Transparency as a Predictor of Loyalty in the Banking Sector: A SEM-ANN Approach

Abstract

The current competitive environment in the banking sector aims to increase customer loyalty (CL). Therefore, this study aims to look at how transparency (TR) influences CL through the interaction of trust (TU) and customer satisfaction (CS). In this study, structural equation modelling (SEM) was applied to 510 samples of public sector banks. The results showed that transparency, trust and satisfaction interact with CL. The study found that the relationships between transparency and CL are non-compensatory and nonlinear. The contribution of the study is that SEM predictors were used as a neural layer in an artificial neural network (ANN) model to predict loyalty. The results showed that the independent variables significantly influence dependent variables when the normalized significance was calculated based on the multilayered observations using the ANN method. Our study’ ANN model can predict customers’ loyalty with 75.5% accuracy. In addition, we have highlighted several useful theoretical implications of transparency on CL.