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Record W2173645876 · doi:10.1111/cjag.12089

Existing Food Habits and Recent Choices Lead to Disregard of Food Safety Announcements

2015· article· en· W2173645876 on OpenAlexaffvenue
Ying Cao, David R. Just, Calum G. Turvey, Brian Wansink

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCognitive dissonanceCommitConfirmation biasIncentiveFood safetyPsychologySocial psychologyPerceptionCognitionMarketingEconomicsWelfare economicsAdvertisingBusinessMicroeconomicsMedicineComputer science

Abstract

fetched live from OpenAlex

On whom do food safety announcements have the least impact? Building on research on cognitive dissonance and confirmatory bias, this study shows that consumers tend to inadequately process (food safety) information, pay limited attention to signals, and make purchase decisions that are biased toward their initial choices. Using an incentive compatible auction mechanism, this study elicited consumers' willingness to pay (WTP) under different informational settings. Results showed that consumers were willing to pay much higher prices when they chose to commit to food items (treatment) than when they were randomly assigned (control), suggesting cognitive dissonance. The gaps in WTP were further enlarged as food safety information was revealed to consumers. Confirmatory bias was supported by findings that those who made an earlier commitment were more reluctant to change their WTPs despite increased risk perceptions. In terms of market responses, demand curves were less likely to shift down in the presence of food safety risks because consumers were less responsive to public information due to their existing habits and psychological biases. Specialized targeted strategies will be necessary to target those who are heavy or recent users of the target food when there is a food safety scare.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.201
GPT teacher head0.288
Teacher spread0.087 · 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.

Study designNot applicable
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

Citations16
Published2015
Admission routes2
Has abstractyes

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