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Original scientific article

SUSTAINABLE FINANCE IN THE AGE OF AI AND GREEN BANKING ADOPTION AMONG INDIAN BANKING CUSTOMERS

By
M. J Sumaiah Shaheen Orcid logo ,
M. J Sumaiah Shaheen

Research Scholar, Faculty of Management, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, Tamil Nadu, India

M Karthikeyan Orcid logo
M Karthikeyan

Assistant Professor (Senior Grade), Faculty of Management, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, Tamil Nadu, India

Abstract

The increasing use of artificial intelligence (AI) in banking opens up possibilities for advancing eco-friendly banking, however, there is little empirical evidence on the interaction between AI service features, environmental and functional perceptions, and customer adoption in India. The research analyzes the relationship of environmental concern, perceived usefulness, AI service convenience, and green perceived value to green banking adoption, using the attitude towards green banking as a mediating factor and the quality of AI adaptation as a moderating factor. Data were obtained from 516 users of digital bank services in India and was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) technique with 5000 bootstrap resamples. It has been established that the quality of AI adaptation was the strongest predictor of green banking adoption (β = 0.433, p < 0.001). Environmental concern (β = 0.228, p < 0.001) and perceived usefulness (β = 0.133, p = 0.013) served as effective predictors of adoption, while the attitude towards green banking played an important positive role (β = 0.232, p < 0.001). AI service convenience had a significant impact on attitude (β = 0.167, p = 0.003) but not on adoption (β = 0.035, p = 0.494). Green perceived value was not significant for both attitude (β = 0.028, p = 0.607) and adoption (β = −0.061, p = 0.195). Attitude was a mediator in terms of the environmental concern (β = 0.070, p = 0.001), perceived usefulness (β = 0.057, p = 0.007), and AI service convenience (β = 0.039, p = 0.025). The moderating effect of AI adaptation quality was not significant (β = 0.000, p = 0.990). The findings indicate that functional usefulness, environmental concern, and AI-driven personalization are more influential adoption mechanisms than green value perceptions alone.

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Citation

This is an open access article distributed under the  Creative Commons Attribution Non-Commercial License (CC BY-NC) License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 

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