Consumer Preferences for Private Label Brand vs. National Brand Organic Juice and Eggs: A Latent Class Approach
Bibliographic record
Abstract
Many retailers have expanded and diversified their private label food product assortment by offering premium-quality private label food products such as organic products. With price being identified as the major barrier for organic food purchases, private label organic food products could be a suitable and more affordable alternative for many consumers. While numerous studies have examined consumer preferences for organic food, very few organic food studies have incorporated the concept of private labels. This study addresses this research gap by studying consumer preferences and willingness to pay for national brand and private label organic food using a latent class model. Specifically, this study analyzes consumer preferences for organic eggs and orange juice and the effect of national branding versus private label. Findings show heterogeneity in consumer preferences for production method and brand, with three consumer segments being identified based on their preferences for both juice and eggs. For eggs, about half of the consumers prefer private label and organic production, whereas one-quarter clearly prefers organic, and another quarter is indifferent about the brand and the organic production. For orange juice, the majority (75%) prefer the national brand. In addition, one-quarter of the consumers prefers organic juice, and about one-third values both organic and the national brand.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".