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Metrics for Analyzing Social Documents to Understand Joint Work

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Title:Metrics for Analyzing Social Documents to Understand Joint Work
Authors:Schubert, Petra
Mosen, Julian
Schwade, Florian
Keywords:Collaboration for Data Science
enterprise collaboration platform
enterprise social software
metrics
social collaboration analytics
show 1 moresocial documents
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Date Issued:07 Jan 2020
Abstract:Social Collaboration Analytics (SCA) aims at measuring and interpreting communication and joint work on collaboration platforms and is a relatively new topic in the discipline of Information Systems. Previous applications of SCA are largely based on transactional data (event logs). In this paper, we propose a novel approach for the examination of collaboration based on the structure of social documents. Guided by the ontology for social business documents (SocDOnt) we develop metrics to measure collaboration around documents that provide traces of collaborative activity. For the evaluation, we apply these metrics to a large-scale collaboration platform. The findings show that group workspaces that support the same use case are characterized by a similar richness of their social documents (i.e. the number of components and contributing authors). We also show typical differences in the “collaborativity” of functional modules (containers).
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63769
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.030
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Collaboration for Data Science


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