A Synthetic Indicator of the Quality of Support for Businesses in Burkina-Faso, Cameroon, and Ghana
Bibliographic record
Abstract
Abstract This paper proposes a synthetic indicator of the quality of support for companies and identifies the factors that can contribute towards improving the quality of such support in three countries (i.e., Burkina-Faso, Cameroon, and Ghana). The study uses static mechanics and applies techniques of factor analysis. A principal component analysis is performed on the data collected from 80 business support structures in the sampled countries. After constructing the indicators, correlates are provided on how the constructed indicators are linked to the objectives of sustainable development. Our results are robust after controlling for variables relating to the general characteristics of the support structure. The findings are consistent with the position that taking sustainable development objectives into account in business support practices would significantly improve business performance in sampled countries and, by extension, in sub-Saharan Africa. The originality of the study stems from the fact that it considers specific sustainable development goals and assesses their contribution to improving the quality of support for companies, a research area that has not been investigated hitherto by the extant literature. Implications for all stakeholders in the entrepreneurial ecosystem and future research directions are discussed.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".