Racial/ethnic differences in mobile payment usage: what do we know, and what do we need to know?
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate the racial/ethnic differences in mobile payment use and to explore the contributing factors to the differences. Design/methodology/approach This study used the 2018 National Financial Capability Study (NFCS) dataset to examine racial/ethnic disparities in mobile payment use. Logistic regression analyses were conducted to confirm racial/ethnic differences, and Blinder–Oaxaca decomposition analyses were performed to identify which factors explain the differences among the groups. Findings The authors discovered that Whites use mobile payment less than Blacks, Hispanics and Asians/others. The results revealed that prior experiences with mobile financial services, including transfer, banking and budgeting applications, all play considerable roles in explaining the disparities between Whites and other racial/ethnic groups. Originality/value This is one of the few studies to examine racial/ethnic disparities in mobile payment use with a particular focus on the influence of users' past experience with technology. The results provide insights for researchers, professionals, educators and policymakers into ways to promote future use of mobile payment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".