Market Orientation and the Performance of Small and Medium-Sized Manufacturing Enterprises in the Accra Metropolis
Bibliographic record
Abstract
The importance of small and medium-sized manufacturing enterprises as an engine of economic growth has been widely recognized. However, despite their invaluable contribution in the Ghanaian economy, their performance has not been impressive due to the orientation of their marketing strategies. This study, therefore, sought to investigate the relationship between market orientation and the performance of small and medium-sized manufacturing enterprises in the Accra metropolis of Ghana. Given the purpose and nature of this study where most of the analyses were quantitative in nature, quantitative research approach was deemed the most appropriate and, therefore, adopted. The descriptive and correlational study designs, too, were adopted for this study. Simple random sampling technique, specifically lottery method, was used to select 346 small and medium manufacturing enterprises in the Accra metropolis. Regression analysis was used to test the hypotheses postulated. It was observed from the findings that positive relationship exists between market orientation and small and medium-sized manufacturing enterprises performance. This means that market orientation contribute positively to the well being of small and medium-sized manufacturing enterprises. The study recommends that policy makers as well as National Board for Small Scale Industries should sensitize owners/managers of small and medium-sized manufacturing enterprises about the importance of employing market orientation practices in their business operations.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".