The Growing Organic Market: Factors that Influence Consumers' Evaluation and Choice
Bibliographic record
Abstract
The growth of organic products has been considerable over the last decade. The annual growth rate of organic food from 1995 to 2007 in the United States has been around 19% (Monier, Hassan, Nichèle, & Simioni, 2009), and in Canada of 20% (Anders & Moeser, 2008); making of this industry a relevant area of study. This research examines whether and to what extent brand and consumer characteristics influence consumers’ attitude toward and choice of organic food products. It considers the impact of brand history (organic versus non-organic brand) and brand credibility (low versus high), as well as the impact of consumer factors, such as scepticism, concern for the environment, price sensitivity, and knowledge. The results of a laboratory study show that the organic brand was better evaluated in terms of quality perceptions than the non-organic brand. This study was also able to demonstrate that, among consumers with high price sensitivity, quality perceptions towards organic products were amplified for the organic brand and diminished for the non-organic brand compared to their low price sensitivity counterparts. Consumer knowledge influences overall evaluation of the brands, being the organic one the best rated; and with respect to concern for environment and scepticism, significant effects were not found. Suggestions for future research and managerial implications are also discussed.
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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 | 0.002 | 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".