Megaprojects as complex adaptive systems: The Hinkley point C case. Issue 8 (November 2019)

• Record Type: Journal Article
• Authors: Daniel, Eric; Daniel, Pierre A.
• Is Part Of: International journal of project management. Volume 37:Issue 8(2019)
• Page Start: 1017
• Page End: 1033
• Publication Date: 2019-11
• Subjects: Complexity theory -- Megaprojects -- Performance -- Complex adaptive systems (CAS) -- Self-organizing; Project management -- Periodicals; Network analysis (Planning) -- Periodicals; 658.40405
• DOI: 10.1016/j.ijproman.2019.05.001
• Languages: English
• ISSNs: 0263-7863

Abstract

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. Highlights: Megaprojects are relevant cases to examine mechanisms of complexity and evolution. Complex Adaptive System theory is compliant with megaproject dynamics of change. Megaprojects are driven by self-organization, coevolution and recombination. Content analysis and historical research provide rich data to examine megaprojects.