Contagion risk: How stakeholders mediate the impact of rivals’ misfortunes on firms
Bibliographic record
Abstract
This study aims to investigate the dynamics of contagion and its impact on firms, specifically focusing on how a rival’s failure to control an event can have adverse consequences for other firms. Through a comprehensive analysis of relevant theories, literature, and real-world cases, the study identifies key factors that contribute to the contagion process and proposes a framework for assessing the associated risk. The research highlights the crucial role of stakeholders in mediating the effects of rivals’ misfortunes on other firms and emphasizes how stakeholders’ identities shape their risk evaluations, thereby affecting the occurrence of contagion. This study contributes to the existing literature by providing a conceptualization of the contagion process and introducing the concept of “stakeholder identity” within the context of organizational and operational risk management. The findings offer practical insights to firms by emphasizing the significance of contagion risk, which is often overlooked in operational risk management strategies. Additionally, the study provides valuable guidance on how firms can effectively assess their vulnerability to contagion, enabling them to proactively manage and mitigate their risk.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".