Digital by Default? A Critical Review of Age-Driven Inequalities in Payment Innovation
Bibliographic record
Abstract
This paper offers a systematic literature review of age-related disparities in the adoption of digital payment systems, a phenomenon that is becoming increasingly relevant as financial transactions become predominantly digital. Using the SPAR-4-SLR protocol, 66 scholarly contributions published between 2014 and 2024 are examined and categorised into four thematic clusters: demographic determinants, behavioural drivers, structural barriers linked to the grey digital divide, and emerging insights from neurofinance. The review highlights a multifactorial set of barriers that limit older adults’ engagement with digital payments, including usability challenges, cognitive and physical limitations, digital skill gaps, and perceived security risks. These obstacles are further amplified by structural inequalities such as socio-economic status, geographic location, and infrastructural constraints. While digital payments are often presented as tools of inclusion, the findings underscore the risk of exclusion for ageing populations without tailored design and policy interventions. The review also identifies areas for further research, particularly at the intersection of ageing, cognitive function, and human–technology interaction, proposing a research agenda that supports more inclusive and age-responsive financial innovation.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".