Study of the growth opportunities and barriers facing engineering and technology-based SMEs in emerging economies: cases from Egypt
Bibliographic record
Abstract
The TFM should provide a review of the evaluation of two SMEs’ experiences doing business in Egypt. The experiences of the two SMEs will demonstrate the business opportunities and obstacles that engineering and technology-based SMEs face in emerging economies. The two chosen SMEs are different in terms of sector and nationality to provide a good perspective. The first SME is an Egyptian company working in the renewable energy that has a success story collaborating in Egyptian governmental project with Spanish, Chinese and Saudi partners. The second SME is a Canadian company working in the construction sector that is entering the Egyptian market to provide a new construction technology solution using wood and clay as a construction material instead of steel reinforced concrete. Both companies not only share the size or the status of start-up growing to medium but also share the first mover advantage as they both introducing new technologies to the Egyptian market. The TFM will attempt to identify, evaluate and analyse the opportunities and barriers both companies face doing business in Egypt either with public or private sector. The TFM shall provide a review of the evaluation of the two cases through conducting interviews with the companies CEOs, the executive teams, and the clients. At least one specific project for each of the two SMEs will be analysed and reviewed as a case study. The objective is to share the analysis of both companies experiences so future engineering and technology companies, either from the same country or from abroad, interested in working in emerging economies in general or in Egypt specifically can better manage their risks.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".