Country of Origin Effect in a Lithuanian Market of Vitamins and Dietary Supplements
Bibliographic record
Abstract
The country of origin (COO)—as an extrinsic cue of a product—influences consumer’s perception of a product and his/her purchase decisions. The COO effect manifests in a different way in various geographical, differently developed economics and different products’ markets. The article analyses the problem how the COO effect manifests in the Lithuanian market of vitamins dietary supplements. This study aims to disclose consumers’ perceptions on vitamins and dietary supplements produced in sixteen countries and highlight the importance of the COO in decision-making when purchasing vitamins and dietary supplements. The research method applied is consumer survey. The consumers’ opinion about the quality, price and safety of vitamins and dietary supplements produced in Lithuania and fifteen foreign countries have been surveyed. The importance of the COO when making a purchase decision in the market of vitamins and dietary supplements is disclosed. The study results indicate that the product’s COO is the factor of moderate importance in the product group researched. The vitamins and dietary supplements produced in developed economies are perceived as the ones of the best quality and most secure to use. The quality and safety of the vitamins and dietary products produced in China, the Ukraine, Poland and India have been rated the worst. The quality, prices and safety of the vitamins and dietary supplements produced in Lithuania are perceived similarly as the quality and safety of the preparations produced in developed countries and higher than the quality and safety of the preparations produced in neighboring countries.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".