Publication Date:
2011
Short description:
Fuzzy methods and satisfaction indices / Sergio Zani, M.A.M., Morlini, I. - In: Modern Analysis of Customer Surveys: with Applications using R[s.l] : Wiley Blackwell, 2011. - ISBN 9781119961154. - pp. 440-456 [10.1002/9781119961154.ch21]
abstract:
This chapter develops a framework that uses fuzzy set theory in order to measure customer satisfaction, starting from a survey with several questions. The basic concepts of the theory of the fuzzy numbers are briefly described. A criterion based on the sampling cumulative function, which assigns values to the membership function with reference to each quantitative, ordinal and binary variable, is suggested. Weighting and aggregation operators for the variables are considered. An application to ABC 2010 annual customer satisfaction survey data shows the usefulness of the fuzzy set approach: the gradual transition from very dissatisfied to really satisfied customers is captured by fuzzy composite indices. The comparison with the classical methods for the measurement of customer satisfaction highlights the advantages of the suggested criterion from both the theoretical and operational points of view.
Iris type:
Capitolo/Saggio
Keywords:
Aggregation operators; Annual customer satisfaction survey; Binary variable; Cumulative function; Customer satisfaction; Fuzzy numbers; Fuzzy set approach
List of contributors:
Sergio Zani, M. A. M.; Morlini, I.
Book title:
Modern Analysis of Customer Surveys: with Applications using R