Exploring Project Management: Methodologies, Sustainability, and AI Transformation

Summary

Project management research is one of the main research streams for Dr. Burga. It encompasses the exploration and analysis of various approaches and methodologies to effectively plan, execute, and control projects which have emerged as important not just in the technical area but in business management, marketing, and research. Two prominent project management methods, waterfall and Agile, have been extensively studied and implemented in the academic literature and in practice. The waterfall approach follows a sequential process, with each phase dependent on the completion of the previous one. On the other hand, Agile methodology promotes flexibility, collaboration, and iterative development. In both cases, Dr. Burga has explored and continues to explore current and emerging issues that can be translated into practice such as the use of management control systems by project managers, the concept of accountability in Agile teams, and the interdependence between project management tools, methods, and human behaviour. In particular, the concept of sustainability has gained significant attention in project management, aiming to minimize negative environmental impacts and maximize social and economic benefits. Sustainable project management integrates environmental considerations, social responsibility, and economic viability throughout the project lifecycle. Dr. Burga is exploring ways of furthering sustainability actions and measures in project management. Furthermore, the emergence of artificial intelligence (AI) has transformed project management practices by automating tasks, improving decision-making, and enhancing efficiency even though ethical and societal concerns are emerging. AI-powered tools enable predictive analytics, resource allocation optimization, and risk assessment, thereby increasing the likelihood of project success when adopted. However, careful consideration is required to ensure ethical use of AI and mitigate potential biases that may arise from algorithmic decision-making.