Understanding Digital Banking Decision-Making System Through Artificial Neural Networks

Research on digital banking has largely examined technology adoption, while less is known about how perceived digital capability enters the prior decision of selecting a banking institution. This study adopts a system-based modelling perspective to examine bank selection as a non-linear decision system in which digitalization acceptance, perceived technology level, usage behaviour, and financial education interact to influence the perceived importance of digitalization in bank choice. Using survey data collected from Romanian university students, we develop and evaluate multiple Artificial Neural Network (ANN) configurations to model these relationships and identify the most effective predictive architecture. University students were selected because they represent a highly digitally exposed consumer segment whose banking choices are particularly relevant for examining technology-oriented decision patterns. The results indicate that the optimal ANN configuration achieves strong predictive performance (MAE = 0.1487; MSE = 0.0314) while maintaining efficient convergence behaviour. Variable importance analysis, computed using both a Random Forest measure and permutation importance on the ANN itself, consistently identifies perceived technology level as the dominant component of the decision system and frequency of use as the least influential, while the relative ranking of digitalization acceptance and financial education varies between the two importance measures. The findings demonstrate the suitability of ANN-based approaches for modelling complex behavioural decision systems characterized by non-linear interactions and heterogeneous component influence. The study contributes to the digital banking literature by extending technology acceptance research from adoption intentions toward bank selection behaviour. From a practical perspective, the findings suggest that banks targeting digitally oriented young consumers should prioritize perceived technological capability and the quality of the digital banking experience, including usability, trust, transparency, and usefulness. The comparatively lower predictive of usage frequency and financial education further indicates that strategies focused on strengthening perceived digital value may be more relevant to bank selection than those aimed primarily at increasing routine service usage.

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Publication Details

Journal
Systems
Published
2026-09-01
DOI
https://doi.org/10.3390/systems14091068
Primary Topic
Technology Adoption and User Behaviour
Type
article
Field-Weighted Citation Impact
0.00
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article

Understanding Digital Banking Decision-Making System Through Artificial Neural Networks

Margareta Ilie, Andreea-Daniela Moraru, Cristina Duhnea, Ionuț Antohi et al.
Systems
Technology Adoption and User Behaviour
article

Understanding Digital Banking Decision-Making System Through Artificial Neural Networks

Margareta Ilie, Andreea-Daniela Moraru, Cristina Duhnea, Ionuț Antohi, Silvia Ghiță-Mitrescu, Răzvan-Ionuţ Drugă
article en

Abstract

Research on digital banking has largely examined technology adoption, while less is known about how perceived digital capability enters the prior decision of selecting a banking institution. This study adopts a system-based modelling perspective to examine bank selection as a non-linear decision system in which digitalization acceptance, perceived technology level, usage behaviour, and financial education interact to influence the perceived importance of digitalization in bank choice. Using survey data collected from Romanian university students, we develop and evaluate multiple Artificial Neural Network (ANN) configurations to model these relationships and identify the most effective predictive architecture. University students were selected because they represent a highly digitally exposed consumer segment whose banking choices are particularly relevant for examining technology-oriented decision patterns. The results indicate that the optimal ANN configuration achieves strong predictive performance (MAE = 0.1487; MSE = 0.0314) while maintaining efficient convergence behaviour. Variable importance analysis, computed using both a Random Forest measure and permutation importance on the ANN itself, consistently identifies perceived technology level as the dominant component of the decision system and frequency of use as the least influential, while the relative ranking of digitalization acceptance and financial education varies between the two importance measures. The findings demonstrate the suitability of ANN-based approaches for modelling complex behavioural decision systems characterized by non-linear interactions and heterogeneous component influence. The study contributes to the digital banking literature by extending technology acceptance research from adoption intentions toward bank selection behaviour. From a practical perspective, the findings suggest that banks targeting digitally oriented young consumers should prioritize perceived technological capability and the quality of the digital banking experience, including usability, trust, transparency, and usefulness. The comparatively lower predictive of usage frequency and financial education further indicates that strategies focused on strengthening perceived digital value may be more relevant to bank selection than those aimed primarily at increasing routine service usage.

SystemsVol. 14(9)
Ovidius University (RO)
Peace, Justice and strong institutions
Openalex Percentile: Top 4%
Technology Adoption and User Behaviour
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