Determining factors of intention to adopt internet banking services: A study on commercial bank users in Bangladesh

  • Received November 20, 2021;
    Accepted February 22, 2022;
    Published March 31, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/bbs.17(1).2022.11
  • Article Info
    Volume 17 2022, Issue #1, pp. 125-136
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This work is licensed under a Creative Commons Attribution 4.0 International License

E-commerce and e-business are necessary components of today’s internet banking due to the developing global economy. Alternatively, in this technological era, the banking sector’s success is associated with creating bank users’ intention to adopt internet banking services. Therefore, the aim of this study is to determine the influencing factors of intention to adopt internet banking services of commercial bank users’ in the Bangladeshi context. A survey questionnaire was formulated based on past works of literature to find out the research objective. The convenience sampling method has been used in this study. For the data collection purpose, 250 bank users were asked request to participate in the research. As a fully completed survey, 180 responses were received where the response rate was 72% and the sample size was n = 180. For correlation analysis and hypotheses testing, SPSS version 26.0 was used. The results of the study show that Perceived Security Risk (PSR), Perceived Usefulness (PU), Perceived Ease of Use (PEU), Social Influence (SI), and Consumer Innovativeness (CI) have a statistical and significant impact on the intention to adopt internet banking services. It is concluded that the bank management committee should utilize PU, PEU, SI, and CI to amplify the level of willingness to adopt and embrace general banking services through internet platforms among bank users in their online banking transactions. For the future research study, this paper outlines several significant implications and offers some directions for the bank management committee of a commercial bank.

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    • Figure 1. Research model of the study
    • Figure 2. Regression analysis results
    • Table 1. Demographic information
    • Table 2. Regression analysis
    • Formal Analysis
      Ayeasha Akhter, Sabeha Jannat, K. M. Anwarul Islam
    • Funding acquisition
      Ayeasha Akhter, Sabeha Jannat, K. M. Anwarul Islam
    • Methodology
      Ayeasha Akhter, Md. Mobarak Karim
    • Project administration
      Ayeasha Akhter, Sabeha Jannat, K. M. Anwarul Islam
    • Resources
      Ayeasha Akhter, K. M. Anwarul Islam
    • Software
      Ayeasha Akhter, Md. Mobarak Karim, K. M. Anwarul Islam
    • Writing – original draft
      Ayeasha Akhter, Md. Mobarak Karim
    • Conceptualization
      Md. Mobarak Karim
    • Data curation
      Md. Mobarak Karim, K. M. Anwarul Islam
    • Investigation
      Md. Mobarak Karim, Sabeha Jannat, K. M. Anwarul Islam
    • Supervision
      Md. Mobarak Karim, K. M. Anwarul Islam
    • Validation
      Md. Mobarak Karim, K. M. Anwarul Islam
    • Visualization
      Md. Mobarak Karim, Sabeha Jannat, K. M. Anwarul Islam
    • Writing – review & editing
      Md. Mobarak Karim, K. M. Anwarul Islam