Effect of Digitisation of School Fee Payment on School Fee Processing Time—A Comparative Study of Traditional and Digital Payment Systems
Bibliographic record
Abstract
This study examines the factors influencing the efficiency of digital payment systems, focusing on payment methods, user continuity, and ease of navigation. Through logistic regression analysis, the research identifies significant associations between these factors and users’ perceptions of time efficiency. Key findings include the negative impact of certain payment methods on perceived efficiency, the delicate balance of factors influencing user continuity, and the positive influence of ease of navigation on time efficiency. The study underscores the importance of user-centric design and suggests recommendations for improving digital payment systems, such as user education, continuous system navigation improvement, and incentives for user continuity. Furthermore, it proposes avenues for future research, including cross-cultural analysis and longitudinal studies, to enhance our understanding of the evolving landscape of digital payments. By offering actionable insights for stakeholders, this research contributes to optimising digital payment systems, aligning with user expectations, and fostering enduring engagement in the digital finance ecosystem.
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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 | 0.004 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".