Student's perception of mobile payment application using TAM model: An empirical study in Saudi Arabia
Bibliographic record
Abstract
The growth in Information and Communication Technology has brought dynamic changes to financial payment systems through smart devices, and mobile payment systems are significant among different smart payment systems. Further, the Technology Acceptance Model's components affect the smart payment user's behavior. Moreover, students are a substantial part of society and are more inclined to use mobile payments. Therefore, the present research examines the influence of technology adoption factors on the perception of students using mobile payments. The study adopted TAM components as influencing factors, such as Perceived usefulness, perceived ease of use, Perceived cost, and Perceived trust. The data was collected from 100 respondents consisting of male and female students. The study employed simple regression analysis to report the results. The results show that the perception of students towards the use of m-payment is strong, with a mean of 1.52. The result is similar to the explanatory variables, ranging from 1.77 to 1.97. The study found that the technology adoption factors, such as Perceived usefulness, perceived ease of use, and Perceived cost, positively influenced the students' perception of using mobile payments with p-values ranging from 0.001 to 0.049. The results of Perceived trust were positive but insignificant. Therefore, the present research observed a significant influence of technology adoption factors on the perception of students using mobile payment.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".