Abstract
Knowledge brokers serve as facilitators of knowledge sharing. The extant literature calls for nuanced analyses of different organizational structures as the spaces knowledge brokers operate in. Our interest lies in formal, semiformal, and informal organizational network structures and in how knowledge brokers are positioned in them. In this paper, we outline a collaborative analysis method, with researchers from different disciplines working together in data sprints. The benefit of this process is that it enables analyzing large organizational networks with deep insights. Amplifying social network analysis with field knowledge offers a deeper understanding of the connections in the network. This paper describes the analysis process and proposes interdisciplinary data processing techniques. We applied the proposed method using an extensive empirical data set that includes intraorganizational social media interactions between employees in a global organization. Our analysis transforms enterprise social media data into a network model that describes an organization’s social structure.
Original language | English |
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Title of host publication | Proceedings of the 55th Hawaii International Conference on System Sciences 2022 |
Pages | 585-593 |
Number of pages | 9 |
ISBN (Electronic) | 978-0-9981331-5-7 |
DOIs | |
Publication status | Published - 2022 |
MoE publication type | A4 Article in a conference publication |
Event | Annual Hawaii International Conference on System Sciences - Manoa, United States Duration: 4 Jan 2022 → 7 Jan 2022 Conference number: 55 |
Publication series
Name | Proceedings of the Annual Hawaii International Conference on System Sciences |
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ISSN (Print) | 1530-1605 |
ISSN (Electronic) | 2572-6862 |
Conference
Conference | Annual Hawaii International Conference on System Sciences |
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Abbreviated title | HICSS |
Country/Territory | United States |
City | Manoa |
Period | 04/01/2022 → 07/01/2022 |
Keywords
- cluster detection
- data-sprint
- knowledge broker
- social network analysis