摘要：Nonlinear MUSA is an extension of MUSA, which employs a derived approach to analyze customer satisfaction and its determinants. It is a preference disaggregation approach, widely welcomed by scholars since 2002, following the principles of ordinal regression analysis. N-MUSA as a goal programing model, evaluates the level of satisfaction among some groups including customers, employees, etcetera according to their values and expressed preferences. Using simple satisfaction survey data, N-MUSA aggregates the different preferences in a unique satisfaction function. The main advantage of this approach is to consider and convert the qualitative form of customer judgments and preferences in an ordinal scale based on a simple questionnaire to an interval scale, in the first place, and to develop various fruitful analytical indices in order to get more knowledge of customers in the second place. In spite of the abovementioned strengths, this paper tackles some computational shortcomings within MUSA and leads to the development of nonlinear form (N-MUSA), which is more effective and efficient in practice. This paper takes MUSA and its drawbacks into account, to introduce N-MUSA as a more efficient alternative, then, deploys it in numerical examples and a real case for more insights.
