Industrial policy environments and the flourishing of African multinational enterprises
Bibliographic record
Abstract
Abstract Research on African organizations has focused on the influence of environmental factors in organizational effectiveness. However, increasing concerns about challenges in Africa and how they negatively affect organizational outcomes have necessitated leveraging the “positive turn” of organizational scholarship to advance a perspective of how industrial policies can permit Africa-originated multinational enterprises (A-MNEs) to flourish. We propose a multilevel model in which the industrial policy environment comprised of agency and policy development positively impacts A-MNE flourishing, a composite index of human, environmental, and economic flourishing. This relationship is mediated by industrial policies – labor, trade, infrastructure, and resources – and moderated by policy fit, relevance, and timeliness. Overall, we shift the old paradigm of organizational outcomes represented by organizational effectiveness to a new paradigm represented by organizational flourishing. This new paradigm seems more appropriate for Africa, which is bedeviled by unusual challenges that limit effectiveness. We discuss empirical testing of the model and implications for managers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".