Are crowdsourced analysts MICE or RASCLS?

Abstract: Great resources in time and money are being invested in crowdsourced intelligence analysis. The research community and intelligence community alike lack understanding of what motivates people to participate in intelligence analysis. Moreover, motivation to engage in crowdcreation platforms in general is poorly researched. The objective of this study is to explore motivation for appreciated participants to join crowdsourced intelligence analysis. For this thesis, four participants in two different intelligence analysis crowdsourcing initiatives, both on crowdcreation platforms, were interviewed via remote video on their motivations to engage in crowdsourced intelligence analysis. In analysing the data gathered, MICE or RASCLS frameworks were applied to compare with how the intelligence community recruits and retains human intelligence sources, i.e. agents on the field. Similarities and differences between the two phenomenons – agent handling vs. crowdsourcing – is examined. This study adds qualitatively rich data on motivations to engage in crowdcreation platforms, and in the intelligence analysis field, to an existing body of research on motivations for participation in crowdsourcing. However, its perhaps most interesting finding is that crowdsourced intelligence analysts, much like human intelligence sources, needn’t necessarily be knowingly engaged with an intelligence community to be an appreciated contributor.