Performance of microenterprises in Ghana: a resource‐based view
Bibliographic record
Abstract
Purpose The purpose of this paper is to assess the financial performance of microenterprises in Ghana by applying the resource‐based theory of the firm. Specifically, it is tested that if firm‐specific resources dominate sector and market‐wide effects in explaining microenterprise performance, as suggested by the resource‐based theory. Design/methodology/approach The relevant literature for both microenterprise performance and the resource‐based theory is reviewed. Data from the 1998/1999 Ghana Living Standards Survey are analysed using ordinary least squares, followed by robustness checks. Findings Factors embodied in firm‐specific resources jointly impact enterprise performance. However, sector/market factors also play a role, suggesting that the interaction between microenterprise, sector, and market factors helps explain enterprise performance. Research limitations/implications All the constructs of the resource‐based theory cannot be tested due to data limitations. Originality/value Small enterprises play a key role in promoting developing country growth, but no study has evaluated microenterprise performance using this particular data set and the resource‐based theory of the firm. Future research should focus on collecting data to further validate this theory.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".