M&E Practices and Implementation of Road Construction Projects in Rwanda

Recognizing that effective road infrastructure is vital for socioeconomic development, this research addressed the persistent challenges that hindered project execution, including delays, cost overruns, and compromised quality often linked to inadequate M&E practices. The study examined the influence of M&E practices on implementation of road construction projects in Musanze District, Rwanda with specific focus on M&E planning, M&E data utilization, and M&E digitalization on implementation of road construction projects. This study used the theory of change and constraints theory. The study employed descriptive survey and correlational research designs with questionnaire and interview for collecting data from a sample of 153 respondent out of 247 key stakeholders. The instruments were pretested in a pilot study involving 10% of the sample to ensure validity and reliability, achieving a Cronbach's alpha coefficient of 0.78. Descriptive and inferential statistics of correlation and regression were done at a significance level of 0.05 with thematic content analysis of qualitative data for triangulation. The findings revealed that road construction projects generally performed above average, with 68.6% of respondents confirming that projects were completed within budget, 69.9% stating that milestones were met on time, and 65.3% satisfied with the quality of materials and road quality. The hypotheses that M&E Planning, M&E Data Utilization, M&E Digitalization and M&E Capacity Building does not significantly influence the implementation of road construction projects were rejected as p = 0.000 < 0.05. In conclusion all four null hypotheses were rejected, demonstrating that M&E planning, data utilization, digitalization, and capacity building significantly influence the implementation of road construction projects. The study recommended enhancing community engagement, strengthening M&E planning, expanding digitalization efforts, prioritizing ongoing capacity building, and fostering a culture of data-driven decision-making. Suggestions for further studies included comparing M&E practices across infrastructure sectors such as water or energy projects, to identify cross-sectoral best practices and potential areas for improvement, exploring emerging technologies like AI and block chain, and analyzing the impact of national and regional policies on M&E practices.