Bibliographic record
Abstract
Purpose The United Nations General Assembly adopted 2005 as the Year of Microcredit, recognizing the link between inclusive financial systems and the economic development of those most at need. Microcredit, or more broadly microfinance (MF) has a role to play in facilitating growth through the provision of credit to entrepreneurs. Association Al Amana for the Promotion of Micro‐Enterprises Morocco (Al Amana) is an unusually successful microfinance institution (MFI) registered as a non‐profit organization in Morocco. Starting in 1997, with capital provided by USAID, it has grown to become the leading MFI within Morocco and the broader Middle East and North Africa (MENA) region. It has seen rapid growth in clients and assets while also reaching a level of financial sustainability uncommon among MFIs. Why has Al Amana been so successful? This paper will seek to examine this question, highlighting key internal and external factors that have contributed to this organization's success. Design/methodology/approach Further, this analysis will examine Al Amana within its regional enabling environment to highlight those critical issues within an entrepreneurial finance climate that are conducive to MF lending. Additional analysis of Al Amana includes a review of their portfolio, key financial indicators, and other bank strategies that have been instrumental in their financial growth and success. Lastly, the analysis will conclude with a discussion of the key factors that will become important to the sustainability and growth of this MFI as it serves the needs of entrepreneurs in Morocco. The methodology for the research consisted of a literature review of MFI activity in Morocco and the MENA region, an examination of key elements within the enabling environment, and a financial review of Al Amana and its key financial results. Findings The results of the research provide a case study of a successful MFI that is contributing to the development of entrepreneurs in its region. It will also highlight the specific organizational factors as well as key elements in the enabling environment that have facilitated the success of Al Amana. Originality/value The paper highlights the success of Al Amana in Morocco and presents some of the challenges facing the organization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".