International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

The paper presents a methodology focused on the leadership style assessment based on exact characteristics of the business environment and their influence on the employees’ motivation. The conceptual framework is to explore the correlation between the applied leadership style, the level of uncertainty of the external environment, and the motivational level of the followers. In this regard, the paper aims to find dependencies among the leadership style and its behavioral application towards the level of motivation. Based on this research design, it is possible for the managers to implement a specific approach that will increase the employees’ motivational level in a concrete level of uncertainty of the business environment. The application of the methodological conditions in organizations from different industries can be beneficial for managers to smooth out an effective and cohesive team, which together overcomes threats from the external environment and the continuous confrontation with new crisis situations.

The pandemic outbreak has significantly impacted many businesses and their ability to continue operating as a going concern. Going concern is a basic accounting principle as well as one of the fundamental assumptions under the International Financial Reporting Standards. The management is responsible to determine whether the going concern assumption is relevant in the preparation of company’s financial statements. With the progress of the novel coronavirus the focus on whether an entity is a going concern should be heightened. Taking into consideration the continuing uncertainty in the Covid-19 environment, the aim of the paper is to discuss the challenges through the lens of the company’s management in the assessment of the going concern assumption. Few questions that should be considered when performing the analyses as well as some of the factors that contribute to a robust assessment are outlined. A special attention is given to disclosure requirements and their improvement in the Covid-19 context.

This research is focus on advanced modelling and design of complex interconnected processes. These processes are characterized by multiple inputs, outputs and state parameters as well as non-linearity, non–stationarity and uncertainty due to environmental disturbances. There are presented two models for deep learning and knowledge extraction using methods of multivalued logical and probable functions and networks model. These models can be applied for complex processes in the area of environment, transportation and complex systems working under uncertainty.