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2013 | OriginalPaper | Chapter

23. Fuzzy Composite Indicators: An Application for Measuring Customer Satisfaction

Authors : Sergio Zani, Maria Adele Milioli, Isabella Morlini

Published in: Advances in Theoretical and Applied Statistics

Publisher: Springer Berlin Heidelberg

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Abstract

Composite indicators should ideally measure multidimensional concepts which cannot be captured by a single variable. In this chapter, we suggest a method based on fuzzy set theory for the construction of a fuzzy synthetic index of a latent phenomenon (e.g., well-being, quality of life, etc.), using a set of manifest variables measured on different scales (quantitative, ordinal and binary). A few criteria for assigning values to the membership function are discussed, as well as criteria for defining the weights of the variables. For ordinal variables, we propose a fuzzy quantification method based on the sampling cumulative function and a weighting system which takes into account the relative frequency of each category. An application regarding the results of a survey on the users of a contact center is presented.

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previous chapter Exploiting Multivariate Outcomes in Bayesian Inference for Causal Effects with Noncompliance
next chapter Symmetric Association Measures in Effect-Control Sampling
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Metadata
Title
Fuzzy Composite Indicators: An Application for Measuring Customer Satisfaction
Authors
Sergio Zani
Maria Adele Milioli
Isabella Morlini
Copyright Year
2013
Publisher
Springer Berlin Heidelberg
Book
Advances in Theoretical and Applied Statistics

Print ISBN: 978-3-642-35587-5

Electronic ISBN: 978-3-642-35588-2

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-642-35588-2_23

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