EFFECT OF PORTFOLIO MANAGEMENT PRACTICES ON THE PERFORMANCE OF SOME SELECTED SMALL AND MEDIUM ENTERPRISES (SMEs) IN NIGER STATE, NIGERIA
Bibliographic record
Abstract
This study examined the effect of portfolio management practices on the performance of small and medium enterprises (SMEs) in Niger State, Nigeria. Portfolio management practices are proxies by corporate risk management, diversification, and security choice. Questionnaires were distributed to the whole SMEs in Kontagora portfolio platform. The study adopted a cross-sectional survey method with Ninety-Two (92) SMEs that has data whereas Twenty-Nine (29) firms were left out from the population of 121 SMEs because they did not have data. The data collected from 89 usable copies of questionnaires were subjected to various statistical analyses using SPSS23 and SmartPLS3.0. The results of this study show that CRM and Diversification have insignificant effects on SMEs performance whereas security choice has a positive and significant effect on the performance of SMEs. Therefore, the study concludes that firms should pay more attention to security choice because the variable has a positive and significant influence in explaining the variability of SMEs performance. Finally, a suggestion for future directions was made accordingly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".