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

Chun Kit Kwong, T. C. Wong, K. Y. Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

74 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)11262-11270
Number of pages9
JournalExpert Systems with Applications
Issue number8
Publication statusPublished - 1 Oct 2009


  • Customer satisfaction models
  • Neuro-fuzzy
  • New product development

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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