Consumer intentions to buy nutrient-rich precooked bean snacks: Does sensory evaluation matter?
Bibliographic record
Abstract
Precooked bean products have the potential of bridging the common bean demand and consumption gap in Kenya. However, sensory evaluation of novel precooked processed products has been inadequate in determining acceptability. This study assessed the sensory evaluation of precooked bean snacks by 269 rural consumers in Machakos County of Kenya. Descriptive results indicated that less than one-quarter (22%) of the consumers were aware of the precooked bean products. The low awareness is a disconnect from the expectations that farming households were probably going to be aware of processed bean products because of their participation in bean value chain. Sensory evaluation showed that 75% of the consumers evaluated the freshness of the bean snacks positively, with about 90% and 63% of them positively assessing the taste of the precooked bean snacks branded Keroma Delicious and Keroma Fruity, respectively. The taste evaluation of Keroma Fruity brand significantly differed depending on age and level of education of the consumer. Similarly,the taste of Keroma Delicious brand also significantly differed by age and educational attainment of consumers. Furthermore, while consumers liked the taste parameters of the products, less than half of them liked the beany flavour of the two products. Results from the binary logit regression model indicated that freshness, sourness, and flavour positively and significantly predicted the probability of future purchases of Keroma Fruity bean snack brands. Consumer intentions to buy Keroma Delicious brand were positively predicted by flavour and marginally by sweetness. To accelerate the consumption of precooked bean products, product development and marketing strategies should recognise the role of sensory attributes in driving acceptability of the bean snacks, deploy processing technologies that retain and enhance sensory attributes, create awareness of the products, and segment the market from a gender lens in order to satisfy the diverse consumer needs and preferences.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".