Effect of Project Risk Management on Success of Community-Based Projects: A Case of Peer Driven Change Project in Burera District, Rwanda

David Niragire; Ronald Kwena

doi:10.51867/AQSSR.1.4.16

Authors

David Niragire University of Kigali, Rwanda Author https://orcid.org/0009-0005-3039-3815
Dr. Ronald Kwena University of Kigali, Rwanda Author https://orcid.org/0000-0002-7301-3027

DOI:

https://doi.org/10.51867/AQSSR.1.4.16

Keywords:

Contingency Planning, Project Risk Management, Project Success, Risk Analysis, Risk Control, Risk Identification

Abstract

This study assessed how project risk management influences the success of community-based projects in Burera District, Rwanda, focusing on the Peer-Driven Change Project. It aimed to evaluate the impact of four practices: risk identification, risk analysis, risk control, and contingency planning. Grounded in the theories of Risk, Constraints, and Community Coalition Action, the research utilized descriptive and correlational research designs to examine relationships between variables. Data was collected from a sample of 177 respondents, using universal sampling across 29 NGOs. Primary data was gathered through five-point Likert scale questionnaires, while secondary data was sourced through document reviews and interviews. Statistical analyses, including descriptive statistics, correlation, and regression were conducted using SPSS software. However, the statistical tools employed for inferential statistics were Pearson’s Correlation, ANOVA, coefficients test and Multiple Linear Repression. Key metrics such as means, standard deviations, and correlation coefficients helped measure relationships between risk management practices and project success. Findings revealed a strong positive correlation between project success and risk control (0.715), risk identification (0.689), and contingency planning (0.642). Risk analysis showed a weaker positive correlation (0.430). Regression analysis indicated a robust model fit, with a correlation coefficient (R) of 0.815 and a coefficient of determination (R²) of 66.4%. This suggests that 66.4% of project success variability can be attributed to the four predictors. The model's adjusted R² of 0.656 confirms its adequacy, and the ANOVA results (F-statistic: 88.67, p-value: 0.000) validate its statistical significance. Regression coefficients highlighted the individual impact of the predictors. Risk identification contributed 0.421 units to project success per unit increase, risk analysis 0.087 units, risk control 0.281 units, and contingency planning 0.252 units. These results emphasize the critical role of robust risk management practices in achieving project success. The study concluded that organizations effectively identify and analyse risks, performing well in risk control and contingency planning is also perceived positively, but noted scope for improvement in risk prioritization. Recommendations include adopting frameworks like ISO 31000 or PMBOK for standardized risk management, enhancing staff skills through targeted training programs, and employing tools like Microsoft Project for real-time risk monitoring and improved communication.

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