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Uncovering Research Streams in the Data Economy Using Text Mining Algorithms

 
: Azkan, Can; Spiekermann, Markus; Goecke, Henry

Technology Innovation Management Review 9 (2019), No.11, pp.62-74
ISSN: 1927-0321
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
DEMAND
Data Economy and Management of Data-Driven Business
English
Journal Article
Fraunhofer ISST ()
Data Ecosystem; Data Economy; Digital Economy; digital transformation; DataMarket; Big Data; literature review; Network Graph; Textmining

Abstract
Data-driven business models arise in different social and industrial sectors, while new sensors and devices are breaking down the barriers for disruptive ideas and digitally transforming established solutions. This paper aims at providing insights about emerging topics in the data economy that are related to companies’ innovation potential. The paper uses text mining supported by systematic literature review to automatize the extraction and analysis of beneficial insights for both scientists and practitioners that would not be possible by a manual literature review. By doing so, we were able to analyze 860 scientific publications resulting in an overview of the research field of data economy and innovation. Nine clusters and their key topics are identified, analyzed as well as visualized, as we uncover research streams in the paper.

: http://publica.fraunhofer.de/documents/N-569601.html