Volume 7.
Issue 8.

This article presents a theoretical analysis of approaches to building customer loyalty in the fishing tourism sector amid shifting behavioral patterns, increasing importance of intangible factors, and growing relevance of the sustainability agenda. The study is based on an interdisciplinary framework incorporating territorial marketing, behavioral psychology, and sustainable tourism concepts. The focus is placed on comparing loyalty models grounded in trust, identity, emotional experience, and perceived sustainability. A content analysis of sources covering various types of fishing tourism (experiential, recreational, cultural) was conducted, identifying key factors influencing tourists’ behavioral and affective attachment to destinations. It was established that a universal trajectory of loyalty formation involves a sequence: engagement in digital interaction, trust formation, sustainable loyalty, and value co-creation. Three theoretical models—service-dominant logic, place identity, and service quality—were examined and compared in terms of focus, cultural context, and applicability. The study highlights regional differences in loyalty strategies and offers practical recommendations for tourism operators, including digital personalization, community development, and emphasis on sustainable consumption. This article will be of interest to tourism researchers, territorial marketing specialists, and practitioners seeking to increase repeat visits and ensure long-term audience retention.

The advent of e-commerce has profoundly reshaped the global financial landscape, transforming traditional banking operations and customer interactions. In India, a rapidly digitizing economy, the integration of e-commerce principles into the banking sector has ushered in an era of unprecedented convenience, accessibility, and efficiency, epitomized by the rise of e-banking and digital payment solutions. However, this transformative journey is not without its complexities. This article provides a comprehensive analysis of the evolving role of e-commerce within the Indian banking industry, meticulously examining the multifaceted issues and concerns that accompany its widespread adoption. Drawing upon existing literature, the study synthesizes challenges related to cybersecurity, technological infrastructure, customer adoption barriers (including the digital divide), regulatory frameworks, and operational complexities. It highlights the critical need for robust security measures, enhanced digital literacy initiatives, adaptive regulatory policies, and continuous technological innovation to fully harness the potential of e-commerce in fostering a secure, inclusive, and efficient banking ecosystem in India.

In this article, the problem of the low efficiency of traditional cold communications with venture capital funds is examined. The relevance of the study is determined by the need to develop automated tools for targeted search of relevant investors capable of overcoming the limitations of warm recommendations and expanding access to capital for startup teams without an extensive network. The aim of the paper is to demonstrate an algorithmic approach based on machine learning methods for identifying relevant investors and to investigate the integration of ML ranking with a disciplined multistep-outreach strategy. The novelty lies in the use of a multilayer feature architecture combining an investment graph, thematic embeddings, soft signals from public channels, and dynamic indicators of fund activity, as well as in the construction of a controlled cycle of cold communications with two follow-ups in each three-day window. The obtained results confirm an increase in the efficiency of the cold channel: algorithmic selection enabled maintaining an open rate at the level of 74–80%, a reply rate in the range of 10–17%, and provided 96 scheduled calls per quarter without a single warm recommendation. The integration of the ML ranking model with a structured cadence strategy increases the controllability of the process, turning fundraising from a lottery into a repeatable business process with continuous model learning on feedback data. Practical implementation includes not only the development of an investor ranking model but also the creation of infrastructure for large-scale mailings: configuration of mail domains, optimization of message templates, A/B-testing, and integration with meeting-scheduling tools. This allows startups to systematically increase open, click, and reply rates as well as conversion into negotiations. The article will be useful to startup founders, venture analysts, and fundraising specialists seeking to improve the efficiency of cold communications with investors.

This paper introduces a comprehensive examination of human resource reform as a strategic catalyst for institutional effectiveness within development finance institutions (DFIs). Drawing on a case study of a Nigerian DFI, the research explores how systematic HR transformation, encompassing standardized job descriptions, redesigned onboarding programs, transparent promotion mechanisms, and enhanced performance management, can strengthen organizational capacity while navigating the complex governance terrain between public accountability and private-sector efficiency. The paper establishes the theoretical foundations for understanding HR innovation in hybrid institutional contexts, articulates the unique challenges facing DFIs in developing economies, and positions HR reform as a critical lever for sustainable institutional resilience. Key contributions include a conceptual framework for hybrid institutional management, empirical insights from the Nigerian context, and practical implications for peer institutions seeking to balance equity imperatives with operational excellence.

This article reviews existing literature on automation and AI in procurement and explores the boundaries and opportunities for the application of artificial intelligence (AI) in electronic procurement systems, considering the ongoing digital transformation in both public and corporate sectors (Vital IT Pros, 2024). The distinction between rule-based automation and intelligent machine learning (ML) – based algorithms is emphasized. The study substantiates the areas where AI solutions can be effectively applied in procurement processes – from predictive analytics to tender documentation processing through natural language processing (NLP) tools. The regulatory and methodological foundations for AI implementation are analyzed, including the EU AI Act, ISO 20400, and the Open Contracting Data Standard (OCDS), while key barriers are identified – technical, ethical, and legal. The risks of algorithmic bias, opacity, and decision-making delegation to autonomous systems are discussed. A set of recommendations is proposed for transparent, ethically grounded, and functionally relevant AI implementation in e-procurement platforms, taking into account stakeholder perspectives.

This article conducts a comparative analysis of the efficiency of various machine learning algorithms in addressing the task of classifying accounting transactions — a component ensuring the accuracy of financial reporting and enhancing operational efficiency. The aim of this study is to analyze different machine learning algorithms for the task of automated classification of accounting entries. The methodological basis of the research includes an extensive review of specialized literature, where the architectures of models such as logistic regression, support vector machine (SVM), random forest and gradient boosting are analyzed, as well as promising neural network solutions employing natural language processing (NLP) technologies. As a result of the experiment, a comparative analysis is presented according to key metrics (accuracy, recall, F1-score) and a hybrid architecture is proposed, combining an NLP module based on the BERT model and a gradient boosting classifier, which demonstrates the best results when processing transactions with complex textual descriptions. The scientific novelty of the work lies in the description of a conceptual model for selecting the optimal algorithm depending on the characteristics of the original data set and in substantiating the advantages of the proposed hybrid architecture, which integrates natural language processing methods for extracting semantic features and ensemble algorithms for final classification. In conclusion it is emphasized that the implementation of intelligent classification automation not only minimizes the influence of the human factor but also transforms the role of the accountant from a data entry operator into a strategic analyst. The obtained data are of interest to researchers in financial engineering and artificial intelligence, practicing accountants and auditors, as well as developers of software products for the automation of financial flow management.

Enterprise Architecture (EA) has changed as a strategic competency that helps organizations to align technologi-cal resources with business aims and, thus, achieve organizational flexibility and support digital transfor-mation. This research is an attempt to analyze the EA aspect as an enabler of agility and a generator of successful initiatives of having a digital transformation in various contexts of organizations. The study based the cross-sectional research design to collect primary data in 212 organizations of the mid and large size in the fields of finance, healthcare, and manufacturing within OECD countries. Structural equation model framework (SEM) quantitative analysis shows that a positive association exists between the mature EA implementation and the improvement in organizational agilities (SEM: 0.72, p < 0.001) with highly significant gains in digital transformation met-rics, notably; IT-business alignment (67% increase), decision-making speed (42% improvement) and operational efficiency (38% gain). The findings further show that EA maturity moderates the association between agility and transformation and imply that it plays a central role in agitating adaptive capacity and innovation. The contribution of this paper to the literature is that the gap between the theory and practice is filled by means of the empirical validation of the effects of EA. The novelty of the research is that the analytical framework is integrated with the focus on enterprise architecture maturity, agility enablers, and digital transformation outcomes as well as it provides academically-grounded idea and practical suggestions that entail the role of the chief information officer (CIO), enterprise architects, and digital strategy leaders. The research establishes the strategic necessity of integrating EA into core business planning in order to generate sustainable competitive advantage in the turbulent digital world. The results are reliable and are generalizable because ethical data collection and rigorous analysis by statistics are carried out.

This article examines the role of machine learning (ML) techniques in market trend forecasting, with a focus on their advantages over traditional approaches. Key algorithms are reviewed, including regression models, neural networks, gradient boosting, and hybrid architectures, along with essential data preprocessing steps such as cleaning, synthetic feature generation, and feature importance evaluation. Using case studies from leading financial institutions (e.g., Renaissance Technologies, JPMorgan Chase), the paper highlights how ML enhances forecast accuracy, optimizes risk management, and accelerates decision-making processes. Several challenges are identified, including dependence on data quality, the risk of overfitting, high computational costs, and the interpretability of complex models. The paper also outlines promising directions for development, such as the integration of transfer learning methods, generative adversarial networks (GANs), and the adaptation of algorithms to non-stationary financial data. The findings emphasize the transformative potential of ML in the context of increasing financial market volatility. This article will be particularly valuable for professionals in finance, especially those engaged in trading and stock market operations, offering practical guidance on selecting optimal ML methods for financial applications. Theoretical insights provided may also serve as a basis for further academic and applied research in artificial intelligence.

The article "Impact of Digital Technologies on Brand Product Strategy Development in Ukraine's Oil-and-Fat Market" analyzes the impact of digital technologies on the formation and adaptation of product strategies of Ukrainian brands in the oil-and-fat industry. The study emphasizes the relevance of understanding how the digital environment, growing consumer expectations for transparency and innovation, as well as increased global competition are changing the strategic behavior of companies.

The work uses a mixed methodology that combines bibliometric analysis, market trend study, SWOT analysis, and content analysis of corporate web resources and social networks. This allowed us to explore the role of specific digital tools in shaping modern product strategies. Based on a content analysis of leading Ukrainian brands, the authors proposed a typology of digital strategies used in the sector.

The results indicate that digitalization contributes to the growth of companies' competitiveness through the personalization of offers, transparency in supply chains and optimization of business processes.

Ukrainian brands actively integrate digital technologies into various communication channels and operational activities, demonstrating a high level of readiness for digital transformations. The article contributes to the development of the discourse on the digital modernization of traditional industries and offers practical recommendations for enterprises seeking to update their product strategies through digital integration.

The article examines the implementation of a work breakdown structure in multidisciplinary projects and its role in ensuring consistency in planning, budgeting, and quality control. The relevance of the study is justified by the need to coordinate diverse engineering and scientific schools accustomed to their work‑structuring templates, which, without a common decomposition language, leads to package duplication, hidden interfaces, and risks of resource overrun. The objectives of the article are to analyze existing standards and practices, identify empirical patterns in how WBS quality influences project timeliness and budget compliance, and formulate methodological recommendations for harmonizing codes, terminology, and integration packages. The novelty of the research lies in the systematic comparative analysis of NASA guidelines and construction case studies, in the content analysis of buffer tasks according to the schedule margin methodology, and in the proposal of a classification of interface tasks along three axes (technical, contractual and organizational); the study demonstrates how to link the WBS‑dictionary with digital PDM, PLM and PPM platforms to enhance transparency and adaptability of project structures. The main conclusions show that a properly constructed WBS functions not only as a work map but also as a mechanism for translation between professional languages, ensures traceability of budget, schedule and requirements, and that integration and interface packages, defined as autonomous elements, transform hidden dependencies into manageable planning objects; the applied empirical threshold rules (8/80, 4%, 40 hours), the RACI role model and schedule margin buffer tasks create a dynamic yet predictable framework capable of adapting to evolving requirements. The article will be helpful to project managers, systems engineers, integration management specialists, and all those involved in planning and control of multidisciplinary projects.

This paper reviews artificial intelligence approaches to predicting the risks of employee turnover and managing strategies designed to retain them. The purpose of the current study is to carry out a systematic review and practical assessment of existing algorithms used as early warnings for personnel turnover in corporate environments and to recommend ways through which the derived models could be incorporated into HR management processes. The relevance of this work is determined by organizations’ enormous costs associated with replacing specialists, the rapid growth of the HR analytics market, and the need to shift from a reactive turnover management model to a proactive talent-retention system. The novelty of the research lies in the comprehensive comparison of classical statistical methods (logistic regression, CoxRF) and modern machine learning algorithms (XGBoost, LSTM-RNN, Bidirectional-TCN, graph neural networks) on both proprietary and open datasets, as well as in the incorporation of interpretability criteria (SHAP, LIME), organizational and ethical barriers, MLOps requirements, and EU regulatory standards into the architecture of predictive HR systems. The findings demonstrate that basic statistical models provide a rapid start and clear interpretability on small samples; however, as data volumes grow, gradient boosting emerges as the “gold standard,” and recurrent and convolutional networks become preferable for analyzing temporal communications. Graph neural networks improve flight-risk detection quality by accounting for social connections, while interpretability tools enable the translation of a score into a concrete retention plan. The key takeaway is the need for an integrated approach: starting from detailed data prep and cleanup, building a cross-functional team, setting up an MLOps loop, designing solutions ethically, training end-users, and monitoring success metrics regularly. This paper will be helpful to HR directors, people analytics specialists, AI-in-HR project managers, as well as academic researchers in the field of human capital management.

The article is devoted to the design of adaptive financial models for fast-growing technology companies that face a shortage of reliable forecasts due to sparse historical data. The relevance of this endeavor stems from the need to move beyond static forecasting toward dynamic valuation capable of responding to changing market conditions and structural business parameters. The novelty lies in the integration of simulation modeling, machine-learning techniques, Bayesian hyperparameter optimization, and streaming data analysis into a unified capital-management architecture. Within this framework, strategies for incorporating sentiment analysis and budget rebalancing are described, scenario-based valuation models are examined, and algorithms for calibrating PEG multiples according to growth phases are developed. Special attention is paid to validating expert assumptions through the analysis of customer metrics. The work establishes the goal of creating a modular structure able to adapt to phases of rapid, mature, and sustainable growth. To achieve this, comparative analysis, case studies, and cash-flow modeling are employed. Publications from CFO Drive, Forbes, IJEAT, Coherent Solutions, and Avenga are reviewed. The conclusion offers recommendations for implementing the framework in companies at various stages of development.

In the digital change era, organizations are becoming more dependent on Information Systems (IS) as part of the implementing strategic decision making throughout various levels of operation. The paper gives a formal, evidence-based literature overview to explore the ways in which IS helps to make better, faster and more optimal decisions with respect to long-term business perspectives. Based on peer-reviewed research of more than 80 studies using approved academic sources like Scopus, IEEE Xplore, and ScienceDirect, and Wiley-Online Library, the review summarizes analyzed scholarly literature of the past ten years. This paper classifies IS types, which include Decision Support System (DSS), Executive Information System (EIS), Enterprise Resource Planning (ERP) and Business Intelligence (BI) system, based on their strategic capabilities. Quantitative factors including reduction in the cycle time of decisions, return on investment in information technology as well as the ability to predict were measured to gauge IS effectiveness. Due to research findings, there is positive and constant relationship between adoption of IS and improvement of strategic performance outcomes in various sectors such as healthcare, manufacturing, finance, and retail. Yet there are a number of obstacles which still remain such as barriers of integration, opposition to digital culture and inability in decision makers to possess adequate analytical skills. The paper has identified such constraints and provided an organizational readiness framework of strategic IS integration. Additionally, it demonstrates upcoming horizons like AI-helped IS, real-time analytics, and morality IS governance as potential ardent research facilities in the future. The uniqueness of the study consists in its integrative comprehensive analysis of disparate knowledge, as well as the creation of the prospective agenda of matching IS potential with strategic organizational goals. The review contains practical suggestions to the business leaders, IT strategists, and policy makers who are willing to derive business competitive advantage out of IS..

The study is devoted to identifying and analyzing the synergistic interaction between the theoretical principles of behavioral finance and applied methodologies for developing high-r     eturn algorithmic strategies in the digital asset segment. In conditions where the efficient market hypothesis demonstrates limitations in its applicability, especially in environments with increased volatility and underdeveloped infrastructure—such as cryptocurrency markets and decentralized finance (DeFi) ecosystems—behavioral biases emerge as important determinants of market inefficiency. The paper presents a framework that combines the targeted exploitation of cognitive patterns, including the disposition effect and the phenomenon of herd behavior, with the application of advanced technological solutions. Based on four original case studies—ranging from the development of a proprietary backtesting mechanism incorporating elements of chaotic process modeling to the construction of a predictive risk management system for DeFi—the practical implementation of the proposed approach is demonstrated. The results obtained confirm the superiority of the hybrid architecture over traditional methods: from effectively reducing crash risk in DeFi carry trade strategies to maintaining portfolio resilience under market stress conditions and generating ultra-high returns (CAGR exceeding 200% with MDD of 30%). The study’s findings reinforce the validity of the adaptive markets hypothesis and confirm the applied value of the synthetic methodology for modern algorithmic trading. The information reflected in the study will be of interest to asset managers, quantitative fund specialists, and researchers focused on creating next-generation algorithms.

This article examines how enterprise resource planning (ERP) systems are deployed to enhance the efficiency of corporate tax accounting and tax-liability management. Its relevance stems from the growing complexity of tax regulations and the pressing need to automate related processes. We synthesize both empirical and analytical evidence on ERP functionality in the tax domain, covering automated calculation, compliance monitoring, report generation, and tax-planning support. The review includes recent studies reporting shifts in tax-risk levels, reductions in compliance costs, and the evolving role of in-house tax departments. We pay special attention to the implementation risks associated with ERP roll-outs and the ethical boundaries of leveraging advanced analytics. The aim is to identify the enduring impacts of integrating ERP into tax-accounting workflows. Employing comparative and systems-based methodologies alongside case-study and literature analysis, we conclude by underscoring ERP’s significance as a tool for strengthening compliance, transparency, and governance in corporate taxation. Practitioners in corporate finance, systems integrators, and tax-planning specialists will find the insights particularly valuable.

The article presents a comparative analysis of the effectiveness of three types of gamification incentives for line personnel in hotel enterprises: individual, team-based, and probabilistic incentives. The purpose of the study is to determine the conditions under which each mechanic demonstrates the highest effectiveness in selling ancillary services, including upselling and cross-selling, in the hotel business, including periods of low occupancy. The methodological basis of the study includes a systematic review of literature indexed in Scopus and Web of Science, comparative analysis, case study, and content analysis of managerial documentation. The empirical object of the analysis is the Volgograd-Intourist Hotel, which applies an original five-component motivation system. By the end of 2025, relative to the 2024 baseline, sales of ancillary services increased by 45%, the average check for ancillary services rose by 30%, and total revenue reached RUB 157.2 million, representing an 11% increase. The study establishes that probabilistic mechanics provide the most stable employee engagement during periods of low demand, team-based incentives reduce destructive competition, and cascading individual bonuses maximize revenue during peak periods. The proposed model allows scaling to hotel enterprises of different classes. The findings may be of interest to hotel managers, revenue managers, and researchers in the field of HR management in the tourism industry.