Dynamic panel investigation of the determinants of South African commercial banks’ operational efficiency

  • Received April 17, 2022;
    Accepted September 22, 2022;
    Published November 2, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/bbs.17(4).2022.04
  • Article Info
    Volume 17 2022, Issue #4, pp. 35-49
  • TO CITE АНОТАЦІЯ
  • Cited by
    4 articles
  • 513 Views
  • 211 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Like any other business, commercial banks are greatly affected by the micro and macro-environment that operate in, no matter how large they are. Capital adequacy ratio, credit risk, money supply, inflation, the exchange rate, and the national gross domestic product have been noted to be the key determinants of bank operational efficiency. This research study looked at the operational efficiency of four large South African banks, namely, Standard Bank, Absa, Nedbank, and First National Bank. A quantitative, descriptive, correlation design was employed, and the System-Generalized Method of Moments (SYS-GMM) techniques were used and revealed that operational efficiency was positively correlated with capital adequacy ratio, credit risk, inflation, and exchange rate, and negatively correlated with profitability, money supply and GDP. SYS-GMM estimates show that capital adequacy ratio, credit risk, inflation and exchange rate positively influenced operational efficiency, while profitability, money supply (M3) and GDP had a negative influence. Thus, it is concluded that bank management should decrease administrative costs, evaluate customers’ creditworthiness before issuing loans, raise bank size as operational conditions require, boost intermediation, and anticipate inflation to operate more efficiently.

view full abstract hide full abstract
    • Figure 1. Conceptual model
    • Table 1. Overview of the top 5 South African commercial banks
    • Table 2. Variable description
    • Table 3. Summary statistics
    • Table 4. Correlation analysis
    • Table 5. Dynamic panel-data estimation, two-step SYS-GMM
    • Table 6. Diagnostics test for two-step SYS-GMM
    • Conceptualization
      Thabiso Sthembiso Msomi, Odunayo Magret Olarewaju
    • Data curation
      Thabiso Sthembiso Msomi, Odunayo Magret Olarewaju
    • Formal Analysis
      Thabiso Sthembiso Msomi, Odunayo Magret Olarewaju
    • Investigation
      Thabiso Sthembiso Msomi, Odunayo Magret Olarewaju
    • Project administration
      Thabiso Sthembiso Msomi
    • Resources
      Thabiso Sthembiso Msomi
    • Software
      Thabiso Sthembiso Msomi, Odunayo Magret Olarewaju
    • Supervision
      Thabiso Sthembiso Msomi, Odunayo Magret Olarewaju
    • Validation
      Thabiso Sthembiso Msomi, Odunayo Magret Olarewaju
    • Visualization
      Thabiso Sthembiso Msomi
    • Writing – original draft
      Thabiso Sthembiso Msomi
    • Methodology
      Odunayo Magret Olarewaju
    • Writing – review & editing
      Odunayo Magret Olarewaju