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Customer satisfaction is assessed by various quantitative and qualitative methods. Several quantitative methods adopt a regression analysis procedure, including Multiple Criteria Decision Aid (MCDA) techniques. However, most of them are compensatory approaches, based on an additive model that assumes preference independence among customer satisfaction criteria. During the last years, several rule-based methods have been proposed in the customer satisfaction analysis problem. Such approaches do not assume an analytical aggregation formula, and thus they may offer an alternative in this problem. The fsQCA method focuses on linguistic summarization of “if-then” type rules. This method provides all necessary/sufficient combinations (rules) of satisfaction criteria, which lead to the output (overall satisfaction). In this context, the criteria (causal conditions) constitute the input variables, while the presence of overall satisfaction is the desired outcome. The main aim of this chapter is to present the current progress in advanced rule-based approaches applied in customer satisfaction analysis, as well as the future prospects of fsQCA. For this reason, the chapter presents the theoretical background of the alternative tool that can identify any non-linear and asymmetric relationship among attribute performance and overall satisfaction. The applicability is illustrated through a case study. The dataset is analyzed using the fsQCA method, and the results are compared with an additive value-based model (MUSA method). The results provide a more detailed and valid analysis of customer satisfaction data and indicate the complementary nature of the alternative approach. Finally, the chapter discusses the potential future research efforts, given that rule-based approaches have gained increasing attention during the last years in analyzing customer satisfaction data. |
