Consumers’ preference and willingness to pay for enriched snack product traits in Shashamane and Hawassa cities, Ethiopia
Bibliographic record
Abstract
Abstract This study investigated the consumers’ preference and willingness to pay for enriched snack product traits. Using a choice experiment framework, we generated 8400 observations from a random sample of 700 respondents in Shashamanne and Hawassa city administrations. Taste parameters and heterogeneities were estimated using the generalized multinomial logit (G-MNL) model. The results reveal nutrition and/or health claim labeling is the most influential trait on the consumers’ decision to buy enriched snack products followed by mango flavor, sorghum chickpea main ingredient, price, and mixed shape. The WTP estimates show that consumers are willing to pay a premium for nutrition and/or health claim labeling equal to 1.43, 1.6, and 8.03 times higher than for a change in the flavor of the products from tomato to mango, the improvement of main ingredients to sorghum chickpea, and change of the product shape from spherical to mixed shape, respectively. The heterogeneities (variations) around the mean taste parameters were partially explained by sex, family size, and educational levels of the respondents. Generally, the consumers in the study areas prefer buying sorghum chickpea main ingredients, a combination of different shapes (mixed shape), mango flavored, and nutrition and/or health claim-labeled enriched snack products. Therefore, we suggest designing and implementing innovative ways of promoting snack products to urban communities with a deliberate focus on these traits to create a snack with the best combination. Given the high literacy of urban consumers and influential role of nutrition and/or health claim labeling trait on consumers’ decision, the trait-based promotion and marketing of the products constitute the right strategy.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".