Network Dynamics and Field Evolution: The Growth of Interorganizational Collaboration in the Life Sciences1

By Walter W. Powell, Douglas R. White, K. Koput, Jason Owen-Smith. American Journal of Sociology, 2005

1,815 citations recorded by the source corpus.

A recursive analysis of network and institutional evolution is offered to account for the decentralized structure of the commercial field of the life sciences. Four alternative logics of attachment—accumulative advantage, homophily, follow‐the‐trend, and multiconnectivity—are tested to explain the structure and dynamics of interorganizational collaboration in biotechnology. Using multiple novel methods, the authors demonstrate how different rules for affiliation shape network evolution. Commercialization strategies pursued by early corporate entrants are supplanted by universities, research institutes, venture capital, and small firms. As organizations increase their collaborative activities and diversify their ties to others, cohesive subnetworks form, characterized by multiple, independent pathways. These structural components, in turn, condition the choices and opportunities available to members of a field, thereby reinforcing an attachment logic based on differential connections to diverse partners.

Subfields: Management of Technology and Innovation, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/8b90414e400fd236cff30026e5dec02e656bc897

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In Management of Technology and Innovation

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Citations in the corpus

The Genesis and Dynamics of Organizational Networks (2012, background): “More recent research has addressed the macrodynamics of networks to understand how organizational fields evolve (Powell et al. 2005).”
Partner Selection Decisions in Interfirm Collaborations: The Paradox of Relational Embeddedness (2010, background): “Consequently, firms need to incorporate both embedded and non-embedded ties in their partner selection decisions (Baum et al., 2005; Nohria and Garcia-Pont, 1991; Powell et al., 2005).”
Overlapping community detection at scale: a nonnegative matrix factorization approach (2013, background): “For example, people sharing multiple hobbies or belonging to several common institutions have a higher chance of becoming friends [23], researchers with many common interests are more likely to work together [26], and proteins involved in multiple common functional modules are more likely to interac”
Networks, Propinquity, and Innovation in Knowledge-intensive Industries (2009, background): “Over time, the internal structure of many organizations changed, co-evolving with transformations in the patterns of affi liation that characterized the industry network (Galambos and Sturchio, 1996; Powell et al., 2005).”