Organizational factors, project features and individual features associated with the use of emerging technologies by project managers in Kenya - Ilovi, F. (Strathmore University, 2025)
A project is a collection of tasks that have a set deadline and must be finished to achieve a certain set of objectives. Project Management is applying knowledge, skills, tools, and techniques to project activities to meet project objectives. The main objective of project management is to ensure projects are delivered on time, at the lowest possible cost and at the highest possible quality. To aid in achieving project management deliverables of time, cost and quality, most projects require effective project management and project management software. Aside from these software, project management has also benefited from the emergence of new technologies such as Blockchain (BC), Machine Learning (ML), Artificial Intelligence (AI) and Robotic Process Automation (RPA). Empirical literature has focused on the use of these technologies in project management but has not evaluated the extent to which the technologies have been adopted by organizations for project management. This adoption is important for organizations and project managers to achieve better project outcomes. This study, anchored on resource-based view and diffusion of innovation theories, aimed to establish organizational features, project features and individual features that influence the use of new technologies by project managers in Kenya. The main population was the 630 members of the Kenya Association of Project Managers (KAPM). Data was collected using an online administered questionnaire. Both descriptive and multivariate analysis was used to analyze data, with the binary logistic regression model. Only 74 respondents participated in the study. The key findings were that artificial intelligence is the popular technology adopted, followed by machine learning, and then blockchain technology across the organizations, project level and individual levels. Robotic process automation has not been adopted. The study found a positive and significant association between firm size and sector in the adoption of Machine Learning and Artificial Intelligence technologies. The rest of the organizational factors are insignificant. The study also found a positive and significant association between project size, project cost and project function in the adoption of Machine Learning and Artificial, while project complexity is negatively associated with the adoption of new technologies, though this is not significant. A project manager’s qualification will likely influence the adoption of machine learning and artificial intelligence. Furthermore, knowledge of artificial intelligence contributes largely to individuals adopting both machine learning and artificial intelligence, but no significant relationship with work experience and project management experience. The results and findings are important as they contribute to empirical and have implication for academia and practice. Further studies can focus on barriers to adopting the emerging technologies such as block chain and robotic process automation.
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