Consumer Interest in a Natural Designation in Food Choice
Bibliographic record
Abstract
In this study, the objective is to identify consumers’ willingness to consume different foods and the factors that could drive their food preferences. One hundred non-academic staff and students at the University of Alberta in Edmonton, Canada participated in the study. Data were collected using focus group discussions, a survey questionnaire and a contingent valuation exercise. In the focus groups, participants discussed their preferences for traits in livestock and their products, their interest in natural foods and their perceptions regarding naturalness of food in relation to the different types of farming and technologies. In the survey questionnaire, participants were asked about their food consumption habits, perceptions, attitudes and preferences for different foods and technologies, generalized trust in people and trust in groups or institutions responsible for food in Canada among other issues. In the contingent valuation exercise, participants chose the price they were willing to pay for pork with different information about carnosine and omega-3 fatty acids. We find that there is heterogeneity in terms of consumers’ perceptions, attitudes and behaviour regarding natural foods. In summary, the cost of food, concerns about human and environmental impacts and trustworthiness of information on labels are some of the factors that influence participants’ decisions to buy pork labeled as coming from disease resilient or feed efficient pigs or pigs that are higher in a human or animal health component. Although some people accept genetic modification, other participants were concerned about its use in improving disease resilience, feed efficiency and human or animal health component in pigs. Although there are some variations in the results, generalized trust in people, food technology neophobia and concerns about product leanness, country of origin of the product, nutrition content, use of hormones and antibiotics in livestock production and environmental foot print of livestock production are associated with attitudes, perceptions and behaviour regarding natural foods. Participants are willing to pay more for pork chops with more information about carnosine and omega-3 fatty acids as compared to pork chops with less information. In comparison to carnosine, participants are willing to pay more for pork chops with information about omega-3 fatty acids. Generalized trust in people, trust in advocacy groups, natural product interest, frequency of purchasing products with a health claim and knowledge of sodium content in pork that have a health claim are associated with willingness to pay for enhanced carnosine and omega-3 fatty acids in pork.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".