Volume 8.
Issue 04.

The study is dedicated to the systematization and analysis of existing approaches to the selection and training of security service personnel. The aim of the research is to examine the specific characteristics of effective recruitment and subsequent training of security staff in large corporations. The methodological framework comprises analysis and synthesis of recent scientific publications on psychophysiological diagnostics of candidates, evaluation of the predictive validity of selection techniques, and the integration of immersive VR/AR technologies and digital twins into training programs. As a result, a synergistic model is proposed that combines multilevel psychodiagnostic profiles with an architecture of adaptive training programs, enabling the creation of personalized development trajectories for employees and enhancing their resilience to stressful and unexpected situations. The scientific contribution lies in the substantiation of a competency-based synergistic model that links candidates’ psychodiagnostic data with adaptive learning modules based on digital twins and VR technologies. The practical significance of the findings consists in their applicability for security managers, human resources directors, and specialists responsible for the development of corporate security systems in large organizations.

In the context of the globalization of e-commerce, effective management of customer disputes (chargebacks) has become a critically important factor for the financial stability of merchants. This article presents a comprehensive analysis of pre-arbitration dispute resolution procedures in the leading international payment systems—Visa and Mastercard. The study examines in detail the dispute lifecycle, the roles of key participants, and the regulatory framework of the process. A comparative analysis is conducted of the structural and philosophical differences in the approaches of Visa (Visa Claims Resolution system) and Mastercard (Mastercom platform), highlighting their impact on merchant strategy. Based on an examination of operational regulations and practical case studies, four fundamental principles of successful pre-arbitration resolution are formulated: proactive prevention and data integrity, procedural expertise and time discipline, strategic evidence management, and economic rationality. The article will be useful for practitioners, legal professionals, and managers responsible for risk management and revenue protection in the field of e-commerce.

This article analyzes the role of state programs in modernizing the national construction industry of New Uzbekistan, as well as in promoting production diversification and innovative development within the sector. The study examines key factors influencing digital transformation, technological renewal, and institutional reforms in the construction industry. It also highlights the importance of aligning technical regulations and standards with international requirements, modernizing construction material production systems, and enhancing investment activity through strategic state initiatives. The findings demonstrate that state policy plays a decisive role in ensuring sustainable growth and improving the competitiveness of the construction sector.

Over the past decade, the C5+1 format (five Central Asian countries + the United States) has evolved from a political-diplomatic platform into a mechanism for promoting projects in regional connectivity, energy, trade, investment, and critical minerals. For Uzbekistan, the economic effectiveness of the C5+1 format is primarily manifested through (1) the reduction of transaction costs and the «coordination effect» at the regional level, (2) expanded access to technologies and financing from the U.S. and related institutions, (3) enhanced export diversification and business environment standards, and (4) increased «negotiating power» amidst competition among external centers of influence. Along with that, economic returns are contingent upon the ability to translate declarations into investment decisions, the quality of institutions and infrastructure, as well as external market conditions and sanction-geopolitical risks. The article proposes an evaluation framework (KPIs) and draws conclusions regarding which areas will yield Uzbekistan the maximum measurable benefits in 2024-2026.

This study examines the current state of the system for providing digital public services to business entities in Uzbekistan and analyzes the results of ongoing reforms in the context of digital transformation. The research highlights that the introduction of integrated digital platforms, particularly the Unified Interactive Public Services Portal, has significantly simplified administrative procedures such as business registration, taxation, licensing, and customs operations. The digitalization of customs services, including electronic declaration systems and newly introduced digital tools, has improved transparency, reduced processing time, and minimized human intervention. The study also considers the impact of the “Digital Uzbekistan 2030” strategy, which aims at full digitalization of public services and mandatory payments. The findings show that digital public services contribute to improving the business environment, reducing bureaucratic barriers, and increasing economic efficiency. At the same time, the research identifies the need for further improvement in service delivery mechanisms, institutional coordination, and the management of “one-stop shop” centers. Overall, the development of digital public services plays a crucial role in supporting entrepreneurship, enhancing transparency, and promoting sustainable economic growth.

This article examines the impact of artificial intelligence (AI) technologies on Uzbekistan's labor market transformation in the context of implementing the national strategy "Digital Uzbekistan - 2030" and the Strategy for the Development of AI Technologies until 2030. Based on the analysis of statistical data, industry reviews, and regulatory legal documents, key mechanisms of AI's impact on employment structure were identified: automation of routine processes, formation of new professional profiles, and changes in requirements for employees' competencies. It is shown that in 2024-2025, the labor market of Uzbekistan is characterized by duality: on the one hand, there is an increasing demand for IT specialists and workers with digital skills; on the other hand, there is a structural shortage of qualified personnel and a gap between the education system and the needs of the economy. Recommendations for adapting employment policy to AI transformation conditions have been proposed.

This article examines how the results of foreign trade economic analysis can be operationalized in customs administration through two linked instruments: differentiated treatment of Authorized Economic Operators and risk-based improvement of customs audit. The core argument is that customs analysis should no longer be treated as a descriptive function limited to turnover, declarations, and payments. It should instead serve as a decision-support mechanism for trader segmentation, facilitation design, post-clearance control, revenue forecasting, and early detection of non-compliance. The paper relies on the empirical materials embedded in the underlying study, including audit results, monthly stability-rating trajectories of selected firms, and the legal and institutional framework presently operating in Uzbekistan. A central proposal is to connect the national business stability rating with customs facilitation and post-clearance control, without replacing the legal content of the AEO regime. Such integration would make customs treatment more dynamic, economically grounded, and proportionate to actual compliance behavior. The thesis also argues that customs audit should be rebuilt around multi-source economic indicators, especially tax discipline, e-invoicing continuity, debt exposure, and profitability, while financial sanctions for customs offences should better reflect the economic significance of the violation. This approach is consistent with current Uzbek reforms and with international standards developed by the World Customs Organization (WCO) and the World Trade Organization (WTO).

This article examines cross-cultural Customer Success management in the international expansion of B2B SaaS companies through the lens of the service model's operational maturity. The relevance of the study stems from the fact that, when entering emerging markets, the demand for high-intensity and informal support is often mistakenly interpreted as an inevitable cultural norm, even though, in practice, it undermines business scalability, profitability, and controllability. The purpose of the article is to substantiate a transition toward a scalable support model in which universal business outcomes are preserved globally, while the means of achieving them are adapted locally. The scientific novelty lies in reconceptualizing regional specificity as a consequence of the vendor’s operational immaturity and in proposing a framework that distinguishes between Outcomes and Execution. The principal findings demonstrate that implementing a multilingual self-service portal and a One-to-Many model reduces onboarding time, shifts routine inquiries to formal channels, and enables the integration of cultural sensitivity with economic sustainability. The article will be useful to researchers, Customer Success managers, and executives of international SaaS companies.

This study presents an empirical evaluation of machine learning models in supporting strategic decision-making within modern organizations. The analysis is conducted using a publicly available dataset from the Kaggle, focusing on employee-related variables to predict organizational outcomes, specifically employee attrition. Multiple machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting, are applied and compared using standard evaluation metrics such as accuracy, precision, recall, F1-score, and Area Under the Curve.The results demonstrate that Gradient Boosting outperforms all other models, achieving the highest accuracy of 90%, precision of 87%, recall of 85%, F1-score of 86%, and AUC score of 0.92. Random Forest also exhibits strong performance with an accuracy of 88% and an AUC of 0.90, indicating robust predictive capability. In contrast, Decision Tree and Logistic Regression models show comparatively lower performance, with accuracies of 82% and 79% respectively, reflecting their limited ability to capture complex, non-linear relationships within the dataset.The findings further reveal that variables such as job satisfaction, overtime, monthly income, and years at the company are the most influential predictors of attrition across all models. Feature importance analysis confirms that employee engagement and workload-related factors significantly impact organizational outcomes. Additionally, ensemble methods demonstrate greater stability and predictive reliability compared to single-model approaches.

The process of decision-making of managers under uncertainty is analyzed, risk and uncertainty are distinguished, and a probabilistic model is presented from the perspective of deep and radical uncertainty. , The influence of cognitive aids of trusted managers, energy, trust, and bias on the quality of medical care decisions is highlighted. Theories are presented, and a basic conceptual model of environmental failures, managerial cognition, organizational processes and decision-making is proposed.