Exploring R&D network resilience under risk propagation: An organizational learning perspective

Abstract

Through the development of a risk propagation model, this paper investigates R&D network resilience under risk propagation from an organizational learning perspective. Drawing from organizational learning theory, we first quantify failure experience in the context of risk propagation. Then a risk propagation model is developed, which is composed of two parts: spontaneous mechanism and propagation mechanism. Finally, we implement agent-based simulation to show how organizational learning and forgetting impact on R&D network resilience and firm vulnerability under risk propagation. Our results show a significant association between organizational learning and both network resilience and firm vulnerability. However, the results differ in different levels of risk. When the propagation probability is low, network resilience can be enhanced through improving the learning ability or decreasing forgetting for both high-vulnerable and low-vulnerable firms. However, in other scenarios, the low-vulnerable firms are more critical to network resilience. Additionally, the impact of firm failure magnitude on enhancing network resilience will gradually decrease with the increase of firm learning rate. Our results will help practitioners understand the effect of organizational learning on R&D network resilience and support decision-making in risk management.