A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach

When developing new products it is important for design teams to understand customer perceptions of consumer products because the success of such products is heavily dependent upon the associated customer satisfaction level. The chance of a new product’s success in a marketplace is higher if users a...

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Main Authors: Kwong, C., Wong, T., Chan, Kit Yan
Format: Journal Article
Published: Elsevier 2009
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/18606
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author Kwong, C.
Wong, T.
Chan, Kit Yan
author_facet Kwong, C.
Wong, T.
Chan, Kit Yan
author_sort Kwong, C.
building Curtin Institutional Repository
collection Online Access
description When developing new products it is important for design teams to understand customer perceptions of consumer products because the success of such products is heavily dependent upon the associated customer satisfaction level. The chance of a new product’s success in a marketplace is higher if users are satisfied with it. In this study, a new methodology of generating customer satisfaction models using a neuro-fuzzy approach is proposed. In contrast to previous research, non-linear and explicit customer satisfaction models can be developed with the use of the proposed methodology. An example of notebook computer design is used to illustrate the methodology. The proposed methodology was measured against the benchmark of statistical regression to determine its effectiveness. Experimental results suggested that the proposed approach outperformed the statistical regression method in terms of mean absolute errors and variance of errors.
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institution Curtin University Malaysia
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publishDate 2009
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spelling curtin-20.500.11937-186062018-03-29T09:06:22Z A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach Kwong, C. Wong, T. Chan, Kit Yan New product development Neuro-fuzzy Customer satisfaction models When developing new products it is important for design teams to understand customer perceptions of consumer products because the success of such products is heavily dependent upon the associated customer satisfaction level. The chance of a new product’s success in a marketplace is higher if users are satisfied with it. In this study, a new methodology of generating customer satisfaction models using a neuro-fuzzy approach is proposed. In contrast to previous research, non-linear and explicit customer satisfaction models can be developed with the use of the proposed methodology. An example of notebook computer design is used to illustrate the methodology. The proposed methodology was measured against the benchmark of statistical regression to determine its effectiveness. Experimental results suggested that the proposed approach outperformed the statistical regression method in terms of mean absolute errors and variance of errors. 2009 Journal Article http://hdl.handle.net/20.500.11937/18606 10.1016/j.eswa.2009.02.094 Elsevier restricted
spellingShingle New product development
Neuro-fuzzy
Customer satisfaction models
Kwong, C.
Wong, T.
Chan, Kit Yan
A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach
title A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach
title_full A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach
title_fullStr A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach
title_full_unstemmed A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach
title_short A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach
title_sort methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach
topic New product development
Neuro-fuzzy
Customer satisfaction models
url http://hdl.handle.net/20.500.11937/18606