| dc.description.abstract | Innovations encounter a relatively high level of uncertainty in their lifecycle path. As innovations are about implementing a new idea, they suffer from a shortage or lack of knowledge dependent on and directly proportional to the radical quality of novelty. They lack information to predict the future and face (high) uncertainty in the background knowledge used for the risk assessment. Incomplete information causes innovation risk analysts to assign subjective assumptions to simplify system models developed for innovation risk assessment. Subjective and non-subjective assumptions as uncertain assumptions are part of the background knowledge and source of uncertainty. This thesis tries to assess and treat innovation assumptions uncertainties by proposing a hybrid model which comprises the semi-quantitative risk assessment (SQRA) approach, extended semi-quantitative risk assessment (EQRA) approach, and knowledge dimension method. SQRA and EQRA highlight the criticality of assumptions and present a systematic approach to assess and treat assumption uncertainties. SQRA applies probabilistic analysis to conduct an assumptions risk assessment, and EQRA provides innovation managers with guidance on developing strategies to follow up uncertain assumptions over the process implementation. The knowledge dimension technique evaluates and communicates the strength of background knowledge applied in assumptions risk assessment to innovation decision-makers expressing whole uncertainty aspects in the background knowledge (assumptions, data, models, and expert judgment). The model can effectively contribute to innovation risks and uncertainties management during the project execution. | |
