EFFECT OF AGILE PROJECT MANAGEMENT ON PERFORMANCE OF MINING PROJECTS IN RWANDA A CASE OF ITSCI PROJECT

Abstract

The goal of this research was to find out how agile project management affects the productivity of mining projects in Rwanda. The study's objectives are to determine the effect of adaptive planning on performance of mining projects in Rwanda, to establish the effect of continuous improvement on performance of mining projects in Rwanda, to assess the effect of iterative project management on performance of mining projects in Rwanda and to examine the effect of Stakeholders collaboration on performance of mining projects in Rwanda. A descriptive survey method was used for this study. Three hundred and eighty-eight respondents with experience in project management made up the study's primary emphasis. A sample size of 197 respondents was determined using Slovin’s formula Secondary information was gathered for this study from records kept by the International Supply chain Initiative Project in Rwanda. In addition, questionnaires were sent to gather primary data. Respondents' experiences and insights formed the basis for the study's analysis and interpretations. Different approaches and incentives were used in the surveys, interviews, and observations with the participants. Participants were asked to critically evaluate the instruments' face-to-face and content validity for the researcher. To assure reliability the study used a test-retest strategy. Reliability was calculated using the Cronbach's coefficient. Reliability of the tool was a test value of 0.7 or above. Concurrent triangulation was used to increase the reliability of the study's findings. The data was collected through the use of a structured questionnaire, and subsequent analysis made use of descriptive statistics. Inferential analysis and Pearson's correlation analysis was used to assess the degree of association between the two sets of variables. A thorough interview approach was used to determine the validity of non-numerical instruments. The study used SPSS tool version 25 to analyze the data. Descriptive and inferential statistical tests was used to analyze quantitative data, and tables and figures were used to display the results. Percentages, rates, and counts are under the purview of descriptive statistical tests, while multiple regression is used in inferential statistical analyses. The results of this thematic analysis of qualitative data were presented in narrative style with direct quotations from the sources used to support them. The results indicates that the constant term is 1.203, with a significant t-value of 8.248 (p < 0.001), suggesting a robust baseline performance. Among the predictors, adaptive planning shows a positive and significant relationship with project performance (B = 0.258, β = 0.368, t = 7.871, p < 0.001), highlighting its crucial role in enhancing outcomes. Conversely, continuous improvement exhibits a negative impact on performance (B = -0.179, β = -0.250, t = -4.199, p < 0.001), indicating potential misalignment or ineffective implementation within the context. Iterative methods also contribute positively to performance (B = 0.171, β = 0.207, t = 2.938, p = 0.004), affirming their utility in adaptive project management. Notably, close collaboration emerges as the most influential variable, with the highest standardized coefficient (B = 0.483, β = 0.578, t = 10.400, p < 0.001), underscoring its significance in driving project success in the mining sector. In conclusion, the findings highlight the significant role of adaptive planning, iterative methods, and close collaboration in driving project performance within the mining sector, while continuous improvement may require strategic reassessment to mitigate its negative impact. Based on these results, it is recommended that mining firms emphasize collaborative practices and flexible planning frameworks to enhance project outcomes, while carefully evaluating and refining continuous improvement initiatives to ensure alignment with project goals. Future studies could explore the underlying causes of the negative effects of continuous improvement in this context and investigate how these strategies might be better adapted to dynamic, resource-intensive industries.