Business, Economics and Management

Abstract: This paper examines the threshold impact of low and high media attention on climate risks and US climate policy, on banks' credit risk among the 230 largest commercial banks in the United States between 2011 and 2022. Using a dynamic threshold regression model, our analysis reveals a non-linear relationship between climate risk and banks’ credit risk. This result suggests that banks may be resilient to climate risk up to a point but become vulnerable once that threshold is exceeded. This finding highlights the importance of physical and transition risks, as well as their implications for financial stability. Our results are robust to a range of alternative measures and model specifications, providing valuable insights for bank managers, regulators, and policymakers. It highlights the need to integrate climate risk considerations into credit risk assessments and policy frameworks to strengthen the banking sector's resilience.

Abstract: This study examines whether ESRS-based ESG disclosures capture a life cycle perspective and whether they provide sufficiently risk-relevant information for sustainable finance decision-making. Drawing on Life Cycle Sustainability Assessment (LCSA), the paper analyzes the extent to which sustainability reports of major European non-financial enterprises reflect upstream, operational, downstream, and end-of-life impacts. The study applies qualitative comparative content analysis to the 2024 sustainability disclosures of Enel, Unilever, and Siemens AG. The findings indicate that ESG reporting remains predominantly entity-centered, indicator-based, and dimensionally segmented. Although value chain impacts and circular economy initiatives are increasingly disclosed, comprehensive cradle-to-grave integration remains limited. Environmental life cycle elements are more visible than social and economic dimensions, while cross-dimensional integration across life cycle stages is weak. These limitations may reduce the ability of ESG disclosures to capture systemic sustainability risks, including transition risks, supply chain vulnerabilities, and long-term value chain externalities. In response, the study proposes an LCSA–ESRS Operationalization Model based on boundary reconfiguration, stage-based indicator mapping, dimensional harmonization, and an accounting translation layer. The paper contributes to sustainability accounting and sustainable finance by showing how life cycle thinking can enhance the decision-usefulness, risk transparency, and systemic coherence of ESRS-based reporting.

Abstract: The tourism and hospitality industry relies fundamentally on the quality of human interactions, yet the sector continues to grapple with significant challenges in effectively and consistently training its workforce using resource-intensive traditional methods. This study addresses these challenges by presenting the design, development, and validation of an intelligent agent for training and evaluation, powered by Google’s Gemini 2.0 Flash model. The system processes internal organizational documentation to build a knowledge base, generates diverse question types for training, and provides automated evaluation and personalized feedback. Validation was conducted in a controlled laboratory environment corresponding to Technology Readiness Level 4 (TRL 4). The system achieved an overall success rate of approximately 82% across all test cases. It demonstrated perfect performance (100%) in social interaction and guided training capabilities. Notably, the automated evaluation engine achieved a 92% agreement rate with expert benchmarks, even for open-ended responses. However, limitations were identified in managing ambiguity and performing deep inferential reasoning beyond explicit documentation. The findings confirm the technical and functional viability of LLM-powered agents for automating hospitality training. This technology offers a scalable, objective solution that significantly reduces resource requirements while enabling personalized learning, although future optimization is needed for complex inference scenarios.

Abstract: The hospitality industry faces ongoing challenges when it comes to training and evaluating staff on critical operational procedures in an efficient and scalable way. Traditional training approaches are often expensive, time-consuming, and difficult to personalize for large teams. This paper presents the design, development, and validation of an intelligent conversational agent powered by Large Language Models (LLMs) aimed at automating training and assessment processes for hotel personnel. The proposed system leverages Google’s Gemini 2.0 Flash model, integrated into a conversational interface built with Chainlit, to support natural language interactions across four core modalities: general inquiries, structured training sessions, practice exercises, and formal assessments. The agent is capable of dynamically generating four types of questions—true/false, multiple choice, open-ended, and scenario-based—using internal hotel documentation as its knowledge base. It automatically evaluates user responses, delivers personalized feedback, and produces detailed performance reports enriched with data visualizations. A Technology Readiness Level 4 (TRL-4) validation was conducted in a controlled laboratory setting, where nine comprehensive functional test cases were executed. The results showed a 95% success rate across all validation criteria, demonstrating the system’s ability to accurately response to general queries, provide targeted training content, generate diverse assessment questions, perform objective evaluations with constructive feedback, etc. This proof of concept highlights the potential of LLM-based conversational agents to transform corporate training in the service industry by offering scalable, personalized, and cost-effective learning solutions. Future work will focus on advancing to TRL-5 validation through deployment with real users in operational hotel environments.

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 whether digital commerce deepening and digital financial use are associated with banking fragility through the household leverage channel. Using country-year data from Euromonitor International Passport for 16 economies over 2015-2025, the analysis links bank nonperforming loans to household debt, app-based mobile commerce, internet banking, smartphone possession, and government effectiveness. The empirical strategy applies dynamic two-way fixed effects models with country and year effects, clustered standard errors, Driscoll-Kraay sensitivity checks, restricted housing-stress controls, crisis-year exclusions, alternative winsorization, mechanism regressions, and placebo leads. The findings show strong persistence in banking fragility and a positive household-debt signal, although the effect is strongest in robust covariance and alternative winsorization specifications. App-based mobile commerce is negatively associated with nonperforming loans in the dynamic models, suggesting that digital commerce may capture formalization, payment efficiency, or digital maturity rather than mechanical overborrowing. Internet banking and the household-debt-by-government-effectiveness interaction are not robust predictors. Overall, digitalization does not mechanically amplify banking fragility; the more consistent channel is household leverage, moderated only weakly by institutional execution in the available panel.

Abstract: Artificial intelligence is increasingly expected to raise productivity by automating tasks, augmenting human work, reducing information-processing costs and supporting new forms of economic organisation. Yet productivity gains do not automatically translate into broadly distributed welfare or into output fully absorbed by market demand. This conceptual review develops the notion of the Distributional Absorption Threshold of AI-Induced Productivity, defined as the point beyond which productivity gains associated with AI are no longer accompanied by proportionate increases in broadly distributed real purchasing power and household consumption. The review argues that the macroeconomic significance of AI-induced productivity depends not only on technological efficiency, but also on the distributive transmission of productivity gains through labour income, disposable income, prices, investment, public expenditure, transfers and external demand. The framework distinguishes between a favourable transmission path, in which AI-induced productivity strengthens purchasing power and effective demand, and a critical transmission path, in which productivity gains are weakly transmitted to households and may generate absorption tension. The review formulates conceptual propositions and proposes possible indicators for future empirical research, including the productivity-real labour income gap and an absorption tension indicator. Its contribution is theoretical: it reframes the AI productivity debate beyond automation anxiety by linking technological change, income distribution and effective demand in a single analytical framework.

Abstract: Large Language Models (LLMs) are increasingly used to answer process mining questions about event logs, models, conformance, performance, fairness, and redesign. Direct prompting can produce plausible but incorrect answers, especially when tasks require careful trace interpretation, formal reasoning, or diagnostic evidence. A common response is to wrap the LLM in a conversational agent framework: the same underlying model is reused under different role prompts—such as “event log interpreter”, “conformance checker”, or “optimization consultant”—and a selector decides which persona speaks next on a shared transcript. The agents in this paper do not call external tools; they differ only in their role description, so any improvement must come from how the conversation is structured rather than from added capabilities. It is therefore not obvious how such agents should be configured, nor how to tell whether a configuration is actually helping. This paper studies selector-mediated configurations composed of process-mining-oriented personas and treats each recorded agent trace as a directed social network over roles. Using LLM-as-a-judge scores aggregated through Social Network Analysis (SNA), we diagnose final-answer quality, role usefulness, and handoff quality, and use these diagnostics to revise agent routing. We provide an open source implementation that executes the configurations, records traces, computes the SNA diagnostics, and feeds the results back into improved routing.

Abstract: This paper discusses the progress of research in the field of "Artificial Intelligence and Macroeconomics/Econometrics" from 1975 to October 14, 2025, using a bibliometric analysis approach. The main research method of this paper is bibliometric analysis using VOSviewer software, which examines the characteristics of published articles such as co-authors' collaborations, geographical distribution, keywords, and reviews of scientific productions of different institutions and journals. By reviewing 256 articles in the Scopus database from 1975 to October 14, 2025, the findings showed that the growth of research in this field has accelerated since the 2010s. The 2023-2024 season has peaked. The United States and China have been the largest producers of scientific papers in this field. The results were grouped into five main research clusters: (1) AI-based economic decision-making; (2) forecasting econometrics and spatial econometrics; (3) innovation and sustainability; (4) cost effectiveness and sectoral risks; and (5) business intelligence and organisational data. Co-citation analysis shows that the intellectual foundation of this field is based on econometrics, statistics, and artificial intelligence. Policy implications suggest that increased interdisciplinary collaboration and open data infrastructure can accelerate the integration of AI into macroeconomic policymaking.

Abstract: This study investigates the varying implications of digital infrastructure on sustainable economic growth in five major emerging countries (Egypt, India, Kenya, Saudi Arabia and Sudan) during the period 2014-2025. We use panel data, System GMM estimation and Random Forest regression and classification to study the impact of internet penetration, mobile broadband subscriptions and fixed broadband subscriptions on sustainable GDP per capita growth. Our results indicate the statistical and economic impact of digital infrastructure on economic growth. The biggest benefits are from internet penetration, then mobile broadband. Fixed broadband is not statistically significant in the full model, which reflects the low access in these countries. Random forest regression (R2=0.538, RMSE=2.31) and classification (F1-score=0.714, AUC-ROC=0.792) models can accurately predict 75% of high and low growth regimes in out-of-sample validation. Internet penetration drives growth in middle income countries (Egypt, India) while mobile broadband drives growth in low income countries (Sudan, Kenya). Average education >6 years doubles the benefits of digital infrastructure growth on human capital. The sustainability implications of these results are relevant for the SDG 4 (quality education), SDG 9 (infrastructure and innovation), and SDG 10 (reduced inequality) programs. There is a digital divide as the MENA countries have 87% internet penetration compared to 44% in Sub-Saharan Africa. We recommend the expansion of mobile broadband in low-income countries and investments in internet infrastructure and human capital in middle-income countries to maximize sustainable growth.

Abstract: Mortality in smallholder cattle and buffalo systems is driven by climate, and the climate signal is not uniform. Heat and cold stress, flooding, and climate-sensitive disease emergence act through different physiological and ecological pathways, yet livestock-loss models usually compress them into a single elevated-mortality state. This paper builds a climate-state-dependent mortality model for the Thai national herd, separating an endemic baseline from a temperature-extreme regime and a moisture- and disease-driven regime. A 100,000-iteration Monte Carlo, calibrated to the 2024 national herd and a 2017 primary farmer survey updated to 2026 prices, generates the annual loss distribution and decomposes it by climate driver. The average year is governed by endemic mortality, which accounts for about 81 percent of expected loss but none of the extreme tail. The tail belongs entirely to the two climate regimes: the moisture- and disease-driven regime carries roughly 69 percent of losses beyond the 95th percentile and the temperature regime about 31 percent. The driver of the typical year is therefore not the driver of the catastrophe. A second result concerns the ecological footprint of insurance. Using a reduced-form behavioral layer, mortality-payout design is shown to suppress adaptive destocking and lift stocking pressure 10 to 16 percent above a sustainable land-carrying-capacity benchmark, so that an instrument promoted for climate adaptation can degrade the rangeland it is meant to protect. The findings argue for regime-specific risk financing, for pairing insurance with heat-adaptation and animal-health investment, and for treating the carrying-capacity externality as a design parameter rather than a side effect. The paper closes with a research agenda for climate-state livestock-loss modeling, intended to give the field a transparent and reproducible starting point.

Abstract: This paper examines global equity-market integration, commodity-price exposure, and volatility spillovers in Ghana’s frontier equity market. Using daily data from January 2011 to December 2025, we estimate a multi-factor asset-pricing model within an ARMA–EGARCH specification for the Ghana Stock Exchange Composite Index (GSECI) and the Financial Sector Index (GSEFSI). The model jointly captures first- and second-moment spillovers from a global equity factor and three key global commodity markets: gold, crude oil, and cocoa, while controlling for asymmetric volatility, return serial dependence, and structural shifts associated with banking-sector recapitalization and the Domestic Debt Exchange Programme (DDEP). The Ghanaian equity market is exposed to the global equity factor, indicating measurable but economically modest global integration, with stronger exposure in the financial sector. Commodity-price exposures are selective, with gold and crude-oil exposures concentrated in the financial sector, whereas the cocoa factor is negatively associated with returns on both indices. The variance results show persistent volatility, inverse asymmetric volatility responses, and differentiated volatility spillovers from global equity and commodity markets. The DDEP period is associated with significant equity-market repricing, particularly in the financial sector. These findings indicate that Ghana’s equity-market dynamics are shaped jointly by global equity and commodity market information, frontier-market frictions, and sovereign–bank conditions.

Abstract: The energy transition requires substantial investments in renewable energy sources and in policy tools that effectively guide market participants' decisions. We examine whether the European Emissions Trading System has effectively driven renewable energy deployment in the power sector. Combining panel econometric analysis of installed renewable capacity across European Member States, a cross-country survey of 127 firms in six EU ETS countries, and an hourly, market-based estimation of inframarginal rents across a panel of eight Member States, we find that public support mechanisms consistently drive renewable-capacity expansion. The EU Allowance price does not exhibit a significant direct effect on renewable deployment after controlling for structural and market variables. In electricity markets, the ETS primarily operates through mechanisms that shape wholesale electricity prices and carbon-cost pass-through, generating persistent inframarginal revenues for low-carbon technologies through marginal price formation. A comparison between regulatory CO₂ factors used for indirect ETS cost compensation and observed market-based pass-through indicators reveals materially divergent magnitudes across Member States, with regulatory-to-observed ratios ranging from 1.2x in Italy to 46.2x in Sweden. The evidence suggests that, in European electricity markets, the ETS currently operates more directly as a wholesale-price transmission and revenue-redistribution mechanism than as an autonomous driver of renewable investment deployment.

Abstract: Manufacturing sectors and ecosystems are undergoing profound transformations as digital platforms converge with generative and agentic AI. This paper argues that manufacturing ecosystems can improve their innovation performance through digital platforms, connected intelligence, and collaborative settings that bring together experts and ecosystem members. This convergence of distributed capabilities across humans, communities, and machines generates intelligent environments that enable ecosystemic and transformative innovation. To examine this hypothesis, the paper follows a three-stage methodology. First, it proposes a modelling framework based on a vector autoregressive model, in which a weighted matrix representing binary couplings among human, collective, and machine intelligence drives the transition of a manufacturing ecosystem from a baseline innovation state to a more advanced one. Second, it presents the SmartGreenEcos experiment, which creates an intelligent environment adapted to a manufacturing ecosystem by combining digital platforms, e-services, and AI agents to support inter-company collaboration, experimentation and innovation. Third, it conducts a simulation-based analysis of the internal dynamics of intelligent environments, with particular attention to the eigenvalues and eigenvectors of the weighted matrix representing connected-intelligence couplings. The results provide insights into the design of intelligent environments and the interaction parameters of connected intelligence that drive innovation, with relevance not only for manufacturing ecosystems but also for other sectoral ecosystems seeking to enhance innovation through intelligent environments.

Abstract: Whether environmental, social, and governance (ESG) disclosure stabilizes share prices or merely masks bad news remains unsettled, and the evidence is conspicuously weak whenever the relationship is assumed to be linear. This study revisits the question by allowing the effect of ESG disclosure on future stock price crash risk to be nonlinear, and by decomposing disclosure into its environmental, social, and governance components. Using an unbalanced panel of non-financial firms listed on the Ho Chi Minh Stock Exchange over 2018–2024, we estimate firm and year fixed-effects models with firm-clustered standard errors, measuring one-year-ahead crash risk by negative conditional skewness (NCSKEW) and down-to-up volatility (DUVOL). Consistent with prior work, the linear association between overall ESG disclosure and crash risk is statistically insignificant. Once a quadratic term is introduced, however, a U-shaped relationship emerges, and dimension-level tests show that this curvature is driven almost entirely by social disclosure: the linear term is negative and the squared term positive and significant for both crash-risk proxies, with turning points of 0.3316 (NCSKEW) and 0.2918 (DUVOL). The U shape is confirmed by the formal test of Lind and Mehlum (2010) for both proxies, and is robust to additional profitability and valuation controls and most strongly for NCSKEW to panel corrected and feasible GLS estimators. The findings support a “too-much-of-a-good-thing” interpretation: social disclosure improves transparency and reduces crash risk up to a moderate threshold, beyond which incremental, hard-to-verify narrative disclosure becomes consistent with impression management and heightens crash risk. Because the turning point lies below the first quartile of social disclosure, most sample firms already operate where additional disclosure raises crash risk. The study reframes the ESG crash-risk debate around the level and dimension of disclosure rather than its mere quantity.

Abstract: This study examines the relationship between corporate FinTech disclosure and ESG reporting performance among non financial firms listed on the Saudi Stock Exchange (Tadawul), with a focus on post Covid period from 2021 to 2024. Using an ESG Disclosure Index constructed from annual reports and a textual measure of FinTech adoption, the analysis provides market-level evidence on the evolution of digital transformation and ESG disclosure in Saudi Arabia. Descriptive results indicate that ESG reporting among Tadawul firms is moderate yet heterogeneous, with governance disclosure consistently stronger than environmental and social components. Correlation analysis indicates a positive association between FinTech disclosure and overall ESG disclosure, particularly within the environmental pillar. Regression results further show that the firms with stronger FinTech disclosure tend to report higher ESGDI scores. The two way fixed effects (TWFE) model yields statistically significant results and the direction of the relationship remains consistent with theoretical expectations. Pillar level analysis suggests that digital transformation is most closely aligned with environmental reporting. Taken together, the results indicate that sustainability disclosure and digital capabilities appear to co-develop in the Tadawul market. Businesses may improve their ability to track, organize, and disseminate ESG-related data by investing in digital reporting systems, analytics, and technology modernization. In this way, FinTech serves as a governance-supporting instrument that improves transparency and reporting discipline in addition to being a financial innovation. The study adds to the expanding body of knowledge by providing important emerging-market-level evidence from the Saudi capital market and highlighting how FinTech can support sustainability-driven growth in an institutional context undergoing rapid transformation.

Abstract: This study examines how accounting information on environmental protection expenditure can support fiscal transparency and sustainability risk management in the public sector. Using harmonised Environmental Protection Expenditure Accounts (EPEA) data for EU Member States, together with GDP and population indicators, the paper develops a comparative framework for analysing public-sector environmental expenditure. The study constructs scaled indicators, including expenditure per capita and expenditure as a percentage of GDP, and examines the functional composition of expenditure through the Classification of Environmental Protection Activities and Expenditure (CEPA). Exploratory clustering and panel regression diagnostics are used to identify cross-country expenditure profiles and descriptive associations with macroeconomic indicators. The findings show substantial variation among Member States and confirm that environmental expenditure should not be interpreted as a direct measure of environmental ambition or performance. Instead, differences reflect accounting scope, institutional arrangements, service-delivery models and infrastructure needs. The paper contributes to sustainability accounting, public financial management and sustainable finance by demonstrating how harmonised accounting information can improve comparability, auditability and decision usefulness in public-sector environmental reporting. It also highlights the relevance of environmental expenditure information for identifying fiscal exposure, infrastructure priorities and sustainability-related risks.

Abstract: This research applies the principles of human resource accounting (HRA) and intangible asset valuation frameworks under IAS 38/IFRS to examine the relationship between the quality of player performance metrics, human capital metrics, and the quality of their financial reporting on the market valuation of football players and the financial performance of the leading football clubs in Europe. Based on a dual-level database, composed by 20 leading European clubs (club-level) and by 120 players (player-level) in the season 2023/24, the study constructs a performance-adjusted valuation model for estimating the interconnection between on-field statistics (goals, assists, expected goals, defensive actions, and performance indices imposed on a composite measure) and accounting or financial number (transfer fees, amortization charges, intangible asset values, book values) and financial results (ROA, club market valuation). The Outcome of multiple OLS Regression Models using Robust Standard Errors shows that Performance Index is the most important predictor of player market value (max 0.497, p < 0.01) whereas club revenue is the most important predictor of club market valuation (max 0.009, p < 0.01, R2 = 0.879). The market to book ratio analysis shows systematic difference between economic value and accounting book value based on player age, duration of contract signed, and performance indicators (Adj. R² = 0.363). The Moderated regression shows existence of positive moderating relationship between IFRS compliance and Big4 audit quality with on-field performance and financial outcomes. The findings add to the intersection of sports finance, accounting and human capital theory, stressing the inadequacy of current IAS 38 provisions in capturing the true economic value of football players as human capital assets.

Abstract: Regulated digital financial assets can become more than financing instruments when issued through platform account infrastructures, but public texts do not directly reveal customer behavior. This article examines Golden Apple’s Russian digital financial asset issuance as a mechanism-informative socio-technical case and asks how public texts make account–governance platform drift visible before customer-behavior evidence is publicly closed. A 63-document public-source corpus was analyzed using source grading, document-level ordinal coding, three-coder reliability checks, PA_v4 focused recoding, PA_v5 construct-boundary recoding, sensitivity analysis, and A/B-only source-restriction robustness. The final model narrows the main mechanism to an Account–Governance Core (AG-Core) combining investor/customer-entry visibility, PA_v4 account-infrastructure visibility, and investor-protection/risk-governance visibility. PA_v5 indicates that PA-Account reached exploratory acceptable reliability, while PA-Channel and the PA_v5 composite remain moderate and reliability-sensitive; PA_v5 is therefore interpreted as a construct-boundary audit rather than a validated standalone scale. Financing-efficiency and consumption-right narratives are retained as supporting narrative conditions. DR-Gap diagnoses the public evidence boundary around the unclosed customer-behavior feedback loop. The article does not estimate conversion, retention, loyalty, repurchase, redemption, platform traffic, investor outcomes, or causal market effects.

Abstract: Corporate social responsibility (CSR) is increasingly expected to fill welfare gaps in rural communities, especially in transition economies where firms often provide schools, healthcare, infrastructure, and other quasi-public goods. If CSR improves rural living con-ditions, it should, in principle, help retain population. Yet migration theory suggests a more ambiguous possibility: development may not only reduce deprivation but also ex-pand the aspirations and capabilities that make mobility feasible. This study examines this paradox using district-level data from eight rural districts in Kazakhstan’s Akmola region from 2010 to 2021. Applying Partial Least Squares Structural Equation Modeling, we estimate how CSR, amenities, agricultural structure, and economic vitality shape net migration. The results show that CSR substantially improves local amenities, but ameni-ties alone do not generate a strong retention effect. Economic vitality remains the central driver of migration outcomes, while agricultural development has limited demographic effects. These findings suggest that CSR can improve rural welfare without necessarily preventing outmigration. In transition economies, corporate-led development may there-fore operate less as a demographic anchor than as part of a broader process through which rural residents become more capable of pursuing opportunities elsewhere.