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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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