Abstract
Internet banking provides users diverse financial service through the Internet. Under the environment of drastic competition, in order to make customers proceed with transactions on the web site, Internet banking not only should provide complete functions of operations but also advance their electronic service quality. The electronic service quality evaluation for Internet banking can be treated as a multiple-criteria decision making (MCDM) problem. As for evaluating the electronic service quality, since the traditional Likert scale cannot deal with uncertain assessments according to human intuition for the service quality evaluation, fuzzy numbers are employed to measure decision-makers’ subjective preferences. This paper treats the given hierarchical network with the fuzzy MCDM as a feed-forward neural network and aims to develop a genetic-algorithm-based method to automatically determine degrees of importance of respective criteria. Then, critical criteria for evaluating service quality can be easily identified. In the empirical study, five domestic banks belonging to financial holding companies in Taiwan are selected to find critical criteria. The findings provide useful information to Internet banks for improving the electronic service quality.
5. Discussion and conclusion
In order to strengthen competitiveness, Internet banking should pay more attention to the improvement of electronic service quality. Although the traditional Likert scale has been the main way for evaluating service quality, the Likert scale cannot deal with cognitive uncertainty arising from human thinking and perception process. Thus, this paper employs fuzzy numbers to represent uncertain performances of overall evaluation and individual attributes for an Internet bank.
Through questionnaire, each respondent can be asked to give the following data: (1) a triangular fuzzy number corresponding to each of the linguistic values; (2) the performance values of individual attributes and the overall evaluation for Internet banks which he or she visited before. Then, for each Internet bank, degrees of importance of respective aspects and attributes under the corresponding aspect can be automatically determined by the proposed genetic-algorithm-based learning method. The learning method is performed for the given hierarchical decision network. In the empirical study, five domestic Internet banks of financial holding companies with respectable FOCAS on 2007 in Taiwan are selected. The average degrees of importance of respective aspects and attributes are reported.
