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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 1475148, 9 pages
Research Article

An Analysis of Bank Service Satisfaction Based on Quantile Regression and Grey Relational Analysis

Department of Information Management, National Taiwan University of Science and Technology, Taipei 10607, Taiwan

Received 6 January 2016; Accepted 13 March 2016

Academic Editor: Feng Yang

Copyright © 2016 Wen-Tsao Pan and Yungho Leu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Bank service satisfaction is vital to the success of a bank. In this paper, we propose to use the grey relational analysis to gauge the levels of service satisfaction of the banks. With the grey relational analysis, we compared the effects of different variables on service satisfaction. We gave ranks to the banks according to their levels of service satisfaction. We further used the quantile regression model to find the variables that affected the satisfaction of a customer at a specific quantile of satisfaction level. The result of the quantile regression analysis provided a bank manager with information to formulate policies to further promote satisfaction of the customers at different quantiles of satisfaction level. We also compared the prediction accuracies of the regression models at different quantiles. The experiment result showed that, among the seven quantile regression models, the median regression model has the best performance in terms of RMSE, RTIC, and CE performance measures.