Examining the Impact of Customer Digital Literacy and Artificial Intelligence Literacy on the Adoption of AI-Enabled Mobile Banking Services
Notice bibliographique
Résumé
Artificial intelligence (AI) is reshaping mobile banking (MB) globally by enabling intelligent, personalised, and automated financial services. While countries such as the United States of America (USA), China, Singapore, Canada, and the United Kingdom have advanced Artificial Intelligence-enabled mobile banking (AIMB) ecosystems, New Zealand remains at an early stage of adoption. Banks are implementing AIMB services primarily to enhance system efficiency; however, banks worldwide have paid less attention to customer competency in adopting platforms such as AIMB. Prior studies have predominantly emphasised system performance and organisational readiness, overlooking customer digital literacy (DL) and AI literacy (AIL) as determinants of adoption. This gap is significant in New Zealand, where AIMB initiatives are emerging but understanding remains limited regarding how DL and AIL shape readiness and influence perceptions of service quality. Without these insights, advanced services risk being deployed misaligned with customer competency, slowing adoption and weakening service improvements. To address this gap, this research explored the DL and AIL levels of New Zealand MB customers while assessing how these literacies shape AIMB readiness, followed by the evaluation of AIMB influences on MB customers’ perceptions of service quality. A quantitative research design was adopted, collecting 276 responses from New Zealand MB customers via a structured online survey. The survey included sections on demographics, DL, AIL, readiness dimensions, and perceived service quality, measured for MB usage and again after participants were introduced to AIMB functionalities using a demonstration video. Data analysis employed descriptive statistics, analysis of variance (ANOVA), correlation, paired-sample t-tests, and regression modelling to examine relationships among the constructs. Findings illustrated that New Zealand MB customers hold moderate to high levels of DL and AIL, with a strong positive correlation showing that customers with higher DL also tend to possess greater AIL. Age and education emerged as significant demographic factors, with younger and more educated customers exhibiting higher literacy levels. Digital literacy significantly enhanced readiness by increasing optimism and reducing discomfort and insecurity, whereas AIL contributed positively to optimism but demonstrated weaker effects on mitigating negative readiness factors, highlighting DL’s stronger role in shaping overall readiness. Exposure to AIMB created a polarising effect on perceived service quality: dissatisfied MB customers perceived clear improvements, while already satisfied customers reported weaker or negative changes, particularly around privacy and fulfilment dimensions. Although efficiency and system availability showed improvements with AIMB, declines in fulfilment and privacy offset these gains, indicating that overall service quality could not increase substantially. This research provides empirical evidence that DL is a critical enabler of AIMB readiness, while fulfilment and privacy dimensions, including overall service quality, demonstrate a declining trend once customers adopt AIMB. For practice, the results highlight the need for New Zealand banks and policymakers to strengthen digital competency-building, ensure equitable adoption across demographics, and embed governance mechanisms to safeguard trust. Collectively, insights provide timely guidance for navigating AI transformation and aligning AIMB services with customer competencies and expectations in the New Zealand banking landscape.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».