Business Development Frameworks for Establishing Innovative Born-Global Firms in Nigeria and Sub-Sahara Africa
Bibliographic record
Abstract
This conceptual paper explores different approaches for establishing born-global firms (BGFs) in developed and developing countries, with a special focus on Nigeria and Sub-Sahara Africa. It reviews the key constructs and frameworks that underpin new business development in born-global firms. Examples of these constructs are business development, dynamic capabilities, innovation, collaboration, entrepreneurship, and organisational learning. The research is important because of the relative lack of BGFs (Google, Amazon, Alibaba, and Facebook, for example) in Sub-Sahara Africa, compared to other parts of the world. Moreover, the frameworks for BGF new business development can be applied in subtly different ways in developed and developing country contexts. For example, BGFs in developed countries focus on niche products and services with breakthrough innovation, whilst those in developing countries, because of limited resources and capabilities, focus on underserved and mass markets, which do not require high level resources and capabilities. Realistic hypothetical examples of BGFs which directly underpin Nigerian and Sub-Sahara African higher education and economic development are used to illustrate the BGF business development constructs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".