FACTORS AFFECTING PURCHASE INTENTION OF HEALTHY DRINKS
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
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.
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 | 0.000 | 0.000 |
| 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