Risk assessment of implementing innovative projects in enterprises using artificial neural networks

The purpose of this paper is to shed new light on the issue of risk assessment in implementing innovative projects. Based on the ideas of George Kleiner and artificial neural network tools, this paper interprets the possibility of completing successful innovation projects in terms of risk management. The argument is buttressed with a case study of a set of Russian enterprises implementing innovative projects. Successful completion of an innovation project depends on a number of risk factors identified at the project start. The identified set of risk factors should include both innovative risk factors and non-innovative risk factors. This fact was established during the cluster analysis of the available data. The paper excludes from consideration the anti-risk management impact and does not take into account the weights of various risk factors in the problem formalization. The practical application of the results of the study is decision makers’ quest to strike a visual interpretation of the final data with a small number of possible scenarios that differ significantly from each other. This research contributes significantly to the literature on the risk assessment model of innovative projects. The failure of managers to balance the assessment of innovative risk factors and non- innovative risk factors is exposed as the root cause of the unsuccessful completion of innovative projects.