The effect of excessive social networking sites on credit overuse behavior through money trust, money anxiety, and money power
Bibliographic record
Abstract
The development of social media technology has an impact on the welfare of users but has side effects on communication and behavior when used excessively. Excessive use of social networking sites impacts user behavior in obtaining fast information and sharing information with other users to show their strengths as a personal profile. Data was collected on young adults who made purchases on credit with pay letters as many as 210 users of social media Twitter, Facebook, and YouTube. The analysis used in the study used Partial Least Square version 4. The research data was obtained by distributing questionnaires via Google Forms. The study results show that excessive SNS use influences money attitudes, including money anxiety, trust, and power. The money trust that users have has an impact on money power. Money attitude affects credit application PayLater overuse behavior. The results showed that money trust did not impact increasing credit application PayLater overuse behavior, while money power and money anxiety influenced credit application PayLater overuse behavior. Research makes a practical contribution for SNS users to continue using it reflectively, so it does not interfere with work activities, family relationships, and the responsible use of money. The theoretical contribution enriches the theory of money behavior, e-payment, and money attitude using social media.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".