Pre-Entry Experience, Postentry Adaptations, and Internationalization in the African Mobile Telecommunications Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it