EFFECT OF TASKS DEPENDENCE MAPPING ON PERFORMANCE OF CONSTRUCTION PROJECTS IN RWANDA CASE OF NYARUTARAMA PROPERTY DEVELOPERS
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Abstract
The performance of construction projects is significantly influenced by the interdependence of tasks and their efficient coordination. This study examines the effect of task dependence mapping on the performance of construction projects in Rwanda, with a specific focus on Nyarutarama Property Developers. Using a mixed-methods approach, data was collected through surveys and interviews with project managers, engineers, and other key stakeholders involved in the construction process. The study explores how mapping task dependencies enhances workflow efficiency, minimizes delays, and optimizes resource allocation. One hundred seventy engineers working for Nyarutarama Property Developers with a concentration on road construction made up the study's target population. The study adopted descriptive survey design. Secondary data was sourced from pertinent papers gathered from a Rwandan construction business. Furthermore, surveys made it easier to get primary data. Respondents' points of view formed the basis of the research's analysis and interpretations. A variety of methods and incentives was used to conduct surveys, interviews, and observations to the participants. The growth of a specific factor is dependent on all the other factors. As part of the study, the researcher asked participants to rate the instruments' content and face validity. In order to determine the reliability of the measurements, the research used test-retest methods. In order to determine how trustworthy, the instruments are, the study used Cronbach's coefficient. For the instruments to be considered reliable, the test result must be at least 0.7. The study used a structured questionnaire to gather primary data, and then the study analyzed it using descriptive statistics. Hence, to determine the strength of the link between the independent and dependent variables, the study used inferential analysis and Pearson's correlation analysis. Applying descriptive and inferential statistical analyses, as well as presenting findings using tables and figures, were part of the quantitative data assessment process. Counts, frequencies, and percentages are some of the metrics used in descriptive statistics tests to characterize and summarize data. In contrast, multiple regression is a statistical tool for investigating correlations and drawing conclusions from a collection of independent variables; it is an integral part of inferential statistical tests. Applying theme analysis approaches to qualitative data allows for the presentation of findings in a narrative format with the addition of direct quotes. The standardized coefficients (Beta) provide a comparison of the relative strength of each predictor in the model, with Task’s Dependence Mapping also having the highest Beta value of 0.260. The t-values and corresponding significance levels indicate the statistical significance of each predictor, with all variables except the constant being significant at p < 0.05. Specifically, Task’s Dependence Mapping (t = 2.799, p = 0.006) and Project Tasks Definition (t = 1.982, p = 0.050) are at the edge of significance. Findings indicate that proper task dependence mapping leads to improved project timelines, cost management, and overall quality of construction outputs. However, challenges such as poor communication and lack of proper planning can hinder its effectiveness. The study recommends the adoption of digital project management tools and enhanced collaboration among stakeholders to improve construction project outcomes. These insights contribute to the growing body of knowledge on construction project management in Rwanda and provide practical recommendations for developers seeking to enhance project performance.
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References
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