What determines the performance of SMEs? Evidence from poultry farming in Ghana
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate factors influencing the performance of poultry farmers and examine the potential disparities in performance among gender, formalization and association membership and the source of such disparities if they are established. Design/methodology/approach This study focussed on the poultry farmers located in the Bono Region of Ghana. Data was gathered on a total number of 155 poultry farmers located in the study area for two rounds. This study augmented the traditional C-D function and estimate the determinants of performance using panel estimation technique. The Binder-Oaxaca was used to investigate disparities in performance. Findings The empirical results established a significantly positive relation between association membership, size, as well as formalization of farms and performance. However, there existed a negative relation between the level of education of managers and performance. Also, the discrimination analysis revealed the existence of discrimination stemming from association membership and formalization. Research limitations/implications Although the data gathered was adequate for the purpose of this study, further studies on poultry production in Ghana/Africa can broaden the scope to other constructs which are not captured in this study. Originality/value This study contributes to the growing literature that delves into the poultry industry of the Ghanaian economy. Conducting a further discrimination analysis aside the determining factors make the study unique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 |
| 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".