Dissertation
Using Distributed Situation Awareness to Understand and Enhance Multi-Agency Emergency Response to Natural Disasters
University of the Sunshine Coast, Queensland
Doctor of Philosophy, University of the Sunshine Coast, Queensland
2025
DOI:
https://doi.org/10.25907/00905
Abstract
Natural disaster events such as fires, floods and cyclones are increasing in rate and intensity worldwide. Apart from the devastating human and economic costs, there are other substantial short and long-term social, health, psychological, cultural, and environmental impacts. Effective disaster management systems rely on multi-agency emergency response in the immediate aftermath of a disaster. Situation Awareness (SA) has commonly been identified as a contributory factor in sub-optimal responses; yet, from a systems thinking perspective it is poorly understood and it is unclear how it can be optimised. Distributed Situation Awareness (DSA) is a contemporary theory that allows the examination of complex multi-agency systems and the identification of factors that promote effective emergency response measures. The theory posits that SA emerges from interactions across an entire system and challenges the notion that it is SA solely an individual's to ‘lose’. The theory is underpinned by a set of tenets that have not been explicitly tested in multi-agency emergency response. This research aimed to explore the application of DSA in a civilian multi-agency emergency response context and to test the tenets of DSA within this domain. Additionally, the research sought to understand how DSA can be optimised to improve disaster management outcomes. A literature review established that while the importance of SA was well accepted, it was under-investigated from a systems perspective. To address this gap three studies were conducted applying the Event Analysis of Systemic Teamwork (EAST) framework, firstly to a retrospective case study, and secondly to data collected during a live training exercise. The third study, a prospective risk assessment, applied EAST Broken Links (EAST-BL) to a generic response model. EAST works on the premise that complex collaborative systems can be understood through a network of networks (Salmon & Plant, 2022), this approach entails the examination of three distinct networks, task, social and information. A task network describes the order and interdependences between tasks, the social network describes the organisation and communications among system members and the information network describes the information that is used and communicated during task execution (i.e., DSA). By adopting different perspectives, these studies enable a comprehensive evaluation of DSA and the testing of its tenets across various multi-agency response scenarios. The findings suggested that failures in information transfer between tasks led to more severe consequences compared to failures between agents. The influential role of social media in developing and maintaining DSA during emergency response was also evident. While some of the DSA tenets required modification the studies confirmed the validity of others. The findings are discussed in terms of their theoretical, methodological, and practical implications.
Details
- Title
- Using Distributed Situation Awareness to Understand and Enhance Multi-Agency Emergency Response to Natural Disasters
- Authors
- Alison O'Brien - University of the Sunshine Coast, Queensland, School of Law and Society
- Contributors
- Gemma Read (Principal Supervisor) - University of the Sunshine Coast, Queensland, Centre for Human Factors and Systems SciencePaul Salmon (Co-Supervisor) - University of the Sunshine Coast, Queensland, Centre for Human Factors and Systems Science
- Awarding institution
- University of the Sunshine Coast, Queensland
- Degree awarded
- Doctor of Philosophy
- Publisher
- University of the Sunshine Coast, Queensland
- DOI
- 10.25907/00905
- Organisation Unit
- Centre for Human Factors and Systems Science; School of Law and Society
- Language
- English
- Record Identifier
- 991107746002621
- Output Type
- Dissertation
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