Satisfaction Analysis
The Multicriteria Satisfaction Analysis (MUSA) method for measuring
and analysing customer satisfaction is presented in this paper. The MUSA method
is a preference disaggregation model following the principles of ordinal regression
analysis (inference procedure). The integrated methodology evaluates the satisfaction
level of a set of individuals (customers, employees, etc.) based on their values
and expressed preferences. Using satisfaction survey's data, the MUSA method aggregates
the different preferences in unique satisfaction functions. This aggregation–disaggregation
process is achieved with the minimum possible errors. The main advantage of the
MUSA method is that it fully considers the qualitative form of customers' judgements
and preferences. The development of a set of quantitative indices and perceptual
maps makes possible the provision of an effective support for the satisfaction evaluation
problem. This paper also presents the reliability analysis of the provided results,
along with a simple numerical example that demonstrates the implementation process
of the MUSA method. Finally, several extensions and future research in the context
of the presented method are discussed.
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Decision Analysis
UTA methods refer to the philosophy of assessing a set of value
or utility functions, assuming the axiomatic basis of MAUT and adopting the preference
disaggregation principle. UTA methodology uses linear programming techniques in
order to optimally infer additive value/utility functions, so that these functions
are as consistent as possible with the global decision-maker’s preferences (inference
principle). The main objective of this chapter is to analytically present the UTA
method and its variants and to summarize the progress made in this field. The historical
background and the philosophy of the aggregation-disaggregation approach are firstly
given. The detailed presentation of the basic UTA algorithm is presented, including
discussion on the stability and sensitivity analyses. Several variants of the UTA
method, which incorporate differentforms of optimality criteria, are also discussed.
The implementation of the UTA methods is illustrated by a general overview of UTA-based
DSSs, as well as real-world decision-making applications. Finally, several potential
future research developments are discussed.
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Dataset Generation
Evaluation of methods/software is considered as a very important procedure for any
user who wants to know the actual performance of a scientific tool. For the implementation
of such evaluation procedure several test cases - scenarios shall be run. Obtaining
the necessary test data sets can be an obstacle due to both privacy issues and also
the time and cost associated with collecting multiple instances of a diverse set
of data sources. An efficient alternative is the design and use of synthetic reference
data sets to undertake black-box testing. These data sets should be generated in
a manner consistent with the functional specification of the problem addressed by
the method/software. According to this approach a data set generator
was developed for the evaluation of preference disaggregation methods that are used
for customer satisfaction analysis, like MUSA (Multicriteria Satisfaction Analysis).
This paper presents a software tool which generates data sets of answers for customer
satisfaction questionnaires. The main objective of this generator is to help the
researchers in their effort to evaluate the quality and reliability of the results
that MUSA or other methods produce.
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