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Record W2031768041 · doi:10.5539/ibr.v4n3p161

Thai Consumers Willingness to Pay for Food Products with Geographical Indications

2011· article· en· W2031768041 on OpenAlexvenueno aff
Pimsiri Seetisarn, Yingyot Chiaravutthi

Bibliographic record

VenueInternational Business Research · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
FundersMahidol UniversityMahidol University International College
KeywordsProduct (mathematics)Willingness to payBusinessCountry of originGeographical indicationFood labelingChristian ministryFood productsAgricultural scienceCertificationSign (mathematics)MarketingAdvertisingEconomicsFood scienceMathematicsGeographyChemistryMicroeconomicsBiology

Abstract

fetched live from OpenAlex

Geographical Indication (GI) is a name, or sign, that refers to a specific characteristic of geographical location or origin. GI is used to both protect and guarantee that the product has a unique characteristic, resulting from being qualifiedly produced in a specific place. There are many products in Thailand that have been certified by the Ministry of Commerce, carrying the GI label. The purpose of conducting this research is to study Thai consumers’ willingness to pay (WTP) for products with GI labels. This experiment was carried out under the nth price auction method on Doi Tung coffee, Tung Kula Ronghai Thai Hom Mali rice, and Chaiya salted eggs. Sixty participants were asked to offer bids for three products each with different types of labels, a normal label; a label stating the product’s origin; and a label which stated the product’s origin and contained a GI sign. The results show that Thai consumers’ WTPs are influenced by the origin of the product. However, the WTPs of GI labels do not significantly differ from the WTPs of labels which state the product’s origin. This implies that Thai consumers value the product’s origin, but do not recognize the importance of the GI label.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.282
GPT teacher head0.310
Teacher spread0.028 · 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

Citations18
Published2011
Admission routes1
Has abstractyes

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