Business, Economics and Management

Abstract: Employee-turnover warning in finance and taxation education must account for different job roles, repeated monthly records, rare departures, and uncertainty about when some fields became available. This study analyzed 1,397 de-identified employee records and reconstructed 6,935 monthly records for predicting voluntary turnover within 90 days. It compared 22 classifiers with the Hierarchical Risk Encoding XGBoost–CatBoost Ensemble (HRE-XCB), which combines Raw-XGB, HRE-XGB, and native CatBoost. Before model development, 278 employees were selected without reading their out comes. None of their earlier records entered feature screening, tuning, encoding, weighting, or cutoff selection. The search evaluated 152 base structures and selected weights of 0.65, 0.15, and 0.20. The June 2022 evaluation contained 278 separated employees and 24 departures. HRE-XCB achieved recall 0.7500, F1 0.3750, and PR-AUC0.3477. Among the equally tuned shortlist, random forest obtained the highest PR-AUC (0.3772), Extra Trees the highest F1 (0.4324), and histogram gradient boosting recall 0.7917 with the lowest Brier score (0.0752). A separate lower-tuning screen was led by preset gradient boosting (PR-AUC 0.4229). In 5,000 employee-cluster bootstrap samples, no tuned model had a PR-AUC difference from HRE-XCB whose 95% interval excluded zero. Component ablation showed that Raw-XGB alone had nearly the same PR-AUC as the complete ensemble (0.3481 versus 0.3477), while Raw-XGB plus HRE-XGB reached 0.3492. The study therefore supports employee-separated evaluation and decision-specific model choice, but not a universal-best algorithm or exact individual departure probabilities.

Abstract: Three decades of research on finance and growth in the Middle East and North Africa disagree with one another. This paper argues the disagreement is manufactured by measurement and identification, not by the economies themselves. A transparent coverage rule fixes the sample at ten MENAT economies (the Middle East and North Africa plus Turkiye) over 1995-2021, and cointegration is assessed with a factor-based test suited to the data's strong common movements. Bank-ratio finance proxies carry only a weak and unstable long-run signal: the within-country correlation between the ratio and remittances has no common sign across economies, and the remittance term beside the ratio coefficient switches sign and significance across samples, so no stable estimate can be anchored on the ratio. Reframing the object of measurement resolves the impasse. A capabilities factor combining the multidimensional financial-institutions index with schooling carries a long-run elasticity of 0.16 to 0.24 per standard deviation, agreeing across pooled mean group, dynamic fixed effects with cross-sectionally robust errors, group-mean fully modified least squares, and a dynamic common correlated effects estimator reading the relationship through common factors. Trade openness contributes robustly across specifications. Policy that treats finance and education as separate levers asks the region the wrong question.

Abstract: For more than two centuries, economic theory has been grounded in the assumption that scarcity constitutes the fundamental condition of human societies and that the principal task of economic systems is the efficient allocation of finite resources. However, the accelerating convergence of artificial intelligence (AI), autonomous production systems, advanced robotics, additive manufacturing, decentralized digital infrastructures, and emerging quantum technologies increasingly challenges this foundational premise. These technological transformations are creating unprecedented capacities for continuous value generation, radically reducing the dependence of economic production on traditional constraints of labor, capital, and material scarcity. Consequently, the scarcity paradigm that has shaped classical, neoclassical, and even many contemporary economic theories is becoming progressively inadequate for explaining the dynamics of emerging socio-economic systems.This article introduces the Infinity Economy as a novel conceptual framework for understanding post-scarcity economic systems in the age of AI and quantum abundance. Rather than conceptualizing economics as the science of allocating scarce resources, the Infinity Economy redefines it as the science of designing and governing generative systems capable of continuously producing cognitive, informational, technological, and relational value. Integrating insights from complexity economics, artificial intelligence, innovation studies, digital political economy, systems theory, and governance scholarship, the paper develops a theoretical architecture that explains how autonomous production, distributed intelligence, decentralized infrastructures, and AI-mediated coordination are transforming wealth creation, exchange mechanisms, and institutional organization.The article makes three principal contributions. First, it critically examines the theoretical limitations of scarcity-based economics under conditions of accelerating technological abundance. Second, it proposes the Infinity Economy as an integrative paradigm grounded in generative value creation, distributed governance, cognitive capital, and regenerative economic systems. Third, it identifies the institutional, ethical, ecological, and governance challenges associated with the transition toward post-scarcity societies while outlining a future research agenda for economics beyond scarcity. By reconceptualizing economic systems as adaptive, self-generating, and AI-enabled ecosystems, the Infinity Economy provides a foundation for rethinking economic theory, public policy, and sustainable development in the twenty-first century.

Abstract: Innovations in artificial intelligence are reshaping how tax administrations approach compliance and audit planning, yet existing AI-based fraud detection studies largely treat the taxpayer population as homogeneous or remain conceptual frameworks awaiting empirical validation. This gap is consequential because audit resources are limited, evasion tactics are increasingly sophisticated, and misallocating scarce audit capacity carries a direct fiscal cost. To address it, this study presents VERITAS, a machine learning-based decision support system operationalizing a segment- and sector-aware architecture for corporate income tax audit planning: a single-layer model for Large Taxpayer case selection, and a novel two-layered model for small and medium enterprises (SMEs) that filters evasion-suspect cases before prioritizing them by expected tax-recovery yield against a target threshold. Ten classification algorithms were compared across 4,063 SME and 1,903 Large Taxpayer financial statements, with correlation-ranked feature selection subsequently applied to each. Random Forest consistently outperformed all alternatives across every segment, sector, and task examined; feature selection improved performance in every case; sector-specific modeling outperformed a generic classifier in three of four SME sec-tors; and a novel business-activity-code feature was retained in most analyses. These findings position VERITAS as a practical innovation in tax audit practice: a deployable, generalizable template for AI-driven audit planning built entirely from data tax administrations already collect.

Abstract: This paper provides a comprehensive analysis of leveraged Exchange-Traded Funds (ETFs) and Exchange-Traded Notes (ETNs), financial instruments that have grown significantly in popularity and market presence since their introduction in the early 2000s. Using data from 2020-2025, we examine the performance characteristics, risk profiles, and potential market impacts of these complex investment vehicles across different asset categories and market conditions. Our analysis reveals significant volatility drag and tracking errors that increase with holding period length and underlying asset volatility. We find that leveraged ETFs tracking technology and semiconductor indices experience the most extreme performance patterns, while fixed income leveraged ETFs show more moderate but still significant decay effects. The paper demonstrates that these products generally fail to deliver their stated multiple of underlying index returns over periods longer than their daily rebalancing horizon, with the divergence increasing during periods of high market volatility. These findings have important implications for individual investors, financial advisors, and regulators concerned with market stability and investor protection.

Abstract: This study constructs a two-period model where a regulator determines the level of precautionary capital, information-generating reporting, and the design of supervisory information systems, while providers respond by participating. When reporting successfully produces a verified diagnostic, continuation capital is set afterward, but reporting incurs various costs, including variable, participation, and fixed setup costs, before its information is utilized. In a Bayesian context, the diagnostic's gross decision value is weakly nonnegative because it can be disregarded. Reporting is only activated if its discounted decision value and any screening benefits outweigh its net costs and setup expenses. Under recursive maxmin assumptions, an admissible prior that makes the adverse state certain introduces a certainty boundary: diagnosis cannot alter the continuation capital once the conditions are met, and precaution substitutes for learning. Priors that are uniformly interior allow for ongoing learning. Recursive smooth ambiguity models positive reporting at finite levels of ambiguity aversion and converges to the maxmin boundary under specified conditions, without implying overall monotonicity. A joint-selection theorem compares scenarios with no reporting, common, and specialized reporting architectures after optimizing intensity. An architecture that keeps the common experiment and adds an ignorable signal slightly improves gross information but may reduce net surplus. Kenya’s virtual-asset framework provides a dated institutional example but neither calibrates the model nor reveals its core mechanism. The results are supported by analytical proofs, independent recalculations, and reproducible code.

Abstract: Job preparation, engagement, and retention are crucial processes for retaining and supporting a quality workforce, and realistic job previews (RJPs), along with simulation-based training, play a crucial role in this process. In this paper, we bring together four sources of literature to review the translation of experiential and simulation-based training into tangible positive outcomes for organizations, most notably employee retention. This paper, relying on the foundational thinking of Kolb's (1984) experiential learning cycle, Issenberg et al.'s (2005) review of the features of high-fidelity simulation training programs, Jun and Eckardt's (2023) social exchange perspective on training and turnover, as well as Albtoosh, Ngah, & Yusoff's (2022) meta-analytic findings, demonstrates that high-quality simulation-based training not only enhances skill attainment but also fosters organizational identification and lower voluntary turnover. Research shows that quality training and development (with its features of feedback, repetition, variability, and integration) is a more important driver of turnover than quantity. By providing employees with structured simulation-based onboarding and real job previews, employers are likely to foster psychological readiness, loyalty, and retention among employees. The findings have implications for human resource strategies, training for industrial safety, and within other industries.

Abstract: Leadership has become one of the most critical determinants of organizational resilience, institutional effectiveness, societal transformation, and sustainable development. Yet despite decades of research, leadership scholarship remains fragmented across multiple theoretical traditions—including transformational, authentic, servant, adaptive, strategic, distributed, and complexity leadership—each emphasizing particular behaviors, competencies, or relational processes. Existing approaches have significantly advanced the understanding of leadership but provide limited capacity to compare leadership readiness across organizations, sectors, or countries using a standardized and multidimensional measurement framework. Consequently, no internationally recognized index currently exists to assess leadership capacity in a manner comparable to widely used composite indicators such as the Human Development Index or the Global Innovation Index.This article addresses this gap by introducing the Leadership Capacity Index (LCI), a novel conceptual and methodological framework designed to measure leadership capacity as a multidimensional capability rather than a collection of isolated leadership traits or styles. The article argues that leadership capacity is an emergent systemic property reflecting the ability of individuals, organizations, and institutions to mobilize people, knowledge, ethical values, innovation, and adaptive learning to achieve sustainable and regenerative transformation under conditions of complexity and uncertainty.Building upon an extensive review of leadership theories and organizational capability research, the paper proposes the Leadership Capacity Theory (LCT), which conceptualizes leadership capacity as a dynamic interaction among ten complementary dimensions: visionary capacity, ethical capacity, adaptive capacity, learning capacity, innovation capacity, collaborative capacity, systems thinking, cultural intelligence, ecological stewardship, and regenerative leadership. These dimensions are integrated into the Leadership Capacity Framework (LCF) and operationalized through the proposed Leadership Capacity Index (LCI), accompanied by a Leadership Capacity Maturity Model and a Leadership Diagnostic Toolkit.Rather than presenting a fully validated quantitative instrument, this article establishes the theoretical foundations for an international leadership measurement system. It outlines future directions for operationalization, psychometric validation, mathematical modeling, artificial intelligence-assisted assessment, and cross-cultural benchmarking. The Leadership Capacity Index is intended to support evidence-based leadership development across governments, businesses, universities, civil society organizations, and international institutions. Ultimately, the article argues that measuring leadership capacity is becoming as strategically important as measuring economic performance, innovation, or human development in addressing the complex challenges of the twenty-first century.

Abstract: Carbon pricing raises the cost of fossil-fired electricity inside the regulated jurisdiction but cannot price emissions beyond the regulator's border. Proof-of-work cryptocurrency mining is an unusually clean setting in which to test whether this asymmetry produces carbon leakage: mining is electricity-intensive, geographically footloose, and reallocates across grids far faster than conventional heavy industry. Using daily data from January 2019 to January 2026 (N = 2,550), we estimate quantile regressions of power-sector CO2 emission growth in the EU27, the Russian Federation and the rest of the world on the interaction between Bitcoin returns and European carbon-allowance returns. In Russia the interaction is positive and significant in the lower tail (beta = 0.066, p = 0.001) and survives ordinary least squares with robust standard errors (p = 0.012) and a dynamic specification controlling for emission persistence (p = 0.010); Russia is the only region whose model is jointly significant. In the EU27 and the rest of the world the interaction is not robustly distinguishable from zero at any quantile. The Russian effect is entirely a post-2020 phenomenon, coinciding with the Chinese mining prohibition and the tenfold rise in European allowance prices, while neither other region shifts across the same break. We find no support for a green-paradox reading of green finance: the Green Bond index enters negatively and significantly across specifications. Instruments confined to the emissions-trading perimeter are structurally unable to govern a mobile, electricity-intensive load.

Abstract: International trade is undergoing a growing transformation driven by geopolitical ten-sions, the adoption of stringent regulatory frameworks, and the increasing incorporation of sustainability criteria; therefore, explaining bilateral trade flows regains importance when analyzed through the gravity model. This study analyzed trends in international trade from geopolitical, regulatory, and sustainability perspectives to propose an expand-ed conceptual gravity model that comprehensively incorporates these dimensions. The re-search was grounded in the hermeneutic paradigm, employing a qualitative-interpretive method that synthesized a systematic literature review with grounded theory procedures for an interactive analysis of 83 scientific articles indexed in Scopus, selected using the PRISMA method and published between 2014 and 2026, which included conceptual op-erationalization as a systematization exercise. The results confirm the convergence among the dimensions identified in the literature, and based on the synthesis of the scientific lit-erature, an expanded gravity model is proposed that strengthens the explanatory power of the gravity model and establishes an agenda for future research aimed at application in various trade contexts using econometric evidence.

Abstract: This study examines the primary factors influencing audit fees among Russell 3000 businesses from 2001 to 2023. The impact of market indices and the existence of key audit matters (KAMs) on audit fees are particularly emphasized to offer an enhanced understanding of audit cost structures. The study employed a panel data analysis with econometric models to measure the correlation between audit fees and other independent variables, using data obtained from the Audit Analytics database, encompassing financial and audit-related factors for firms within the Russell 3000 index, excluding the financial sector. This analysis revealed that assets, earnings, and revenue are the primary predictors of audit fees, although book value exerts no substantial influence due to the counteracting impacts of positive and negative values. These findings facilitate auditors and companies’ improved understanding of audit pricing structures and, support better-informed negotiations and resource planning in audit engagements.

Abstract: Research on airport proximity has mostly concentrated on permanent residents, ignoring vacation resort markets despite their increasing economic significance. Our study examines 9,841 resort property sales from 1983 to 2023 in Mobile and Baldwin Counties, Alabama. We use a hedonic pricing model and geographically weighted regression (GWR) to analyze how airport proximity impacts resort values. A global regression indicates no overall relationship, but GWR shows that this masks strong, spatially varying effects that a single coefficient cannot capture. Properties near airports tend to sell for less, with larger discounts near general aviation airports than commercial airports. This reverses the accessibility premium seen in residential markets for nonurban general aviation. We introduce the Amenity Separation Effect to explain that resort buyers prioritize tranquility over mobility. As a result, airport accessibility does not compensate for its disadvantages, unlike in residential markets. These findings can help improve property assessment, appraisal practices, noise policies, and disclosure requirements in tourism-dependent coastal economies.

Abstract: Green bonds and green bank loans coexist as instruments for financing the low-carbon transition, yet the strategic mechanism through which issuers select between them remains weakly formalized. This paper models green debt instrument choice as a three-stage extensive-form game with perfect information in which a firm first selects a financing route, investors then decide whether to subscribe to a green bond issuance, and a bank sets lending conditions under regulatory incentives. The subgame-perfect equilibrium is characterized analytically by backward induction and is then implemented numerically: the equilibrium correspondence is mapped over the parameter space and evaluated through a Monte Carlo experiment of 200,000 parameter drawing across the eight admissible states of demand, issuer credibility and regulatory regime. Equilibrium green bond issuance requires two conditions to hold jointly, namely investor participation and a demand-driven advantage exceeding the fixed issuance and certification cost. The simulation shows that green bond adoption falls from 79.2% under high demand and strong credibility to 1.5% under moderate demand and weak credibility, with a sharp discontinuity at the participation threshold, and that bank concessionally shifts financing levels without altering the bond-versus-loan margin. The framework provides a tractable basis for assessing sustainability-related financing risk and for the empirical separation of feasibility, optimality and institutional transmission channels.

Abstract: This study examines how green marketing and sustainability information overload shape consumers' perceptions of greenwashing and behavioral intentions toward green hotels in Vietnam. Integrating Stimulus-Organism-Response theory and Media Richness Theory, the study models green marketing and information overload as external stimuli, perceived greenwashing, green trust, and green attitude as organism states, and intention to stay, word of mouth, and willingness to pay as behavioral responses. Data were collected from 248 Vietnamese consumers aged 20 years and above after exposure to a simulated green hotel website video. Green marketing reduced perceived greenwashing, whereas information overload increased it. Perceived greenwashing did not affect green trust or green attitude but had a positive direct effect on behavioral intention. Green attitude and green trust significantly strengthened behavioral intention, and perceived greenwashing mediated the information overload-behavioral intention relationship. The findings highlight the need for concise, verifiable, and credible sustainability communication in green hospitality marketing.

Abstract: This paper presents a comprehensive and critical examination of the theoretical and empirical underpinnings of liquidity management within the commercial banking sector, with a specific focus on the Republic of Armenia. We argue that liquidity, far from being a monolithic metric, is a multifaceted construct that encapsulates an institution's capacity to meet its financial obligations in a timely and cost-effective manner. This study advances the discourse by integrating seminal theoretical frameworks—from the classical "golden banking rule" to the modern dynamics of asset-liability management (ALM) and funding liquidity risk—with a granular empirical analysis of regional financial heterogeneity. The analysis is structured around two central pillars. First, we decompose the evolution of key regional economic indicators, including firm registrations, wage dynamics, and unemployment, to map the landscape of real economic activity against the backdrop of national liquidity conditions. Second, we employ a correlation matrix to dissect the intricate, often non-linear, relationships between bank-specific variables (e.g., liquid assets to total assets, non-performing loans (NPLs), profitability) and macroeconomic aggregates (e.g., GDP growth, wage inflation). Our findings challenge the conventional wisdom of a homogeneous national liquidity framework, revealing that regional disparities are profound and that the predictive power of traditional liquidity measures is contingent upon the specific economic context. The paper concludes by positing that effective liquidity management is predicated on a "state-contingent" mechanism, one that is responsive to both micro-prudential signals and macroeconomic shocks, and that this is particularly crucial in small, open economies where external vulnerabilities are pronounced.

Abstract: This study aims to identify the financial and non-financial factors affecting audit opinions by using explainable artificial intelligence (XAI) models, which provide an important advantage in terms of transparency in the auditing sector. In this context, 2,415 firm-year observations from 238 manufacturing firms listed on Borsa Istanbul for the period 2010–2024 were used. In the study, the independent audit opinion was used as the dependent variable, while 18 financial and non-financial attributes were employed as independent variables. Descriptive statistics and a correlation heatmap analysis were conducted. To identify the most influential factors affecting audit opinions and to comparatively reveal which factors are more important across different models, explainable predictive models were developed using logistic regression, decision tree, random forest, XGBoost, LightGBM, and CatBoost models. Three different analyses were performed: permutation feature importance analysis, SHapley Additive exPlanations (SHAP) analysis, and SHAP beeswarm plot analysis. The built-in feature importance heatmap and the SHAP feature importance heatmap for all models were presented comparatively. A final heatmap was then constructed based on the averages of the built-in feature importance scores and SHAP values. According to the results of the final heatmap, the type of audit opinion in the previous year (X17) was identified as the most influential factor with a score of 0.78, followed by asset turnover ratio (X7) with 0.57, operating sector (X18) with 0.54, current ratio (X1) with 0.54, acid-test ratio (X2) with 0.51, and the natural logarithm of total assets (X10) with 0.50, all of which were found to have the highest explanatory power. The final comparison table, constructed by averaging both the models’ built-in importance scores and SHAP values, indicates that the results are not random but are based on a robust foundation. The findings of this study make significant contributions to both the academic literature and practitioners by guiding decision-makers in identifying the factors that should be prioritized.

Abstract: Background: The Middle Corridor has become a strategically important Eurasian transport route, yet limited research has examined the configuration of intermodal networks for hazardous materials transportation under its unique operational and regulatory conditions. This study develops a decision-support framework for optimizing hazardous materials transport along this corridor. Methods: A multi-objective mixed-integer linear programming (MOMILP) model was formulated to optimize transportation cost, risk, and transit time simultaneously. The model incorporates modal selection, intermodal terminal transfers, infrastructure capacity constraints, border delays, and regulatory compatibility under ADR, RID, and IMDG requirements. A simplified Middle Corridor case study was used to evaluate Pareto-optimal network configurations and sensitivity to safety preferences and infrastructure capacity. Results: The numerical analysis shows that rail-dominant intermodal configurations consistently achieve the lowest transportation risk while requiring only modest increases in transit time. Sensitivity analysis indicates that assigning moderate weight to safety leads to stable rail-oriented solutions, whereas railway capacity limitations force shifts toward higher-risk road transport under increasing demand. Conclusions: The proposed framework provides a transparent and mathematically rigorous approach for balancing safety, efficiency, and cost in hazardous materials transportation. It offers practical decision support for infrastructure planning, intermodal terminal development, and policy evaluation along the Middle Corridor.

Abstract: The COVID-19 pandemic and subsequent economic crises uncovered deep vulnerabilities in social, economic, and institutional systems across the world. In this context, the corporate social responsibility (CSR) and sustainability strategies emerged as strategic tools for resilience and collaboration overcoming the crisis. This study examines the role of CSR and sustainability-oriented approaches in strengthening partnerships between for-profit and non-profit organizations in Slovenia during periods of crisis. Focusing on the various cross-sector collaborations formed or adapted during crises in the context of COVID and other events, the research explores how organizations mobilized resources, shared knowledge, and co-created social value to respond to urgent community needs. Using a qualitative method based on interviews the study identifies the various CSR and Sustainability strategies that companies and NPO/NGOs in Slovenia have adopted to combat the impact of pandemic and subsequent crises in society. Particular attention is given to how sustainability-driven innovative strategies enable longer-term, trust-based partnerships for achieving better organizational outcomes and provide recommendations for companies and NPO/NGOs moving forward. Findings indicate that organizations with embedded CSR and sustainability practices were better positioned to pivot from transactional relationships to strategic alliances with non-profits. These partnerships enhanced crisis response capacity and contributed to economic and social resilience to various crisis situations. Moreover, the crises acted as a catalyst for redefining value creation, shifting the focus from compliance management toward shared impact and collaboration.

Abstract: Electricity-generation mixes can converge in a final-year snapshot while following different transformation pathways, which matters for identifying energy-security exposures. This article analyzes EU-27 electricity-generation mixes for 2000–2024 (27 countries, 25 years, eight generation components) as compositional time trajectories. Shares were regularized by simple multiplicative replacement, transformed with the isometric log-ratio transformation, compared using multivariate dynamic time warping with a Sakoe–Chiba constraint, and clustered with partitioning around medoids (PAM; k-medoids). The selected four-cluster solution is represented by Spain, Bulgaria, Malta, and Latvia and describes broad renewable diversification, solid-fossil-fuel legacy transformation, high-concentration island transition, and renewable-dominant restructuring. The raw-share baseline and the compositional trajectory solution agree weakly (Adjusted Rand Index = 0.1891), while endpoint-only clustering for 2024 also diverges from full-trajectory clustering (Adjusted Rand Index = 0.1950; 10 countries change assignment after label alignment). Across 40 robustness specifications, 14 countries are core cases and 13 are border cases. This boundary sensitivity is a substantive diagnostic: stable interpretation of border countries requires information beyond generation shares. Final mix similarity is therefore not a substitute for pathway similarity, and structural energy-security exposure should be interpreted through diversification, concentration, and the sequence of fuel substitution.

Abstract: The aim of this scientific article is to identify the key aspects of the concept of corporate social responsibility (CSR) and to quantify their impact on employee stability (ES). The subject of the research was selected aspects of CSR (strategic CSR management; effective implementation & communication; financial commitment; internal use & employee satisfaction; internal dissemination) and ES (organizational image; ethical corporate culture; safety and health protection; rewards, KPI and loyalty; growth through training). The quantitative research was carried out using a uniquely created questionnaire. The research sample contained 442 key managers from the business environment of the Czech Republic. The statistical hypotheses were evaluated using the structural equation modeling (SEM) method. The quantitative research revealed interesting findings. There are direct positive effects of the use of the aspects of the CSR concept on employee stability. Effective implementation & communication, together with financial commitment, are the most significant determinants that directly influence employee stability. A surprising finding is that growth through training has no links to employee stability. On the contrary, organizational image, together with rewards and loyalty, are the key aspects with links to employee stability. The findings bring new impulses for owners and top managers, who are the key persons responsible for the management and sustainable growth of the enterprise and its activities.