تحلیل چندمعیاره رضایت: به‌کارگیری و موارد ضعف MUSA در عمل (مطالعه صنعت بانکداری)

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 استاد دانشکده مدیریت دانشگاه تهران، ایران

2 استاد دانشکده صنایع دانشگاه شریف، تهران، ایران

3 دانشیار دانشکده مدیریت دانشگاه تهران، ایران

4 دانشجوی دکترای OR، دانشکده مدیریت دانشگاه تهران، ایران

چکیده

تحلیل چندشاخصه رضایت (MUSA) از جمله تکنیک‌های نوین توسعه داده شده به‌منظور تحلیل رضایت مشتریان، مبتنی‌بر برنامه‌ریزی آرمانی خطی است. این تکنیک به‌منظور غلبه بر ضعف مدل‌های پیشین تحلیل چندمعیاره رضایت، شامل فرض مقیاس فاصله‌ای داده­ها و برازش ضعیف مدل­ها توسعه داده شده است. این تکنیک از داده­های حاصل از قضاوت مشتریان در قالب پرسشنامه رضایت‌سنجی استفاده کرده و همزمان با تبدیل داده­ها به مقیاس فاصله­ای، رضایت مشتریان و عوامل مؤثر بر آن را تعیین می­کند.. هدف از این مقاله، مرور ادبیات نوین تحلیل رضایت و تبیین روش‌شناسی MUSA به‌همراه شناسایی نقاط قوت و ضعف آن است. در این راستا، پس از شرح مدل‌سازی، نتایج حاصل از اجرای این تکنیک در یک مثال واقعی با نتایج حاصل از دو روش سنتی رایج تحلیل رضایت، شامل رگرسیون معمولی و ترتیبی مقایسه شده است. یافته‌های پژوهش نشان‌دهنده عملکرد مناسب‌تر این تکنیک نسبت‌به دو روش دیگر همزمان با تأیید اعتبار ملاکی آن است. درنهایت در بخش نتیجه‌گیری این مقاله، به بررسی نقاط قابل‌بهبود روش‌های تحلیل چندمعیاره رضایت مشتریان پرداخته می‌شود تا راه‌گشای پژوهشگران و متخصصان در پژوهش‌های آتی باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Multi Criteria Satisfaction Analysis: Employing and Weak Points of MUSA in Practice (Case of Banking Industry)

نویسندگان [English]

  • mohammad reza Mehregan 1
  • Modares Yazdi Modares Yazdi 2
  • Hasangholipour Hasangholipour 3
  • Safary Safary 3
  • Dehghan Nayeri Dehghan Nayeri 4
1 Prof., Faculty of Management, University of Tehran, Iran
2 Prof., Industrial Engineering Department, Sharif University of Technology, Iran
3 Associate Prof., Faculty of Management, University of Tehran, Iran
4 Ph.D. Student, Faculty of management, University of Tehran, Iran
چکیده [English]

MUSA is one of the novel techniques in CSA, which lays its foundation on linear goal programming, developed for overcoming prior CSA models’ weaknesses such as coping with ordinal nature of data and low fitness. Employing a simple questionnaire, MUSA develops the interval scale and the level of satisfaction as well as determining its determinants in addition to several fruitful indices. This paper aims to review the contemporary CSA literature while being more elaborated on MUSA’s methodology. According to that, after defining its modeling approach, MUSA compared to ordinary and ordinal regression models in case of banking firm. Findings depicted that MUSA in addition to criterion validity outperforms regression models where as it is vulnerable to asymmetrical data sets. The paper at the end reviews the weak points of multi criteria satisfaction analysis methods, hopping that, marketing scholars and practitioners effectively put them in practice

کلیدواژه‌ها [English]

  • Customer Satisfaction
  • Multi criteria analysis
  • MUSA
  • Ordinal Regression
نوری، ا.، فتاحی، ک. (1390)، اندازه‌گیری رضایت مشتریان در بانک توسعه صادرات ایران با استفاده از روش تجزیه‌و‌تحلیل چندمعیاره رضایت (MUSA). پژوهش‌های مدیریت در ایران- مدرس علوم انسانی، 15(2)، 205-229.##
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.##
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, 271-280.##
Behra, R., Fisher, W., Lemmink, J. (2002). Modeling and evaluating service quality measurement using neural networks. International Journal of Operation and Production Management, 22(10), 1162-1185.##
Busacca, B., Padula, G. (2005). Understanding the relationship between attribute performance and overall satisfaction: theory, measurement and implications. Marketing Intelligence and Planning, 23(6), 543-561.##
Chen, L.(2012). A novel approach to regression analysis for the classification of quality attributes in the Kano model: an empirical test in the food and beverage industry. Omega, 40, 651-659.##
Chen, W. (2009). Analysis of a customer satisfaction survey using Rough sets theory: a manufacturing case in Taiwan. Asia Pacific Journal of Marketing and Logistics, 21(1), 93-105.##
Eboli, L., Mazzulla, G. (2009). An ordinal logistic regression model for analysing airport passenger satisfaction. EuroMed Journal of Business, 4(1),40-57.##
Fečikovà, I. (2004). An index method for measurement of customer satisfaction. The TQM Magazine, 16(1), 57-66.##
Gerson, R.F. (1993). Measuring Customer Satisfaction. Menlo Park, CA.##
Grigoroud, E., Spyridaki, O. (2003). Derived vs. Stated importance in customer satisfaction surveys, Operational Research: An International Journal, 3(3), 229-247.##
Grigoroudis E., Siskos, Y. (2010). Customer Satisfaction Evaluation. New York, Springer.##
Grigoroudis, E., Kyriazopoulos, P., Siskos, Y., Spyridakos, A., Yannacopoulos, D. (2007). Tracking changes of e-customer preferences using multicriteria analysis. Managing Service Quality, 17(5), 538-562.##
Grigoroudis, E., Litos, C., Moustakis, V., Politis, Y., Tsironis, L. (2006). The assessment of user-perceived web quality: Application of a satisfaction benchmarking approach. European Journal of Operational Research, 187, 1346-1357.##
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, 599-618.##
Grigoroudis, E., Siskos, Y. (2002). Preference disaggregation for measuring and analyzing customer satisfaction: the MUSA method. European Journal of Operation Research, 143, 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, 334–353.##
Grigoroudis, E., Siskos, Y. (2010). Customer Satisfaction Evaluation: Methods for Measuring and Implementing Service Quality. London, Springer.##
Grigoroudis, E., Spyridaki, O. (2003). Derived vs. stated importance in customer satisfaction surveys. Operational Research: An International Journal, 3(3), 229-247.##
Hu, H., Chiu, S., Cheng, C., Yen, T. (2011). Applying the IPA and DEMATEL models to improve the order winner criteria: A case of Taiwan’s network communication equipment manufacturing industry. Expert systems with applications, 38, 9674-9683.##
Hung-yu, L., Jian, L., Yun-xian, G. (2006). design of customer satisfaction measurement index; system of EMS service. The Journal of China Universities of Posts and Telecommunications, 13(1), 109-113.##
Jacquet-Lagréze E., Siskos, J. (1982). Assessing a set of additive utility functions for multicriteria decision-making: The UTA method. European Journal of Operational Research, 10 (2), 151-164.##
Kwong, C.K., Wong, T.C., Chan, K.Y. (2009). A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach. Expert system with application, 36, 11262-11270.##
Lovaglio, P.G. (2004). The customer satisfaction in a reduced rank regression framework. The TQM Magazine, 16(1), 33-44.##
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, 347-360.##
Naumann, E., Giel, K. (1995). Customer Satisfaction Measurement and Management: Using the Voice of the Customer. Cincinnati, Thomson Executive Press.##
Politis, Y., Siskos, Y. (2004). Multicriteria methodology for the evaluation of a Greek engineering department. European Journal of Operational Research, 156, 223-240.##
Siskos, Y., Grigoroudis, E. (2002), Measuring customer satisfaction for various services using multicriteria analysis. Aiding Decisions with Multiple Criteria: Essays in Honor of Bernard Roy. Dordrecht, Kluwer Academic Publishers, 457-482.##
Siskos J. (1985). Analyse de regression et programmation linéaire. Revue de Statistique Appliquée, 23(2), 41-55##
Siskos, Y., Grigoroudis, E., Zopounidis, C., Saurais, O. (1998). Measuring customer satisfaction using a survey based preference disaggregation model. Journal of Global Optimization, 12, 175-195.##
Taieb, H. A., Msahli, S., Sakil, F. (2010). Modeling consumer satisfaction degree of functional textile. Journal of Modeling and Simulation of Systems, 1(2), 84-89.##
Taylor, S.A. (1997). Assessing regression-based importance weights for quality perceptions and satisfaction judgments in the presence of higher order and/or interaction effects. Journal of Retailing, 73(1), 135-159.##