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Record W2964109309 · doi:10.1186/s12889-019-7323-y

Cueing healthier alternatives for take-away: a field experiment on the effects of (disclosing) three nudges on food choices

2019· article· en· W2964109309 on OpenAlexfundno aff
Tracy Cheung, Marleen Gillebaart, Floor M. Kroese, David Marchiori, Bob M. Fennis, Denise T. D. de Ridder

Bibliographic record

VenueBMC Public Health · 2019
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNudge theorySalience (neuroscience)Food choiceMedicineAdvertisingMarketingSocial psychologyPsychologyBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: The current field experiment demonstrates the effectiveness of nudging to promote healthy food choices. METHODS: Three types of nudges were implemented at a take-away food vendor: 1) an accessibility nudge that placed fruits at the front counter; 2) a salience nudge that presented healthy bread rolls to be more visually attractive; and 3) a social proof nudge that conveyed yoghurt as a popular choice. We additionally assessed whether nudging effects would remain robust when a disclosure message was included. The field experiment was conducted over a seven-week period. The measured outcome was the sales of the targeted healthy food products. RESULTS: The accessibility nudge significantly increased the sales of the fresh fruits. The impact of the salience nudge was limited presumably due to existing preferences or habits that typically facilitate bread purchases. As the sales of the yoghurt shakes remained consistently low over the seven-week period the impact of the social proof nudge remained unexamined. Critically, disclosing the purpose of the nudges did not interfere with effects. CONCLUSIONS: Current findings suggest nudging as an effective strategy for healthy food promotion, and offer implications for topical debate regarding the ethics of nudges.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.371
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.357
Teacher spread0.289 · 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

Citations42
Published2019
Admission routes1
Has abstractyes

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