Consumer acceptance of a new traceability technology: A discrete choice application to ginseng
Bibliographic record
Abstract
A new technology has been developed for enhanced authenticity and traceability of food and natural health products. While the paths for authorization through Health Canada are clearly laid out, consumer response is uncertain. This thesis examines consumer acceptance of this new technology and to what extent it might interact with currently existing signals of traceability in the marketplace, such as brand, manufacturers' guarantees, or place of production labeling. It makes use of two main research stages: qualitative, which consists of the literature review and focus group interviews, and quantitative, which consists of a consumer survey with a discrete choice experiment applied to a particular ginseng product. Results of conditional logit estimations, conducted with choice experiment data from an Ontario panel, reveal that consumers value molecular tags as an acceptable signal of traceability and authenticity to ensure safety and quality of ginseng products. Canadian consumers treat molecular tags independently from other existing authenticity signals. Results indicate that although molecular tag carries a positive premium ($3.43 per bottle), this is not the largest premium among attributes examined. Consumers are most willing to pay for ginseng products labeled with a Canadian guarantee, followed by products labelled 'product of Canada', then by local brand and molecular tag, respectively. Therefore, it would be possible to charge premiums for the information carried by molecular tagging technology in ginseng products.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".