The role of financial literacy, digital literacy, and financial self-efficacy in FinTech adoption

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The primary aim of this study is to delve into the factors influencing individuals’ readiness to embrace financial technology (FinTech) services in Bangladesh. Specifically, the study focused on Bangladeshi fintech consumer’s knowledge about contemporary digital financial tools, such as mobile-based payment service apps. Data collection was carried out using a survey questionnaire tailored to the Bangladeshi context. Participants were invited to participate in the survey, and their responses were gathered upon their consent. A five-point Likert scale, ranging from ‘1’ for ‘Strongly Disagree’ to ‘5’ for ‘Strongly Agree,’ was employed to gauge the questionnaire items. The final sample size was 450 respondents. To assess the hypotheses, a 5% significance level was employed, with data analysis conducted using SPSS software. The findings underscore a positive and statistically significant impact of financial literacy, digital literacy, and financial self-efficacy on the adoption of FinTech services in Bangladesh. Collectively, these variables elucidate 48.20% of the variance (R2=0.482) in predicting individuals’ adoption behavior of FinTech. Financial self-efficacy (β = 0.574; t-value = 8.394) has the highest effect on FinTech adoption compared to the other two factors. Additionally, a substantial correlation coefficient (r=0.634) is present between digital literacy and FinTech adoption. This study contributes to the extant literature on FinTech services by providing valuable insights that enhance scholars’ understanding of the emerging financial technologies’ significance and their predominant impacts within the Bangladeshi FinTech ecosystem. These findings hold implications for policymakers, financial institutions, and stakeholders seeking to promote FinTech adoption and foster financial inclusion in Bangladesh.

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    • Figure 1. Study framework
    • Table 1. Sources of study variables
    • Table 2. Reliability and validity analysis
    • Table 3. Demographic information
    • Table 4. Descriptive analysis of the study variables
    • Table 5. Normality test analysis
    • Table 6. Correlation matrix
    • Table 7. Multicollinearity analysis
    • Table 8. Regression coefficient analysis
    • Table 9. Hypotheses results
    • Conceptualization
      K. M. Anwarul Islam, Muhammad Saifuddin Khan
    • Data curation
      K. M. Anwarul Islam
    • Formal Analysis
      K. M. Anwarul Islam, Muhammad Saifuddin Khan
    • Investigation
      K. M. Anwarul Islam, Muhammad Saifuddin Khan
    • Methodology
      K. M. Anwarul Islam, Muhammad Saifuddin Khan
    • Project administration
      K. M. Anwarul Islam, Muhammad Saifuddin Khan
    • Resources
      K. M. Anwarul Islam, Muhammad Saifuddin Khan
    • Software
      K. M. Anwarul Islam, Muhammad Saifuddin Khan
    • Validation
      K. M. Anwarul Islam, Muhammad Saifuddin Khan
    • Visualization
      K. M. Anwarul Islam, Muhammad Saifuddin Khan
    • Writing – original draft
      K. M. Anwarul Islam, Muhammad Saifuddin Khan
    • Writing – review & editing
      K. M. Anwarul Islam, Muhammad Saifuddin Khan
    • Funding acquisition
      Muhammad Saifuddin Khan
    • Supervision
      Muhammad Saifuddin Khan