Can development agencies’ project data, design, monitoring and evaluation, scrutinized by AI technologies, systematically inform evidence-based policymaking? Can real-time local media and social media contextualize such data? A PhD research project has explored such a domain by focusing on project evaluations and interagency partnerships.
To date, some 20% of interagency partnerships fail to achieve their objectives fully, incurring significant financial implications, and having less impact on beneficiaries. Unravelling the keys to enabling more effective collaboration among development cooperation agencies is paramount. This research delves into the determinants of successful partnerships, presenting a unique dataset of 750 factors clustered into ten key areas, including local context, management, and project quality.
The study employs a blend of text mining, natural language processing, machine learning, and statistical methods to identify and automate the most critical factors for successful collaboration. The intended output of this project is a decision-support tool for organizations to improve partnership selection.
