Food Authenticity, Technology and Consumer Acceptance
Bibliographic record
Abstract
Traceability and authenticity issues have gained increasing prominence in food markets and create both opportunities and challenges for the food industry in providing credible information to consumers. Internal molecular tagging is an emerging technology with the potential to deliver traceability and authenticity assurances. A key question for the food industry in adopting new technologies is consumer acceptance. This paper explores consumer attitudes toward traceability and authenticity and the role of information in affecting consumer acceptance of new technologies, using molecular tagging as an example. Data were gathered from an online survey conducted in Canada in December 2010. To determine whether product-specific effects exist, two versions of the survey were used, focusing on salami and on apple juice. In a discrete choice experiment respondents were presented with choice sets describing an apple juice (salami) product containing different combinations of four attributes: traceability technology (molecular tag Vs RFID), price, brand, country of origin. Of particular interest was the effect of information on consumers’ choices. Therefore, respondents were randomly assigned to one of four information treatments: positive information on molecular tagging technology or further information on the issue of food authenticity and adulteration, or a combination of both. The control group was provided with neutral information on the technology and no additional information. Results from Conditional Logit and Random Parameter Logit models reveal that initial consumer acceptance of the technology is low, however, information matters. Highlighting the problems of adulteration reduces resistance more effectively than providing positive technology information, and the effects appear to be product specific across a juice product versus a processed meat product. Other proxy signals (country of origin, brand), resonate strongly with consumers and tended to have a larger impact on willingness-to-pay.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".