Come Close and Co-create: Proximities in Pharmaceutical Innovation Networks

In studying firm behavior, economists tend to have an under-socialized view of the firm, while sociologists tend to have an over-socialized view of the firm. Socialization in this respect refers to the extent to which a firm is embedded in- and affected by its relational environment. On the one extreme, economists building on transaction costs economics assume the market to be an anonymous environment where firms can behave opportunistically without reper

When Clusters become Networks

Policy makers spend large amounts of public resources on the foundation of science parks and other forms of geographically clustered business activities, in order to stimulate regional innovation. Underlying the relation between clusters and innovation is the assumption that co-located firms engaged in innovative activities benefit from knowledge that diffuses locally. In order to access this knowledge, firms are often required to form more- or less formal relations with co-located firms. Empirical evidence shows however that besides some success cases like Silicon Valley and the Emilia- Romagna region where firms collaborate intensively, many regional clusters are mere co-locations of firms. To enhance our understanding of why some clusters become networks of strategic collaboration and others don’t, we study link formation within European biopharmaceutical clusters. More specifically we look at the effect of cluster characteristics such as number of start-up firms, established firms or academic institutions, or the nature of the collaborations on the probability of local link formation

Pharmaceutical Research Strategies

This study analyses 1400 research projects of the top 20 R&D-spending pharmaceuticals to identify the determinants of successful research projects. We provide clear evidence that externally sourced projects and projects involving biotechnologies perform better than internal projects and chemical projects, respectively. Controlling for these effects, we find that big pharma should either build a critical mass of disease area knowledge or diversify projects over different DA’s in order to obtain higher success probabilities. Projects in which a firm has built a critical mass of disease knowledge (through at least 10 projects per DA) are significantly more likely to reach clinical testing. Moreover, within large disease areas, the success probabilities of internal projects increases when a few (less than 20%) externally sourced projects are involved. We interpret this finding as knowledge spillovers from external to internal projects, as the limited number of external projects enables the same people to be involved in both external and internal research projects and apply externally generated knowledge internally