.61 kB)
Intelligent models for predicting levels of client satisfaction
journal contribution
posted on 2015-01-14, 14:47 authored by Robby SoetantoRobby Soetanto, David G. ProverbsPresents 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 ResearchVolume
5Issue
2Pages
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 PublishingVersion
- 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
2004Notes
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/jcrISSN
1793-687XPublisher version
Language
- en