Research output per year
Research output per year
Research output: Contribution to journal › Article › peer-review
Contemporary disaster risk management requires the exchange and integration of risk information across societal sectors and administrative borders. However, differences in how risk is described can be an obstacle to making sense of the material. This paper focuses on the challenge of aggregating risk assessments from multiple stakeholders and aims to establish the characteristics of risk descriptions that are most conducive for attaining a comprehensive understanding of risk. In an empirical study, risk management students from two different scholarly traditions (engineering and social sciences) rated how useful different combinations of risk descriptions from two fictive municipalities were for (i) comparing their levels of risk and (ii) making decisions on risk-reducing measures in the area covered by both municipalities. Adopting a within-subjects design, the participants were faced with six different combinations of risk descriptions, which varied with respect to how specific assessments of consequences and likelihood were expressed, and whether a supporting narrative was provided. The study also explored the effects of combining risk descriptions of the same type (e.g. where both expressed consequences and probabilities with qualitative ordinal scales) with two of dissimilar types (e.g. one qualitative ordinal and one quantitative). Overall, the results indicate that disaster risk management systems would benefit from greater consistency in the way interdependent stakeholders describe risks, and from greater use of quantitative assessments. Furthermore, a supporting narrative can provide useful contextual information that may facilitate the comparison of incongruent risk descriptions. Challenges related to these findings are discussed.
Original language | English |
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Pages (from-to) | 497-512 |
Number of pages | 16 |
Journal | Journal of Risk Research |
Volume | 22 |
Issue number | 4 |
Early online date | 2017 Nov 1 |
DOIs | |
Publication status | Published - 2019 |
Research output: Thesis › Doctoral Thesis (compilation)