Computational modeling shows how network structure influences innovation diffusion in dynamic organizational fields, indicating potential strategies for effective adoption.
Computational modeling simulated innovation diffusion through six prototypical in-terregional network structures and two distributions of partnering tendencies in dy-namic organizational fields. Compared to regional constraints, connections among all geographic regions decreased clearly beneficial innovation diffusion (a low-threshold adoption model) but increased ambiguous innovation diffusion (a social influence model). Compared with uniform partnering tendencies, normally distributed partner-ing tendencies increased diffusion of ambiguous innovations. Overall, local and inter-regional network structures interacted with the observability of an innovation’s ben-efits to determine diffusion. As an organizational population develops, net-works of relations form among its members. Adap-tive change can diffuse through these networks (Kraatz, 1998), but structural disparities may deter-mine whether an innovation sweeps through the field or languishes in obscurity. To the extent that
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Deborah E. Gibbons (2004) studied this question.
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