Location
Grand Wailea, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2020 12:00 AM
End Date
10-1-2020 12:00 AM
Description
In recent years enterprise collaboration systems (ECS) integrated with social network capabilities have become popular tools for supporting knowledge management (KM) strategies and organizational learning. Increased usage has resulted in higher interest in understanding and classifying the roles that ECS users adopt online. Previous research has investigated user role identification by considering: the degree of participation in an ECS, the user interactions with shared content, the user role in the ECS network, and the user KM-role observed within an interaction. Although all of these factors provide insights into ECS user engagement, they fail to fully consider the knowledge sharing perspective. In this paper, we define "bridge users" within the context of KM and present a framework for identifying them using semantic analysis of user-generated content. Further, we present results and observations from tests of our pipeline on the ECS of a large multinational engineering company with more than 100k users.
Identifying Bridge Users: the Knowledge Transfer Agents in Enterprise Collaboration Systems
Grand Wailea, Hawaii
In recent years enterprise collaboration systems (ECS) integrated with social network capabilities have become popular tools for supporting knowledge management (KM) strategies and organizational learning. Increased usage has resulted in higher interest in understanding and classifying the roles that ECS users adopt online. Previous research has investigated user role identification by considering: the degree of participation in an ECS, the user interactions with shared content, the user role in the ECS network, and the user KM-role observed within an interaction. Although all of these factors provide insights into ECS user engagement, they fail to fully consider the knowledge sharing perspective. In this paper, we define "bridge users" within the context of KM and present a framework for identifying them using semantic analysis of user-generated content. Further, we present results and observations from tests of our pipeline on the ECS of a large multinational engineering company with more than 100k users.
https://aisel.aisnet.org/hicss-53/cl/distributed_collaboration/7