Quantifying knowledge exchange in R&D networks: a data-driven model

Abstract

We propose a model that reflects two important processes in R&D activities of firms, the formation of R&D alliances and the exchange of knowledge as a result of these collaborations. In a data-driven approach, we analyze two large-scale data sets extracting unique information about 7500 R&D alliances and 5200 patent portfolios of firms. This data is used to calibrate the model parameters for network formation and knowledge exchange. We obtain probabilities for incumbent and newcomer firms to link to other incumbents or newcomers which are able to reproduce the topology of the empirical R&D network. The position of firms in a knowledge space is obtained from their patents using two different classification schemes, IPC in 8 dimensions and ISI-OST-INPI in 35 dimensions. Our dynamics of knowledge exchange assumes that collaborating firms approach each other in knowledge space at a rate µ for an alliance duration τ . Both parameters are obtained in two different ways, by comparing knowledge distances from simulations and empirics and by analyzing the collaboration efficiencyĈ n . This is a new measure, that takes also in account the effort of firms to maintain concurrent alliances, and is evaluated via extensive computer simulations. We find that R&D alliances have a duration of around two years and that the subsequent knowledge exchange occurs at a very low rate. Hence, a firm's position in the knowledge space is rather a determinant than a consequence of its R&D alliances. From our data-driven approach we also find model configurations that can be both realistic and optimized with respect to the collaboration efficiencyĈ n . Effective policies, as suggested by our model, would incentivize shorter R&D alliances and higher knowledge exchange rates.

Knowledge sharing in inter-organisational teams: the role of the advice network and the substitutive role of the formal network in an R&D alliance

“…So, we assume that the ability of an alliance member to share knowledge with members from the other partner(s) is most influential to his/her alliance-related work performance. This ability depends on the propensity of the alliance member to exchange knowledge with members from the other partner(s) (Cohen and Levinthal 1990;Tomasello, Tessone, and SCHWEITZER 2016;Vaccario et al 2018), and the number of alliance members with whom one can interact and collaborate profitably (Tomasello, Tessone, and SCHWEITZER 2016;Vaccario et al 2018). With alliance members within the same firm it is relatively easy to interact, but the collaboration with alliance members of the partner firm(s) probably is much more profitable.…”

Does the position in the inter-sectoral knowledge space affect the international competitiveness of industries?

“…Operti and Carnabuci (2011) and Tortoriello (2015) provide additional empirical evidence consistent with the theoretical framework formulated here. A more structured modelization of knowledge space has been adopted by Tomasello et al (2016) and Vaccario et al (2017) in studying R&D alliances and knowledge exchange among firms. Finally, to these three factors related to knowledge, it is possible to add some remarks about the variety of channels and organizational forms through which knowledge crosses industry boundaries.…”

Fabless Semiconductor Firms’ Financial Performance Determinant Factors: Product Platform Efficiency and Technological Capability

“…Demand for system IC (Integrated Circuit) has increased as well. It is adequate to use the most recent 10 years of data to verify each firm's technology trend [59]. The patent pool comes from patent applications.…”