Examining the Impact of Customer Digital Literacy and Artificial Intelligence Literacy on the Adoption of AI-Enabled Mobile Banking Services
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".