COMMITTED TO EXCELLENCE Current scientific papers show the high research competence at AIT.
I. Wanzenböck, M. Neuländtner, T. Scherngell: "Impacts of EU funded R&D networks on the generation of Key Enabling Technologies: Empirical evidence from a regional perspective"; Papers in Regional Science, 99 (2019), 1; p. 3 - 24.
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Digitalisation causes a complete paradigm shift of production and innovation options. Digitalisation understood as the use of digital technologies to change a business model and provide new revenue and value-producing opportunities. The term digitisation describes the technological shift of “analog information into digital format”. Digitalisation is the conversion of processes, interactions, communications, business functions and business models into digital ones. A digital transformation to refers to the process of organizational and societal changes, including the ability to adopt technologies rapidly which affects social as well as technical elements of business models, processes, products and the organizational structure. M. Hörlesberger: "Innovation management in a digital world"; Journal of Manufacturing Tech nology Management, 30 (2019), 8; 10 pp.
Movement data is ubiquitous. Whether via GPS or mobile r adio, an increasing number of people, vehicles and goods can be traced using their data tracks. Exploratory analyses of movement data pose a considerable challenge due to the heterogeneity of movement data sets and analysis tasks. There is a lack of established tools for the exploratory analysis of movement data as well as a lack of guidelines for the implementation of existing movement data analysis concepts using generally available analysis tools. In order to close this gap, we present three open source technology packages for the exploratory analysis of movement data and discuss their capabilities and limitations. A. Graser, M. Dragaschnig: "Open Geospatial Tools for Movement Data Exploration"; KN - Journal of Cartography and Geographic Information, (2020), p. 1 - 8. AIT/Bösendorfer (2), AIT/Zinner
Cross-regional R&D networks are essential for regional innovativeness. Yet, we lack insights into technology field specific effects of a region's network connectivity. This study investigates key enabling technologies (KETs) to compare knowledge creation effects of EU funded R&D networks for different technological fields. By applying spatially filtered regression models together with marginal effect interpretations for non linear models we quantify and compare network effects across KET fields. Results show that the generally positive network effects differ depending on region internal endowments and the nature of the underlying technologies. Policy implications arise for the interrelations between EU research, industrial and regional policy.
AIT AUSTRIAN INSTITUTE OF TECHNOLOGY
