Continuance intention to use digital payments in mitigating the spread of COVID-19 virus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigated the continuous intention to use digital payment solutions in online transactions to mitigate the spread of the COVID-19 virus. Primary data was collected from individuals using digital payment systems in Bangkok, Thailand using a structured questionnaire from a total of 400 respondents. The study adopted the Technology Acceptance Model (TAM). Data were analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) in AMOS 26. The results revealed that factors that significantly affected continuous intention to use digital payments were perceived ease of use, satisfaction, attitude, and social distancing. Satisfaction mediated the effects of perceived usefulness, perceived ease of use, and social distancing on continuous intention to use. Attitude mediated the effect of perceived usefulness on continuous intention to use. The study recommends that concerned policymakers and institutions should consider users’ satisfaction, social distancing, and perceived ease of use when developing digital payment systems.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it