The investigation and modelling of aviation accident causation is dominated by linear models. Aviation is, however, a complex system and as such suffers from being artificially manipulated into non-complex models and methods. This thesis addresses this issue by developing a new approach to investigating aviation accident causation through information networks. These networks centralise communication and the flow of information as key indicators of a system‟s health and risk. The holistic approach focuses on the system itself rather than any individual event. The activity and communication of constituent elements, both human and non-human agents, within that system is identified and highlights areas of system failure. The model offers many potential developments and some key areas are studied in this research. Through the centralisation of barriers and information nodes the method can be applied to almost any situation. The application of Bayesian mathematics to historical data populations provides scope for studying error migration and barrier manipulation. The thesis also provides application of these predictions to a flight simulator study in an attempt of validation. Beyond this the thesis also discusses the applicability of the approach to industry. Through working with a legacy airline the methods discussed are used as the basis for a new and forward-thinking safety management system. This holistic approach focuses on the system environment, the activity that takes place within it, the strategies used to conduct this activity, the way in which the constituent parts of the system (both human and non-human) interact and the behaviour required. Each stage of this thesis identifies and expands upon the potential of the information network approach maintaining firm focus on the overall health of a system. It is contended that through the further development and application of this approach, understanding of aviation risk can be improved.

Most law firms have done little to address the opportunities and threats related to potentially disruptive technology (DT), such as artificial intelligence (AI) and machine learning (ML). The purpose of this multiple case study was to explore strategies that law firm leaders in the United States used to address the potentially detrimental influences of DT, such as AI and ML, on their organizations. The systems approach to management was employed as the conceptual framework. Data were collected from 6 participants at 2 international law firms with offices in California using semistructured interviews and organizational artifacts. Data were analyzed using Miles, Huberman, and Saldana's data analysis method, resulting in 4 themes: recognizing the legal ecosystem and legal firms are open systems, but organizational subsystems often function as semiclosed systems; acknowledging that while DT represents the most significant potential challenge in the near future, the immediate challenge is improving technology, which requires organizational adjustments; recognizing the need for firms to invest more heavily in innovation generation activities; and realizing the need for increased utilization of augmenting technologies, such as AI or ML, to streamline nonadvisory outputs. The findings of this study might support best practices for addressing DT and contribute to social change by outlining ways in which firms can lower costs to clients while increasing access to legal services for those in underserved communities.