۱- Introduction

۲- Literature review

۳- Research aim and methods

۴- The HPC case study

۵- Discussion

۶- Conclusion

References

Abstract

Megaprojects are complex projects which impact millions of people, involve public and private stakeholders, and present challenges related to decision making and performance shortfalls. They are relevant cases for studying faulty management thinking as well as performance evolutions and self-organizing dynamics. Our paper builds on the theory of Complex Adaptive Systems (CAS) to understand and model processes of evolution in the Hinkley Point C nuclear power plant megaproject. The results show that CAS properties apply to megaproject changes and provide a theoretical and practical framework for examining and modeling megaproject management dynamics. We designed a research methodology combining content analysis and historical research for its relevance in conducting organizational research in conditions of complexity and non-linearity. This original research design makes it possible to conduct causal analyses of relations between key megaproject events and thus build models of evolution dynamics in stakeholder success expectations, change mechanisms in the implementation of project outputs, and self-organizing patterns.

Introduction

A megaproject costs more than 1 billion dollars, involves many private and public stakeholders and impacts millions of people (Flyvbjerg, 2014). Megaproject management is an emerging field of research and the project management literature is still in search of its ‘classics’ to clearly define the scope of this new field (Flyvbjerg and Turner, 2018; Pollack et al., 2018). Its impact on millions of people, its large quantities of stakeholders and its multi-dimensional ambitions make megaproject interesting object of research for studying inappropriate management thinking and lack of performance (Flyvbjerg, 2014). Megaprojects are complex and evolve in unpredictable political, societal and economic environments involving hundreds of reciprocal ties (Chapman, 2016). Despite the use of advanced planning techniques and risk analysis tools, modeling risk interactions and impacts on the performance of megaprojects remains a major challenge (Boateng et al., 2015), and the causes for massive cost overruns in large projects stem from the limited capacity of humans to estimate, plan or anticipate the impacts of uncertainties (Eden et al., 2005). The megaproject management (MPM) research field has to address challenges related to decision-making risks and performance shortfalls, which emerge when classical project management theories are applied to the management of megaprojects (Li et al., 2018).