IMPROVING RISK MANAGEMENT MODELS FOR THE DEVELOPMENT AND IMPLEMENTATION OF INNOVATIONS
Abstract
The growing of informational influence on economic agents and the increasing social tension worldwide impact the development and implementation of innovations. The purpose of this article is to improve existing innovation risk management models by obtaining a forecast of a specific target indicator, ensuring the objectivity of assessing the social, informational, and business environment. To account for the impact of social transformations on the processes of developing and implementing innovative project solutions, a system of four key indicators has been developed. The social catastrophe profile quantifies the characteristics of social shifts. The target audience profile reflects the behavioral patterns of stakeholders. The business environment profile integrates global and local economic indicators of economic entities. The information environment characterizes the intensity of disinformation spread. A classification of modern DSS is presented according to two fundamental criteria: the method of decision-making and the nature of interaction with the user during their operation. Artificial neural networks were used for the decision support system in innovation risk management. The combination of convolutional and recurrent neural networks was used as an architectural solution. A comparative analysis of their models was conducted based on three key criteria: execution time efficiency, forecasting accuracy, and training data economy with an accuracy threshold of 80%. The evaluation results, obtained using the linear additive aggregation method, confirmed the effectiveness of the proposed approach for parallelization and preprocessing of exogenous data. Both modified versions of the algorithms demonstrated high forecasting accuracy, however, the updated RCNN showed superior performance. The developed approach is practically significant as it allows for the effective forecasting of economic indicators in socially oriented, information-sensitive innovative projects during emergencies. This ensures timely crisis response and appropriate management strategy adjustments.
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