The use of performance measures: case studies from the microfinance sector in Kenya
Bibliographic record
Abstract
Purpose The intent of microfinance institutes (MFIs) in developing countries is to provide loans to very poor people in order to help them transform their lives. MFIs tend to receive subsidies; sustainability is being sought to free MFIs from non‐market dependencies. Sustainability is expected to be achieved with “best practices,” of which management with performance measures is a component. The purpose of this paper is to examine the use of performance measures by three Kenyan MFIs, which are classified as formal and client based, and likely to use rational and explicit performance measures. Clients in these MFIs are placed into self‐help groups with two responsibilities: to provide mutual support and advice to the borrowing client; and to provide the MFI with a guarantee that loans of group members will be repaid. Design/methodology/approach Based on a review of the economics and performance measurement systems literatures, research questions were developed along with an interview guide. Case studies were used to administer an interview guide which was distributed to the respondents prior to the face‐to‐face interviews. Findings The study concludes that MFIs have relatively well‐developed performance measures that support their particular businesses. There was a good balance between the use of financial and non‐financial performance measures. However, output measures were more commonly used than process measures. The nature of the MFIs suggests the importance of performance measurement. The managers of the MFIs are concerned with performance measurement, as expected within a bureaucracy, and a top‐down demand is present. In addition, group members or clients are interested in performance measurement as each member guarantees the loans of all fellow group members who have loans with the MFI. Thus, the customers exert a bottom‐up demand for performance measurement. Originality/value The findings support the view that performance measures are a means for managing MFIs and are a likely requirement for sustainability. Furthermore, the findings have identified performance measures (similar to those at banks) that are appropriate for the three MFIs in Kenya. The findings are important since the identified performance measures may be adopted by other evolving MFIs in this relatively new sector. In addition, the findings contribute to a better understanding of the genesis of the less popular results and determinants performance measurement framework of Fitzgerald et al.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".