Intra-cohort social network structure and cohort performance

Abstract

We investigate the social network relationships between startups in the same accelerator. We use network theory to explain and predict peer effects in the form of social capital, leading to cohort-level performance. We hypothesize and find that higher performing cohorts have fewer unconnected startups than under-performing cohorts. Consistent with the ‘network closure’ and ‘structural holes’ arguments, we also hypothesize an inverse curvilinear relationship between cohort network density and cohort performance. We quantify the intra-cohort social network structures of 1537 startups comprising 154 Techstars cohorts using their activity on Twitter. We find that there exists an upper limit to the impact of cohort network density on performance, after which any further increase becomes negative. We contribute to theory on peer effects in accelerator cohorts by demonstrating the importance of intra-cohort social networks. Our study has implications for stakeholders in accelerators and for the startups that join them.