Intelligence analysis in multidisciplinary teams - affordances of the collaborative online SWARM platform for public service and education application

Abstract

Intelligence analysts from Australian Commonwealth, State and Territory Government agencies and departments regularly work in teams with other roles, referred to in this research as multidisciplinary teams (MDTs). MDTs operate within a ‘framework’, which provides the central mechanism for team coordination and structure. However, there is little available research on intelligence MDT frameworks including challenges.

To address this gap and explore potential MDT frameworks, this thesis examines intelligence MDTs and the Hunt Lab’s collaborative software prototype SWARM Platform as a framework to support the functions of intelligence MDTs. The Hunt Lab developed the Platform in 2017 to improve collaboration and quality of reasoning of intelligence analysts. We investigated whether the Platform can move beyond its origins to provide a framework to support the business and training functions of intelligence MDTs, including affordances for managers and educators.

As a first step, we developed a maturity model, informed by the intelligence teamwork literature for the purposes of defining and evaluating the key indicators of intelligence MDTs. We detail the development of our maturity model that contains seven pillars against which MDT performance could be evaluated: Mission, Accountability, Agility, Efficiency, Communication, Collaboration and Output. This maturity model formed the central coding mechanism for this research to evaluate MDT performance.

We conducted three studies to address our research questions focusing on current MDT workplace application and whether the Platform supports business and training MDT framework functions. Study 1 applied the maturity model to a case study of intelligence MDT performance in the Australian Government, where we identified Mission and Collaboration as a strength but Efficiency as a weakness of their MDT framework.

In Studies 2 and 3, we evaluated the Platform as a potential framework to support the business and training functions of intelligence MDTs, first in a small-scale pilot within an Australian Government workplace and then on a larger scale in an analytical competition involving public and intelligence organisation teams. In these two studies, we found the Platform mostly supported key MDT pillars but also identified varying weaknesses across pillars of Accountability, Efficiency and Communication.

Despite the Platform’s broad support for the MDT maturity pillars, this research identified technical and cultural issues that prevent adoption of the Platform as a business framework without further prioritised research. Instead, our research indicates the Platform has more immediate benefits in supporting training functions as a computer-supported collaborative learning (CSCL) tool, where research participants reported both cognitive and social learning outcomes in the same studies.

This research developed insights relevant to MDT frameworks more broadly including scaffolding strategies to improve multiple maturity model pillars. These strategies include teamwork schema development; launch meetings; accountability practices; and data management. We also developed insights into engaging and researching government with intelligence functions that are an emergent but not unforeseen result from this research. These findings have implications for future collaboration between the tertiary research sector and government with intelligence functions.