Factors Influencing Risk Management Decision of Small and Medium Scale Enterprises in Ghana
Bibliographic record
Abstract
This research seeks to study the factors that enhance or preclude owners of SMEs in Ghana in making risk management decisions. The study was conducted with managers of SMEs in four regions in Ghana. The researchers adopted a quantitative approach and employed STATA 10 and SPSS version 20 in the analysis. Stratified and simple random sampling techniques were used to select the sample units. The probit model was used in the analysis of data. A total of 447 SMEs were sampled for the study, with at least 111 from each of the selected regions. The probit results show that the demographic factors indicate a positive influence on the likelihood that managers will take risk management decisions. All of the business related demographic factors are significant at various levels and positive, except for risk-loving. The economically related factors, such as the estimated amount at risk, the estimated cost of risk management and the estimated total monthly income after tax all have a positive influence on risk management decision making. However, government and tax policies are perceived to negatively influence risk management decisions by managers. We recommend that institutions working closely with SMEs acquire the expertise to train the managers of SMEs on risk management practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".