Digitizing Project Portfolio Management in the Architecture, Engineering & Construction Industry: The Application of Data & Analytics for Evidence-Based Decision-Making

This master thesis research has the aim to investigate how Data & Analytics enhance monitoring, reporting and control in project portfolio management practices to improve portfolio decision-making in the Architecture, Engineering and Construction (AEC) industry. Adequately and regularly reconsidering the project portfolio, with interdependencies towards the internal and external complex and rapidly changing environment, has shown to be a determinant for being successful in creating competitive advantage and securing the future of organisations that are project-based. Especially in the information technology (IT) field, rapid technological changes aim for dynamic organisational capabilities. This is reflected by the demand for digitized project portfolio management (PPM) using Data & Analytics. Although literature in the academic research field of project and project portfolio management describe different frameworks for PPM, there is a lack of context and practice. Information regarding comprehensive PPM frameworks that incorporate organisational, technological, and environmental factors found through empirical research are missing. Moreover, industry specific factors are missing as frameworks are generic and standardized, existing PPM practices miss adequacy and regularity and are often based on intuition, power, opinion, and leadership. To enhance competitive advantage within the AEC industry, a comprehensive approach in PPM is investigated with a focus on digitization and Data & Analytics. The conceptual framework that is developed is built on the three pillars, people, processes, and technology. Adherent to the conceptual framework, key success factors (KSFs) for portfolio performance and project success in the AEC industry are found that in the end drive business success. Findings indicate that the whole range from descriptive analytics to prescriptive analytics apply to PPM practices. Most value can be derived through starting with improved descriptive analytics through visualization and dashboarding. The developed conceptual framework is theoretic and implementation of the framework in practice is subject to influencing factors. Findings indicate different categories of influencing factors, under which digitalization in the AEC industry, general barriers to PPM, project management and data management maturity, and barriers to Data & Analytics. Some factors are covered through the framework, other factors are refuted with resolutions.Management of Technology (MoT