TY  - GEN
A1  - Semke, Lisa-Marie
A1  - Tiberius, Victor
T1  - Corporate Foresight and Dynamic Capabilities
BT  - An Exploratory Study
T2  - Postprints der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe
N2  - Firms engage in forecasting and foresight activities to predict the future or explore possible future states of the business environment in order to pre-empt and shape it (corporate foresight). Similarly, the dynamic capabilities approach addresses relevant firm capabilities to adapt to fast change in an environment that threatens a firm’s competitiveness and survival. However, despite these conceptual similarities, their relationship remains opaque. To close this gap, we conduct qualitative interviews with foresight experts as an exploratory study. Our results show that foresight and dynamic capabilities aim at an organizational renewal to meet future challenges. Foresight can be regarded as a specific activity that corresponds with the sensing process of dynamic capabilities. The experts disagree about the relationship between foresight and sensing and see no direct links with transformation. However, foresight can better inform post-sensing activities and, therefore, indirectly contribute to the adequate reconfiguration of the resource base, an increased innovativeness, and firm performance.
T3  - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe - 128 
KW  - corporate foresight
KW  - dynamic capabilities
KW  - forecasting
KW  - Germany
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-474487
SN  - 1867-5808
IS  - 128
SP  - 180
EP  - 193
ER  - 
TY  - GEN
A1  - Ayzel, Georgy
A1  - Varentsova, Natalia
A1  - Erina, Oxana
A1  - Sokolov, Dmitriy
A1  - Kurochkina, Liubov
A1  - Moreydo, Vsevolod
T1  - OpenForecast
BT  - The First Open-Source Operational Runoff Forecasting System in Russia
T2  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
N2  - The development and deployment of new operational runoff forecasting systems are a strong focus of the scientific community due to the crucial importance of reliable and timely runoff predictions for early warnings of floods and flashfloods for local businesses and communities. OpenForecast, the first operational runoff forecasting system in Russia, open for public use, is presented in this study. We developed OpenForecast based only on open-source software and data-GR4J hydrological model, ERA-Interim meteorological reanalysis, and ICON deterministic short-range meteorological forecasts. Daily forecasts were generated for two basins in the European part of Russia. Simulation results showed a limited efficiency in reproducing the spring flood of 2019. Although the simulations managed to capture the timing of flood peaks, they failed in estimating flood volume. However, further implementation of the parsimonious data assimilation technique significantly alleviates simulation errors. The revealed limitations of the proposed operational runoff forecasting system provided a foundation to outline its further development and improvement.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1338 
KW  - OpenForecast
KW  - open
KW  - operational service
KW  - runoff
KW  - forecasting
KW  - Russia
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-473295
SN  - 1866-8372
IS  - 1338
ER  -