خلاصة:
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
ملخص الجهاز:
"The main objective of the MUSA method is the aggregation of individuals’ judgments into a collective value function, assuming that client’s global satisfaction depends on a set of criteria or variables representing service characteristics dimensions (Grigoroudis et al.
(View the image of this page)Considering the result of above-mentioned LGP, MUSA methodology has developed some fruitful indices resulting in deep analysis of satisfaction.
(View the image of this page)It is worth mentioning that the average of the optimal solutions given by the n LPs may be considered as the final solution of the problem, which is a stability analysis within MUSA methodology (Grigoroudis & Siskos, 2002).
N-MUSA optimal solution can be applied for satisfaction engineering through developing several fruitful indices as well as customer demanding level, determinants weights and improvement strategies in addition to figure out the satisfaction level based on a simple questionnaire.
(View the image of this page)In the first numerical example, the results of both models are the same indicating the same effectiveness of both models, but as far as efficiency is concerned, MUSA needs employing 4 LPs to reach the results, which can be provided just by a simple quadratic programing in N-MUSA.
(View the image of this page)Conclusion This paper reviewed the MUSA method with its preference on disaggregation approach to deep customer satisfaction analysis, which is based on the ordinal data obtained from satisfaction surveys.
In addition to the efficiency, it is worth mentioning that, according to Grigoroudis and Siskos (2002), the average of post optimality analysis in MUSA may be able to present the final solution; it needs more consideration in practice."