Exploring the Impact of Using Financial Technology Application on Financial Welfare (Case Study in Medan City, East Medan District)
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".