Affective Commitment in Co-operative Organizations: What Makes Members Want to Stay?
Bibliographic record
Abstract
Affective member commitment is seen as an essential ingredient for sustainable and successful co-operation. It provides co-operatives with flexibility and helps to alleviate the problems of free-riding, property rights, and horizon differences. The importance of affective commitment is highlighted as co-operatives face the challenges of an increasingly globalized business environment. Co-operatives need to promote their members’ desire to remain as members and active users of the organization they own. In this paper, we review extant co-operative literature on members’ affective commitment and develop proposals on the sources of this type of commitment within a co-operative context. Hence, the focus of this paper is on the sources rather than the outcomes of affective commitment. Affective commitment is explained through three theoretical frameworks, namely organizational identification, organization-based self-esteem, and psychological ownership. Linkages are identified and a theoretical model is presented. Our work creates value for future research and practice of co-operation by summarizing extant knowledge on the sources of affective commitment, specifying previously unspecified relationships, and identifying avenues for future research.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".