Effect of Project Risk Management on Performance of Social Development Projects in Rwanda: A Case of VUP Financial Services Project in Gakenke District
DOI:
https://doi.org/10.53819/81018102t2338Abstract
The general objective of the study was to assess the effect of project risk management on performance of social development projects in Rwanda. This study had the following specific objectives: to determine the effect of project risk identification on performance of VUP financial services project in Gakenke District, find out the effect of project risk analysis on performance of VUP financial services project in Gakenke District, assess the effects of project risk response on performance of VUP financial services project in Gakenke District and analyze the effect of project risk control on performance of VUP financial services project in Gakenke District. Target population of this study was 122 including 14 VUP staff at district level, 72 VUP at sector level and 36 Gakenke District Local leaders. The Solvin formula utilized to determine the appropriate sample size, as it provides a straightforward approach for computing the number of participants required for the study. The formula provides sample of 94 for the study. Cluster sampling used for selecting groups, or clusters, of participants from the population. The clusters chosen based on position and categories of population in VUP. The study also employed purposive sampling as the sampling technique. Questionnaire, interview and documentation research used as tools of data collection. The statistical analysis of the collected data conducted using Statistical Package for Social Sciences (SPSS) which is a widely used tool for statistical computation in research. The model summary presents the results of a regression analysis on project risk control, project risk identification, project risk analysis, and project risk response, on the dependent variable of project performance within the context of the VUP Financial Services project in Gakenke District. The value of R is 0.729, indicating a linear effect of the predictors on project performance. The R-squared value is 0.532, indicating that approximately 53.2% of the variance in project performance can be attributed to the combined effects of project risk control, risk identification, risk analysis, and risk response. This indicates a moderate level of explanation and indicate that the included predictors collectively contribute to understanding project performance. Gakenke District should establish a comprehensive risk identification framework regarding VUP financial services project involving diverse stakeholders, implementing rigorous risk analysis practices with advanced tools, fostering a proactive risk response culture through cross-functional collaboration.
References
Alhawari, S. (2016). Relationship among project management processes and knowledge repository for project success. International Journal of Enterprise Information Systems 12(4), 16–30
Cagliano, A. C., Grimaldi, S., and Rafele, C. (2015). Choosing project risk management techniques. A theoretical framework. Journal of Risk Research 18(2), 232–248
Connell, J. P., Kubisch, A. C., Schorr, L. B., & Weiss, C. H. (2020). New Approaches to Evaluating Community Initiatives: Concepts, Methods, and Contexts. Aspen Institute.
Ojiako, U., Chipulu, M., Maguire, S., & Marshall, A. (2021). Managing Projects for Performance Improvement: Tools and Techniques for Tracking and Reviewing Progress. Routledge.
Patil, S., and Ade, R. (2015). A global software engineering knowledge management approach for intensive risk mitigation. International Journal of Managing Public Sector Information and Communication Technologies 6(1), 1–8
Pinto, J. K., & Slevin, D. P. (2019). Project Success: Critical Factors and Behaviors. Routledge.
Seijts, G. H., & Latham, G. P. (2020). The Work of Leaders: How Vision, Alignment, and Execution Will Change the Way, You Lead. Wiley.
