On strategies enhancing technological learning and technological performance in R&D- intensive corporations.

The purpose of this project is to gain a deeper understanding of the interplay between technology licensing and firms' technological performance. We aim to provide insights into the role of internal patent portfolios as a determinant of a firm's decision to engage in licensing and its impact on subsequent technological performance. First, we investigate what types of technology firms tend to in-license, and how this affects the composition of their technology portfolios. Second, we explore how this portfolio composition drives firms' decisions to out-license, that is, whether a diversified technology portfolio and the presence of general purpose technologies are important conditions for out-licensing. Third, we seek to explain how licensing impacts a firm's technological performance, by examining both in-licensing and out-licensing. We want to answer these questions by tapping into a data source that has so far never been used to track technology licensing deals, namely text analytics of unstructured big data on firms in five R&D-intensive industries.. Empirically, we adopt a mixed-methods approach, combining quantitative and qualitative methods. Our focus will be on firm-level as well as on the more detailed firm- and technology-level, allowing us to maximally take advantage of the richness of the quantitative data.

Jaar van publicatie:2020

Toegankelijkheid:Open