Delibration on service quality evaluation of internet banking by using ES-Qual, a Case study in an Iranian Bank

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Abstract

Nowadays, Due to the increasing development of Internet and communication services, Manufacturers and suppliers have been found the special importance of the Internet and use it to offer its services. In this study, using the model ES-Qual (E-SQ), internet banking service quality in an Iranian bank in four dimensions "Efficiency (Eff), system availability (Sys), Fulfillment (Ful), privacy (Pri)" is discussed. In the event of difficulties in internet banking transactions in order to solve the problems and doing it timely, appropriate and suitable for protection of people privacy, some Criterias are considered in this model called "Responsivness (Res), Compensation (Com), contact (Con)".In this study, the kind of research is applied research and research methods are descriptive and inferential (correlation), The population in This research is bank customers in Tehran, which use of internet banking.Gathering information from books and magazines and the analysis of data, the questionnaire and interview preparation and descriptive and inferential statistics (correlation analysis) were used.Questionnaire survey, distribute randomly between internet banking customers electronically and manually distributed, to analyze data and software regression Lisrel, in both descriptive and inferential statistics were used.

 

Keywords: Internet banking services, quality of service, ES-QUAL, Internet.

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Delibration on service quality evaluation of internet banking by using ES-Qual, a Case study in an Iranian Bank. (2015). Global Journal of Computer Sciences: Theory and Research, 5(1), 51–58. https://doi.org/10.18844/gjcs.v5i1.33
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References

Adomavicius, G., Sankaranarayanan, R., Sen, S., & Tuzhilin, A. (2005). Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems (TOIS), 23(1), 103-145.

Ricci, F. (2002). Travel Recommender Systems, IEEE Intelligent Systems, 55-57.

Basu, C., Hirsh, H., & Cohen, W. (1998, July). Recommendation as classification: Using social and content-based information in recommendation. In AAAI/IAAI (pp. 714-720).

Breese, J. S., Heckerman, D., & Kadie, C. (1998, July). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence (pp. 43-52). Morgan Kaufmann Publishers Inc..

Pazzani, M. J. (1999). A Framework for Collaborative, Content-Based and Demographic Filtering, Artificial Intelligence Review, 13(5-6), 393-408.

Tran, T., & Cohen, R. (2000). Hybrid Recommender Systems for Electronic Commerce, AAAI Technical Report WS-00-04.

Castillo, L., Armengol, E., Onaindía, E., Sebastiá, L., González-Boticario, J., Rodríguez, A., ... & Borrajo, D. (2008). SAMAP: An user-oriented adaptive system for planning tourist visits. Expert Systems with Applications, 34(2), 1318-1332.

Huang, Y., & Bian, L. (2009). A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet. Expert Systems with Applications, 36(1), 933-943.

Schiaffino, S., & Amandi, A. (2009). Building an expert travel agent as a software agent. Expert Systems with Applications, 36(2), 1291-1299.

García-Crespo, Ã., López-Cuadrado, J. L., Colomo-Palacios, R., González-Carrasco, I., & Ruiz-Mezcua, B. (2011). Sem-Fit: A semantic based expert system to provide recommendations in the tourism domain. Expert systems with applications, 38(10), 13310-13319.

Garcia, I., Sebastia, L., & Onaindia, E. (2011). On the design of individual and group recommender systems for tourism. Expert systems with applications, 38(6), 7683-7692.

Hsu, F. M., Lin, Y. T., & Ho, T. K. (2012). Design and implementation of an intelligent recommendation system for tourist attractions: The integration of EBM model, Bayesian network and Google Maps. Expert Systems with Applications, 39(3), 3257-3264.

Lucas, J. P., Luz, N., Moreno, M. N., Anacleto, R., Figueiredo, A. A., & Martins, C. (2013). A hybrid recommendation approach for a tourism system.Expert Systems with Applications, 40(9), 3532-3550.

Yang, W. S., & Hwang, S. Y. (2013). iTravel: A recommender system in mobile peer-to-peer environment. Journal of Systems and Software, 86(1), 12-20.

Chiang, H. S., & Huang, T. C. (2015). User-adapted travel planning system for personalized schedule recommendation. Information Fusion, 21, 3-17.