Metrics for Analyzing Social Documents to Understand Joint Work

dc.contributor.author Schubert, Petra
dc.contributor.author Mosen, Julian
dc.contributor.author Schwade, Florian
dc.date.accessioned 2020-01-04T07:11:01Z
dc.date.available 2020-01-04T07:11:01Z
dc.date.issued 2020-01-07
dc.description.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).
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.030
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/63769
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Collaboration for Data Science
dc.subject enterprise collaboration platform
dc.subject enterprise social software
dc.subject metrics
dc.subject social collaboration analytics
dc.subject social documents
dc.title Metrics for Analyzing Social Documents to Understand Joint Work
dc.type Conference Paper
dc.type.dcmi Text
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