FACTORS AFFECTING PURCHASE INTENTION OF HEALTHY DRINKS
Bibliographic record
Abstract
Today, choosing healthy foods and providing adequate nutrients is crucial for the body. Someone better choose clean foods and beverages that have undergone hygienic processing to prevent contamination with harmful ingredients. One of the products that can assist customers in meeting their nutritional needs to increase endurance and avoid illness is healthy beverages. This study examined how health awareness, food safety, and perceived advantages affect the buying intention of healthy drinks. This research employs a non-probability approach with purposive selection. 224 respondents were recruited by disseminating surveys online via Google Forms, and the data was evaluated using SmartPLS4.0-SEM. The results of this study show that health consciousness, food safety, and perceived benefits all have positive but minor effects on purchase intentions for healthy beverages in Jakarta. The results of this study suggest that food safety and health consciousness can increase consumer demand for healthful drinking products. Therefore, healthy drinks can pay attention to these factors to increase consumer interest in buying healthy beverage products.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".