Evaluation of user satisfaction using evidential reasoning-based methodology

Tang, Dawei and Wong, T. C. and Chin, K. S. and Kwong, C. K. (2014) Evaluation of user satisfaction using evidential reasoning-based methodology. Neurocomputing, 142. pp. 86-94. ISSN 0925-2312 (https://doi.org/10.1016/j.neucom.2014.01.055)

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Abstract

For the sake of gaining competitive advantages, it is important to evaluate the satisfaction level of a product or service from the users' perspective. This can be done by investigating the relationship among customer attributes (customer requirements) and design attributes (product configurations). However, such relationship would be highly non-linear in nature. In this regard, many approaches have been proposed over traditional linear methods. Particularly, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method has been prevalently utilized in modeling such vague and complex relationship among these attributes and evaluating user satisfaction towards certain products or services. Despite the fact that the ANFIS method can explicitly model the non-linear relation among these attributes, it may be restricted if uncertain information can be observed due to subjectivity and incompleteness. To overcome these limitations, a belief rule base (BRB) approach with evidential reasoning (ER) is applied in this paper. For justification purpose, both the ANFIS and BRB methods are applied to the same case. Comparison results indicate that the BRB is capable of minimizing the human biases in evaluating user satisfaction and rectifying the inappropriateness associated with the ANFIS method. Also, the BRB method can generate more rational and informative evaluation results.

ORCID iDs

Wong, T. C. ORCID: https://orcid.org/0000-0001-8942-1984

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  Item type:	Article
  ID code:	49090
  Dates:	Date Event 22 October 2014 Published 26 January 2014 Accepted
  Notes:	“NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, [VOL 142, 22 October 2014] DOI: 10.1016/j.neucom.2014.01.055
  Subjects:	Technology > Engineering (General). Civil engineering (General) > Engineering design Technology > Manufactures
  Department:	Faculty of Engineering > Design, Manufacture and Engineering Management
  Depositing user:	Pure Administrator
  Date deposited:	27 Aug 2014 08:55
  Last modified:	16 Dec 2024 01:29
  Related URLs:	Scopus publication
  URI:	https://strathprints.strath.ac.uk/id/eprint/49090