Examining the Relationship Between Bank Efficiency, Market Power, and Contemporary Technologies – Smart Banks and Cryptocurrency

Abstract

The aim of this study is to explore the effect of digital transformation on the efficiency of retail banks in India. Digital transformation is represented by two contemporary technologies, namely: smart branches and cryptocurrencies. The paper tests the direct effects of these factors on efficiency and indirect effects mediation by the market power of commercial banks. This aim is pursued and attained using the quantitative research design and methods of Data Envelopment Analysis (DEA) and panel regression analysis. The sample comprises thirty-four commercial banks in India retrieved from the Bureau van Dijk database for the period from 2012 to 2018. The conceptual framework of this study is represented by the extension of the Structure Conduct Performance (SCP) Paradigm. The main dependent variables are represented by Efficiency Scores estimated using DEA with the assumptions of constant returns to scale, variable returns to scale, and decreasing returns to scale. The market power of commercial banks in India is measured by their concentration ratios linked to total assets. The key variables of digital transformation are the support of cryptocurrencies and the availability of Smart Branches. The results reveal that the support of cryptocurrencies is shown to produce no significant effects either on market power or efficiency of banks confirming there is no direct and indirect relationship between digital currencies and the efficiency of banks. Smart branches demonstrate a significant positive effect on market power but no direct effect on efficiency. Therefore, it is evidenced that the technology of smart branches currently has an indirect effect on the efficiency of banks in India, mediated by the market power of banks. The results have implications for banks and bank managers who are recommended to invest in smart branches to maintain and increase their market share and serve more customers. However, the work is limited by the availability of data with higher frequency, the limitation of DEA to study only technical efficiency, and the limitations of regression analysis, which omits qualitative factors and is not able to explain all changes in dependent variables.