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Mixed methods analysis of enterprise social networks


Behrendt, Sebastian; Richter, Alexander; Trier, Matthias (2014). Mixed methods analysis of enterprise social networks. Computer Networks, 75:560-577.

Abstract

The increasing use of enterprise social networks (ESN) generates vast amounts of data, giving researchers and managerial decision makers unprecedented opportunities for analysis. However, more transparency about the available data dimensions and how these can be combined is needed to yield accurate insights into the multi-facetted phenomenon of ESN use. In order to address this issue, we first conducted a systematic literature review to identify available data dimensions and integrated them into a conceptual framework. We then adopted this framework as part of a mixed methods research approach to comprehensively analyze an empirical ESN case. With our results serving as a proof of concept we show the insights that can be derived from different data dimensions and how combining these can improve the validity of the analysis. The application of the framework also allows us to derive a detailed guideline for combining different data sources in ESN analysis to support researchers and decision makers.

Abstract

The increasing use of enterprise social networks (ESN) generates vast amounts of data, giving researchers and managerial decision makers unprecedented opportunities for analysis. However, more transparency about the available data dimensions and how these can be combined is needed to yield accurate insights into the multi-facetted phenomenon of ESN use. In order to address this issue, we first conducted a systematic literature review to identify available data dimensions and integrated them into a conceptual framework. We then adopted this framework as part of a mixed methods research approach to comprehensively analyze an empirical ESN case. With our results serving as a proof of concept we show the insights that can be derived from different data dimensions and how combining these can improve the validity of the analysis. The application of the framework also allows us to derive a detailed guideline for combining different data sources in ESN analysis to support researchers and decision makers.

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Citations

12 citations in Web of Science®
19 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:2014
Deposited On:09 Oct 2014 11:22
Last Modified:08 Dec 2017 07:25
Publisher:Elsevier
ISSN:1389-1286
Publisher DOI:https://doi.org/10.1016/j.comnet.2014.08.025
Other Identification Number:merlin-id:10174

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