Changing customer mindset in adopting digital financial services during the COVID-19 pandemic: Evidence from India

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Digital Financial Services (DFS) have been growing steadily all over the world. The COVID-19 crisis has reinforced the need for DFS. This study aims to examine the growth of DFS in the global and Indian markets and to analyze the factors that change the mindsets and attitudes of adults towards the adoption of DFS during the pandemic. The growth of DFS is analyzed using secondary data. The changing customer mindset is studied and analyzed through primary data collected by a survey approach. The unit of analysis includes adults who use or prefer to use DFS. A total of 384 respondents, determined by Krejcie and Morgan formula, were personally interviewed. 384 is taken as sample size as this sample size avoids type II errors in the data analysis. The collected data were processed in SPSS21 software. The study results found that technological benefits (67.9%) have the most significant positive effect on changing people’s mindsets and attitudes towards DFS followed by the pandemic forces (50.7%). Peer influences (33.2%) and perceived trust (38.3%) have also affected the change in mindsets and attitudes of adults regarding DFS. But the change in mindset is significantly and positively influenced by perceived risk (50.1%) rather than affecting negatively. So, the factors are confirmed again. The factors that drive changes in mindsets and attitudes of adults towards the adoption of DFS are Pandemic Forces & Convenience, Perceived Safety and Security, User Benefits and Experiences, Peer Influences, and Perceived Trust during the pandemic.

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    • Figure 1. Factors changing adult’s mindset and attitudes towards the intention to use digital financial services
    • Figure 2. Confirmatory factor analysis
    • Table 1. Research design
    • Table 2. Tests for normality
    • Table 3. Difference between gender and the core variables
    • Table 4. Difference between marital status and the core variables
    • Table 5. Difference between age and the core variables
    • Table 6. Difference between education and the core variables
    • Table 7. Difference between occupation and the core variables
    • Table 8. Linear regression analysis
    • Table 9. Total variance explained by extracted factors
    • Table 10. Rotated component (factor) matrix
    • Table 11. Goodness of fit
    • Table A1. Loadings of five variables on five factors extracted. Component matrix
    • Conceptualization
      Thangaraj Ravikumar, Rajesh R., Arjun B. S.
    • Data curation
      Thangaraj Ravikumar, Rajesh R., Krishna T. A., Haresh R., Arjun B. S.
    • Formal Analysis
      Thangaraj Ravikumar, Arjun B. S.
    • Investigation
      Thangaraj Ravikumar, Krishna T. A., Haresh R., Arjun B. S.
    • Methodology
      Thangaraj Ravikumar, Krishna T. A., Haresh R.
    • Resources
      Thangaraj Ravikumar, Krishna T. A., Haresh R.
    • Software
      Thangaraj Ravikumar, Krishna T. A., Haresh R., Arjun B. S.
    • Supervision
      Thangaraj Ravikumar, Haresh R., Arjun B. S.
    • Writing – original draft
      Thangaraj Ravikumar
    • Project administration
      Rajesh R., Krishna T. A., Haresh R., Arjun B. S.
    • Validation
      Rajesh R., Arjun B. S.
    • Writing – review & editing
      Rajesh R., Haresh R.
    • Visualization
      Krishna T. A., Arjun B. S.