Uncovering Research Streams in the Data Economy Using Text Mining Algorithms
| Autor: | Markus Spiekermann, Henry Goecke, Can Azkan |
|---|---|
| Přispěvatelé: | Publica |
| Rok vydání: | 2019 |
| Předmět: |
Big Data
data market lcsh:Management. Industrial management Data Economy literature review Computer science 020209 energy Digital Economy Textmining 02 engineering and technology Business model DataMarket Field (computer science) Management of Technology and Innovation lcsh:Technology (General) 0502 economics and business 0202 electrical engineering electronic engineering information engineering Network Graph text mining 05 social sciences Computer Science Applications Systematic review Economy lcsh:HD28-70 Key (cryptography) lcsh:T1-995 digital transformation 050203 business & management Data Ecosystem |
| Zdroj: | Technology Innovation Management Review, Vol 9, Iss 11, Pp 62-74 (2019) |
| ISSN: | 1927-0321 |
| DOI: | 10.22215/timreview/1284 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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