COVID-19 implications for corporate social responsibility, corporate governance and profitability in banks: The case of Egypt

  • Received September 26, 2021;
    Accepted December 3, 2021;
    Published December 16, 2021
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/bbs.16(4).2021.13
  • Article Info
    Volume 16 2021 , Issue #4, pp. 149-168
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This work is licensed under a Creative Commons Attribution 4.0 International License

This paper aims to measure the relationship between Corporate Social Responsibility (CSR), Corporate Governance (CG), and profitability in listed Egyptian banks. COVID-19 is expected to affect this relationship if the year 2020 is taken. Profitability is measured by earnings per share (EPS), return on equity (ROE), and return on assets (ROA). CSR is measured as a dummy variable and CG is measured by the chief executive officer (CEO) duality. There are three control variables, such as the Islamic variable, which classifies a bank into Islamic or conventional, bank age, and bank size. The paper uses multiple regression and logistic regression models. The final sample is 12 banks consisting of 9 conventional banks and 3 Islamic banks (IBS). The results show no impact of profitability on CSR. The results prove a significant positive impact of profitability on CG; there is a significant negative relationship between CEO duality and EPS at a 0.05 level. CSR has a significant impact on CG at a 0.001 level. The results show a clear impact of COVID-19 on the impact of CSR on profitability only when measured by ROA at 0.001 in the period 2014–2019.

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    • Table 1. Model variables and their measurements
    • Table 2. Results from the impact of CSR on EPS, Model (1)
    • Table 3. Results from the impact of CSR on ROE, Model (2)
    • Table 4. Results from the impact of CSR on ROA, Model (3)
    • Table 5. Results from the impact of ROE and EPS on CSR, Model (4)
    • Table 6. Results from the impact of ROA and EPS on CSR, Model (5)
    • Table 7. Summary of results and their relation to literature
    • Table A1. Variables entered/removed
    • Table A2. Model summary
    • Table A3. ANOVA
    • Table A4. Coefficients
    • Table B1. Variables entered/removed
    • Table B2. Model summary
    • Table B3. ANOVA
    • Table B4. Coefficients
    • Table C1. Variables entered/removed
    • Table C2. Model summary
    • Table C3. ANOVA
    • Table C4. Coefficients
    • Table D1. Variables entered/removed
    • Table D2. Model summary
    • Table D3. ANOVA
    • Table D4. Coefficients
    • Table E1. Variables entered/removed
    • Table E2. Model summary
    • Table E3. ANOVA
    • Table E4. Coefficients
    • Table F1. Variables entered/removed
    • Table F2. Model summary
    • Table F3. ANOVA
    • Table F4. Coefficients
    • Table G1. Omnibus tests of model coefficients
    • Table G2. Model summary
    • Table G3. Variables in the equation
    • Table H1. Omnibus tests of model coefficients
    • Table H2. Model summary
    • Table H3. Variables in the equation
    • Table I1. Omnibus tests of model coefficients
    • Table I2. Model summary
    • Table I3. Variables in the equation
    • Table J1. Omnibus tests of model coefficients
    • Table J2. Model summary
    • Table J3. Variables in the equation
    • Table K1. Using the period from 2014 to 2020: Correlation matrix
    • Table L1. Using the period from 2014 to 2019: Correlation matrix
    • Conceptualization
      Zakia Abdelmoneim, Mahmoud Elghazaly
    • Data curation
      Zakia Abdelmoneim
    • Formal Analysis
      Zakia Abdelmoneim
    • Investigation
      Zakia Abdelmoneim
    • Methodology
      Zakia Abdelmoneim
    • Project administration
      Zakia Abdelmoneim, Mahmoud Elghazaly
    • Supervision
      Zakia Abdelmoneim, Mahmoud Elghazaly
    • Validation
      Zakia Abdelmoneim
    • Visualization
      Zakia Abdelmoneim
    • Writing – original draft
      Zakia Abdelmoneim, Mahmoud Elghazaly
    • Writing – review & editing
      Zakia Abdelmoneim