Governance and Management of a Savings and Credit Cooperative: The Successful Example of a Haitian SACCO
Bibliographic record
Abstract
This research aims to understand the different success factors of a savings and credit cooperative (SACCO) through the study of the managerial dimensions and its style of governance. Semi-directed interviews were conducted with three directors who are founding members of the Haitian ‘MEN ALE MEN VINI (MAMEV)’ cooperative. NVIVO version 11 software was used as a tool for coding and data analysis. The interviews concluded that the success of the cooperative is linked to the personal attributes of the various directors, effective management, good governance and an efficient decision-making system. This research also showed that the success of cooperatives is partly related to the notoriety, leadership, willingness and level of commitment of its directors. It is also related to the good governance manifested in the participatory management and decision-making, the transparency of the organization and the quality of the work of the cooperatives. Also, the research has shown that anchoring the cooperative in the community, bringing the population closer to the cooperative and offering services adapted to the community’s needs have a positive influence on the membership and make the cooperative more sustainable. Finally, the research showed that the adoption of an effective governance mode helped to ensure the sustainability, viability, modernity, efficiency and social, financial and economic soundness of the cooperative. The experience of this SACCO not only helped the members to counteract the myth of incapacity that prevailed in Haitian community at the time but also improved the living conditions of the members as well as those of community members. The research provides a novel contribution to the resilient and sustainable model of governing and managing SACCOs. It also presents a new model of the Success Keys of SACCOs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".