Abstract
Staying alive (and at large), is a career advantage when you manage an insurgent group. If instead, your objective was to detonate a suicide bomb, success would be measured differently. These divergent goals must be considered when examining the social network within which individual actors are embedded, as each outcome may require a different supporting structure, warranting the application of different theory and associated metrics. Breaking from the extant literature that is principally concerned with assessing the cellular structure of attack groups and the centrality of actors, this chapter applies a business model of competitive advantage to examine how varied egonet structures correlate with the operational success of command staff—here the objective is to stay alive. Investigating the utility of Burt’s (Structural holes: the social structure of competition. Harvard University Press, Cambridge, 1992), Burt (Adm Sci Q 42(2):339–365, 1997). theory of structural holes, we find that the communication patterns of central leaders of Al Qa’ida and the Islamic State of Iraq (ISI), who were active since 2006 and survived at-large until November 2015, involved smaller egonets that had fewer non-redundant ties, lower density, and were significantly less likely to involve reliance on a central actor for information. In short, less social capital and lower constraint improved the likelihood of survival.
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Notes
- 1.
While we are aware of the differences between insurgent and terror groups, the terms are used interchangeably in this chapter.
- 2.
The reader should be aware that there are different conceptions of social capital. Our adoption of Burt’s [1] perspective (and suggested metrics) does not indicate that it is the only useful conceptualization. Moreover, the theory did not remain in a vacuum: several additions were made (see [2]). Constrained by the limits of what can reasonably be covered in a chapter, we encourage readers to see Borgatti et al. [14] for a brief synopsis of the political and sociological discussion on this topic and the various measures available to study the attendant network structures. Readers are also advised to explore Granovetter’s [15, 16] discussion of the information benefits of weak ties, as well as some of Burt’s more recent writings.
- 3.
For a more detailed description of this coding process see Bush and Bichler [8].
- 4.
Degree centrality measures the number of direct connections an actor has; Bonacich’s power and eigenvector centrality identifies individuals who may have only a few connections, but those associates are highly connected; and, betweenness centrality can identify which individuals are situated along the shortest paths among all other pairs in the network. For more information about these centrality measures see Borgatti et al. [24], Hanneman and Riddle [25].
- 5.
One journalist also appeared in the top 25 listing for Al Qa’ida. Since this person did not have a known role within an insurgent group they were excluded from this analysis.
- 6.
Recall from our previous discussion that social capital is a dynamic byproduct of competition borne from relational structures that affords some individuals significantly more access and control of resources than others [1].
- 7.
Admittedly, searching the status of each person using publicly available sources is a limitation for two reasons. First, some individuals go by several names and it is possible that a notice of death or arrest was missed. Second, many individuals assume names of others who have fought before them. To determine which person was killed or arrested, we considered their known locations, role within their respective organizations, and age. Again, we may have missed something. However, we compared our coding with published findings (see Wu et al. [13]) which confirmed our classification of Al Qa’ida operatives.
- 8.
While the documents used to code communication chains are dated between September 2006 and April 2011, we decided that coding death or capture required a longer time frame. All actors active in September 2006 were alive in January 2006, so extending back from the documents was reasonable. Moreover, we wanted to code the ability of individuals to remain alive, and thus, expanding the window forward to when this chapter was written seemed appropriate.
- 9.
Efficiency is a standardized version of effect size that controls for the size of an egonet. Generally, it is better to use this measure. However, we found that efficiency was highly correlated with density (Pearson Correlation of −0.992), and thus, it was not used here.
- 10.
Borgatti [26] demonstrates that when applied to a dichotomous network (as we have done here) the effect size is simply the egonet density scaled by a factor of n − 1, such that effect size can be calculated as: \(n - \frac{2t}{n}\); where, t equals the number of ties in the egonet, excluding all ties to the ego.
- 11.
The difference in effect size reported in Table 6 would seem to indicate that actors associated with ISI had fewer structural holes, and thus, less redundancy in the network; however the effect size reported does not control for the size of the network. Once network size is controlled for, individuals named in ISI documents exhibited more structural holes in their egonets (AQ = 0.82; both = 0.80; ISI = 0.87).
- 12.
For brevity, only the logistic regression is reported.
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Bichler, G., Bush, S. (2016). Staying Alive in the Business of Terror. In: Masys, A. (eds) Disaster Forensics. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-41849-0_11
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