Pre-Entry Experience, Postentry Adaptations, and Internationalization in the African Mobile Telecommunications Industry
Bibliographic record
Abstract
We study the evolution of the African mobile telecommunications industry from its effective beginning and explore the sources of ownership advantages among indigenous firms, by assembling historical qualitative and quantitative firm-level data. Our historical qualitative findings suggest that a few start-ups gained industry-specific knowledge through their pre-entry experience, directed their postentry development of capabilities toward adaptations to challenging market and operational conditions, and leveraged their adaptive capabilities to enter and compete in other African countries. Using our quantitative panel data, we show that these firms successfully internationalized across the continent. In particular, compared with other start-ups, they had higher rates of foreign entry in African countries that had relatively weaker rule of law, and greater market reach in African countries that had relatively larger low-income consumer segments. These patterns corroborate that their capabilities for overcoming the industry’s challenging market and operational conditions were their key ownership advantages. Through our triangulated analysis, we show that inherited industry knowledge provides a foundation for postentry capability development, and entrepreneurial leadership guides this process to create ownership advantages for regional internationalization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".