The Influence of Risk Landscape Dynamics on Program Governance: An Investigation on Artemis

The Space Economy is reshaping the risk profile of space programs, redefining risk management approaches, and influencing space program governance dynamics. The extant body of knowledge is limited in explaining this phenomenon and presents three main shortcomings: (1) there is a limited understanding of the nexus between project governance and risk management approaches employed in the Space Economy. (2) The impact of differing mission and business risk profiles on the design and evolution of project governance is underinvestigated. (3) The dynamics of the risk landscape across different stages of the program are underinvestigated. Our key aim is to address these gaps by answering the following question: How do mission and business risk profiles influence the space program governance? We use the Artemis program as a case study, as it encompasses diverse governance architectures, varying risk landscape and allocation models, and projects developed in the Space Economy. The research consists of three phases. First, through secondary sources, we mapped the governance structure of Artemis and its constituent projects. Second, we categorised project risks using a taxonomy and framework previously developed and published by the authors. Finally, we analysed how risks influence program governance and how governance, in turn, is used to manage risks over time. We discussed the evolution of different governances and risk landscapes of the Artemis program and projects (comparing three paradigmatic and polar cases: SLS, PPE, and HLS). Our research provides valuable insights for managers of space organisations and officers of space agencies engaged in planning new space programs, showing how different program governance can be adopted to manage risks in the dynamic Space Economy landscape. Future research can build upon our results as a reference for evaluating the risks associated with new space programs and the most suitable program governance models over time.