Simulation of Effective Electronic Banking Factors on Profitability Using a System Dynamics Approach: A Case Study of Maskan Bank

Abstract

The objective of this study was to dynamically simulate and analyze the factors affecting electronic banking and their impact on the profitability of Maskan Bank using a system dynamics approach and scenario evaluation. This study was developmental in purpose and descriptive–simulation in nature, conducted using a system dynamics modeling approach. Secondary data were collected from Maskan Bank over a five-year period from 2020 to 2025. The research model was developed using Vensim DSS software and incorporated endogenous and exogenous variables including population growth rate, bank interest rate, electronic banking services, social networks, customer base, customer satisfaction, and bank profitability. Model validation was performed using multiple system dynamics validation tests including boundary adequacy, structural assessment, dimensional consistency, behavior reproduction, extreme condition testing, and sensitivity analysis. In addition, various scenarios were simulated to evaluate the impact of changes in electronic banking and social network services on bank profitability. Sensitivity analysis results indicated that increases in population growth rate and bank interest rate significantly enhanced bank profitability. Simulation results further demonstrated that reducing or eliminating electronic banking components and social network services led to a substantial decline in bank profitability. However, increasing electronic banking and social network services beyond certain levels did not produce a proportional increase in profitability. The model also showed that increased use of electronic banking improved customer satisfaction, increased customer acquisition, enhanced deposit levels, and ultimately contributed to improved bank profitability. The findings indicate that electronic banking plays a critical role in sustaining bank profitability, but its expansion alone does not guarantee increased profitability, as its effectiveness depends on complementary factors such as customer behavior, interest rates, and demographic conditions. Furthermore, system dynamics modeling proved to be an effective tool for analyzing complex banking system behavior and predicting the outcomes of electronic banking policies. These results provide valuable insights for bank managers in strategic decision-making and in designing effective electronic banking development policies.