The Role of Sensory Experiences and Information on the Willingness to Pay for Organic Wheat Bread
Bibliographic record
Abstract
This study examined the size and the determinants of the price premium a sample of Edmonton-area consumers was willing to pay for organic wheat bread. The development of these premiums included consideration of providing information on health or environmental advantages of organic production and consideration of sensory (taste) acceptance. To do this conventional and organic wheat was grown under similar conditions and milled and baked into 60% whole wheat bread under identical conditions. Samples of these breads were presented to consumers for sensory acceptance and a survey of their attitudes, behaviours and characteristics. The survey included a closed-ended contingent valuation question to examine consumers' willingness-to-pay (WTP) premiums for the organic bread. A trained sensory panel was used to quantify differences in the sensory characteristics of the two breads. The results suggest that in the absence of taste information respondents' WTP when environmental information was provided was greater than WTP when health information was given. When sensory taste information was included, however, the WTP estimates under the health information treatment were about twice those under the environmental information. The trained sensory panel observed the two treatments of bread to differ in texture but not in flavor, aroma or colour characteristics. The implications of these findings are that successful marketing of organic foods depends on circumstance - in the absence of sensory experience the environmental benefits of organic production appear to be more appealing than potential health benefits. Health claims are only viewed positively when the product can be tasted. Sensory scientists should use caution in interpreting WTP estimates in that they must take into account the type of information and placement of WTP questions in their study designs. We also found that sensory variables when included in the regression model had statistically significant effects on WTP estimates. The economic significance of including these variables in the logit model was limited, however. Nevertheless we feel that under some circumstances economists may find it advantageous to include sensory information in their models of food demand.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".