Financial Management Practices Among Micro Enterprises and their Implications for Loan Repayment: A Case of Solidarity Group Lending of DCB Commercial Bank in Dar es Salaam
Bibliographic record
Abstract
The aim of this study was to determine the implications of financial management practices among micro enterprises for loan repayment. The study was confined to Solidarity Group Lending (SGL) customers of DCB Commercial Bank Plc (DCB). Specific objectives included: to identify common practices of managing finances among SGL customers; to determine the extent to which the commonly identified financial management practices influence loan repayment; and to find out challenges facing SGL customers during loan repayment in DCB. A case study research design and cluster sampling were used while data were collected using questionnaires from 80 respondents. Data were analyzed using multiple regressions, and simple descriptive statistics of frequencies, percentages, mean, and range. Results indicate that the common practices of managing finances among the respondents were cash holding 73.8% (n= 59) and short term investments 38.8% (n=31). Regression results revealed that about 70% of variations in ease of loan repayment is influenced by cash holding and short term investment techniques at p=0.000 level of significance (i.e. R = 0.841, R2 = 0.707 and p < 0.05). Key challenges of loan repayment among the respondents were: losses from business (82.6%), payment delays from debtors (67.5%), and difficulty in managing group members to attend their respective loan centers (72.6%). The study recommends that SGL customers need to be educated and sensitized on various financial management techniques and their implications so that they select appropriate techniques in managing profitability and liquidity in their businesses to enhance smooth loan repayment.
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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 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".