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Methodology for the design of technology strategies in a volatile business environment

: Schuh, G.; Patzwald, M.; Scholz, P.


Grabis, J. ; Institute of Electrical and Electronics Engineers -IEEE-; Riga Technical University:
60th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2019. Proceedings : October 10-11, 2019, Riga, Latvia
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-5710-8
ISBN: 978-1-7281-5709-2
International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS) <60, 2019, Riga>
Conference Paper
Fraunhofer IPT ()

Companies are facing steadily increasing levels of dynamics and uncertainty in their business environments. This phenomenon often is subsumed under the acronym VUCA (Volatility, Uncertainty, Complexity, Ambiguity) as more industries are undergoing transformative change in ever-shorter timeframes. Especially in the asset-heavy manufacturing sector, companies are forced to innovate more frequently outside their core business and to build up entirely new competences in order to avoid the commodification of their physical products. Subsequently, maintaining competitive advantage by managing competences and technologies in a long-term technology strategy is extremely challenging in this environment. Due to a strong emphasis on extensive analyses, traditional strategy processes are time-intensive and, thus, inherently bear a higher risk of strategy pivots in a volatile environment. As strategic positioning remains a crucial factor for a company's sustainable success, a new and adjusted methodology for designing technology strategies is required in order to manage their competence- and technology portfolio consistently in the long-term. Therefore, this paper aims to conceptualize the building blocks of a methodology for the design of technology strategies in volatile business environments. For this reason, the theoretical background is briefly introduced, existing approaches are reviewed and their deficits are discussed to identify model requirements before deriving the methodology and its sub-models.