Analysis of Factors Influencing Financial Performance of Savings and Credit Co-operative Societies in Lesotho: Evidence From Maseru District
Bibliographic record
Abstract
SACCOS play a major role of providing financial access to poor people who are excluded from the services of Formal Financial Institutions (FFIs). However, they also face number of challenges which may affect their performance. Most of the previous studies in the area of SACCOS did not concentrate on their performance. The aim of this study therefore was to assess performance of SACCOS in Maseru District, Lesotho. The study adopted a cross-sectional research design where data were collected at one point in time. A sample size of 369 respondents was computed by the use of formula by Yamane (1967). Respondents in the sample were selected by using simple random sampling technique. However, respondents from individual SACCOS were proportional to the total number of members in particular SACCOS. This was done in order to make the sample representative of all SACCOS in the study area. Analyses of data were done by using different techniques which include: mathematical equations (i to vii); different financial ratios; tables; graphs; bar charts and other types of descriptive statistics like mode and percentages. It was found that socio economic characteristics of members were supportive to financial performance of the SACCOS. Furthermore, SACCOS in the study area achieved high performance in terms of ratios of members’ capital; loan delinquency; volumes of savings in the SACCOS; and growth of total assets. On the other hand, the SACCOS realised poor financial performance in terms of ratio of fixed assets to total assets; and share capital owned by members.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".