Assessing the Impact of Erratic Governance on Local and International NGOs in Zambia: An Exploratory Study Using Machine Learning and Artificial Intelligence

This study explores the impact of erratic governance on local and international NGOs in Zambia, using a mixed-methods approach that combines survey data, in-depth interviews, and machine learning (ML) and artificial intelligence (AI) techniques. The study finds that erratic governance practices, including funding constraints, operational challenges, and limited access to services, significantly affect the operations and effectiveness of NGOs in Zambia. Weak institutional frameworks, corruption, lack of transparency and accountability, political instability, and limited civic engagement are identified as key factors contributing to erratic governance. The study demonstrates the potential of ML and AI in analyzing and predicting the impact of erratic governance on NGOs, including predictive modeling, risk analysis, data visualization, automated reporting, and decision support systems. The findings of this study have implications for policymakers, NGO managers, and development practitioners seeking to promote more effective and sustainable development outcomes in Zambia.