Kalloniatis Alexander, Ali Irena, Neville Timothy, La Phuong, Macleod Iain, Zuparic Mathew, Kohn Elizabeth
Defence Science and Technology Group, David Warren Building, 24 Scherger Drive, Canberra, ACT 2609, Australia.
Defence Science and Technology Group, David Warren Building, 24 Scherger Drive, Canberra, ACT 2609, Australia.
Appl Ergon. 2017 May;61:178-196. doi: 10.1016/j.apergo.2017.02.002. Epub 2017 Feb 14.
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 information 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.
我们引入了一种新颖的模型及相关数据收集方法,以研究为通用作战图(COP)提供信息的军事人员分布式组织如何产生态势感知(SA),这是组织绩效的关键组成部分。所提出的基于实证得出的态势感知加权网络(SAWN)模型借鉴了两种关于态势感知的科学模型,一种是恩德斯利提出的涉及感知、理解和预测的模型,另一种是斯坦顿等人提出的模型,该模型认为态势感知存在于跨越一组任务的活动中人员和信息对象的社会及语义网络中。SAWN的输出是一个加权半二分网络,它表示人员(“人类节点”)与文档和系统显示等信息制品(“产品节点”)之间的交互;链接权重表示个人从信息对象或向信息对象及其他个人获取或提供的恩德斯利态势感知水平。SAWN方法通过来自澳大利亚军事人员在两种截然不同的场景(稳态行动期间和危机威胁背景下)开展工作的案例研究的汇总实证数据进行说明。加权网络分析的一个关键结果是,随着工作人员在工作中寻求“增值”,我们能够量化态势感知在组织中的流动情况。