Rickaby, Baker A.; (2025) Managing Uncertainty in Real Time: A Case Study of the High Speed 2 Railway Project. Doctoral thesis (Ph.D), UCL (University College London).

Text Baker_Rickaby_UCL_PhD_Thesis_Managing_Uncertainty.pdf - Accepted Version Access restricted to UCL open access staff until 1 April 2028. Download (3MB)

Abstract

Managing uncertainty is crucial for the delivery of large infrastructure projects. When uncertainties materialise, they often lead to disruptions in the progress of these projects. Numerous studies have explored these unknowns and the strategies, preparations, techniques, and tools employed to anticipate and mitigate them. However, prior research offers limited insights into the real-time management of uncertainties as they arise. A few studies have examined uncertainty beyond traditional risk management, encompassing the unpredictable unknowns that projects encounter. However, these contributions still primarily focus on understanding uncertainty before it materialises. This research provides a novel contribution to understanding how uncertainty is organised in large infrastructure projects. It also introduces the Dynamic Project Uncertainty Management (DPUM) framework that integrates the study’s insights on addressing uncertainty in real time. A case study research methodology was adopted involving multiple embedded cases within HS2, such as the COVID-19 pandemic. The researcher spent approximately two years fully seconded in the case study organisation (HS2 Ltd). This involved longitudinal fieldwork, engaged scholarship and collection of many sources of data (e.g., semi-structured and informal interviews, documentation and observations). The research identifies how uncertainties materialised through three explanatory mechanisms: emergence, recognition and integrated response. These mechanisms unfolded through real-time trajectories depending on the severity of the disruption. Addressing uncertainties in real time required a range of routine and novel activities. The main contribution of the research is that it improves our understanding of project uncertainty management through a process theory perspective, particularly in large infrastructure projects. These contributions include a real-time temporal view of uncertainty, classification of uncertainty in real time, and expansion and validation of existing conceptualisations provided by existing studies. The study emphasises the need to adapt and target project management approaches to specific challenges and the requirement for diverse strategies to manage uncertainties.

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