Electronic payment acceptance model: A study on United Arab Emirates consumers
Bibliographic record
Abstract
This paper aims to investigate if trust, perceived usefulness, and perceived ease of use affect the intention to use e-payment. Also, the study explores if attitudes towards the use of e-payment influence consumer's intentions to use the e-payment system which is supported by testing the moderation effect of Self-Efficacy, and Computer Anxiety on the attitude to use such systems in higher education institutes. The study found that there are a variety of effects of the Electronic Payment Acceptance Model in the United Arab Emirates that pertain to sociological, legal, and economic aspects. The United Arab Emirates can benefit from a more robust and inclusive digital payment ecosystem by comprehending and implementing the lessons gained from such research. Among the lessons learned from this study is that using electronic payment leads to many benefits, it is not possible to benefit from all these benefits if the acceptance rate of technology, especially electronic payment, is low. For this reason, this research came to provide solutions to the possibility of increasing the acceptance of technology among individuals and organizations through a complete model and studying the impact of its factors and the factors that moderate the relationship in it.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".