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The aim of the research is to assess the effectiveness of innovative approaches to attracting investment in the regions of Ukraine, taking into account the impact of digitalization, structural reforms and PPPs. The study is based on multiple linear regression using panel data for 20 regions of Ukraine (2019–2023), ordinary least squares (OLS) estimation and fixed effects. The analysis showed that digitalization (β = 0.47, p = 0.001), PPP (β = 0.50, p = 0.000), and public spending (β = 0.45, p = 0.000) are the most effective mechanisms for attracting investment. At the same time, the unemployment rate (β = -0.40, p = 0.000) is negatively correlated with investment activity. The largest investment volumes are concentrated in Kyiv, Lviv, and Odesa, while the smallest are in Chernihiv, Zakarpattia, and Volyn regions. Innovative approaches that combine digital technologies, PPP and structural economic reforms are more effective in attracting investment compared to traditional methods. Further analysis should focus on developing a comprehensive model for assessing the effectiveness of digital platforms, artificial intelligence, and algorithmic financial mechanisms for attracting investment in small communities.

The aim of the study is to assess the impact of innovative development of small businesses in Ukraine on its competitiveness in the context of economic and social changes, as well as to identify factors that affect the effectiveness of innovation strategies in small businesses. The research employed the following methods: Correlation, regression analysis, and analysis of variance (ANOVA). The study established a positive impact of state support on the financial results of enterprises, the correlation between the financial result and the state support level is 0.76. A correlation was also found between the cost of credit resources and financial indicators (correlation coefficient 0.61), as well as a moderate positive relationship between investment in innovation and profitability (0.39). The digitization index (DiGiX) showed a weak impact on the financial result (−0.07), which indicates a significant time for the payback of investment in digitalization. The study covers the period from 2017 to 2024, and it was found that state support and access to credit resources are the main factors determining the success of innovation strategies in small businesses. The results can become the basis for further development of state policies and strategies to stimulate innovation development in Ukraine.

The growth and sustainable development of companies depend on their ability to adequately and effectively identify and utilize innovative strategies. The aim of the study is to determine the results of the innovative strategy of international small and medium-sized enterprises (SMEs) in times of economic crisis. The research involved 360 owners and managers of international SMEs in the European Union. The study used tabular and graphical methods, surveys, questionnaires, and correlation and regression analysis. It was found that 82.7% of SMEs improved their production processes, while 62.8% implemented innovations by acquiring new products or equipment. It was established that 82% of enterprises implemented innovative strategies in the commercial or trade sector, 73.2% in the service sector, and 77.7% in other sectors. The models developed in the study contribute to the modern development of the economy since they analyze the relationship between innovative strategies and determinants affecting their successful implementation and implementation. Prospects for further research include analyzing the types of innovative strategies that prevail in the activities of international SMEs, as well as their size and sectors of activity.

The aim of the study was to assess and analyse the shadowing of the national economy, as well as to determine the impact of its shadowing on the reduction of tax revenues to the state budget. The research employed general scientific, economic, and mathematical assessment methods, such as regression analysis, correlation analysis, interval forecasts, as well as econometric models of Lacko's household electricity approach, as well as modelling of the shadowing level using the multiple indicators-multiple-causes (MIMIC) model. The calculations and analysis gave grounds to determine the level of the shadow economy of Ukraine based on the state statistics of shadowing trends. There is currently an upward trend estimated to be 37.1% of GDP in 2022 compared with international data, which indicates a rapid growth of the shadow economy from 27% in 2019 to 44% in 2022. An alternative method of assessing the shadowing level was proposed in order to level the peculiarities of calculating the level of shadowing of the economy. According to the calculations, it was 43% in 2022. It was determined that the rapid growth of the shadow economy is an indicator of a reduction in tax revenues, especially in 2022 — by almost 8%. The correlation analysis proved that the increased shadowing level is an indicator of the reduction of tax revenues to the budget of Ukraine. The conducted analysis has certain limitations, therefore it is advisable to further test the hypothesis regarding the indicator of tax revenue reduction on a larger range of data and to apply an alternative method of assessing the shadowing level for developed economies, such as the countries of the European Union (EU).

The state faces significant challenges in fulfilling its functions during a recession, and crises only exacerbate the problems that arise. However, it is precisely during crises that adequate social security, defense, and other state functions are critically important for ensuring national and individual public interests. The purpose of the study was to analyze the impact of economic recession on the level of financial support for state functions during a crisis. The study used methods of statistical analysis, comparative analysis, analogy, abstraction, and generalization. As a result of the study, the revenues of the state budget of Ukraine and its financing were analyzed as the main sources of financial support for state functions. It was found that the share of tax revenues decreased from 85.37% in 2021 to 45.04% in 2023, which poses a threat to the state's ability to perform its functions. Significant budget financing amounts (31094 million USD in 2022 and 42479.15 million USD in 2023) positively influenced the country's defense capability but led to an increase in the state debt. Insufficient gold and foreign exchange reserves and the state debt reaching the GDP volume are the most negative trends. By analyzing international experience, it was determined that Ukraine should pay high attention to optimizing internal revenue sources, especially tax revenues, through fiscal incentives and ensuring tax discipline. It is also necessary to develop long-term strategies for repaying the state debt and international cooperation. The research results can be useful for government officials in the process of developing budget and tax policies.

The state faces significant challenges in fulfilling its functions during a recession, and crises only exacerbate the problems that arise. However, it is precisely during crises that adequate social security, defense, and other state functions are critically important for ensuring national and individual public interests. The purpose of the study was to analyze the impact of economic recession on the level of financial support for state functions during a crisis. The study used methods of statistical analysis, comparative analysis, analogy, abstraction, and generalization. As a result of the study, the revenues of the state budget of Ukraine and its financing were analyzed as the main sources of financial support for state functions. It was found that the share of tax revenues decreased from 85.37% in 2021 to 45.04% in 2023, which poses a threat to the state's ability to perform its functions. Significant budget financing amounts (31094 million USD in 2022 and 42479.15 million USD in 2023) positively influenced the country's defense capability but led to an increase in the state debt. Insufficient gold and foreign exchange reserves and the state debt reaching the GDP volume are the most negative trends. By analyzing international experience, it was determined that Ukraine should pay high attention to optimizing internal revenue sources, especially tax revenues, through fiscal incentives and ensuring tax discipline. It is also necessary to develop long-term strategies for repaying the state debt and international cooperation. The research results can be useful for government officials in the process of developing budget and tax policies.

The use of artificial intelligence (AI) builds up the accounting system efficiency, increases data entry accuracy and simplifying the accounting process. The aim of the study is to prove the effectiveness of modern AI-based information technologies (IT) in accounting and the possibilities of AI application for process optimization. The effectiveness and efficiency were proven using comparison methods, statistical analysis, graphical cause-and-effect analysis, modelling using the linear regression method. The assessment was carried out using quantitative and qualitative indicators of labour productivity and process optimization. The results of the study showed that 18 accounting department employees on average are needed to perform standard transactions in the companies studied without AI. With AI, 1 person can handle such a volume of work. Accordingly, with the implementation of AI, the average reduction in Transaction Processing Time per Week is 696.26 hours. Regression analysis confirmed that the implementation of AI increases the companies’ productivity in terms of Transaction Processing Time. Reducing the Data Processing Complexity by one unit leads to a reduction in transaction processing time by 592.69 seconds. Each percent increase in Data Entry Accuracy contributes to a reduction in processing time by 5135.51 seconds. The prospects for implementing AI in accounting include further improving algorithms to increase the accuracy and speed of transaction processing, optimizing material and time consumed.  

The use of artificial intelligence (AI) builds up the accounting system efficiency, increases data entry accuracy and simplifying the accounting process. The aim of the study is to prove the effectiveness of modern AI-based information technologies (IT) in accounting and the possibilities of AI application for process optimization. The effectiveness and efficiency were proven using comparison methods, statistical analysis, graphical cause-and-effect analysis, modelling using the linear regression method. The assessment was carried out using quantitative and qualitative indicators of labour productivity and process optimization. The results of the study showed that 18 accounting department employees on average are needed to perform standard transactions in the companies studied without AI. With AI, 1 person can handle such a volume of work. Accordingly, with the implementation of AI, the average reduction in Transaction Processing Time per Week is 696.26 hours. Regression analysis confirmed that the implementation of AI increases the companies’ productivity in terms of Transaction Processing Time. Reducing the Data Processing Complexity by one unit leads to a reduction in transaction processing time by 592.69 seconds. Each percent increase in Data Entry Accuracy contributes to a reduction in processing time by 5135.51 seconds. The prospects for implementing AI in accounting include further improving algorithms to increase the accuracy and speed of transaction processing, optimizing material and time consumed.

Emotional intelligence (EI) as the ability for self-analysis, self-motivation, and self-regulation is necessary in the process of financial activity. It ensures informed decision-making, considering consequences and perspectives. The aim is to identify the influence of emotional intelligence on the effectiveness of decision-making by financiers. The following tests were used: EQ (Emotional/Empathy Quotient) test, Melbourne Decision Making Questionnaire, the Rathus Assertiveness Schedule, the Schubert Risk Propensity Test. Statistical processing of the results included the use of descriptive analysis, ANOVA, and regression analysis. The study found that financial workers with a high level of emotional intelligence have a pronounced “vigilance” decision-making style (F=113.4, p≤0.01), high assertiveness (F=103.3, p≤0.01), and risk propensity (F=137.3, p≤0.01). It was proved that emotional intelligence explains the “vigilance” decision-making style (β=0.943, R²=0.572), risk propensity (β=0.896, R²=0.424) and explains 48% of assertiveness (β=0.945, R²=0.483). It was confirmed that emotional intelligence is a predictor of successful financial decisions, it determines high assertiveness, vigilance, and risk propensity. Such results are useful for the development of training and retraining programmes for financiers. At the same time, including the development of assertiveness and risk propensity in professional and corporate training in parallel with the development of EI will give greater chances for the success of financial decisions. The obtained results are important for the financial and economic sphere, as they prove the effectiveness of EI in decision-making. This contributes to the improvement of the system of training competitive financiers and allows to expand aspects of the study of financial success.

Emotional intelligence (EI) as the ability for self-analysis, self-motivation, and self-regulation is necessary in the process of financial activity. It ensures informed decision-making, considering consequences and perspectives. The aim is to identify the influence of emotional intelligence on the effectiveness of decision-making by financiers. The following tests were used: EQ (Emotional/Empathy Quotient) test, Melbourne Decision Making Questionnaire, the Rathus Assertiveness Schedule, the Schubert Risk Propensity Test. Statistical processing of the results included the use of descriptive analysis, ANOVA, and regression analysis. The study found that financial workers with a high level of emotional intelligence have a pronounced “vigilance” decision-making style (F=113.4, p≤0.01), high assertiveness (F=103.3, p≤0.01), and risk propensity (F=137.3, p≤0.01). It was proved that emotional intelligence explains the “vigilance” decision-making style (β=0.943, R²=0.572), risk propensity (β=0.896, R²=0.424) and explains 48% of assertiveness (β=0.945, R²=0.483). It was confirmed that emotional intelligence is a predictor of successful financial decisions, it determines high assertiveness, vigilance, and risk propensity. Such results are useful for the development of training and retraining programmes for financiers. At the same time, including the development of assertiveness and risk propensity in professional and corporate training in parallel with the development of EI will give greater chances for the success of financial decisions. The obtained results are important for the financial and economic sphere, as they prove the effectiveness of EI in decision-making. This contributes to the improvement of the system of training competitive financiers and allows to expand aspects of the study of financial success.