The effect of R&D collaborations on firms' technological positions

Abstract

We develop an agent-based model to reproduce the processes of link formation and knowledge exchange in a Research and Development (R&D) inter-organizational network. In our model, agents form links based on their network features, i.e. their belonging to one of the network's circles of influence and their previous alliance history, and then exchange knowledge with their partners, thus modifying their positions in a metric knowledge space. Furthermore, we validate the model against real data using a two-step approach. Through the Thomson Reuters SDC alliance dataset, we estimate the model parameters related to the link formation, thus reproducing the topology of the resulting R&D network. Subsequently, using the NBER data on firm patents, we estimate the parameters related to the knowledge exchange process, thus evaluating the rate at which firms exchange knowledge and the duration of the R&D alliances themselves. The underlying knowledge space that we consider in our real example is defined by IPC patent classes, allowing for a precise quantification of every firm's knowledge position. Our novel data-driven approach allows us to unveil the complex interdependencies between the firms' network embeddedness and their technological positions. Through the validation of our model, we find that real R&D alliances have a duration of around two years, and that the subsequent knowledge exchange occurs at a very low rate. Most of the alliances, indeed, have no consequence on the partners' knowledge positions: this suggests that a firm's position - evaluated through its patents - is rather a determinant than a consequence of its R&D alliances. Finally, we propose an indicator of collaboration performance for the whole network. We find that the real R&D network does not maximize such an indicator. Our study shows that there exist configurations that can be both realistic and optimized with respect to the collaboration performance. Effective policies to obtain an optimized collaboration network - as suggested by our model - would incentivize shorter R&D alliances and higher knowledge exchange rates, for instance including rewards for quick co-patenting by allied firms.