Abstract
The impact of current crises show that previously “unthinkable” risks need to be recognized and treated. Newly emerging threats (e.g. war, disruption of global trade, resource scarcity) are not perceived as risks due to subjective perception, and cognitive biases make risk assessment more difficult. The aim of the work is, on the one hand, to find and present practically applicable methods for identifying previously unrecognized risks. On the other hand, it examines cognitive biases and presents measures to objectify risk assessment. Data was collected through expert interviews and subsequent qualitative content analysis. At the same time, relevant topics were researched in the literature. What regulatory framework conditions are companies subject to? Which methods are currently recommended for risk identification? Which cognitive biases are relevant and what measures are there to mitigate them? Although a large number of possible methods for identifying risks have been developed, only a few methods are actually applied. This paper provides an overview and recommendation for the creative use of risk identification methods. The literature shows that risk management is still primarily seen as a management system for “reactive risk monitoring”. However, the expert interviews reveal a development towards an instrument of strategic control. The combination of operational risk management “bottom-up” with strategic risk issues “top-down” in a counter-current method is crucial in order to be able to assess risks at all levels of the company. From the interview results it can be deduced that with a pronounced risk culture and a risk-based approach, subjective and biased decisions practically do not occur in modern risk management. The proposed measures have been prepared in such a way that they can be integrated into established risk management processes.
| Original language | German |
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| Qualification | Master of Science |
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| Award date | 25 Oct 2024 |
| Publication status | Published - 25 Oct 2024 |
Research Field
- Dependable Systems Engineering
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