Adaptation and Learning in Social Networks

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2018-12

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Abstract

This dissertation broadly addresses the issue of learning in social networks. The dissertation builds on existing literature that leverages learning as a mechanism for predicting the performance effects of different network structures, and focuses on two competing structures: open networks in which none of an actor’s partners are connected to each other, and closed networks in which all of an actor’s partners are connected to each other. The dissertation examines these issues at both the inter-organizational and intra-organizational levels. At the inter-organizational level, the dissertation addresses two related issues. First, prior network research has favored a simplistic view of learning, conceptualized as a firm’s acquisition of information from its network partners. Second, in its focus on information acquisition, this work has prioritized a single informational characteristic—informational diversity. This dissertation uses formal simulation models to advance this literature by (1) accounting for learning not only as information transfer but also as a firm’s ability to adapt in response to performance feedback, and (2) by demonstrating the importance of redundant rather than diverse information for learning from networks. It is shown that once these two issues are properly accounted for, open and closed networks may each generate performance advantages in contexts thought impossible from the perspective of prior work. At the intra-organizational level, prior work conceptualizes learning as an employee’s process for developing expertise. However, the literature on expert development has ignored the importance of an actor’s social network configuration for influencing whether the actor progresses from novice to expert. This dissertation advances the literature on intra-organizational networks by proposing a conceptual model which explains the importance of an actor’s network structure for his or her development as an expert. These insights are then leveraged to propose organizational interventions that may be implemented to improve an employee’s advancement toward expertise within the firm. Overall, the dissertation advances existing research by bringing closer the disparate literatures on network structure and learning at the individual and organizational levels. Opportunities for future research are discussed.

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Social networks, Computer simulation, Organizational learning, Expertise, Business networks

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