Decision making process of community organic food consumers: an exploratory study
Bibliographic record
Abstract
Purpose Substantial changes in the organic food sector and recent studies on the Canadian organic food market are showing promising trends. However, community organic food markets are different from organic food mainstream markets. In a domain growing in theoretical and practical importance, the main objective of this paper is to develop an understanding and analysis of “community organic food market”. Design/methodology/approach Focus groups were conducted and data collected were analyzed using content analysis. Findings Five main themes emerged and brought contributions in terms of: organic food definition and recognition, organic food consumers' motivations, trust with regard to organic food, labeling and certification process, organic food distribution channels, and the proposition of a conceptual model of decision making with regard to organic food consumers in small communities. Research limitations/implications Data collection was conducted in only one small community location and should be extended to other small communities as well as urban city centers. Practical implications This study provides some insights to managers in terms of the market mix and target marketing of organic food niche markets. Originality/value The paper explores the difference between an organic food mainstream market and an organic food niche/community market using a consumer behaviour perspective.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".