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Record W4404069604 · doi:10.1177/00222429241299392

To Dispose or Eat? The Impact of Perceived Healthiness on Consumption Decisions for About-to-Expire Foods

2024· article· en· W4404069604 on OpenAlexfundno aff
Jeehye Christine Kim, Young Eun Huh, Brent McFerran

Bibliographic record

VenueJournal of Marketing · 2024
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNational Research Foundation of Korea
KeywordsDispose patternConsumption (sociology)BusinessMarketingAdvertisingFood scienceWaste managementEngineeringChemistrySociology

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.068
GPT teacher head0.420
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractyes

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