Universität zu Köln

Azkan, Can, Spiekermann, Markus and Goecke, Henry (2019). Uncovering Research Streams in the Data Economy Using Text Mining Algorithms. Technol. Innov. Manag. Rev., 9 (11). S. 62 - 75. OTTAWA: CARLETON UNIV GRAPHIC SERVICES. ISSN 1927-0321

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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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Azkan, Can UNSPECIFIED UNSPECIFIED UNSPECIFIED Spiekermann, Markus UNSPECIFIED UNSPECIFIED UNSPECIFIED Goecke, Henry UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-128915
Journal or Publication Title:	Technol. Innov. Manag. Rev.
Volume:	9
Number:	11
Page Range:	S. 62 - 75
Date:	2019
Publisher:	CARLETON UNIV GRAPHIC SERVICES
Place of Publication:	OTTAWA
ISSN:	1927-0321
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language INFORMATION; INNOVATION Multiple languages Management Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/12891