Abstract
Scenarios illustrate probable, plausible, and possible future developments and serve as a framework for strategic planning and decision making. They try to draw holistic images considering various aspects of today’s world. Still, their development is complex and time-consuming. For example, at the beginning of the scenario development process, the literature needs to be screened in order to capture the state of the art and get an overview on influential aspects for the scenario stories. Here, this work concentrates on and proposes two alternative text mining approaches to improve this initial phase of scenario preparation. Text mining automatically processes texts and aggregates their content (scientific publications and reports in this case). This enables to summarize the topic and identify driving aspects. In order to draw a comparison, two different approaches are applied on two different cases. As the results show, the delimitation and structuring of the scenario field are supported and input for discussing the influences is delivered.
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For further details on the project please visit www.ettis-project.eu.
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Acknowledgments
The authors would like to thank Ewa Dönitz (Fraunhofer ISI) for her permission to work with the data of the ETTIS Scenarios. Furthermore, we want to thank a number of friends and colleagues for their comments on our work and their encouragement.
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Kayser, V., Shala, E. (2016). Generating Futures from Text—Scenario Development Using Text Mining. In: Daim, T., Chiavetta, D., Porter, A., Saritas, O. (eds) Anticipating Future Innovation Pathways Through Large Data Analysis. Innovation, Technology, and Knowledge Management. Springer, Cham. https://doi.org/10.1007/978-3-319-39056-7_13
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