Exploring the Impact of Using Financial Technology Application on Financial Welfare (Case Study in Medan City, East Medan District)
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 aims to examine the factors that affect Financial Welfare in the context of technology usage to enhance people's Financial Welfare. A case study was conducted in the Medan Timur District of Medan City. The analytical method used is descriptive statistical analysis and Structural Equation Modeling. The population and sample in this study were 245 Millennial Generation people in the East Medan District of Medan City. The sampling technique used was purposive sampling. The results of this study indicate that Financial Training has a negative and not significant effect on Financial Welfare in the Millennial Generation in Medan City, Financial Stress has a positive and significant effect on Financial Welfare in the Millennial Generation in Medan City, Financial Training has a positive and significant effect on Use of Financial Technology in the Millennial Generation in Medan City, Financial Stress has a positive and significant effect on Use of Financial Technology in the Millennial Generation in Medan City, Use of Financial Technology has a positive and significant effect on Financial Welfare in the Millennial Generation in Medan City, Use of Financial Technology can mediate the relationship between Financial Training and Financial Welfare, Use of Financial Technology unable to mediate the effect of Financial Stress on Financial Welfare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it