Efficient Structures for Innovative Social Networks

What lines of communication among members of an organization are most productive in the early, ideation phase of innovation? We investigate this question with a recombination and selection model of knowledge transfer operating through a social network. We find that ideation is accelerated when people in the organization dynamically churn through a large (ideally the entire population) set of conversational partners over time, which naturally begets short path lengths and eliminates information bottlenecks. Group meetings, in which the content of conversations is available to all for consideration, are another way to learn in parallel and accelerate the ideation process, although for complex problems they may not offer significant advantages over the best decentralized networks. The idealized core-periphery graphs emerge as an important family on the time–cost efficient frontier. New sociometrics for the analyses of innovation processes emerge from this investigation.

• 저널Management Science · 56(7) · 1127–1145
• 토픽Complex Network Analysis Techniques · Statistical and Nonlinear Physics
• DOI10.1287/mnsc.1100.1168
• 저자William S. Lovejoy, Amitabh Sinha