International business research challenges in Africa
Bibliographic record
Abstract
Purpose This paper aims to explore the challenges researchers in/on Africa face when conducting research on the continent. It examines the reasons behind Africans’ relatively limited contribution to the business literature in the global sphere and why not culturally sensitive and nuanced research on Africa is spreading unchallenged. Design/methodology/approach The study combines knowledge creation and institutional theories to explain why African business scholars struggle in researching the continent and in contributing significantly to global knowledge creation. It also explores the debate about why Africa’s narratives in business seem dominated by not culturally sensitive and nuanced voices and approaches. It uses a participant observation method. Findings The study found that African scholars have not yet contributed significantly to global knowledge creation because of Africa’s institutional weaknesses and lack of government support for research, coupled with challenges at the interviewing, organizational and scholars’ levels. The study points to the specificities of the continent as well as to African interviewees’ particularities and the type of interactions with the researchers. The paper proposes new avenues to address those multilevel challenges and offers key lessons for future studies. Originality/value To the best of the authors’ knowledge, this study is the first to systematically investigate the fundamental reasons behind business research challenges in/on Africa from knowledge creation and institutional standpoints. This study also contributes to the growing debate on Africans’ meager contribution to business literature as well as the controversy regarding culturally sensitive vs not culturally sensitive knowledge creation on Africa. Finally, it proposes avenues to understanding and overcoming those challenges.
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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 | 0.039 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".