Network Structure and Innovation Ambiguity Effects on Diffusion in Dynamic Organizational Fields

Abstract

Computational modeling simulated innovation diffusion through six prototypical interregional network structures and two distributions of partnering tendencies in dynamic 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 partnering tendencies increased diffusion of ambiguous innovations. Overall, local and interregional network structures interacted with the observability of an innovation's benefits to determine diffusion.

Distributed leadership in teams: The network of leadership perceptions and team performance

“…A different possibility is to make use of the availability of cheap computing power and increasingly sophisticated simulation techniques to model the linkages between leadership networks and group outcomes. Network simulation allows researchers to overcome the difficulty of collecting sociometric data from a large number of field-based teams, and it offers a practical tool for systematically varying the many different factors that are likely to influence the structure of leadership networks in teams (see Newman, 2003, for a review of generalized models of network growth and change; and see Gibbons, 2004, for a recent application of this methodological approach in organizational studies). Irrespective of whether researchers use field-based, laboratorybased, or simulation techniques, we urge them to borrow freely from the extensive work that has already been done on different social network structures (see Carrington, Scott, & Wasserman, 2005;Wasserman & Faust, 1994).…”

Global alliance networks: A comparison of biotech SMEs in Sweden and Australia

“…Using an SNA approach, Fleming and Juda (2004) contend that relatively few key players can catalyze the agglomeration of many small networks into larger ones and boost innovation across whole regions. Indeed, research in a number of fields has demonstrated the importance of identifying central network links as opinion leaders in the diffusion of innovations, again, from an SNA perspective (Deroian, 2002;Gibbons, 2004). The importance of social networks in the diffusion of innovations has been shown in industries and settings as diverse as construction (Pryke, 2004), small firms in technology and engineering (Hanna & Walsh, 2002), and computer-related services among university faculty (Durrington, Repman, & Valente 2000).…”

Elucidating strategic network dynamics through computational modeling

“…A few notable exceptions are the projects by Abrahamson and Rosenkopf (1997), Gibbons (2004), and Gavetti and Levinthal (2000), which provided some initial glimpse into the potential of using computational modeling for addressing inter-organizational relationships from a dynamic network perspective. Abrahamson and Rosenkopf (1997) and Gibbons (2004) looked into the role of networks in the diffusion of innovations, while Gavetti and Levinthal (2000) used an NK model (see Kauffman 1993 for a description) to examine the role of cognition in search behaviors.…”

“…The event has a resource demand for at most K e resource types. In the real world, such events can happen when firms face crises or new product innovations (e.g., Gibbons 2004;Lin et al 2006;Staw et al 1981). For this study, K e is set at 5, so no event will demand more than 5 types of recources even if the dimensionality of resource space for the firms in the network is larger (K > 5).…”