Success Factors of Upgrade Programs of SMEs in a Changing Environment, Resources Dependency Perspective: The Case of Algeria
Bibliographic record
Abstract
The SME sector is exposed to an arduous and fierce competition to the disadvantage of enterprises that are actually non-competitive. Therefore, the problems of SMEs in developing countries are rich and complex. Algeria isn't immune to these difficult conditions; the SME is exposed to an open and intense environment. Public authorities have developed and have been implementing upgrade programs to promote the SME sector. Thus, the study seeks to find the key success factors of upgrade programs, and explores the dimension of adaptation of SMEs in developing and emerging countries, Algeria is a practical case. The study is theoretical. It aims to determine a theoretical model on the success factors of upgrade programs. The research operates a diverse literature review in emerging and developing countries. To explain this research, we will use the theory of resources dependency. While exploring the literature on upgrade programs in emerging and developing countries; our research was able to theoretically identify different success factors of the upgrade programs. In addition, research has managed to develop a theoretical model on the success factors of the upgrade programs.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".