The growing need to develop, transform, and integrate new knowledge into economic processes has driven a notable increase in research, development, and innovation (R&D&i) projects. These projects face significant uncertainty, as outcomes often differ from initial expectations. They also aim for transformative impacts across diverse sectors and require collaboration among industry, academia, government, and civil society stakeholders. Aligning these varied interests with project objectives can be challenging, frequently limiting the scalability of results in socio-economic contexts. Consequently, effective project management methodologies capable of handling such complexities are increasingly crucial. This study presents a comprehensive theoretical review of methodological frameworks for managing R&D&i projects. It examines predictive and adaptive approaches to identify attributes best suited for project lifecycle phases. The primary goal is determining which phases benefit from traditional management techniques and which require agile practices to address uncertainty and change. Based on this analysis, the study proposes an innovative hybrid framework integrating best practices from both approaches, enhancing operational efficiency and impact.
