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The purpose of the study is to analyze the risk of violent conflict with the global conflict risk factors in the Middle East economies by using an integrated fuzzy decision approach. For this purpose, five different dimensions and 24 different criteria are defined by analyzing similar studies in the literature. The dataset is borrowed from the European Commission, and experts appointed for the linguistic evaluation of each dimension and criterion. Additionally, fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology is used to weigh dimensions and criteria and Multi-objective Optimization on the basis of Ratio Analysis (MOORA) approach is considered to rank the countries with respect to the conflict risk. Social dimension was concluded to have the highest importance of the Global Conflict Risk Index. Moreover, Syria, Libya, and Saudi Arabia were identified as the countries that have high conflict risk. Because these countries have high risk of facing conflict in the future, it is strongly recommended that they should primarily focus on social factors in order to minimize this risk.

In this study, it is aimed to correctly weigh the criteria for the selection of qualified manager in a healthcare facility and to make the right selection among candidates with an objective method. In this study, in which the health manager selection was carried out with fuzzy Analytical Hierarchy Process (AHP) and MULTIMOORA methods, 8 candidates were evaluated according to 12 personnel selection criteria. Comparative matrices of qualified health manager selection criteria were presented to the expert opinion and analyzed with the fuzzy AHP method. According to the analysis result, among the 12 criteria; The “Crisis Management Skill” criterion is in the first place with 12.5% weight; The “Social Responsibility Awareness” criterion was found to be in the last place with 3.2% weight. The MULTIMOORA method was applied by analyzing the interview scores and criterion weightings of the candidates evaluated by the jury. According to the results of MULTIMOORA, Candidate 1 first place and Candidate 6 ranked last.

The aim of this study is to examine the effect of trade wars between countries on social factors. For this purpose, six different criteria are selected based on the comprehensive literature review. Additionally, fuzzy DEMATEL approach is considered to weight these criteria. The findings indicate that working life is the most significant criterion. However, free time and leisure and family are at the bottom of the list. Additionally, it is also defined that working life is the most influencing criterion. These results demonstrate that trade wars have the highest impact on the working life of the people. In other sayings, people’s fear of losing their jobs goes up in case of the trade wars. To solve this problem, companies should make official statements to their employees about the situation caused by the trade wars. By making clear explanations, uncertainties in this process can be minimized.

The military operation of the Russian Federation on the territory of Ukraine exerted additional pressure on prices on the aluminum market since aluminum supplied by Russia accounts for about 10% of the total volume of US imports. It is known that Russia has become the largest aluminum producer after China, and now there is also an increase in aluminum production. Since electricity prices remain relatively low in Russia, especially in energy-surplus Siberia, the increase in output along with the increase in metal prices is a positive factor, since 70% of the primary aluminum produced is exported and only less than a third is consumed domestically. At the same time, high aluminum prices may constrain the expansion of domestic consumption of the metal and may force manufacturers to look for a cheaper alternative. In general, the increase in aluminum prices coincides with the general “supercycle” of raw materials in the last year and a half, and there is a chance to stabilize aluminum prices at current high levels, which will be facilitated, among other things, by new metallurgical projects in Russia. At the same time, it is worth noting that limited metal supplies will haunt the industry for most of 2022, and some experts predict that it may take up to five years to solve the problems.

In scientific works on forecasting price volatility (of which the overwhelming majority, in comparison with works on price forecasting) for energy products: crude oil, natural gas, fuel oil, the authors compared the effectiveness of forecasting models of generalized autoregressive heteroscedasticity (Generalized Autoregressive Conditional Heteroscedastic model, GARCH) with regression of support vectors for futures contracts. GARCH models are a standard tool used in the literature on volatility, and the vector machine nonlinear regression model is one of the machine learning methods that has been gaining huge popularity in recent years. The authors have shown that the accuracy of volatility forecasts for energy and aluminum prices significantly depends on the volatility proxy used. The model with correctly defined parameters can lead to fewer prediction errors than GARCH models when the square of the daily yield is used as an indicator of volatility in the evaluation. In addition, it is difficult to choose the best model among GARCH models, but forecasts based on asymmetric GARCH models are often the most accurate. The work is based on a model with a representative investor who solves the problem of optimizing utility in a two-period model. The key assumption of the model is the homogeneity of energy and aluminum investor preferences, that is, preferences do not change over time. There are also works with an attempt to solve this problem in a continuous state space. A completely new theory has been put forward that allows predicting the movement of the underlying asset without using historical data, so this topic is very relevant.

This study investigates the causal relationship between financial innovation (FI) as proxied by the bank credits to private sector, as percentage of GDP and economic growth in case of Azerbaijan using annual data covering the period from 1992 to 2018. For this purpose, the Toda–Yamamoto causality test with the framework of vector autoregressive (VAR) model is utilized to test causal relationship between the variables. The estimation results reveal that there is bidirectional causal relationship between FI and economic growth. The findings of the study suggest the researchers and policy makers to understand the role of FI in economic growth for macroeconomic stability and sustainable development purposes in Azerbaijan and other developing oil-rich countries.

In modern conditions energy investment in Russia the world and cash flow status sectors is sufficient tense: financial institutions are forced to act in the following conditions: deficiencies in money supply and underestimated financial assets and liabilities of active site balance of payments. However, fast ones actions regulators contribute to mitigation measures the crisis. This very clearly shows the importance of monetary policy in Russia in regards to modern economic relations. Modern reality of development of energy investment and a new period of functioning of the economy were determined by necessity for revision of theoretical data basics and practical analysis of monetary policies, which determine the basic direction of this study. Monetary policy does not apply to financial institutions. It is fully autonomous, as end goals of monetary and credit control system regulations coincide with basic principle objectives of the macroeconomic policies of countries.

Introduction: Cost competitiveness, customer focus, and sustainability compliance are essential for new-age firms to survive and succeed in the VUCA market environment. This study examines how automobile corporations have improved cost competitiveness, productivity, and product quality.

Purpose: This study examines the importance of cost competitiveness, customer focus, and sustainability compliance for the long-term survival of organisations in VUCA markets, looking at the practical efforts made by automobile corporations to enhance cost competitiveness, productivity, and quality.

Methodology: The study utilises a comprehensive analysis of the strategies and initiatives implemented by the selected automobile companies. It involves a review of relevant literature, case studies, financial data analysis, and interviews with key industry experts, providing a holistic understanding of the actions taken by these organisations to achieve their goals.

Findings: The study reveals that cost competitiveness, customer focus, and sustainability compliance are critical factors for the long-term survival and success of organisations in the automotive industry. The analysed automobile companies have undertaken practical efforts to improve cost competitiveness, enhance productivity, and ensure high-quality products, enabling them to navigate the challenges and maintain a competitive edge.

Significance: The findings of this study contribute to a deeper understanding of the importance of cost competitiveness, customer focus, and sustainability compliance in the automotive industry. It highlights the need for organisations to constantly monitor both qualitative and quantitative profit to avoid complacency and ensure long-term efficiency. The study’s insights are relevant to businesses operating in other sectors, as they face similar challenges in the VUCA market environment.