Improving dual importance analysis based on a Shapley value associated with a fuzzy measure when interactions of criteria are significant
Jiunn-I Shieh and
Hsin-Hung Wu
International Journal of Industrial and Systems Engineering, 2019, vol. 31, issue 2, 168-183
Abstract:
Kano's model is very useful to classify customer needs into different categories by completely using self-stated evaluations. However, the derived evaluation approach uses a less direct way of uncovering the evaluations that are most reliable to reflect the respondents' view from the survey. In addition, interaction effects among items, particularly non-linear interactions, are often incurred in practice. This study proposes a framework of using the dual importance graph with self-stated performance and derived importance computed by a Shapley value associated with a fuzzy measure method to classify the service items into different types of Kano's category by considering both linear and nonlinear effects among items. A case of evaluating the service quality of a particular hospital is illustrated to show how this proposed framework works. The result shows that using the Shapley value-based dual importance graph is more practical to deal with interactions of items.
Keywords: Kano's model; dual importance diagram; Shannon interaction information; fuzzy measure; Shapley value. (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.inderscience.com/link.php?id=97735 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:31:y:2019:i:2:p:168-183
Access Statistics for this article
More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().