Customer loyalty and trust in South African retail banking

  • Received April 4, 2023;
    Accepted May 30, 2023;
    Published June 13, 2023
  • Author(s)
  • DOI
  • Article Info
    Volume 19 2023, Issue #2, pp. 211-222
  • Cited by
    1 articles

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

Customer loyalty and trust are key elements for the success of retail banking. For this reason, it is crucial to investigate the predictors of these elements. This study aims to model service quality, customer satisfaction, and commitment influencing customer loyalty and trust in South African retail banking. The target population is a growing banking customer segment – Generation Y. A sample of 271 Generation Y customers participated in the survey. Their responses were analyzed using AMOS, whereby a structural equation model was developed. Although the structural model suggests that service quality (β = 0.097; p = 0.175) is an insignificant predictor of customer loyalty in retail banking, the influence remains positive. Moreover, the model infers that customer satisfaction (β = 0.793; p = 0.001) predicts customer loyalty in retail banking and that customer satisfaction (β = 0.715; p = 0.001) and commitment (β = 0.257; p = 0.001) influence trust in retail banking. All the model fit indices (NFI = 0.95; RFI = 0.92; IFI = 0.97; TLI = 0.96; CFI = 0.97; RMSEA = 0.06; SRMR = 0.03) infer that the model is reliable, valid, and ultimately good fitting measurement tool of customer loyalty and trust in retail banking. The results provide insights into the most critical factors in building customer loyalty and trust among Generation Y customers in South African retail banking. Moreover, they can help to develop marketing and customer service strategies to improve these outcomes.

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    • Figure 1. Predictors of retail banking loyalty and trust
    • Table 1. Demographic data
    • Table 2. Principal components analysis
    • Table 3. Measurement model reliability and validity
    • Table 4. Measurement model estimates and model fit
    • Table 5. SPSS output
    • Table 6. Structural model paths
    • Table 7. Structural model fit
    • Conceptualization
      Marko van Deventer, Ephrem Habtemichael Redda
    • Data curation
      Marko van Deventer
    • Formal Analysis
      Marko van Deventer
    • Investigation
      Marko van Deventer, Ephrem Habtemichael Redda
    • Methodology
      Marko van Deventer
    • Project administration
      Marko van Deventer
    • Writing – original draft
      Marko van Deventer
    • Writing – review & editing
      Marko van Deventer, Ephrem Habtemichael Redda
    • Validation
      Ephrem Habtemichael Redda
    • Visualization
      Ephrem Habtemichael Redda