ArticleHeliyon2023
Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of expectation confirmation model and antecedents of artificial intelligence enabled digital banking.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Research on factors influencing teaching staff's continued use intention of AIGC tools: based on structural equation modeling and artificial neural networks.Scientific reports · 2026Article
- The adaptive engagement framework: enhancing banking customer experience through AI-powered invisible marketing.Scientific reports · 2026Article
- Acceptance of AI-based tools in consumer financial decision-making: An application of the extended technology acceptance model.PloS one · 2026Article
- Advancing Nursing Education: Evaluating Artificial Intelligence (AI) and Escape Room (ER) Approaches for E-Learning and Interpretation Skills.Nursing research and practice · 2026Article
- The impact of artificial intelligence on behavioral intentions to use mobile banking in the post-COVID-19 era.Frontiers in artificial intelligence · 2025Article
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- Applications of artificial intelligence and machine learning in the financial services industry: A bibliometric review.Heliyon · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the era disruptive technology the emergence of artificial intelligence has fundamentally improved banking operations. The execution of artificial intelligence is no longer discretionary for financial institutions and now it is considered an essential tool to meet customer expectations. Although artificial intelligence enabled digital banking is faster efficient and effective however user acceptance of digital banking driven by artificial intelligence is in its initial stages. Therefore, current study develops and integrated research framework with expectation confirmation model and examines digital banking user satisfaction and acceptance of AI enabled digital banking. Data were collected from digital banking user through structured questionnaire. Overall, 320 respondents were approached and requested to participate in digital banking survey. In return 251 valid responses were received and analyzed with structural equation modeling. Findings of the structural model indicate that satisfaction is jointly determined by expectation confirmation, perceived performance, trendiness, visual attractiveness, problem solving, customization, communication quality and revealed substantial variance R^2 51.1% in digital banking user satisfaction. Therefore, satisfaction and corporate reputation have shown considerable variance R^2 48.3 in user acceptance of AI enabled digital banking. Moreover, the research framework has revealed substantial predictive power Q^2 0.449 to predict digital banking user satisfaction and Q^2 0.493 user acceptance of artificial intelligence enabled digital banking. Concerning with hypotheses relationships exogenous factors have shown positive and significant impact user satisfaction except trendiness and customization. Practically, this research has suggested that policy makers should pay attention in improving user expectation confirmation, perceived performance, visual attractiveness, communication quality and corporate reputation which in turn enhance satisfaction and boost digital banking user's confidence to accept artificial intelligence enabled digital banking. This study is original as it integrates expectation confirmation model with the antecedents of artificial intelligence and examines user behavior towards acceptance of artificial intelligence enabled digital banking.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.