Creating demand for foreign brands in a ‘home run’ market: tobacco company tactics in South Korea following market liberalisation
Bibliographic record
Abstract
OBJECTIVE: To analyse the tactics transnational tobacco companies (TTCs) used to increase market share in South Korea after market liberalisation in 1988, and the subsequent impact of TTCs' activities on the domestic industry and ultimately public health. METHODS: Internal tobacco industry documents were searched iteratively and analysed by keyword related to strategies for increasing market share in Korea since liberalisation. RESULTS: Following market liberalisation, TTCs faced entrenched cultural and institutional barriers in Korea which hindered increased sales of cigarette imports. TTCs identified population groups more favourably inclined towards imported brands, developed new distribution channels and used promotional activities targeting these groups. The growth in market share by TTCs suggests that these activities were successful at challenging the Korea Tobacco & Ginseng Corporation (KTGC) monopoly. In response, KTGC shifted to a proactive marketing approach and adopted strategies similar to TTCs. This, in turn, made the Korean market highly competitive. Findings show that, after market liberalisation, there was an upward trend in cigarette consumption and smoking prevalence among the targeted population groups, notably youth and young women. CONCLUSIONS: Governments engaging in trade negotiations that may lead to the opening of domestic tobacco markets need a fuller understanding of previous industry activities for expanding into emerging markets as well as how the domestic industry can change accordingly. To protect public health, the adoption of comprehensive tobacco control measures, guided by WHO Framework Convention on Tobacco Control, are needed as part of such negotiations.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".