Pricing strategies for organic vegetables based on Indonesian consumer willingness to pay
Bibliographic record
Abstract
An awareness of the dangers of chemicals contained in food could potentially have led to a significant increase in demand for organic food in Indonesia.Yet the demand for organic products remains relatively low.This could be attributed to high price, limited product choice, consumer distrust toward organic products, satisfaction with conventional food, or a lack of consumer perceived differences in the taste of organic products.The purpose of this article is threefold.First, to analyze the factors that influence the Indonesian consumers' willingness to pay (WTP) for several types of organic vegetables.Second, to calculate the price increase incurred by consumers of organic vegetables.Third, to determine a recommended pricing strategy based on consumers' WTP for certain common organic vegetables, including broccoli, cauliflower, cabbage, pak choi, lettuce, and carrots.Using an accidental sampling technique, samples were derived from 154 respondents living in urban areas.Descriptive analysis, crosstab, logistic regression analysis and the contingent valuation method were all employed.Findings suggest that the variables of age and income significantly affect WTP.The highest percentage of WTP was for cabbage, followed by carrots, broccoli, cauliflower, pak choy, and lettuce.The recommended pricing strategy is the default value pricing method.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".