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Record W2978124686 · doi:10.1017/age.2019.21

Willingness to Pay, Quality Perception, and Local Foods: The Case of Broccoli

2019· article· en· W2978124686 on OpenAlexaff
Xiaoli Fan, Miguel I. Gómez, Phillip S. Coles

Bibliographic record

VenueAgricultural and Resource Economics Review · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWillingness to payQuality (philosophy)PerceptionVariety (cybernetics)TasteConsumer demandPrice premiumBusinessMarketingEconomicsFood qualityFood sciencePsychologyMathematicsMicroeconomicsBiologyStatistics

Abstract

fetched live from OpenAlex

A large and growing body of literature has studied consumer willingness to pay (WTP) for local foods in the United States. However, these studies implicitly assume that consumers perceive local foods to have superior quality than nonlocal foods. Little is known about WTP for local foods when taking into account differences in consumer perception of food quality between local and nonlocal foods. In this article, we conduct an economic experiment to assess the effect of locally grown information on consumer WTP and quality perceptions of three broccoli varieties (one commercial variety grown in California and two newly developed local varieties). Our results show that consumers rate both the appearance and the taste of the two local broccoli varieties lower than the California variety when evaluating food quality blindly. However, consumers’ evaluations of the two local varieties improve substantially after being told the two varieties are locally grown. Results also indicate that consumers are willing to pay a price premium for the two local varieties after being told that they are locally grown. Our results provide evidence that locally grown information has a positive effect on both consumer WTP and quality perception of local foods.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.175

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.014
GPT teacher head0.221
Teacher spread0.207 · 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 designOther design
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

Citations69
Published2019
Admission routes1
Has abstractyes

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