Pandemic-Era Trends in US Automatic Payment Adoption: A 2022 Behavioral Analysis
Bibliographic record
Abstract
Introduction: This study analyses the adoption trends of automated payment methods among different racial groups in the United States during the COVID-19 pandemic in 2022. It investigates the correlations among financial literacy, risk attitudes, customer sentiment, and the adoption of automated payment mechanisms for bill settlements. Method: An analysis of responses from a diverse demographic is conducted using logistic regression on data from the 2022 Survey of Consumer Finance. This analysis investigates the trends associated with subjective and objective financial literacy, consumer sentiment as evaluated by economic perceptions, and risk attitudes among several generations, taking into account race as well. Results: Initial results suggest disparities in the degrees to which financial literacy, risk attitudes, and consumer sentiment are connected with adoption rates among various racial groups. The statistical analysis highlights disparities in the adoption of automated payments among different racial and ethnic groups. Discussion: The findings underscore the complex interplay among socio-economic status, behavioral variables, and technology adoption in the period after the pandemic. This methodology aligns with current scholarly works on behavioral finance, which emphasize the need to consider both individual psychological aspects and wider social impacts in the process of making financial decisions. By incorporating racial diversity into the research, the study provides a valuable understanding of how cultural and demographic factors interact with behavioral aspects in the particular setting of financial technology adoption during a time of substantial economic transformation.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".