Consumer demand heterogeneity and valuation of value-added pulse products: a case of precooked beans in Uganda
Bibliographic record
Abstract
This study investigated consumer demand heterogeneity and valuation of a processed bean product—“precooked beans” with substantially reduced cooking time. Common bean is the most important source of protein for low- and middle-income households in Uganda. Its consumption is, however, constrained by long cooking time, high cooking energy and water requirements. As consumption dynamics change due to a rapid expansion of urban populations, rising incomes and high costs of energy, demand for fast-cooking processed foods is rising. An affordable, on-the-shelf bean product that requires less time, fuel and water to cook is thus inevitable. A choice experiment was used to elicit consumer choices and willingness to pay for precooked beans. Data used were collected from 558 households from urban, peri-urban and rural parts of central Uganda and analyzed using a latent class model which is suitable when consumer preferences for product attributes are heterogeneous. Study results revealed three homogeneous consumer segments with one accounting for 44.3% comprising precooked bean enthusiasts. Consumers derive high utility from a processed bean product with improved nutrition quality, reduced cooking time and hence save water and fuel. The demand for the processed bean is driven by cost saving and preference for convenience, which are reflected in willingness to pay a premium to consume it. Heterogeneity in attribute demand is explained by sex and education of the respondents, volumes of beans consumed, location and sufficiency in own bean supply. Our findings suggest that exploring avenues for nutritionally enhancing while optimizing processing protocols to make precooked beans affordable will increase consumer demand. These results have implications for market targeting, product design and pricing of precooked beans.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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 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".