Organizational learning across multi-level networks

This paper examines organizational learning through a multilevel network lens. We assess how interpersonal knowledge transfer is sustained by the organizational structure of interunit work-flow ties and by the level of specialism of the connected units. To do this, we apply Multilevel Exponential Random Graph Models on data collected in a multiunit government institution in Italy. Results indicate that our approach allows simplifying and better understanding of organizational learning. Units are more likely to retain knowledge transfer ties within their boundaries. Unit boundary-spanning tends to occur only when knowledge transfer ties are sustained by hierarchical interunit work-flow ties. (C) 2015 Elsevier B.V. All rights reserved.