The research presented in this thesis demonstrates that in the management of risks arising from the development of complex socio-technical systems there are four contingent organisational states described as: • Type 1 - High Reliability Organisation • Type 2 - Normal Accidents Organisation • Type 3 - Low Reliability Organisation • Type 4 - Murphy's Law Organisation This thesis has shown that negentropy and redundancy are the forces that seek to achieve effective organisational behaviour and lead to a more steady predictable Type 1 state of algorithmic organisational stability. However, such stability is contingent upon limiting, as far as possible, environmental disturbances. If systemic overload and instability increase significantly during decision toward implementation, high reliability may not eventuate and a crisis may unfold. Redundancy and anticipation are important in order to achieve effective and/or efficient operations with rational decision-making and negative feedback control loops. However, resilience may be more important when decision-making is non-rational, lead times are less than lag times and where economy or equity and positive feedback loops provide a more appropriate fit with the environment. In conclusion, this research has shown that likelihood and consequence of surprises in a complex socio-technical system (an Intelligent Building project for a bank in Singapore) are contingent upon the causal texture of the environment, the dominant risk culture of the key stakeholders, and the dynamic reliability of the system determined by the ratio of lead to lag times and gain to load. Redundancy and negative feedback are important particularly in rational decision systems. Resilience and positive feedback are important in non-rational decision systems. Instability and surprise can occur in transition from one decision state to another, especially during overload conditions. A decision-paths' schema is developed to show that the Intelligent Building as originally intended did not eventuate. This thesis thus demonstrates the analytic value of contingency theory.
|Date of Award||2006|
|Supervisor||Alan JARMAN (Supervisor) & Jenny STEWART (Supervisor)|