Impact of Knowledge Management Practices on Project Management in Research-Driven Startups: A Case Study of Probingon AB

Research-based startups often face the challenge of balancing their passion for innovation with the need for formal knowledge management (KM) and project management (PM) practices. While technical tasks are generally given priority, KM and PM are often neglected. This thesis explores how KM practices impact the choice and modification of PM approaches in a research and development (R&D) focused startup. A qualitative case study methodology was utilised to examine the internal KM practices of Probingon AB, a Swedish medical technology company that develops clinical-grade wearable diagnostic devices. Data were gathered through semi-structured interviews and participant observations. A hybrid abductive thematic analysis was used to conduct the data analysis. The theoretical framework that guides this thesis is built around five phases of KM: capture, store, share, apply, and integrate. It focuses on how KM practices operate across projects and impact PM in terms of communication, decision-making, and project tracking. The results revealed significant challenges in PM due to poor KM practices. Formal documentation is frequently ignored in favour of technical tasks, leading to informal communication and incomplete records. PM tools such as Gantt charts and LeanTime were underutilised, as they are perceived to be too rigid for Probingon's dynamic workflows. The thesis concludes by arguing that PM and KM should not be treated as separate practices but must be interconnected. It also suggests that only a company-specific hybrid PM approach can significantly improve project outcomes when backed by structured KM practices. In the case of Probingon AB, this thesis recommends a selective combination of Agile principles and Stage-Gate elements. While this Agile–Stage-Gate hybrid may work for Probingon AB, different combinations may be more effective for other R&D startups. Thus, carefully considering organisational size, needs, regulatory demands, and development cycles is essential, as the wrong combination can do more harm than good.