Financial security of companies is of the strategic importance. An effective credit risk management greatly impacts on a company security because its failure can threaten the existence of the company. These aspects are closely related to the sustainability of the SME sector, which is determined by many negative processes in the current post-crisis period. The aim of this article is to research the dependence between the entrepreneur’s ability to manage the credit risk in their company effectively and their knowledge of the corporate capital. Within the set goal, we looked at the differences in the attitudes of entrepreneurs depending on a company size, gender and education of entrepreneurs. To analyze acquired data, we used descriptive statistics, regression analyses and Z-score in our research. The originality of the article is that the whole process and result trajectory is focused on highlighting the financial security and sustainability of the searched sector. The results of our research brought an interesting finding. On the one hand, entrepreneurs declared a high capability of the effective credit risk management in their companies and, on the other hand, demonstrated a low level of knowledge in managing the corporate capital. This trend creates a potential possibility of a growth of corporate financial risks. The research results confirmed that the theoretical knowledge of the corporate capital has a significant impact on the formation of effective attitudes of the entrepreneur to manage the credit risk. Larger companies, men and entrepreneurs with higher education have much better level of knowledge of the corporate capital management. The research results enable to form a platform for a deeper insight into the financial security processes in companies and in the sustainability of the SME sector, especially in the current post-crisis period.
Sustainability project is an important part of project management and depends on many factors, such as financial resources, human resources, scheduling operations and especially potential risks. This paper presents a way to work with uncertain information processing project risk analysis with regard to its sustainability. Risk management is an important part of various disciplines, e.g. Project management, Crisis management, Change management, Information Security Management System, etc. Risk analysis is mostly based on expert estimates. However, this may be a problem with brand new tasks as identification of different threats and their numerical evaluations can be interpreted as a decision-making task which can be formalised as a decision tree. A decision-making task solution requires knowledge of all relevant input information items (III), such as probabilities, penalties and profits. If all those numerical values are known then the well-known methods of decision trees evaluations can be used. However, if complex project management problems are solved then a substantial set of relevant data items is missing or its accuracies are prohibitively low. The aim of this paper is to present easy approach how missing elements of the III set can be obtained and integrated into incomplete data sets. The paper contributes a common sense heuristics to obtain missing elements of the III set which can generate all numerical values, i.e. a problem under complete ignorance is solve, and a reconciliation mechanism based on linear programming which allows results of common sense heuristics simply integrate into incomplete data set, i.e. a problem under partial ignorance is solved. The results are therefore divided into two parts. In the first part solves a problem under total ignorance. The second part of the case study evaluates some unknown probabilities, therefore solves a problem under partial ignorance. Both tasks, i.e. partial and total ignorance are demonstrated using a quasi-realistic decision tree. The decision tree has one root node, 6 lotteries and 15 terminals; the total number of unknown probabilities is 21 under total ignorance and 18 probabilities are evaluated under partial ignorance.