Relationship between e-service quality dimensions and online banking customer satisfaction

  • Received October 6, 2022;
    Accepted February 10, 2023;
    Published March 30, 2023
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/bbs.18(1).2023.15
  • Article Info
    Volume 18 2023, Issue #1, pp. 174-183
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This work is licensed under a Creative Commons Attribution 4.0 International License

Recently, the demand for internet banking has been gradually increasing the number of online banking customers, where the banking clients or customers do not need to visit a bank branch for their transactions. The principal focus of this inquiry is to ascertain how various aspects of e-service quality relate to online baking customers’ happiness, particularly with regard to Bangladeshi commercial banks. Data information was collected from three commercial banks in Bangladesh using an online survey questionnaire, and this study is quantitative and exploratory in nature. The study considered those bank customers who use frequently online/internet banking services. The sample size was n=200, and the study adopted a non-probability sampling approach. Five-point Likert scale was used to measure an item-wise question where “1” stands for “Highly Disagree” and “5” stands for “Highly Agree”. The consequences of this study demonstrate that e-service quality dimensions play a significant role in creating customer satisfaction for online banking customers. The study proposes four hypotheses, and the hypotheses are accepted in this research. Based on this study, bank management should have utilized e-service quality dimensions such as perceived security risk, perceived ease of use, perceived website quality, and perceived responsiveness to create the relationship between e-service quality dimensions and online banking customer satisfaction. Bank management will receive some guidance on developing policies and strategies to improve the satisfaction level of online customers.

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    • Figure 1. Investigation framework
    • Figure 2. Regression coefficient result analysis
    • Table 1. Demographics profile of respondents
    • Table 2. Regression analysis
    • Table 3. Hypothesis testing summary
    • Conceptualization
      K. M. Anwarul Islam, Md. Mobarak Karim
    • Data curation
      K. M. Anwarul Islam, Md. Mobarak Karim
    • Investigation
      K. M. Anwarul Islam, Md. Mobarak Karim
    • Methodology
      K. M. Anwarul Islam, Md. Mobarak Karim
    • Software
      K. M. Anwarul Islam
    • Supervision
      K. M. Anwarul Islam, Serajul Islam
    • Writing – original draft
      K. M. Anwarul Islam, Md. Mobarak Karim
    • Writing – review & editing
      K. M. Anwarul Islam, Md. Mobarak Karim
    • Formal Analysis
      Serajul Islam, Md. Shariful Haque, Tania Sultana
    • Funding acquisition
      Serajul Islam, Md. Shariful Haque, Tania Sultana
    • Project administration
      Serajul Islam, Md. Shariful Haque, Tania Sultana
    • Resources
      Serajul Islam, Md. Shariful Haque
    • Validation
      Serajul Islam, Md. Shariful Haque, Tania Sultana
    • Visualization
      Serajul Islam, Md. Shariful Haque, Tania Sultana