The Situation Awareness Weighted Network (SAWN) model and method: Theory and application Alexander Kalloniatis a, * , Irena Ali a , Timothy Neville a, b , Phuong La a , Iain Macleod a , Mathew Zuparic a , Elizabeth Kohn a a Defence Science and Technology Group, David Warren Building, 24 Scherger Drive, Canberra, ACT 2609, Australia b Faculty of Arts and Business, Centre for Human Factors and Sociotechnical Systems, The University of the Sunshine Coast, Sippy Downs, Australia article info Article history: Received 13 July 2016 Received in revised form 22 December 2016 Accepted 2 February 2017 Keywords: Situation awareness Empirical model Case studies Network analysis abstract We introduce a novel model and associated data collection method to examine how a distributed organisation of military staff who feed a Common Operating Picture (COP) generates Situation Awareness (SA), a critical component in organisational performance. The proposed empirically derived Situation Awareness Weighted Network (SAWN) model draws on two scientific models of SA, by Endsley involving perception, comprehension and projection, and by Stanton et al. positing that SA exists across a social and semantic network of people and information objects in activities connected across a set of tasks. The output of SAWN is a representation as a weighted semi-bipartite network of the interaction between people (‘human nodes’) and information artefacts such as documents and system displays (‘product nodes’); link weights represent the Endsley levels of SA that individuals acquire from or provide to in- formation objects and other individuals. The SAWN method is illustrated with aggregated empirical data from a case study of Australian military staff undertaking their work during two very different scenarios, during steady-state operations and in a crisis threat context. A key outcome of analysis of the weighted networks is that we are able to quantify flow of SA through an organisation as staff seek to “value-add” in the conduct of their work. Crown Copyright © 2017 Published by Elsevier Ltd. All rights reserved. 1. Introduction Situation Awareness (SA) is fundamental to decision-making in many contexts, from individual aviators, to teams in civilian safety and control systems, emergency response, and e our focus - mili- tary Command and Control (C2). Various units of analysis have been proposed: the individual, the collective or the systemic. Early approaches studied the state of knowledge of, say, an individual F15 pilot about a rapidly changing environment (Endsley, 1988, 1990; Taylor, 1990) with focus on cognition (Endsley, 1995) across three levels: Perception, Comprehension and Projection. This approach has evolved to addressing teams whose SA is recognised as critical for performance (Fiore and Salas, 2004; Shu and Furuta, 2005; Chiappe et al., 2014), particularly for dynamic and complex situa- tions (Burke et al., 2006; Stachowski et al., 2009). Here ‘team SA’ is based on either aggregation of individual SA measures (Rentsch and Klimoski, 2001) or notions of ‘shared SA’ where overlaps (often represented using a Venn diagram) in the SA of individuals for overlapping requirements (Endsley and Jones, 1997) are iden- tified. However, modern technology now offers the promise that distributed organisations, or even ‘virtual teams’, may be as effec- tive as close knit teams of collocated members. Such arrangements are truly socio-technical systems (Ropohl, 1982; Clegg, 2000) where humans and technological components interact through integrated social and technical processes. An alternative approach, the Distributed SA (DSA) model, takes this dimension as its raison d'etre,(Stanton et al., 2006). DSA sees cognition as not purely “in the head” of an operator but jointly held across system components using Hutchins (1995) distributed cognition, manifested through a “computational ecology” of tools. Here, SA is emergent in systems comprising interacting human and technological agents (Stanton et al., 2006; Salmon et al., 2009, 2010; Neville et al., 2016). DSA has an associated data collection method (Walker et al., 2006) known as Event Analysis for Systemic Teamwork (EAST) which links with an earlier ‘ecological’ approach (Smith and Hancock, 1994) emphasising the dynamical nature of the environment and the requirement for the human to adapt e see also (Plant and * Corresponding author. E-mail address: Alexander.Kalloniatis@dsto.defence.gov.au (A. Kalloniatis). Contents lists available at ScienceDirect Applied Ergonomics journal homepage: www.elsevier.com/locate/apergo http://dx.doi.org/10.1016/j.apergo.2017.02.002 0003-6870/Crown Copyright © 2017 Published by Elsevier Ltd. All rights reserved. Applied Ergonomics 61 (2017) 178e196