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Record W2259164335

일본과 미국의 인삼시장 세분화 연구

2006· article· ko· W2259164335 on OpenAlexaboutno aff
박성훈

Bibliographic record

Venue식품유통연구 · 2006
Typearticle
Languageko
FieldBiochemistry, Genetics and Molecular Biology
TopicGinseng Biological Effects and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsGinsengBusinessMarketingChinaPromotion (chess)Order (exchange)PurchasingAdvertisingPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The export volume of Korean Ginseng products has been diminishing since 1990 because of their weakened international competitiveness. Nowadays, Korean Ginseng products are faced with lower price competitiveness and ineffectiveness of marketing strategies comparing with other Ginseng products from competing countries in the foreign markets. Furthermore, the superiority of processing technologies is being threatened by other developing countries like China and Canada. Thus, it is required to make a dramatic turning point for the export promotion of Korean Ginseng products. The objectives of this research are to examine the existing export strategies of Ginseng products, and to make some new export marketing strategies in order to promote the export of Korean Ginseng products. To achieve these objectives, ① the structures and trends of Ginseng markets of Japan and U.S.A. were analysed, ② questionnaire surveys on the foreigners` consumption of Ginseng products in each country were conducted, and finally, ③ market segmentation and making new export marketing strategies were implemented. In conclusion, the marketing basis for the export of Korean Ginseng products like appearance of Ginseng root, cultivation years, and the traditional fame have to be substituted for the emphasizing of functional differences, various products line-up, and modem senses. And it is required to develope the practices of consumer-oriented target marketing in accordance with the widespread selective purchasing behavior of foreign consumers.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score0.964

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 head0.006
GPT teacher head0.234
Teacher spread0.228 · 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 designBench or experimental
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

Citations0
Published2006
Admission routes1
Has abstractyes

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