Attitude based on Tri Kaya Parisudha in increasing intention to reuse e-money
Bibliographic record
Abstract
The purpose of the study is to explain the effect of perceived risk and subjective norm to the attitude based on Tri Kaya Parisudha and reuse intentions, attitude to reuse, and role attitude in mediating the effect on perceived risk and norm subjective to reuse intention from the use of e-money in the elderly. Quantitative approach is conducted for this study. The subject is the elderly/senior citizen who uses e-money, with a sample of 150 respondents. Purposive sampling is used as a method of sampling determination with the SEM-PLS technique. The result of this study are attitude based on Tri Kaya Parisudha capable mediate influence perception risk and norm subjective to reuse intention, also this study found the attitudes based on Tri Kaya Parisudha turned out to be able to mediate the effect of risk perception and also the influence of subjective norms on the intention to reuse e-money in the elderly in the city of Denpasar and the elderly who, even though they are elderly, still use e-money to meet their needs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".