Authors:
Arman Arzani
1
;
Marcus Handte
1
;
Matteo Zella
2
and
Pedro José Marrón
1
Affiliations:
1
University of Duisburg-Essen, Essen, Germany
;
2
Niederrhein University of Applied Sciences, Krefeld, Germany
Keyword(s):
Knowledge Transfer, Founding Potential, Researcher Profiling, Innovation Identification.
Abstract:
Technology transfer is central to the development of an iconic entrepreneurial university. Academic science has become increasingly entrepreneurial, not only through industry connections for research support or transfer of technology but also in its inner dynamic. To foster knowledge transfer, many universities undergo a scouting process by their innovation coaches. The goal is to find staff members and students, who have the knowledge, expertise and the potential to found startups by transforming their research results into a product. Since there is no systematic approach to measure the innovation potential of university members based on their academic activities, the scouting process is typically subjective and relies heavily on the experience of the innovation coaches. In this paper, we study the discovery of potential founders to support the scouting process using a data-driven approach. We create a novel data set by integrating the founder profiles with the academic activities f
rom 8 universities across 5 countries. We explain the process of data integration as well as feature engineering. Finally by applying machine learning methods, we investigate the classification accurracy of founders based on their academic background. Our analysis shows that using a Random Forest (RF), it is possible to successfully differentiate founders and non-founders. Additionally, this accuracy of the classification task remains mostly stable when applying a RF trained on one university to another, suggesting the existence of a generic founder profile.
(More)