Managing Internationalization Strategies for Africa: Addressing Marginalization through Dependency T
Bibliographic record
Abstract
Africa offers unparalleled opportunities for international business expansion, yet its markets are deeply influenced by historical marginalization and systemic inequities. This paper underscores the critical need for businesses entering African markets to adopt strategies informed by dependency theory and intersectionality. Dependency theory provides insights into structural economic imbalances, while intersectionality reveals how overlapping social identities shape consumer experiences. These frameworks enable firms to address marginalization through inclusive practices, equitable partnerships, and culturally relevant engagement. Practical tools such as geospatial data analysis, participatory action research, and leadership development are explored to guide businesses in fostering sustainable and ethical market engagement. This exploratory study highlights the potential for successful internationalization into the African market. It emphasizes that successful internationalization in Africa demands a paradigm shift toward inclusivity, innovation, and social equity, ensuring mutual benefits for businesses and marginalized communities.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".