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Record W2099966792 · doi:10.22004/ag.econ.8152

Identification of Niche Market for Hanwoo Beef: Understanding Korean Consumer Preference for Beef using Market Segment Analysis

2004· article· en· W2099966792 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2004
Typearticle
Languageen
FieldNursing
TopicNutrition, Health and Food Behavior
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHanwooBusinessPurchasingProduct (mathematics)Niche marketQuality (philosophy)Identification (biology)MarketingFood scienceMathematics

Abstract

fetched live from OpenAlex

Korean Hanwoo beef producers are interested in improving the image of Hanwoo beef for Korean consumers, as the Korean beef market is becoming increasingly open to international competition. This study examines the consumer profile and positioning for the Hanwoo beef product in South Korea. A survey of 480 consumers is conducted to analyze preferences for 33 attributes of beef purchasing decisions. Factor analysis was used to determine factors that are important in beef purchasing decisions, and cluster analysis was used to identify a niche market for branded Hanwoo beef. Factor analysis results indicated that effective labeling and quality assurance of Hanwoo products, the meat quality, price and branding are important to the positioning and marketing of the Hanwoo beef product. Consumers with medium to high income, married and aged between 30 to 39 years, and those that appreciate Hanwoo quality but do not trust the current labeling system are most likely to purchase branded Hanwoo beef and represent a potential niche market, according to cluster analysis results.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.121
GPT teacher head0.318
Teacher spread0.197 · 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