Eyes on Africa: redefining managerial focus for untapped opportunities in Sub-saharan Africa
Bibliographic record
Abstract
Sub-Saharan Africa (SSA) presents promising yet often overlooked opportunities for international business. This study investigates how senior managers from developed countries, particularly Canada, cognitively recognize or dismiss opportunities in geographically and institutionally distant markets like SSA. We draw on in-depth case studies of seven Canadian companies internationalizing into SSA countries. Our framework rests on an attention-based view (ABV), which we refine and extend by emphasizing cognitive processes that precede the noticing of opportunity-relevant information. We offer nuanced insights into this pre-noticing phase, showing how managers’ initial expectations and mental categorization of SSA shape their later attention to market cues. We also highlight how the salience and perceived credibility of information sources, including diaspora networks, can influence whether these cues are acted upon. Our findings suggest that persistent SSA disengagement by Western firms could stem from SSA-specific attentional biases or failures, rather than just concerns about informational or institutional voids. The evidence-based insights add to a managerial cognition view of firm internationalization. They also help reframe conventional entry barriers, such as information problems linked to foreignness or outsidership, as cognitively constructed. Meanwhile, senior managers and policymakers gain insights into cognitive filters that can hamper business expansion into historically stigmatized but high-potential markets.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".