Investigating Factors that Influence SME’s Choice of Services Rendered by Microfinance Institutions: Evidence from La-Nkwantanang Municipality in Ghana
Bibliographic record
Abstract
There is a recent wave of collapsing Microfinance Institution’s in Ghana which causes SMEs to think critically about the MFI’s they choose to bank with. This has given birth to the investigation of factors that influence SMEs choice of services rendered by microfinance institutions in Ghana. The study adopted the descriptive research design. Stratified random sampling technique was used to select the SMEs for this study and data was collected from a sample of 384 using questionnaires and 279 were returned. The study revealed that electronic banking, convenience and security influences, reputation and legal regulation, interest rate and service provided by the microfinance institution are essential factors that influence the choice of SMEs. The study concludes that the reputation of a business is also essential to its survival, the trust and confidence of the SME can have a direct and profound effect on microfinance institutions. The study recommended that, microfinance institutions should make it a must to obtain all necessary banking licenses from Bank of Ghana before they commence business in order to avoid the embodiment of fear of collapse into potential SME’s who might be willing to transact business with them and also educate those who render services on behalf of the bank. Customer service is very important and should be considered as the first priority of the bank.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".