Using Loyalty Points to Encourage Healthy Food Choices in Online Shopping
Bibliographic record
Abstract
We investigate the effectiveness of using loyalty points to influence consumers’ choices for healthier products among two different food categories (deemed unhealthy vs. ambiguously healthy). To test the effectiveness of the loyalty points, we run an experiment with 1003 participants through Qualtrics across Canada. The conditional logit model results indicate that consumers' utility for both ‘low-in’ products and loyalty points are significantly positive. However, the utility will decrease if the consumers are told more loyalty points are given to the ‘low-in’ products because they are healthier. Several socio-demographic groups, such as women and people who avoid ‘high-in’ fat food products are more sensitive to ‘low-in’ labels. When comparing yogurt and popcorn, ‘low-in’ and loyalty points work differently. However, these results are not statistically different for most attributes. We suggest that the food processing industry and retailers should keep these results in mind when implementing ‘low-in’ labels, loyalty points, and information in stores.
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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 | 0.004 | 0.014 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".