Vol. 7 No. 07 (2025)

This study explored how procurement practices relate to competitive advantage within organizations, using the Unified Theory of Acceptance and Use of Technology (UTAUT) to understand the role of technology in supply chain management. Researchers employed a quantitative approach, analyzing 245 responses from 100 regional universities using descriptive statistics and structural equation modeling (SEM) with SmartPLS software. The findings revealed a strong positive correlation between effective procurement methods and competitive advantage, leading to improved financial performance, return on investment, and profit margins. Regression analysis confirmed that efficient procurement strategically enhances economic performance. The UTAUT model highlighted that performance expectancy, effort expectancy, social influence, and facilitating factors influence the adoption and use of procurement technology. The study demonstrates how aligning procurement digitalization with the UTAUT framework can optimize sourcing, foster innovation, and boost overall profitability in supply chain management. Ultimately, this research contributes to a deeper understanding of the link between procurement practices and achieving a competitive edge in organizational supply chain management.

This article examines the issue of enhancing the operational resilience of small trucking companies under conditions of high rate volatility, driver shortages, and tightening regulatory requirements. The relevance of the study is determined by the extremely low level of digital readiness in the sector against the backdrop of the rapid growth of the global digital freight market. The aim of the work is to identify strategic approaches that allow enterprises with limited IT budgets and a shortage of qualified personnel to successfully implement digital dispatch platforms and record measurable economic benefits. The novelty of the study lies in the development of a systematic, phased implementation methodology: from the pilot launch of basic telematics to full integration with external accounting systems and payment modules. A unified roadmap is proposed, including the selection of model tariffs, mechanisms for engaging champions among drivers and dispatchers, as well as a recommended set of five key KPIs (ETA accuracy, empty‐run ratio, fleet utilization, driver idle time, and customer satisfaction) for regular performance monitoring. The most significant findings demonstrate that phased deployment of cloud solutions with open APIs and monthly payment minimizes capital expenditure and reduces operational risks, while microlearning modules and continuous KPI analysis accelerate personnel adaptation and ensure a sustainable effect: up to 9% fuel savings, 15% reduction in accident‐related costs, improved ETA accuracy, reduced unplanned downtime, and increased fleet profitability. Integration with ELD, accounting, and freight marketplaces creates conditions for continuous improvement and scalability. The article will be useful for managers of small fleets, IT consultants, and experts in the digital transformation of transport companies.

This article substantiates the necessity of transitioning to an integrated digital sales ecosystem as a key factor of competitiveness. The relevance of the study is determined by the rapid growth of electronic commerce and the approach of the online channel share to 20% in global retail, which renders traditional methods of sales management economically inefficient. The author emphasizes that digital transformation should be regarded not as a one-off project but as a continuously accelerating positive feedback loop requiring end-to-end integration of CRM, POS, BI, and ERP/OMS. The objective of the study is to systematize and analyze contemporary digital solutions applied to sales management in the retail business, as well as to identify the mechanisms of their interaction and their impact on key operational indicators. The methodological basis comprised a comparative analysis of reports by UNCTAD, Emarketer, McKinsey, Intellias, and leading industry research, as well as content analysis of practical case studies and statistical data. The theoretical part examines the four layers of the sales tech stack, while the empirical part provides examples of the implementation of AI modules, predictive analytics, and omnichannel platforms. The novelty of the research lies in the comprehensive consideration of the chain CRM → POS → BI → ERP/OMS as a single data loop that enables enterprises to achieve operational transparency of sales, responsiveness to demand, and process scalability. Additionally, current trends in marketing automation, SFA applications, and AR/VR solutions are analyzed, as well as the organizational and behavioral factors influencing the success of digital initiatives. Key findings: integration of digital tools ensures up to 65% reduction in revenue loss through AI demand forecasting and a 5–15% increase in revenues; omnichannel transforms the customer journey, increasing the average basket value and customer retention; the implementation stages (audit – pilot – phased migration) are critical for minimizing risks; the main barriers remain data fragmentation, employee resistance, cyber threats and the risk of vendor lock-in, overcoming which requires a systemic approach to training, security and data management. This article will be useful for executives of retail companies, IT directors, digital transformation consultants, and researchers in the field of retail.

This research aims to evaluate customer satisfaction with industrial products in Helmand province, Afghanistan, and identify which dimensions and factors of the marketing mix have the most significant impact on customer satisfaction. Sixty-five questionnaires were gathered from customers who visited industrial production companies within the past three days to collect data. The collected data was analysed using SPSS 26.0 and OLS and correlation techniques. The findings indicate that all dimensions of marketing (7Ps) have a positive and significant relationship with customer satisfaction. Among the variables, price was identified as the most influential factor affecting customer satisfaction compared to other variables. Based on the model, the obtained R Square is 0.451, which means that the independent variables can explain 45.1% of the variance in the dependent variable (customer satisfaction). Overall, the study's results show that all independent variables significantly impact the dependent variable

The article examines the impact of artificial intelligence on the transformation of human resource management functions, analyzing the practices of embedding AI modules in the Oracle Fusion Cloud HCM platform and assessing their economic and strategic effects. Against the backdrop of rapid growth in AI penetration into business processes and active participation of HR units in the selection of AI solutions, the relevance of this study is determined by the need to optimize recruitment, retention and development of personnel, as well as to free up to 12 hours of working time per week for strategic tasks. The novelty of the work lies in its comprehensive approach, combining an overview of industry surveys (McKinsey, Engagedly, SHRM), analysis of Oracle technical documentation (Dynamic Skills, Skills Nexus, Activity Centers, Fusion HCM Analytics), and corporate case studies (Carv, Candidate, Forrester-TEI, Adecco). Data have been synthesized concerning the level of HR-task automation, the architecture of Oracle’s unified object model, and the contributions of pre-trained AI agents in recruiting processes, employee performance appraisal, and benefits management. The main findings demonstrate that AI implementation in HR ensures a significant reduction in routine operations (81% of respondents consider automation a priority), improvement of employee experience (73%), decrease in time-to-hire (by up to 70% through automated interview scheduling) and enhanced accuracy of candidate selection (a 14% increase in diversified responses). Using the Dynamic Skills module creates a “live” competency inventory, Activity Centers prompt the “next best action,” and the Digital Assistant and other chatbots return up to one hour per day to employees. Additionally, the author has proposed the Set-up Extractor Tool for automating the migration of Oracle HCM Cloud configurations, eliminating the risks of manual copying and version conflicts. The article will be helpful to HR service leaders, HR-technology implementation specialists, and digital transformation consultants.

This article investigates the phenomenon of volatility clustering in the cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH), through empirical time-series analysis. The study employs quantitative methods, including GARCH modeling, to identify persistent patterns in the price fluctuations of the two leading digital assets. The analysis is based on trading data over an extended period, encompassing both phases of high market turbulence and periods of relative stability. Adopting an interdisciplinary approach that integrates behavioral finance, econometrics, and financial market theory, particular attention is given to identifying autocorrelation, memory effects, and the structure of market shocks. The findings demonstrate that volatility clustering in BTC and ETH significantly differs from similar phenomena in traditional financial markets, largely due to their speculative nature, asset novelty, and the influence of both institutional and retail participants. The identified patterns enhance risk profiling for crypto assets and may be applied in hedging strategies, automated trading algorithm development, and investment portfolio optimization. Additionally, the study highlights the importance of accounting for both micro- and macroeconomic factors influencing market behavior. The article is intended for researchers in digital finance, risk managers, analysts, investors, and anyone examining unstable assets in conditions of high uncertainty and a rapidly changing informational landscape.

The article provides a detailed account of approaches applied to the digital transformation of traditional business processes. In the context of a rapid technological shift, such transformations become indispensable for the survival and competitiveness of economic actors. However, despite a proliferation of publications, both academic and practitioner literature remain fragmented in their definitions of the nature of transformational steps, their scope, and the organizational mechanisms involved. The objective of this paper is to undertake a critical analysis of the conceptual foundations of digital transformation and to identify the primary directions that underpin the rethinking and reconfiguration of established operational models. Special attention is given to juxtaposing strategic, institutional, and industry‐applied approaches, as well as to exploring the tensions between normative rhetoric and the empirical feasibility of these changes. A typology of the approaches under review is presented, key limitations and barriers are delineated, and the author’s position on the novelty of processes for the digital reconfiguration of business architecture is articulated. The scientific and practical value of this work lies in systematizing diverse viewpoints on the topic and interpreting them through an interdisciplinary lens. The material set forth will be of use to scholars in management, digital economics, organizational theory, and applied informatics, as well as to consulting professionals and business architects engaged in facilitating digital transformations.

Demand forecasting plays a crucial role in optimizing supply chain operations, particularly in multi-echelon supply chains where goods move through various stages, including manufacturers, wholesalers, and retailers. Traditional time-series models like ARIMA and SARIMA have been widely used for demand forecasting, but their limitations in handling complex, non-linear relationships and incorporating external factors such as promotions and weather events have led to the exploration of machine learning (ML) and deep learning (DL) techniques. This study evaluates and compares the performance of AI-driven demand forecasting models, including ARIMA, SARIMA, Random Forest (RF), Gradient Boosting Machines (GBM), and Long Short-Term Memory (LSTM) networks. The results demonstrate that the LSTM model outperforms traditional methods and other machine learning algorithms in terms of accuracy, as measured by lower MAE, RMSE, and MAPE values across all echelons of the supply chain (retailer, wholesaler, and manufacturer). The superior performance of LSTM highlights its ability to capture long-term dependencies and handle the complexity of multi-echelon supply chains. This study provides valuable insights into the effectiveness of AI-driven forecasting models for real-world supply chain applications, particularly in managing dynamic demand patterns and optimizing operations.

This article aims to conduct a comprehensive study of the economic and logistical factors that determine the formation of the final price when abandoning multi-stage intermediary chains in favor of the factory-to-site model. The relevance of the work is due to the high level of cumulative markups in the classical supply scheme, especially for developing landlocked markets, where transport and warehousing costs multiply the price of building materials and hinder the introduction of innovative solutions in infrastructure projects. The novelty of the research lies in combining quantitative analysis of marginal markups (using the Lerner index and PPI data), logistical assessment of hidden costs (according to UNCTAD and SCFI data), modeling of currency effects based on historical exchange rates, and practical case analysis of pilot projects in Kyrgyzstan, during which empirical confirmation of the claimed savings was obtained. As a result of a comparative analysis of the traditional and direct supply models, it was found that eliminating national distributors, wholesale warehouses, and retailers ensures a reduction in the final cost of supplies by 30–40 % through the removal of the accumulated markups at each link. Additional savings are achieved through factory cutting of materials to order size, just-in-time delivery, optimization of warehousing and installation operations within a lean approach, as well as the use of currency risk hedging instruments. The factory-to-site model demonstrates high transparency of cost structure and scalability of the methodology to neighboring markets, which is confirmed by an increase in annual turnover from $0.5 million to over $6 million with supply volumes increasing up to 100,000 m². The article will be useful for procurement managers, logisticians, investment analysts, and government specialists in the regulation of the construction market.

In this article, an analysis is carried out of the characteristics of fault-tolerant replication in vector search systems, driven by the rapid expansion of generative artificial intelligence capabilities and related methods, including Retrieval-Augmented Generation (RAG). The key challenge in this area is to guarantee both high availability and immutability of information, which is achieved through the implementation of various fault-tolerant replication schemes. The present study is aimed at the systematization and comparative analysis of existing replication models in the context of vector search systems, with attention to the trade-offs between data consistency, service availability, and system response time. The work employs methods of systematic and comparative analysis, as well as a review of academic publications and technical documentation of leading industry solutions. As a result of the conducted analysis, three main classes of replication approaches are identified: leader-follower (primary-backup), consensus-based protocols, and shared-storage architectures. It is shown that the choice of a specific replication scheme is determined by the combination of requirements for throughput, latency, and level of fault tolerance, as well as financial and operational constraints. The conclusions of the study point to the high promise of hybrid solutions that combine elements of different models to achieve an optimal balance between reliability and cost. The material will be useful for system architects of distributed applications, experts in database design, and researchers working on high-load AI systems.

This article examines contemporary algorithmic approaches to multiparametric immunization of interest rate risk in a fixed-income portfolio, explicitly accounting for non-parallel shifts in the yield curve. Using the Nelson–Siegel framework, the bond-price sensitivities to the three primary factors—level, slope, and curvature—are characterized, and the traditional Duration and Duration–Convexity immunization strategies are reviewed. It is demonstrated that attempting to hedge all three factors simultaneously with classical techniques often yields extreme portfolio weights, excessive leverage, and poor out-of-sample performance. To overcome these limitations, we implement L¹ (Lasso) and L² (Ridge) regularization—subject to a strict overall leverage cap—on U.S. Treasury data. An empirical replication of a “retirement bond” (a pension-payment stream) shows that leverage-constrained Lasso strategies reduce the median absolute deviation of the funding ratio while also lowering turnover. These results confirm the hypothesis that regularization improves both the robustness and economic efficiency of interest-rate hedging for institutional investors with long-dated liabilities. The insights presented will interest financial-engineering researchers specializing in stochastic yield-curve modeling and optimal portfolio-management methods. Portfolio managers, institutional risk officers, and quantitative teams at hedge funds seeking to integrate high-frequency and machine-learning algorithms into their volatility-reduction workflows and to ensure stable returns amid changing market rates will also find practical guidance here.

The article examines the theoretical basis for the role of emotional intelligence in executive coaching practice. In a dynamic VUCA environment complicated by digital transformation and technological stress, the relevance of this research is determined by the need to enhance leaders’ adaptability through the development of emotional competencies. The work aimed to conduct a systematic analysis of classical and contemporary models of emotional intelligence (ability approach, mixed model, and trait approach), to assess their diagnostic instruments, and to substantiate the mechanisms for integrating EI into the executive coaching cycle. The novelty of the study lies in its multidisciplinary synthesis of data, encompassing the psychometric properties of the MSCEIT, EQ-i, and ESCI, as well as neuro-visualization experiments (fMRI) and HRV biofeedback, alongside consideration of coaching industry trends. For the first time, meta-analytic results on the effectiveness of individual and group coaching interventions have been combined with real-world cases of job crafting and mindfulness training, enabling the construction of a comprehensive methodology for diagnosis, the formulation of emotionally concrete objectives, and practical micro-practices. The main findings demonstrate that developing EI through the structured coach-cycle diagnosis → goals → interventions → verification yields a statistically significant improvement in management outcomes, a reduction of subordinates’ techno-stress, and an enhancement of authentic leadership. Neurophysiological data confirm the effectiveness of PEA sessions for activating self-awareness and ensuring durable transfer of changes into behavior. At the same time, HRV biofeedback and the CSMC model demonstrate measurable business dividends in terms of reduced burnout and turnover. This article will be particularly useful to consultants and practitioners in executive coaching, HR directors, and researchers in organizational psychology.

The methodology introduced in this article combines a comprehensive model for assessing both market demand and saturation, an adaptive content-design principle based on the 50/40/10 formula, the use of hybrid video-production structures, and experimental hypothesis testing on a dataset of 157 channels. This integrative approach accommodates varied conditions and heterogeneous strategy formats. The study simultaneously identifies fundamental obstacles faced by novice creators—most notably the confirmation effect and survivorship bias—and proposes effective instruments for neutralizing these psychological traps, thereby fostering more informed and balanced decision-making. Empirical analysis indicates sustained audience growth of 8–12 % per month, viewer retention between 55 % and 65 %, and click-through rates of up to 9 %, all of which clearly outperform traditional approaches and underscore the practical significance of the proposed concept. The work is intended for professionals and researchers in digital promotion who design competitive video projects within YouTube’s dynamic, saturated media environment, and the findings possess high practical value and universal applicability across thematic segments.