Business, Economics and Management

Abstract: The accelerating diffusion of artificial intelligence (AI) in Europe raises pressing distributional questions about employment, social cohesion, and sustainable development in disadvantaged regions. Research has concentrated on advanced urban economies, leaving the implications of AI for peripheral small and medium-sized enterprises (SMEs) operating under weak human capital, thin digital infrastructure, and constrained social capital — underexplored. We examine the interplay between AI adoption, social capital formation, workforce dynamics, and sustainable development in Eastern Macedonia and Thrace (EMT), one of the EU's least developed regions. Drawing on Bitsani's Biocultural City framework [11], which treats human, social, and cultural capital as interdependent dimensions of regional sustainability, we thematically analysed twelve semi-structured interviews with SME owners and managers conducted in early 2025 using Atlas.ti, yielding 19 codes grouped into six categories. Knowledge deficits and financial constraints emerge as primary barriers, while external technology partnerships, targeted education, and economic incentives operate as enablers, all mediated by social and human capital availability. AI adoption in peripheral economies is not a purely technological or financial challenge but a social and human capital challenge, embedded in a biocultural environment shaped by brain drain, institutional thinness, and weak civic intermediation. Without parallel investment in digital literacy, organizational culture, and inter-firm networks, AI will reproduce rather than reduce employment inequalities. The study draws policy implications for EU Cohesion programming and Sustainable Development Goals 4, 8, 9, 10, and 17.

Abstract: Enhancing agricultural economic resilience (AER) is essential for global food security. As a key policy tool for stabilizing agricultural production, policy-based agricultural insurance lacks rigorous causal evidence on its impact on resilience. Using 2012–2023 provincial panel data from China, this study measures AER via the entropy method and identifies policy effects using a multi-timepoint difference-in-differences (DID) model. We find that policy-based insurance for the three major staple crops significantly strengthens AER, primarily by promoting agricultural technological innovation (ATI) and regional industrial structure upgrading (RIS). The improvement effects are more pronounced in central and western regions, non-major grain-producing areas, and regions with higher natural risks. Our findings confirm that the staple crop insurance policy effectively boosts agricultural resilience, suggesting that differentiated implementation can support more sustainable and targeted agricultural risk governance.

Abstract: Egan et al. (2026) estimate that interchange fees transfer approximately $30 billionper year from cash and debit card users to credit card users, assuming merchants setuniform prices. We extend their sufficient-statistics framework to incorporate merchantsurcharging and show that it attenuates the pooled cross-subsidy by $1–2 billion (3–7%). The correct aggregation uses transfer-weighted sector shares, not expenditureshares; the naive alternative overstates the correction fivefold. Using transaction-leveldata from the Diary of Consumer Payment Choice (2022–2024), we document thatsurcharging has nearly doubled since 2021 and is concentrated in sectors where smallbusinesses face high interchange costs. At the transaction level, credit card purchasesby consumers with household income below $25,000 are surcharged at twice the rate of1those above $150,000 (p = 0.038, respondent-clustered standard errors with merchant-category fixed effects). However, this gradient is fragile: it does not survive aggregationto the respondent level, is present in 2024 but not in 2022, and is largely absorbed bycontrolling for rewards card status. Surcharging widens inequality in the net benefitsof card use primarily through card segmentation—non-rewards cardholders face highersurcharge rates—rather than through an independent income channel.

Abstract: Accurate product-to-catalog invoice matching is a foundational internal control critical to financial oversight and audit quality, yet it is often bottlenecked by inconsistent vendor descriptions. Traditional rule-based matching fails to address this "long tail" of supplier heterogeneity, leading to costly manual reconciliation. This study presents an end-to-end system for automated invoice reconciliation. We introduce a novel “augment-both-sides” strategy: catalog entries are proactively enriched with LLM-generated keywords and synonyms before vectorization, while incoming invoice line items undergo query expansion to bridge the semantic gap between vendor terminology and master data. A final LLM-based reranker applies context-aware judgment to produce highly accurate Top-3 match candidates. We evaluate this system using three diverse entity resolution benchmark datasets, Abt-Buy, Amazon-Google and Walmart-Amazon, structured to simulate real-world ERP environments. The system achieves a Top-3 Recall of 93.14% to 97.96% across all domains, effectively narrowing the search space for accounting and auditing professionals from thousands of SKUs to a precise set of candidates. These results demonstrate that the architecture functions as a highly reliable intelligent decision aid, standardizing complex reconciliations, and structuring the reconciliation task for subsequent human verification.

Abstract: The accurate prediction of credit default risk remains a significant challenge for financial institutions operating within increasingly complex data environments. This study pro-poses a hybrid Long Short-Term Memory (LSTM) and eXtreme Gradient Boosting (XGBoost) model that integrates deep learning and ensemble machine learning techniques to enhance predictive performance while preserving interpretability. The LSTM component effectively captures temporal patterns in borrower behavior, and its output is utilized as a meta-feature within the XGBoost framework. The model is evaluated using a bench-mark credit dataset and is compared with conventional machine learning approaches. The results indicate that the proposed hybrid model outperforms standalone models across key evaluation metrics, achieving high accuracy, F1-score, and ROC–AUC. To enhance transparency, Shapley Additive Explanations (SHAP) are employed to analyse feature contributions and directional effects. The findings reveal that repayment behavior, particularly recent delinquency, serves as the most influential predictor of default risk, followed by indicators of financial capacity. The feature derived from the LSTM demonstrates the strongest overall impact, thereby confirming the significance of temporal dependencies in credit risk prediction. This study illustrates that the integration of deep learning with ensemble techniques establishes a robust and interpretable framework for credit risk assessment, thereby providing practical value for enhancing financial decision-making and risk management.

Abstract: This study examines the determinants of informality in ASEAN-5 (Indonesia, Malaysia, the Philippines, Thailand, and Singapore) using Partial Least Squares Structural Equation Modeling (PLS-SEM) on panel data from 2015–2022. Five hypotheses tested the effects of institutional quality, social protection, labor market policy, economic growth, and technological advancement (as mediator). Results show that institutional quality significantly reduces informality (β = –0.378; p = 0.015), while social protection, labor market policy, and economic growth exert positive and significant effects, reflecting policy design–implementation gaps and growth patterns that fail to generate formal employment. Technological advancement does not mediate the growth–informality relationship (β = 0.011; p = 0.335). The model explains 88.8% of the variance in informality (adjusted R² = 0.888). Policy implications highlight the need for stronger institutions, inclusive social protection, adaptive labor regulations, and digitalization integrated with e‑governance to foster formalization.

Abstract: This study investigated the influence of marketing infrastructure and digital marketing strategies on the performance of small and medium enterprises (SMEs) in Nigeria, emphasizing the mediating role of digital transformation. While prior research has established the importance of digital marketing strategies in driving performance, much of the focus has been on large firms, with limited attention to SMEs and the transformative effects of digital transformation. To address this gap, data were collected from 400 SME managers and owners registered with the Small and Medium Enterprises Development Agency of Nigeria (SMEDAN) through an online survey. The data were analyzed using partial least squares structural equation modelling (PLS-SEM). Findings revealed that marketing infrastructure and digital marketing strategies do not directly improve marketing performance; rather, digital transformation serves as a critical mediator that enables this relationship. The study concludes that SMEs that embrace digital transformation, by integrating digital technologies across operations, achieve superior marketing outcomes, including enhanced brand awareness, customer acquisition, conversion rates, and customer satisfaction, ultimately leading to higher sales, profitability and and business sustainability.

Abstract: This paper presents a full implementation of data-driven modelling of the dynamics of the options on Bitcoin, using high-frequency data from the Deribit exchange. To this end, we provide a synthesis of methods established in prior papers, namely the works involving “neural SDE market models,” to build a pipeline to go from raw options quotes to a functioning non-parametric model. The options surface is decomposed into a low-dimensional latent space designed to minimise arbitrage in reconstruction and the temporal evolution of these factors are modelled with a stochastic differential equation (SDE). The drift and diffusion of the SDE are learnt from data using neural networks, thereby forming a ’Neural SDE’. These networks are constrained in order to guarantee the absence of static arbitrage and to minimise dynamic arbitrage in the resulting model. The networks are trained using a likelihood-based objective function in an SDE transition discretisation. The framework produces arbitrage-free simulations of option surfaces and enables risk management applications such as Value-at-Risk estimation and hedging applications.

Abstract: This study examines how family background shapes individual occupational status within a behavioral science framework, using pooled data from the 2018 and 2020 waves of the China Family Panel Studies (CFPS). Grounded in New Human Capital Theory, it further investigates the moderating roles of cognitive and non-cognitive abilities in this relationship. The results indicate that family background exerts a significant and persistent positive effect on both initial and current occupational status, suggesting the enduring influence of intergenerational advantage. Robustness checks using alternative indicators, including father’s occupational status and mother’s education, confirm the stability of the findings. In addition, digital skills, appearance investment, and selected Big Five personality traits—agreeableness, openness, and conscientiousness—significantly strengthen the positive association between family background and occupational outcomes. These findings suggest that, beyond structural advantages, individual behavioral and psychological characteristics play a critical role in enabling individuals to effectively transform family resources into labor market success. Overall, the study provides empirical evidence on how behavioral factors interact with family background to shape occupational inequality in contemporary China.

Abstract: This working paper develops a methodological approach for integrating mathematical optimization with a digital twin environment in the analysis of first-mile milk collection systems. The approach combines a mixed-integer linear programming (MILP) model for network design with a digital representation that enables the evaluation of system behavior under changing operating conditions. The optimization model determines the baseline configuration, including the location of collection points, capacity allocation, and producer assignments. This configuration is then embedded into the digital twin, where its performance is examined under a representative perturbation scenario involving a 20% reduction in milk supply. The analysis shows that the baseline configuration, while efficient under nominal conditions, is sensitive to variations in supply, leading to reduced utilization and higher unit costs. Allowing limited operational adjustments within the fixed network structure improves performance, although economic indicators do not fully return to baseline levels. The results also reveal uneven effects across performance dimensions, indicating the presence of trade-offs between economic, operational, and environmental outcomes. The contribution of this study lies in connecting optimization-based design with a digital evaluation environment that enables the assessment of network configurations beyond their initial formulation. The approach provides a structured way to examine how a given design responds to changing conditions without requiring immediate structural modifications. The analysis is illustrative and intended to demonstrate the integration mechanism. Future work will extend this approach through systematic scenario design, quantitative validation, and the incorporation of real-time data.

Abstract: There are lots of perennial species that enable multiple harvests over years from one planting in nature. These crops require no repeated tillage and can promote root accumulation, thus leaving rural landowners with time for reproduction and further production, but this model is difficult for complex knowledge and operational difficulty. Focusing on the supplementation of distinctive species in rural household agriculture, this paper sorts out existing problems and compiles a biological resource list including perennial crops and self-reproducing animals. Combined with methods such as using bamboo trellises and other climbing structures to block light for non-crops, a household-based perennial agricultural scheme of "one-time work, continuous harvest" is constructed to ease reproductive pressure and accelerate civilizational development. Studies show that perennial, self-propagating, storable crops allow people to run a food company, avoid repetitive labor, and gain stable family food dividends; some resilient perennial species can gain competitive advantages with simple artificial tools, and combining the innate advantages of plants with the acquired strengths of tools can resist various risks; A diversified species lifespan table helps people plan investment according to species longevity and their own needs, allowing some species to form a cycle where longer lifespan is accompanied by larger root tubers and higher fruit yields.

Abstract: This study examines the role of women executives in shaping crisis management strategies and firm outcomes during the COVID-19 pandemic, focusing on small and medium-sized enterprises (SMEs) in Türkiye. Drawing on survey data from 207 SMEs across 12 sectors in Istanbul and using nonparametric tests and regression models controlling for firm age, size, and sector, we analyze whether the presence of women in executive positions influenced firm resilience and strategic responses under crisis conditions. The findings reveal that firms with women executives demonstrated significantly stronger cash flow sustainability and cost management outcomes. However, no significant differences were observed in revenue generation or overall performance. In terms of strategy, women executives were more likely to adopt cost-control measures such as operational cost reduction, telecommuting, and staff expense adjustments. At the same time, they were more likely to pursue ambidextrous strategies combining cost control with revenue gene-ration rather than relying solely on defensive approaches. Despite this balanced strategic orientation, only the cost-related dimension translated into measurable outcomes. This indicates an intention–outcome gap, where revenue-generating efforts did not yield significant advantages under severe crisis conditions. The results suggest that women’s leadership advantages during crisis may be domain-specific, emerging primarily in areas of internal organizational control rather than market-dependent outcomes.

Abstract: Purpose – This article aimed to offer insight into and discuss how the concepts of Industry 5.0 and Society 5.0 are perceived and understood by managers in the Norwegian construction industry. The research questions were limited to the perceptions and opinions of a group of bachelor's students in construction site management. Design/methodology/approach – The study used qualitative data collection, encompassing participants with different functions and experiences in the Norwegian construction industry. The student group was thus interdisciplinary. Everyone was a part-time student and had a full job at the same time. The participants represented two classes, with a total of 70 students divided into 15 work/study groups. Findings – The empirical evidence shows that the groups had insight into the concepts of Industry 5.0 and Society 5.0. They reflected on opportunities and obstacles. A transition to Industry 5.0 and Society 5.0 will require a focus on competence and self-directed learning, as well as a willingness to invest in competence and technology. This can be seen in light of a knowledge-intensive society and sustainable development. The participants emphasised the Norwegian working life model as a strength in the possible implementation of Industry 5.0 as it has several similarities with the concept of Industry 5.0 in terms of being human-centred. Originality/value – The article contributes insights into how the concepts of Society 5.0 and Industry 5.0 are perceived and understood by bachelor's students in construction site management. The study provides an in-depth analysis of the concept of resilience and the sub-concepts of vulnerability and capacity in a Norwegian context. Practical implications – The practical impact of the study can be linked to the students' participation as both informants and in assisting analysis of the empirical material, which has given them a foundation to communicate the topic of Industry 5.0 and Society 5.0 in their workplaces and other forums in which they participate as leaders in the construction industry.

Abstract: U.S. banks are investing unprecedented amounts in artificial intelligence, with annual spending at institutions like JPMorgan Chase, Bank of America, and Citigroup now exceeding $2–$4 billion each. Yet a critical national financial resilience problem persists: most U.S. banks cannot confidently determine whether these massive AI investments generate positive risk-adjusted returns, creating capital allocation inefficiency and potential systemic vulnerability. This research proposal outlines a comprehensive mixed-methods research design for investigating how senior executives in U.S. global banks govern enterprise AI investments, manage emerging financial risks, and measure return on investment when scaling AI across national banking operations. Drawing on the Resource-Based View, Paradox Theory, and the Technology-Organization-Environment framework, this proposal develops an integrated conceptual framework linking AI governance mechanisms, operating model configurations, and multi-dimensional ROI measurement specifically calibrated to the U.S. regulatory environment (Federal Reserve, OCC, FDIC). The proposed study would employ an embedded multiple-case design with semi-structured interviews of 30–40 C-Suite executives across 6–8 U.S.-headquartered global banks, supplemented by secondary analysis of SEC filings, FRED economic data, FDIC call reports, and Model Risk Management documentation. We propose a novel risk-adjusted ROI calculation framework incorporating direct financial benefits, indirect value creation, strategic option pricing, and probabilistic risk adjustments aligned with U.S. banking stress testing practices. Anticipated methodological barriers include organizational resistance, access constraints to senior executives, and causal attribution challenges—each addressed with specific mitigation strategies outlined in this proposal. This proposal aims to contribute empirically validated ROI measurement tools for executive decision-making at U.S. systemically important financial institutions and demonstrates a scholar-practitioner approach to bridging academic rigor with national financial stability priorities.

Abstract: This study advances a modular microfoundational framework to examine how individual-level actions aggregate into macro-level technological innovation capabilities and operational performance in innovation intermediaries in emerging economies. Grounded in microfoundations theory (Coleman's bathtub model) and cybernetic principles (Viable System Model), we dissect three interdependent modules to diagnose systemic issues within institutional voids: (i) macro-level system viability and technological emergence; (ii) meso-level organizational practices mediating R&D collaboration; and (iii) micro-level behaviors of boundary-spanning agents driving knowledge integration. Empirical evidence from a Brazilian Research and Technology Organization (RTO) reveals how context-specific microfoundations determine operational efficiency and technological emergence. Theoretically, we contribute by operationalizing Coleman's micro-macro link to enable cross-context benchmarking of innovation intermediaries and decoding how meso-micro-level actions co-evolve with ecosystem-level innovation. By shifting the diagnostic focus to the fine-grained dynamics of individuals and their interactions, our study offers actionable levers for managers and policymakers to optimize operational viability in contexts of institutional uncertainty. Implications for innovation policy, ecosystem governance, and the design of intermediary organizations in late-development settings are discussed.

Abstract: The most widely used bankruptcy predictor, Altman’s Z-Score, assigns a positive coefficient to asset turnover: faster firms are rated safer. Under crisis conditions, that assumption reverses. We introduce the Solvency Margin (SM), a diagnostic calculable from standard financial statements that measures, in dollars, how far an organization is from the threshold where operations become impossible. Unlike static liquidity ratios, the SM yields a concrete speed limit: the maximum operating velocity at which an organization can survive a defined shock. We validate the SM against pre-crisis financial data across three crises in two domains. In the automotive sector, SM computed from FY2019 filings showed directional predictive power among ten major automakers in both the 2021 semiconductor shortage (ρ = 0.50, p = 0.14) and the 2020 COVID-19 pandemic (ρ = 0.53, p = 0.12; ρ = 0.70, p = 0.036 excluding one governance-driven outlier). In the 2023 U.S. banking crisis, SM augmented with a Deposit Stability Factor predicted crisis outcomes among eighteen regional banks (Spearman ρ = 0.62, p = 0.006), correctly ranking three of four failed institutions in the bottom three positions. Monte Carlo simulation (450,000+ runs) confirms threshold behavior across a wide range of conditions. We present a five-step calculation method and a three-lever decision framework for practitioners.

Abstract: The essence of free trade zones lies in addressing development challenges through institutional opening-up and innovation-driven growth. Sustainable development constitutes the fundamental goal of free trade zone construction, opening-up and innovation serve as the core driving forces for their development, and a sound business environment acts as a critical guarantee for their efficient operation. Therefore, based on the panel data of 22 free trade zones in China from 2013 to 2022, this paper adopts Principal Component Analysis (PCA) and Analytic Hierarchy Process (AHP) to conduct a comprehensive evaluation of their sustainable development levels from six dimensions: environmental optimization, economic development, opening-up, radiation-driven capacity, business environment, and scientific and technological innovation. The results indicate that, first, the overall comprehensive scores of free trade zones in sustainable development show an upward trend with obvious regional divergence in growth rates. Coastal free trade zones maintain robust growth momentum, inland ones achieve steady progress, and border free trade zones witness modest growth. Second, the comprehensive scores of the 22 free trade zones in 2022 present a gradient distribution, reflecting prominent regional development imbalance. On this basis, targeted policy recommendations are put forward in this paper.

Abstract: The proposition that expanding education uniformly advances the 2030 Agenda has acquired the status of settled consensus — embedded in SDG 4, amplified by UNESCO, and routinely invoked in national development strategies. This paper shows that the consensus is empirically half-true. Using a balanced panel of 193 countries observed over 2000–2023, I estimate 96 two-way fixed-effects regressions connecting eight measures of education — spanning expenditure, enrolment, completion, attainment, and accumulated stock — to twelve Sustainable Development Goal outcomes. The estimates reveal a pronounced block asymmetry. On the social side, educational expansion is a first-order anti-poverty instrument: a one-standard-deviation increase in secondary enrolment is associated with a 0.16-log-point reduction in the $2.15/day extreme-poverty headcount and a 4.35-point reduction on the 0–100 SDG-1 composite, both significant at the 0.1% level. On the environmental side, the same treatment produces a β = +0.048 (p = 0.014) coefficient on production-based CO₂ per capita and a β = −0.260 (p = 0.031) coefficient on forest area — effects that are statistically significant but directionally perverse. Income inequality worsens rather than improves with schooling expansion (β = +0.71 on the Gini, p = 0.006). The asymmetry survives Driscoll–Kraay standard errors, Oster (2019) sensitivity bounds (δ > 1), and two-year lagged specifications. The findings qualify the optimistic narrative that frames education as "the key" to sustainable development: schooling is a central social block instrument, but cannot substitute for dedicated environmental policy. The 2030 architecture needs instrument–goal pairs, not universal keys.

Abstract: The transition to energy independence requires the application of flexible approaches and the diversification of distributed energy generation technologies. This article substantiates the feasibility of using a hybrid power supply system incorporating renewable generation sources and a cogeneration plant to enhance the reliability of power supply to centralised heat supply boiler houses. Criteria and approaches to structuring and balancing the hybrid system are proposed, based on an analysis of the nature of electricity consumption and the nature of generation by its structural elements, using the example of Ukraine’s district heating enterprises. The structure of the hybrid system has been determined, taking into account seasonal variations in operation and the stochastic nature of load and generation, whilst applying economic and environmental approaches in accordance with the institutional requirements of the European Union and Ukraine. A discrete mathematical model of the energy balance with a system of technical constraints is proposed to justify the parameters of the hybrid system’s components. It is proposed that the parameters of the structural elements of the photovoltaic power station, wind power station, storage battery and cogeneration plant be determined on the basis of actual electricity consumption data from a district heating boiler house. Operating modes of the hybrid power supply system have been established depending on technological requirements and conditions for integration with the centralised electricity grid. The results obtained can be used in the design of hybrid power supply systems for district heating enterprises.

Abstract: Sustaining high performance in private healthcare requires that internal organizational processes actively reinforce the values employees carry into their daily work. This study examines how organizational communication and internal branding shape employee commitment, positioning commitment as a foundational condition for sustainable organizational performance in health services. Drawing on quantitative data from 247 healthcare professionals, a structured questionnaire validated through reliability analysis and Confirmatory Factor Analysis was used to assess the direct and indirect pathways between these constructs. Multiple regression analysis confirmed that both organizational communication and internal branding are significant positive predictors of employee commitment, with communication carrying a marginally stronger direct effect. Mediation analysis further revealed that internal branding mediates the relationship between organizational communication and employee commitment, accounting for approximately 29 percent of the total effect. These findings suggest that coherent communication structures and well-anchored internal branding practices function as complementary mechanisms that, together, support the workforce stability and internal alignment that sustainable service delivery requires. The study concludes by proposing an integrated managerial framework that brings these two strategic levers into closer coordination, with the aim of advancing long-term organizational sustainability in the private healthcare sector.