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.

Paola Zappa

Garry Robins

Social Networks

Social Networks is an interdisciplinary and international quarterly. It provides a common forum for representatives of anthropology, sociology, history, social psychology, political science, human geography, biology, economics, communications science and other disciplines who share an interest in the study of the empirical structure of social relations and associations that may be expressed in network form. It publishes both theoretical and substantive papers. Critical reviews of major theoretical or methodological approaches using the notion of networks in the analysis of social behaviour are also included, as are reviews of recent books dealing with social networks and social structure.

Social Networks is an interdisciplinary and international quarterly journal. It serves as a common forum for representatives from various fields, including anthropology, sociology, history, social psychology, political science, human geography, biology, economics, and communication science, who share an interest in studying the empirical structure of social relations and associations that can be expressed in network form. The journal publishes both theoretical and substantive papers, as well as critical reviews of major theoretical or methodological approaches that utilize the concept of networks in analyzing social behavior. Additionally, it includes reviews of recent books related to social networks and social structure.

《Social Networks》是一本跨学科的国际季刊。它为人类学、社会学、历史、社会心理学、政治科学、人文地理学、生物学、经济学、传播科学等多个学科的代表提供了一个共同的论坛，这些学科的研究者都对社会关系和社会关联的实证结构感兴趣，这些结构可以用网络形式表达。期刊发表理论性和实质性的论文，同时也包括对使用网络概念分析社会行为的主要理论或方法论的批判性评审，以及对涉及社会网络和社会结构的近期书籍的评审。

Organizational learning across multi-level networks

Results indicate that the Multilevel Exponential Random Graph Models approach allows simplifying and better understanding of organizational learning.

Category	Quartile
社会学	2
社会学, 人类学	2
社会学, 社会学	3

Organizational learning across multi-level networks

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