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Inter-technology relationship networks: Arranging technologies through text mining

 
: Hofmann, P.; Keller, R.; Urbach, N.

:

Technological forecasting and social change 143 (2019), S.202-213
ISSN: 0040-1625
Englisch
Zeitschriftenaufsatz
Fraunhofer FIT ()

Abstract
Ongoing advances in digital technologies – which enable new products, services, and business models – have fundamentally affected business and society through several waves of digitalization. When analyzing digital technologies, a dynamic system or an ecosystem model that represents interrelated technologies is beneficial owing to the systemic character of digital technologies. Using an assembly-based process model for situational method engineering, and following the design science research paradigm, we develop an analytical method to generate technology-related network data that retraces elapsed patterns of technological change. We consider the technological distances that characterize technologies' proximities and dependencies. We use established text mining techniques and draw from technology innovation research as justificatory knowledge. The proposed method processes textual data from different information sources into an analyzable and readable inter-technology relationship network. To evaluate the method, we use exemplary digital technologies from the big data analytics domain as an application scenario.

: http://publica.fraunhofer.de/dokumente/N-558583.html