The Ripple Effect of Reputation Spillover: How Corruption Fugitives Shape Consumer Perceptions of Fugitives’ Host Countries and Their MNEs?
Bibliographic record
Abstract
The relocation of corruption fugitives raises an important question: how does their presence in host countries affect the brand credibility of multinational enterprises (MNEs) from those destinations? Drawing on the reputation spillover perspective, this study theorizes and tests a ripple effect of reputational damage. Consumers in fugitives’ home countries attribute corruption-related reputational damage to the host country’s image and extend it to MNE brands originating there. This spillover effect is moderated by perceived corruption in consumers’ home country and by national animosity between the home and host countries. By uncovering how corruption fugitives indirectly shape foreign brand evaluations, this study broadens the understanding of nonmarket actors as carriers of reputational contagion across borders. It also highlights the paradox that anti-corruption efforts, while normatively desirable, can generate unintended reputational externalities that complicate the balance between global policy enforcement and international business legitimacy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".