Simulating learning in interorganizational networks: The insidious role of task interdependence and relational instability in system-level learning

In this paper we develop a multi-agent simulation model to explore the issue of learning in interorganizational networks. Though interorganizational network researchers generally agree that when firms form into networks they will gain access to new knowledge, the question of learning beyond the firm at the boundaries between firms or at the level of the network itself remain less explored. We simulate the impact of task interdependence and relational instability on learning in interorganizational networks comprised of multiple disparate specialist firms. We find that relational instability in networks slows learning and that task interdependence moderates the impact of increasing relational instability on network productivity rates. The findings have significant implications for interorganizational network theory. Furthermore, the simulation results provides insights into appropriate firm and network strategies for change.

Presented at:
North American Association for Computational Social and Organizational Science (NAACSOS) Conference, University of Notre Dame, South Bend, Indiana, USA, 22-23 June, 2006
Best Paper Award

 Record created 2007-04-24, last modified 2018-03-17

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