Decoding AI adoption in banking: Insights from artificial neural network modelling
Keywords:
Artificial Intelligence (AI), Artificial Neural Networks (ANN), Banking Sector, Fin-Tech, UTAUT-2, Perceived Risk, Knowledge, Openness to ChangeAbstract
AI is revolutionizing the banking sector addressing intricate challenges, streamlining operations, and enhancing customer experiences. This research delves into the key elements affecting customers' decisions to adopt Artificial Intelligence (AI) in the banking industry, filling a significant gap in the current literature. By combining various components from the ‘Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)’, such as ‘Performance Expectancy’, ‘Effort Expectancy’, ‘Social Influence’, ‘Hedonic Motivation’, ‘Facilitating Conditions’, ‘Behavioural Intentions’, and ‘Habit’, with additional dimensions like ‘Knowledge’, ‘Openness to Change’, and ‘Perceived Risk’, the study proposes a holistic framework to understand AI adoption. Drawing on primary data from 511 banking customers in India, the research utilizes ‘Artificial Neural Network (ANN)’ modelling to assess the relative significance of these factors. Sensitivity analysis indicates ‘Habit’ as the most influential factor (normalized importance: 100%), followed by ‘Openness to Change’ (70%) and ‘Facilitating Conditions’ (67%). This highlights the significance of routine behavior, flexibility, and infrastructure. Moderate influences were found for ‘Effort Expectancy’ (45%) and ‘Hedonic Motivation’ (33%), while ‘Social Influence’, ‘Knowledge’, and ‘Perceived Risk’ had a relatively minor effect. This study's uniqueness lies in its integration of theoretical constructs and innovative analytical methods. The results provide valuable guidance for banks to develop AI solutions that cater to customer needs, focusing on habit formation and adaptability, while offering a solid basis for further exploration in this field.
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Copyright (c) 2025 Ram Singh, Anu Kohli, Jhalkesh Sharma
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