Mohammed Arshad Khan

Purpose: This article aims to investigate the main drivers of mobile banking among Delhi–NCR consumers. The TAM (technological acceptance model) was used as a framework for this study. Only a few studies have looked at how online banking users in India plan to use other similar services, such as m-banking. In order to do this, a theoretical model was made using the technology acceptance model. This model was then expanded to include the factors that make m-banking users more likely to use mobile banking. These adoption factors include the feeling of being watched, the ability to do things on your own with a mobile device, social dominance, and the role of customer support as a mediator. The use of m-banking is the thing that matters. Scope: In the last two decades, digital mobile devices have become the primary preferred method of consumer communication. Throughout the past year, mobile banking has become increasingly popular. The increasing number of smartphones in use, as well as the government’s push for cashless transactions, provide an opportunity for the Indian banking industry to rapidly expand its usage of mobile and online banking. Methodology: The data were collected through a structured questionnaire distributed to 376 respondents from different sustainable investment classes. The use of convenience sampling was imposed. Structure equation modeling (SEM), reliability, convergence, discriminate validity, and model fitness were achieved through SmartPLS 3. Findings: The study found that the adoption factors had a significant impact on perceived surveillance, mobile self-reliance, and social domination and mediating role of customer support to use mobile banking. These latest findings will inform banks and financial institutions on the rise of m-banking in India, as well as provide insight into digital banking channels and add to the literature on the topic of digital banking adoption.

This research aims to analyze the impact of bank performance determinants on bank performance by applying robust regression analysis. For this, the relationship between return on assets and net interest margin with bank performance determinants has been discussed using robust regression. Robust regression offers a better and more realistic analysis owing to reducing the impact of outliers and influential data, and it is recommended for more precise results.