The “People” Factor in Cooperatives: An Analysis of Members' Attitudes and Behavior
Bibliographic record
Abstract
Members' attitudes and perceptions play a significant role in their behavior toward their organization and the performance of such organizations. This study examines the role of these “people” factors in a sample of fruit and vegetable growers' cooperatives in the Mid‐Atlantic United States. Although the Theory of Planned Behavior is used as the framework of analysis, the objective of this study was not to test the theory. Study findings provide additional insights into how cooperative members' beliefs and knowledge may shape their attitudes and the consequent behavior. Given the gradual decline of both cooperative memberships and the number of cooperatives in the United States and other countries, a good understanding of members' attitudes and behaviors is necessary because a cooperative's success may depend on it. Les attitudes et les perceptions des membres d'une coopérative jouent un rôle important dans le comportement envers l'organisme et la performance de ce dernier. La présente étude a examiné le rôle de ces facteurs ≪humains≫ dans un échantillon de coopératives de producteurs de fruits et légumes dans les États du centre du littoral atlantique. Bien que la théorie du comportement axé sur un objectif ait été utilisée comme cadre d'analyse, l'objectif de l'étude ne consistait pas à tester la théorie. Les résultats de l'étude ont fourni des éclaircissements supplémentaires sur la façon dont les croyances et les connaissances des membres d'une coopérative peuvent façonner leurs attitudes et leur comportement subséquent. Compte tenu du déclin progressif du nombre de membres de coopérative et du nombre de coopératives aux États‐Unis et dans d'autres pays, il est important de bien comprendre les attitudes et les comportements des membres puisque le succès d'une coopérative peut en dépendre .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".