Machine learning based detection of organizational structures in startups

Startups are often wrongly stylized in society's perception by a lack of structures and a lack of organization - not for nothing, for example, is there the myth of teams thrown together in a garage to develop the next Unicorn.
However, more recent findings from academia and practice stand in contrast to this. Young companies have to deal with various organizational issues right from the start, such as when to establish a marketing function and how to structure it.

For this reason, the research project deals with this and similar questions. The project uses machine learning to derive information about internal structures from publicly available texts and thus approximates a company's organizational structure. In this way, the development of a company can be observed over time. In addition, other sources of information are used to examine how organizational structures affect the achievement of milestones (such as venture capital funding). The project thus contributes to the identification of best practices for the organizational set-up of startups.