INFLUENCE OF PROJECT MANAGEMENT PRACTICES ON PERFORMANCE OF CDF SCHOOL INFRASTRUCTURE DEVELOPMENT PROJECTS IN RUNYENJES CONSTITUENCY EMBU COUNTY, KENYA
Grace Wamuru - University of Nairobi, Kenya
Dr. Reuben Kikwatha - University of Nairobi, Kenya
ABSTRACT
Project Management and the practice of the same have become indispensable to the modern-day project manager and they form the basis of much of what is achieved during the course of a project. The implementation of CDF projects has been marred by repeated accusation of abuse of funds, patronage due to excessive powers of the members of parliament, incomplete projects, and lack of technical capacity, poor planning and a litany of other weaknesses which threaten to undermine the very success of the projects. Lack of proper project management practice is a cause of many projects failing to achieve its set goals and objectives. The purpose of this study was to determine the influence of project management practices on performance of CDF school infrastructure development projects in Kenya. Specifically, the study sought to determine the influence of scope management, time management, stakeholder management, and risk management on the performance of CDF school infrastructure development projects in Runyenjes constituency Embu County. This study was guided by the following theories: stakeholder theory, Agency Theory and theory of project management. Descriptive survey research design was applied to undertake the study. The target population for this study was composed of the 97 constituency development fund projects in Runyenjes Constituency. The study’s unit of analysis was the project management committees (PMCs). The sample size of 325 respondents was selected using stratified sampling method. Structured questionnaires were employed to get primary data. Data that was collected from the field was filtered, sorted and cleaned in line with research objectives. The data was then coded, and entered into and analyzed using statistics software (SPSS, Version 25.0). Quantitative data was analyzed using descriptive statistics. It involved calculation of percentages, frequencies, means and standard deviation. Multiple regression analysis helped analyze inferential data. The results were presented using tables.