Analyzing Food Purchasing Behavior Helps Improve Consumers' Health on E-Commerce Platforms in Viet Nam
Why this work is in the frame
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Bibliographic record
Abstract
The project aims to understand and analyze the factors influencing food buying behavior to help improve consumers' health on e-commerce platforms. As life gets busier, consumers tend to search for and choose healthy food products online. Understanding this shopping behavior will help businesses and product suppliers on the e-commerce platform better orient their business strategies. This study combines both qualitative and quantitative research methods. Based on data collected from a survey of 400 people, the data was analyzed using SmartPLS 4 software. The collected results are processed through 3 steps: descriptive statistics, measurement model testing, and structural model testing. This study analyzes food buying behavior that helps improve consumers' health on e-commerce platforms. The study results are expected to help identify the main factors affecting purchasing decisions, thereby providing business strategy recommendations for businesses and promoting online healthy food consumption.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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