Banking resilience and government response during the COVID-19 pandemic: Evidence from Nigeria

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In a global pandemic, there is a need for banks to improve service delivery through financial technologies. Since the fight against COVID-19 is the community responsibility, the role of banks in channeling cash to all stakeholders is essential for the contemporary human race. This study investigated the impact of the government response to COVID-19 on the resilience of banks. A multivariate Structural Equation Model (SEM) was used to specify the links between the exogenous factors (government’s social and financial responses) and the endogenous variables (resilience of bank customers, employees and investors). A research survey approach was used where 543 respondents were sampled. A self-constructed online questionnaire was used to harvest responses from customers, employees and investors of the selected banks. The result of the analysis showed a significant relationship between government’s social response and the resilience of bank customers. However, such a relationship does not hold between government’s social responses and other resilience indicators (employees and investors). Furthermore, the result revealed that government’s financial responses do not affect the resilience of banks. The study concluded that the government’s social response during the COVID-19 pandemic influenced bank customers’ resilience in Nigeria. It was recommended that banks, as part of the policy, develop tools to complement government actions during the pandemic, thereby ameliorating its impact on their customers.

Acknowledgment
The authors will like to acknowledge all respondents who took part in the survey.

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    • Figure 1. Path diagram showing the relationship between government responses and banking resilience
    • Figure A1. Path Diagram
    • Table 1. Demographic characteristics of respondents
    • Table 2. Descriptive statistics
    • Table 3. Standardized factor loadings
    • Table 4. Composite reliability and average variance extracted
    • Table 5. Model fit indices
    • Table 6. R-Squared estimates of the model
    • Table 7. Estimates of the regression coefficients
    • Table A1. Regression weights: (Group number 1 – Default model)
    • Table A2. Standardized regression weights: (Group number 1 – Default model)
    • Table A3. Covariances: (Group number 1 – Default model)
    • Table A4. Correlations: (Group number 1 – Default model)
    • Table A5. Variances: (Group number 1 – Default model)
    • Table A6. Squared Multiple Correlations: (Group number 1 – Default model)
    • Table A7. CMIN
    • Table A8. Baseline comparisons
    • Table A9. RMSEA
    • Table A10. Demographic Distribution
    • Conceptualization
      Taofeek Sola Afolabi, Thomas Duro Ayodele, Oyinlola Morounfoluwa Akinyede, Olanrewaju David Adeyanju, Harley Tega Williams
    • Formal Analysis
      Taofeek Sola Afolabi
    • Funding acquisition
      Taofeek Sola Afolabi, Thomas Duro Ayodele, Oyinlola Morounfoluwa Akinyede, Olanrewaju David Adeyanju, Harley Tega Williams
    • Methodology
      Taofeek Sola Afolabi
    • Software
      Taofeek Sola Afolabi
    • Writing – original draft
      Taofeek Sola Afolabi, Oyinlola Morounfoluwa Akinyede, Harley Tega Williams
    • Writing – review & editing
      Taofeek Sola Afolabi, Thomas Duro Ayodele, Oyinlola Morounfoluwa Akinyede, Olanrewaju David Adeyanju
    • Project administration
      Thomas Duro Ayodele, Oyinlola Morounfoluwa Akinyede
    • Supervision
      Thomas Duro Ayodele, Oyinlola Morounfoluwa Akinyede
    • Investigation
      Oyinlola Morounfoluwa Akinyede
    • Validation
      Olanrewaju David Adeyanju, Harley Tega Williams
    • Visualization
      Olanrewaju David Adeyanju
    • Data curation
      Harley Tega Williams