A Logistic Regression Model of Customer Satisfaction of e-banking service quality in Bangladesh

Cite this:
MustafizMunir, M. M. (2016). A Logistic Regression Model of Customer Satisfaction of e-banking service quality in Bangladesh. Journal of Business Management and Economics, 4(5), 18–26. https://doi.org/10.15520/jbme.2016.vol4.iss5.188.pp18-26
© 2022 Interactive Protocols
Article Views
144
Altmetric
1
Citations
-

Abstract

Appraisal of customer satisfaction varies from one study to another. Most of studies focused on evaluating factors influencing customer satisfaction and quality of services. In this paper logistic regression is used to find out the relationship between customer satisfaction and e-banking service quality for five conventional schedules Banks in Bangladesh, such as one is owned by government, two conventional private commercial banks (one of them is Islamic), one is specialized government bank and another one is a foreign bank. There are three independent(Information Quality, Service Quality, System Quality)and one dependent (Customer Satisfaction)variables have been considered for this research.Data were collected randomly from seven divisions of Bangladesh.  A sample size of 350 customers who are using at least an e-banking service or product from aforesaid commercial banks in Bangladesh was resulted for research work. This research is only based on primary data. This data were collected from the field survey through questionnaire. Findings showed that the three independent variables are positively related with customer satisfaction. The paper recommends that the Banks should improve their information quality about e-banking services all over Bangladesh. Besides these, they need to improve customer service by practicing new techniques for customer handling. Data were analyzed using Statistical Package for Social Sciences (SPSS) version 22. Analysis was done using logistic regression to determine importance of the factors that influence customer satisfaction.  A chi-square test was used to indicate how well the logistic regression model fits the data.

 Special Issue

Article Metrics Graph

Content

Section

Source