Nonlinear Multi attribute Satisfaction Analysis (N-MUSA): Preference disaggregation approach to satisfaction

Document Type : Research Paper


1 1. Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran

2 2. Faculty of Management, University of Tehran, Tehran, Iran


 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.


Main Subjects

Ahmadi, M., & Ranjbary, M. (2013). A fuzzy clustering method with and without supervisor for customers' satisfaction measurement. Global Journal of Science, Engineering and Technology, 6, 31-41.
Aouadni, I., & Rebai, A. (2016). Measuring job satisfaction based on fuzzy multi-criteria satisfaction analysis (FMUSA) method and continuous genetic algorithms. International Conference on Control, Decision and Information Technologies (CoDIT), St. Julian's, 405-410.
Al-Eisa, A. S., & Alhemound, A. M. (2008). Using a multiple-attribute approach for measuring customer satisfaction with retail banking services in Kuwait. International Journal of Bank Marketing, 27(4), 294-314.
Arabatzis, G., & Grigourdis, E. (2010). Visitors’ satisfaction, perception and gap analysis: The case of Dadia-Lefkimi-Souflion National Park. Forest Policy and Economics, 12(3), 163-172.
Arbore, A., & Busacca, B. (2009). Customer satisfaction and dissatisfaction in retail banking: Exploring the asymmetric impact of attribute performances. Journal of Retailing and Consumer Services, 16(4), 271-280.
 Celik, E., Gul, M., Aydin, N., Taskin G., & Fuat G. A. (2015). A comprehensive review of multi criteria decision-making approaches based on interval type-2 fuzzy sets. Knowledge-Based Systems, 85, 329-341.
Deng, W., Chen, W., & Pei, W. (2008). Back propagation neural network based importance-performance analysis for determining critical service attributes. Expert Systems with Application, 34(2), 1115-1125.
Dolinsky, A. L. (1991). Considering the competition in strategy development: an extension of importance-performance analysis. Journal of Health Care Marketing, 11(1), 31-36.
Fečikovà, I. (2004). An index method for measurement of customer satisfaction. TQM Magazine, 16(1), 57-66.
Gerson, R. F. (1993). Measuring customer satisfaction: A guide to managing quality service. Menlo Park, CA: Crisp Publications.
Grigoroudis, E., Kyriazopoulos, P., Siskos, Y., Spyridakos, A., & Yannacopoulos, D. (2007). Tracking changes of e-customer preferences using multicriteria analysis. Managing Service Quality, 17(2), 538-562.
Grigoroudis, E., Litos, C., Moustakis, V., Politis, Y., & Tsironis, L. (2008). The assessment of user-perceived web quality: Application of a satisfaction benchmarking approach. European Journal of Operational Research, 187(3), 1346-1357.
Grigoroudis, E., & Politis, Y. (2015). Robust extensions of the MUSA method based on additional properties and preferences. International Journal of Decision Support Systems, 1(4), 438-460.
Grigoroudis, E., Politis, Y., & Siskos, Y. (2002). Satisfaction benchmarking and customer classification: An application to the branches of a banking organization. International Transactions in Operational Research. 9(5), 599-618.
Grigoroudis, E., & Siskos, Y. (2002). Preference disaggregation for measuring and analyzing customer satisfaction: The MUSA method. European Journal of Operational Research, 143(1), 148-170.
Grigoroudis, E., & Siskos, Y. (2004). A survey of customer satisfaction barometers: Some results from the transportation-communications sector. European Journal of Operational Research, 152(2), 334-353.
Grigoroudis, E., & Spyridaki, O. (2003). Derived vs. stated importance in Customer satisfaction surveys. Operational Research: An International Journal, 3(3), 229-247.
Hirata, T. T. (2009). Customer satisfaction planning. New York: Taylor and Francis Group.
Hooley, J. G., & Hussey, K. M. (1994). Quantitative methods in marketing. London: The Dryden Press.
Huang, R., & Sarigöllü, E. (2008). Assessing satisfaction with core and secondary attributes. Journal of Business Research, 61(9), 942-949.
Matsatsinis, N. F., Ioannidou, E., & Grigoroudis, E. (1999). Customer satisfaction evaluation using data mining techniques. Presentation to the European Symposium on Intelligent Techniques 99 (ESIT’99), Kolympari, Chania. Retrieved from
Mihelis., G., Grigoroudis, E., Siskos, Y., Politis, Y., & Malandrakis, Y. (2001). Customer satisfaction measurement in the private bank sector. European Journal of Operational Research, 130(2), 347-360.
Politis, Y., & Siskos, Y. (2004). Multicriteria methodology for the evaluation of a Greek engineering department. European Journal of Operational Research, 156(1), 223-240.
Senthikumar, N., Ananth, A., & Arulraj, A. (2011). Impact of corporate social responsibility on customer satisfaction in banking service. African Journal of Business Management, 5(7), 3028-3039.
Siskos Υ., & Grigoroudis E. (2002). Measuring customer satisfaction for various services using multicriteria analysis. In D. Bouyssou, E. Jacquet-Lagrèze, P. Perny, R. Słlowiński, D. Vanderpooten, & P. Vincke (Eds.), Aiding decisions with multiple criteria: Essays in honor of Bernard Roy (pp. 457-482), Kluwer, Dordrecht.
Zamani-sabzi. H., Phillip, K.  J., Gard, C., & Abudu, S. (2016). Statistical and analytical comparison of multi-criteria decision-making techniques under fuzzy environment, Operations Research Perspectives, 3, 92-117.