Exploring individual’s knowledge transfer motivations : a social network perspective

Abstract

While desirable and highly promoted by every organization, transferring one’s best knowledge to the rest of the organization is a hard, challenging task. To facilitate knowledge transfer within an organization, in-depth understanding about the antecedents of knowledge transfer is required. The economic perspective that assumes the atomized human being who is trying to maximize own utility was not satisfactory to fully explain the mechanisms of knowledge transfer. To resolve this issue, we adopted a sociological perspective which claims that human being is embedded within social networks (social relationships one keeps with his/her friends or colleagues) and the social networks influence human being’s behaviors. Theoretical framework of this thesis postulates that the social networks influence the human being’s psychological factors, which subsequently influence the behaviors. Based on this theoretical framework, we suggest that the social networks influence the knowledge sources’ motivations to transfer their knowledge to the colleagues and these motivational mechanisms differ by the type of knowledge transfer (open vs. closed). By analyzing survey responses from eight R&D groups of five firms in Korea using structural equation modeling, we find that the properties of the social networks such as strength of ties, network centrality, and perceived expertise affect five motivational factors of knowledge transfer. Among the motivational factors, interpersonal factors such as identification and reciprocity are shown to have significant effects on closed knowledge transfer, while self-efficacy and reward affect open knowledge transfer. Second study utilizes multilevel analysis to look deep into the knowledge transfer process both from the knowledge source’s and knowledge recipient’s perspective. We examine the dyadic- and individual-level antecedents of knowledge transfer based on a multilevel research model. Using hierarchical linear modeling method, we find...