Avoid, acquiesce … or engage? New insights from <scp>sub‐Saharan</scp> Africa on <scp>MNE</scp> strategies for managing corruption
Bibliographic record
Abstract
Abstract Research summary Many questions remain about how MNEs manage corruption. Moreover, what is known derives largely from the perspective and experiences of developed country MNEs. To address this limitation, we compare developed country and developing country MNEs' approaches for managing corruption in sub‐Saharan Africa. Through an inductive, qualitative research design, we discover how and why firms engage in “avoidance” and “acquiescence” strategies. We also uncover a novel “engagement” strategy pioneered by developing country firms that involves such tactics as finding innovative substitutes for corrupt activities, leveraging partnerships with governments and other firms, and forming deeper, long‐term relationships in the host country. These findings suggest that MNEs have more room for active agency and more proactive strategies for managing corruption than has typically been assumed in the literature. Managerial summary Multinational firms face challenges in host countries where corruption is common, due to concerns that they will need to engage in corrupt acts in order to survive. Some respond by simply not operating in these countries, while others fall into the trap of engaging in illicit activities. We consider an alternative perspective: that firms may use deeper positive engagement with the host country to reduce pressures to engage in corruption, by building their popular acceptance and strengthening their bargaining power. Although we find that this “engagement” approach was first used by developing country firms, developed country firms have also begun using this strategy. The logic underlying this approach can help managers succeed abroad while reducing the need to get their hands dirty in the process.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".