The Willingness to Pay for Local, Domestic, and Imported Bundled Fresh Produce by Online Shoppers
Bibliographic record
Abstract
This study applies a Censored Normal Tobit Model on the 2016 survey data from 1,205 online shoppers in the South region of the United States to explain their Willingness To Pay (WTP) for a bundle of fresh produce from different origins. This study indicates that online shoppers are willing to pay $6.91, $6.38, and $5.22 for four pounds of bundled fresh produce that are locally, domestically grown, and imported respectively. We found that income category, interests in online shopping, interest level for local, interest level for organic, and monthly spending on fresh produce have a significant positive impact on the WTP for locally grown fresh produce. Results indicate that being married, high income, interests in online shopping, interests in local produce, interests in organic, and the monthly spending on fresh produce increase the WTP for domestically grown fresh produce, while age and being a female diminishes it. We further found that age, being a female, and interest in the freshness of the produce decrease the WTP for imported produce. Based on the findings from this study, we have suggested a couple of marketing implications and suggestions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".