To Dispose or Eat? The Impact of Perceived Healthiness on Consumption Decisions for About-to-Expire Foods
Bibliographic record
Abstract
Perceived healthiness of food is generally regarded as a positive attribute in food choices, as it positively impacts consumers’ preferences. The current research demonstrates that in contexts where there is a time delay between a food's production and its consumption (referred to as “about-to-expire” food), strong perceptions of a food's healthiness can be detrimental. This is because consumers hold a lay theory that healthy food expires more quickly. In eight studies (N = 3,552), the authors find that merely portraying food as healthy increases the perception that it expires quickly and that this effect attenuates when consumers hold the lay theory weakly or have a high level of knowledge about food expiration. Importantly, this lay theory leads consumers to avoid consuming healthy (vs. nonhealthy) about-to-expire food, resulting in increased disposal intentions and decreased preferences. In designing sales promotions for about-to-expire food, managers should consider the healthiness of food products, as consumers prefer different types of sales promotions and require different magnitudes of price discounts for healthy (vs. nonhealthy) about-to-expire food. Finally, adding an expiration date label that provides unambiguous guidance (i.e., “consume by”) can effectively mitigate the detrimental effect of perceived healthiness on the consumption for about-to-expire food.
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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 | 0.002 | 0.011 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".