Enterprise Social Media for Knowledge Brokering

Increasing use of enterprise social media (ESM) for organizational communication and collaboration has fundamentally changed how knowledge is shared and created. Despite the benefits, the abundant availability of information within organizations can sometimes cause confusion, feelings of inadequacy, and even information overload. This doctoral dissertation focuses on knowledge brokering, as it bridges gaps between diverse domains and ensures that relevant information and insights are effectively used within organizations. Discussions on the ESM platforms of two knowledge-intensive organizations were examined to answer the main research question: "How does ESM influence knowledge brokering?" This dissertation comprises four essays. Essays 1 and 2 explore how knowledge brokers can be identified based on their ESM discussions. Essay 3 provides a comprehensive literature review on knowledge brokering and highlights the affordances of ESM for knowledge brokering. Finally, the empirical study reported in Essay 4 utilizes ESM data to uncover attention patterns within organizational communication. Overall, this doctoral dissertation sheds light on how knowledge brokering can leverage ESM to connect individuals, share knowledge, and create new insights, potentially mitigating information overload. It makes four main contributions. First, building on social network theory, this study extends knowledge brokering literature by demonstrating how knowledge brokers, both individuals and intraorganizational groups, can be identified through digital discussions on ESM based on their communicative actions. Second, it identifies the dual role of ESM affordances in both enabling and constraining knowledge brokering, and proposes a framework to analyze and understand the complex interplay of knowledge brokering and ESM. Third, it demonstrates how ESM reveals patterns in attention dynamics within organizational communication. Fourth, it presents innovative methodological approaches to analyze large sets of real-time ESM data, free from self-reflective bias, enabling the observation of individuals and teams in their authentic environments. Furthermore, by complementing qualitative analysis with computational techniques, it introduces a dynamic perspective to the traditionally static view of networks in social network theory, and reveals how organization level patterns are influenced by and influence individual actions over time.