Knowledge spillovers from artificial intelligence faculty: The impact of universities on regional innovation ecosystems

Details

Supervisors

Prof. Dr. Christian Schwens ; Dr. Tim Haarhaus

Faculty

Louvain School of Management

Degree label

Master [120] : ingénieur de gestion, à finalité spécialisée

Abstract

This study discusses a potential relationship between AI faculty and private sector innovation in AI innovation ecosystems. Based on the literature on innovation ecosystems and knowledge spillovers, it is hypothesized that AI professors have a positive impact on private firm innovation within a region. This influence arises from knowledge spillovers, facilitated by personal interactions within the professors’ broad networks in academia and industry, as well as through the education of students and industry professionals. A random effects regression analysis was conducted using panel data from the USPTO over the period 2000 to 2020. The study finds a significant positive relationship between the number of AI faculties and the number of AI patents granted to private firms within the same US county. These results contribute to the existing literature on knowledge spillovers and the relevant actors within innovation ecosystems. They highlight the impact of universities, particularly their faculties, in driving innovation. The channels of knowledge spillovers from faculty are further examined by considering various moderating factors. The results show that human capital in the IT sector has a positive effect on knowledge spillovers at higher levels of the variable. However, there is no significant impact of AI conferences and external investment in university R&D. This shows that the mere existence of exchange platforms and resources may not be sufficient, and highlights the importance of actively mobilizing tacit knowledge through spillover agents. Moreover, the present study advances the understanding of spillover dynamics by demonstrating that the impact of faculty on innovation intensifies over time, with the strongest effects observed after approximately seven years. This clarifies the complexity of the processes involved in AI innovation and patenting. By focusing exclusively on AI faculty and patents, this study is the first to identify the role of spillovers from academic individuals to the private sector in this field. Thereby, it also complements the literature on AI innovation ecosystems and AI education. In addition, several practical implications were derived. It is recommended that universities and governments leverage the role of faculty and promote exchanges within innovation ecosystems. Companies should actively engage with academia, increase their absorptive capacity, and consider nearby universities in their location decisions. Finally, this study discusses potential limitations related to the selection and explanatory power of the variables, the external validity of the results due to its geographical scope, and endogeneity issues. Therefore, it leaves room for fur-ther research and refinement.