Uncovering Research Streams in the Data Economy Using Text Mining Algorithms

Technology Innovation Management Review

Can Azkan¹  Markus Spiekermann²  Henry Goecke³ 

[1] 

Fraunhofer Institute ;

 

Can Azkan is a scientist and PhD candidate at the Fraunhofer Institute for Software and Systems Engineering ISST in Germany. He studied Mechanical Engineering at the Technical University of Dortmund and the San Diego State University, while he gained practical experience in the field of industrial engineering and digital business models in machine und plant engineering. His research at Fraunhofer ISST focuses on value co-creation in emerging data ecosystems and the management of data as a corporate asset.

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关键词: big data;    data economy;    data ecosystem;    data market;    digital economy;    digital transformation;    literature review;    network graph;    text mining.;   

DOI  :  http://doi.org/10.22215/timreview/1284

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown