The effects of front-of-package warning labels on consumer attitudes and purchase intentions toward reformulated food products
Bibliographic record
Abstract
To address the growing concern of non-communicable diseases and obesity, the Government of Canada will introduce a mandatory front-of-package (FOP) warning label for prepackaged foods “high in” saturated fats, sugars, and sodium. This intervention, set to be implemented in January 2026, aims to nudge consumers into making better-informed and healthier food choices. Through a series of three experimental studies, this research seeks to examine: (1) the influence of the proposed FOP label on consumer attitudes and purchase intentions toward reformulated products; (2) the mediating effects of perceived healthiness and perceived tastiness; and (3) the moderating roles of goal salience (health vs. indulgence) and product type (healthy vs. unhealthy). Study 1 found that warning labels led to declining consumers’ attitudes and purchase intentions toward reformulated products, with perceived healthiness mediating the effect. Study 2 revealed that participants with a prominent health goal exhibited less favorable attitudes toward the reformulated product with (vs. without) the warning label, while those with an indulgent goal showed no difference in attitudes. Study 3 showed that participants demonstrated heightened sensitivity to the warning label when displayed on healthy (vs. unhealthy) food products. This thesis contributes to the literature on consumer behavior in the food industry, offering insights into the dynamics between health and indulgence within the framework of warning labels and product reformulation. The findings hold managerial implications for new product development, packaging, and communication strategies, and can help inform governments and policymakers about the effectiveness of warning labels for reformulated 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".