How knowledge loss and network-structure jointly determine R&D productivity in the biotechnology industry

Organizational learning scholarship has shown that experience accumulates in manufacturing operations over time, affecting organizational productivity. To date, we have a limited understanding of how the loss of this experience by decay conditions R&D productivity. We advance this research by developing a model in which a scientist's R&D productivity is a function of her or his technological expertise. This technological expertise is subject to loss over time. Using a long panel (1970-2007) of data from the U.S. biotechnology industry, we find that knowledge loss decreases a scientist's R&D productivity because it reduces the technological expertise the scientist possesses and uses in R&D projects. Knowledge loss also affects R&D productivity through spillovers of knowledge. In our model, spillovers of external knowledge jointly depend on a scientist's network centrality and cohesion, the scientists' prior knowledge, and the extent to which external knowledge is available. Our analysis indicates that since knowledge loss limits decreases the scientist's prior knowledge, it diminishes her or his ability to assimilate and use external knowledge present in a firm and an industry. This in turn decreases the scientists' R&D productivity. Knowledge loss thus is critical to understand R&D productivity and knowledge spillovers.

Automated summary

Organizational learning scholarship has shown that experience accumulation in manufacturing operations affects organizational productivity. However, the impact of knowledge loss on R&D productivity is not well understood. This study develops a model that demonstrates how knowledge loss decreases a scientist's R&D productivity by reducing their technological expertise and affecting knowledge spillovers. The findings highlight the importance of managing and retaining knowledge to sustain innovation and productivity in organizations.