Factors influencing customers’ decision to save with microfinance institutions: the case of Advans Cameroon
Bibliographic record
Abstract
Savings are an indispensable resource for Microfinance Institutions (MFIs). They must mobilise sufficient savings to meet their commitments and become independent from grants providers. They must therefore convince customers, which in this paper include the public in general and micro and small-sized enterprises, to entrust their savings. This requires an understanding of these customers’ characteristics, as well as their needs and expectations. This leads us to the following research question: what are the factors affecting customers’ decision to save with a MFI? The analyses conducted with Advans Cameroon customers show that the Customer life cycle, the MFI characteristics and the MFI–Customer relationship have a direct influence on the decision to save. Specifically, age and revenue have a positive influence on savings. Conversely, the number of person in charge negatively affects savings. An increase in the number of branches leads to an increase in savings, while services quality are very important.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".