Out of Africa: How Africa-Based Studies Matter in Management and Organization Research
Bibliographic record
Abstract
Africa gives rise to unique research questions (and answers) that mainstream M&O research does not address. This conclusion emerges from our integrative evaluation of contextual features typically emphasized in Africa-based studies. In our review, we used a multiphase process that involves systematically searching, content-coding and synthesizing the 191 studies that made up our final sample. By interpreting each study and integrating patterns across all studies, we identified two overarching themes that capture the essence of Africa-based M&O studies (i.e., strong interest in a context shaped by the colonial past, and emerging attention to the social fabric governing economic, social, and political lives in Africa). Our data further reveals that, despite being frequently referenced as major sources of M&O challenges in Africa, neither Africa’s colonial past nor its lingering effects are studied, conceptualized, or leveraged as relevant features of the stories developed. Rather, they are framed as background and/or catch-all features in problem identification and/or discussions of findings. We suggest a research agenda for developing future Africa-based studies that can generate societal, practitioner, policy, and educational impact, in addition to the scholarly impact that is desired.
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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 | 0.136 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.028 | 0.035 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| 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".