Abstract Criminal networks are frequently at risk of disruption through arrest and interorganizational violence. Difficulties in designing empirical studies of criminal network recovery, however, have problematized research into network responses to disruption. In this study, we evaluate criminal network resilience by examining network recovery from disruption in an array of different criminal networks and across different disruption strategies. We use an agent‐based model to evaluate how criminal networks recover from disruption. Our results reveal the vulnerabilities and time to recovery of numerous criminal organizations, and through them, we identify which disruption strategies are most effective at damaging various criminal networks.
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Organizational Resilience to Disruption Risks: Developing Metrics and Testing Effectiveness of Operational Strategies
This study draws from the system resilience literature to propose three different metrics for evaluating the resilience performance of organizations against disruptions: the initial loss due to the disruption, the maximum loss, and the total loss over time. In order to show the usefulness of the developed metrics in practice, we deploy these metrics to study the effectiveness of two resilience strategies: maintaining operational slack and broadening operational scope, by empirically analyzing the performance of manufacturing firms that experienced a disruption during the period from 2005 to the end of 2014. The results show that maintaining certain aspects of operational slack and broadening business scope and geographic scope can affect these different metrics in different ways. Our results help decisionmakers in risk management to gain a better understanding of the conditions under which the recommended strategies actually improve organizations’ resilience, as well as the ways in which they may do so.
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- PAR ID:
- 10352095
- Date Published:
- Journal Name:
- Risk analysis
- Volume:
- 42
- Issue:
- 3
- ISSN:
- 0272-4332
- Page Range / eLocation ID:
- 561-579
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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