Uncovering Research Streams in the Data Economy Using Text Mining Algorithms

Can Azkan; Markus Spiekermann; Henry Goecke

doi:10.22215/timreview/1284

Technology Innovation Management Review (Nov 2019)

Uncovering Research Streams in the Data Economy Using Text Mining Algorithms

Can Azkan,
Markus Spiekermann,
Henry Goecke

Affiliations

Can Azkan: <div>Fraunhofer Institute </div><div> </div><div>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.</div>
Markus Spiekermann: <div>Fraunhofer Institute</div><div> </div><div>Markus Spiekermann currently works as Head of Department "Data Business" at the Fraunhofer Institute for Software and Systems Engineering in Dortmund, Germany. He leads research projects and is active in several related advisory boards. His main research focuses on the topics of data engineering and data management, alongside on the valuation of data assets especially within data ecosystems. Before his time at Fraunhofer, he worked as IT-Professional and Software Engineer from 2008 to 2016. He obtained his Bachelor and Master of Science degree in the field of information systems with a focus on IT Management at the FOM University of Applied Sciences in Essen.</div>
Henry Goecke: <div>German Economic Institute</div><div>Since 2017 Dr. Henry Goecke has been head of the Research Group "Big Data Analytics" at the German Economic Institute. Previously he worked at the German Economic Institute as scientific assistant of the Director, at the IW Consult as Senior Economist, at the TU Dortmund University as research and teaching assistant as well as lecturer at the University of Cologne and the Hochschule Fresenius. He studied Economics at the TU Dortmund University, Strathclyde University of Glasgow, and the Leuphana University of Lüneburg. His research interests are on the impact of social media, artificial intelligence, big data, and data economy.</div>

DOI: https://doi.org/10.22215/timreview/1284
Journal volume & issue: Vol. 9, no. 11
pp. 62 – 74

Abstract

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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.

Published in Technology Innovation Management Review

ISSN: 1927-0321 (Online)
Publisher: Carleton University
Country of publisher: Canada
LCC subjects: Technology: Technology (General); Social Sciences: Industries. Land use. Labor: Management. Industrial management
Website: http://timreview.ca

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