The The Influence of Perceived Quality, Perceived Ease of Use, and Perceived Transaction Security on User Satisfaction in Flip Application (A Study on the Millennial Generation and Generation Z)
Bibliographic record
Abstract
The development of technology, especially information technology, has penetrated various sectors in Indonesia, from education to banking. With this, banking access can be used by all groups flexibly through fintech services. Flip is a real example of how technology services can collaborate with the banking sector to expand the reach of its services. Flip. The application helps its users to make free interbank transfers, send money abroad via Flip Globe, top up e-wallets, pay electricity bills, buy electricity tokens, top up credit, buy data packages, Internet & TV, BPJS Health, Credit Installments at affordable costs. This study aims to test the effect of perceived quality, perceived ease of use, and perceived transaction security on user satisfaction in the flip application (a study of the millennial generation and generation z). The population in this study were all residents in the city of Surabaya who had made transactions using the Flip application. The data collection method was through a survey using a questionnaire to 155 respondents with a non-probability sampling method through Purposive Sampling. Data analysis techniques include testing validity, reliability, classical assumptions, multiple linear regression, coefficient of determination, F test and t test. The results showed that simultaneously the variables perceived quality, perceived ease of use, and perceived transaction security have a significant effect on user satisfaction. Partially perceived ease of use and perceived transaction security have a significant positive effect on user satisfaction, while perceived quality has a positive but insignificant effect.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".