Legitimacy Spillovers and Political Risk: The Case of FDI in the East African Community
Bibliographic record
Abstract
Research Summary In order to successfully invest abroad, firms must consider how their actions will affect their acceptance by host country stakeholders such as the government and host society. When perceived as more legitimate, firms will likely experience less political risk; greater illegitimacy will likely result in more political risk. Importantly, such stakeholders often form similar opinions about firms from the same home country—resulting in legitimacy spillovers within a given host environment across similar firms. Moreover, stakeholders in one country form opinions of firms based on their actions in other countries, resulting in across‐country legitimacy spillovers. This article examines legitimacy and illegitimacy spillovers encountered by Chinese, American, Indian, and European firms in East Africa, which resulted in differences in their acceptance and their political risk. Managerial summary Although the literature has begun to consider how firms’ legitimacy affects their political risk, the role of legitimacy spillovers has not been addressed. Using the legitimacy‐based view of political risk and the literature on cognition and social judgments, we develop theory about how within‐country and across‐country spillovers of legitimacy (or illegitimacy) based on firms’ home country origins can affect their political risk. We examine the case of FDI in six East African countries that have experienced a rapid increase in foreign investment despite the presence of political risk. This case shows evidence of legitimacy spillovers resulting in systematic variation in firms’ political risk. Moreover, we find an important role for moderating factors such as the media, historical ties, and distance between countries. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".