Business, Economics and Management

Abstract: This study examines whether the accumulated stock of private credit provides early-warning information for subsequent deterioration in banking-sector asset quality. It combines annual Passport banking indicators with World Development Indicators for 58 countries over 2010–2024; the preferred sample contains 746 country-year observations. A second-order dynamic fixed-effects model links log(1 + NPL) to lagged private credit to GDP, real credit growth, lending rates, bank capital, GDP growth, inflation, and unemployment. Its preferred coefficient is 0.00377, implying that a 10-percentage-point increase in private credit to GDP is associated with approximately 0.15 percentage points more NPLs one year later at the sample median. On a strictly common 609-observation sample, the credit-depth coefficients at one-, two-, and three-year horizons are 0.00501, 0.00960, and 0.01266; a stacked country-clustered Wald test rejects equality, although these are horizon-specific predictive projections rather than cumulative causal effects. Lending rates and unemployment are positive, whereas annual credit growth and capital ratios are not robust predictors. Pooled interactions do not reject equal slopes across broad country partitions. System GMM passes conventional tests but violates a persistence-bound credibility check. The evidence therefore supports an early-warning interpretation, not a causal claim.

Abstract: The rapid workplace diffusion of conversational artificial intelligence (CAI) introduces a new social actor, yet existing reviews often neglect the intrapsychic processes through which employees construe these systems. This systematic review synthesizes the psychological mechanisms through which CAI reshapes the employee experience. Following PRISMA guidelines, we analyzed 84 SSCI-indexed studies (2020–2026) using Sociotechnical Systems theory, the Computers as Social Actors paradigm, and the Job Demands-Resources model. Findings reveal a significant theoretical turn: CAI is increasingly conceptualized as a partner or supervisor rather than a tool, fundamentally driven by anthropomorphic cues. The synthesis identifies a double-edged reshaping of work: cognitively, human intelligence augmentation competes with skill threat; emotionally, constant support contrasts with social fabric erosion; and career-wise, inclusion coexists with work alienation and generative AI loafing. These dimensions are psychologically coupled, with cognitive threats often cascading into emotional anxiety and moral expediency. The review provides an integrated conceptual model linking multi-layered antecedents to these outcomes. By shifting the focus from productivity to psychological experience, this study offers theoretical foundations for human-AI collaboration and outlines a future research agenda on trust dynamics and algorithmic fairness.

Abstract: Due to the increased threats of the global Covid19 crisis and the necessity to ensure high-quality care while maintaining a safe environment, the demand for effective implementation of people analytics has piqued decision-makers' attention in the health care sector. Although there is a growing interest in people analytics’ theoretical arguments, empirical research on people analytics adoption is still under researched. Up to the researchers' knowledge, this is the first trial to investigate a comprehensive model for studying the contextual forces that drive the successful adoption of people analytics to promote high-quality care and a safe environment in the health industry.

Abstract: In auctions, a bidder naturally prefers to bid against a small number of bidders. In an actual case, involving the acquisition of a large firm, a would-be bidder actually paid another to refrain from bidding. A question that arises is how much the other potential bidder, possibly risk averse, should demand for not participating in the auction; another is what is the maximum the first bidder should pay. We explore that issue in a setting of a first-prize sealed bids auction. We analyze situations of two or three potential bidders. We assume that if there is only a single bidder in the auction, it is charged a fee to acquire the firm. If the number of potential symmetric would-be bidders is large, we ask how many is best to attempt to pay off to refrain from bidding.

Abstract: Environmental considerations shape corporate strategy, risk management, and firm valuation. Yet, empirical evidence on the links between environmental performance, environmental disclosures and corporate financial performance is mixed and often omits risk implications. We develop a holistic framework to examine the endogenous inter-relations among corporate environmental performance (CEP), environmental disclosure (CED), financial performance, and risk. We then analyse the simultaneous links between CEP, CED, and both accounting- and market-based measures of performance and risk for a large panel of US listed firms. The findings reveal that environmental performance and environmental disclosure have fundamentally distinct economic implications. Environmental performance is associated with lower accounting profitability but higher market valuation and lower operating and market risk. These findings suggest that while substantive environmental initiatives may involve short-term costs, they enhance long-term value and organisational resilience. In contrast, environmental disclosure is associated with higher accounting profitability but lower market valuation and higher operating and market risk. This finding suggests that environmental communication may strengthen stakeholder relations while simultaneously increasing investor awareness of environmental exposures and sustainability-related uncertainties. We also find a strong positive association between environmental performance and environmental disclosure, suggesting that environmental reporting increasingly reflects underlying environmental actions rather than symbolic signalling alone. Overall, the results highlight the importance of distinguishing between environmental actions and environmental communication when evaluating corporate environmental strategy, firm value, and risk. Overall, the results carry implications for managers, investors, and regulators seeking to evaluate the role of corporate environmental strategy in corporate resilience, transparency, and value creation.

Abstract: Environmental, Social, and Governance (ESG) frameworks are increasingly popular and yet, these are often under-supported with short term managerial choices to abandon sustainability commitments in the face of unknown and financial difficulty. In this study, the research design is taken into account in the Pāli Canon, Buddhist economics scholarship, the strategic resilience literature and previous work at this time in designing the Buddhism-Resilience-ESG mediation model (BREM). The BREM claims that this methodology in Buddhist ESG management is the basis of three layers of resilience, which are absorptive, adaptable and transformative and it is that those three factors of strategic resilience constitute the linkage between the Buddhist aspect of ESG governance in this practice and the long term performance of the corporation. The model is organised around five foundational Buddhist principles drawn from the Pāli Canon, each mapped onto the three resilience capacities that mediate ESG governance and long-term performance. We show the altruism-performance paradox presented in the earlier works is solved by the BREM because in Buddhist ESG governance, resilience of asset rather than performance sacrifice with it.

Abstract: Agentic artificial intelligence (AI) is a consequential technological frontier in banking because it shifts AI from passive assistance and generative interaction toward goal-directed workflow execution. Responsible and sustainable banking transformation thus depends on the readiness conditions under which agentic AI can move from pilots to governed production and value realization. This study develops a configurational forecasting framework for agentic AI deployment readiness in banking. Because comparable initiative-level evidence remains scarce and commercially sensitive, the paper adopts a transparent, case-informed synthetic configurational simulation rather than claiming to analyze actual bank projects. Drawing on public banking AI cases, technology-diffusion and foresight literature, AI governance research, and role-based stakeholder archetypes, we construct a synthetic dataset of 90 banking-related agentic AI initiatives and apply fuzzy-set Qualitative Comparative Analysis (fsQCA). The bounded simulation indicates that production maturity is associated with the conjunction of data readiness, leadership commitment, governance maturity, workflow redesign capability, human-agent collaboration maturity, and low legacy-system complexity. A supplementary analysis shows that deployment alone is insufficient: value is realized only when deployment is combined with redesigned workflows, governed data use, and human-agent collaboration, whereas non-deployment arises from distinct failure configurations rather than the mere inverse of success. These patterns reframe governance as an enabler of bounded autonomy rather than a constraint. The study offers a reproducible readiness logic for responsible value realization, customer protection, workforce capability, and financial-system resilience.

Abstract: Rural entrepreneurship is increasingly recognised as a critical driver of economic resilience and regional development, particularly in developing regions such as the Eastern Cape, South Africa. This exploratory study examines how entrepreneurial activities stimulate local economic growth, job creation, innovation, and community empowerment in Mnquma Local Municipality, where persistent unemployment and poverty remain significant challenges. Adopting an exploratory research design underpinned by a positivist philosophy, the study synthesises existing literature on the relationships among employment generation, economic contribution, innovation, diversification, community engagement, supply chain development, and economic resilience. Data were collected from 349 respondents drawn from a population of 3,750 formally registered rural entrepreneurs. The data were analysed using descriptive statistics, reliability and validity tests, correlation analysis, and Partial Least Squares Structural Equation Modeling (PLS-SEM) in SPSS and SmartPLS to examine the relationships among employment generation, economic contribution, innovation, diversification, community engagement, supply chain development, and economic resilience. The findings suggest that strong rural entrepreneurship enhances local adaptability and supports sustainable economic performance in the face of external shocks. The study emphasises the importance of inclusive, evidence-based policies that promote rural enterprise development through improved financial mechanisms, infrastructure investment, and strengthened stakeholder networks, thereby providing localised insights to inform policy and sustainable development in South Africa.

Abstract: This study examines ESG data gaps, digital readiness and environmental-security relevance from a multiview Life Cycle Sustainability Assessment (LCSA) perspective, comparing Bulgaria as an EU member state and Moldova as an EU-aligned transition economy. Using publicly available sustainability reports, integrated reports, non-financial statements, ESG disclosures and public-sector documents, the study analyses a balanced sample of 36 organisations, equally distributed between the two countries and covering corporate, financial and public-sector entities. The methodology combines qualitative content analysis with semi-quantitative scoring through two instruments: the ESG Digital Readiness Index (ESG-DRI) and the Data Gap Matrix (DGM), covering 540 indicator-level observations. The results show a moderate overall level of ESG digital readiness, with a mean ESG-DRI score of 2.20. Bulgaria records stronger readiness than Moldova, while financial institutions show the highest sectoral readiness. The DGM results reveal that ESG information is generally available, but remains insufficiently granular and weakly auditable. Auditability is the weakest dimension, while LCSA relevance and environmental-security materiality are comparatively high. The findings suggest that ESG reporting in both countries is progressing toward more structured sustainability disclosure, but stronger data systems, internal controls, audit trails and life-cycle data integration are needed to support CSRD/ESRS-aligned, assurance-ready and environmental-security-oriented reporting.

Abstract: Approximately 70% of organisational development (OD) initiatives based on methodologies such as Total Quality Management (TQM), Business Process Reengineering (BPR), Lean, and Six Sigma are reported to fail. This may cause managers to worry about choosing the right methodology, but failure is often a consequence of the environments in which these approaches are applied. Critical Systems Thinking (CST) has long argued that such outcomes stem from poor implementation and inadequate contextual understanding, emphasising the need for critical awareness, political agency, and multimethodology. Yet when OD work becomes a “bullshit job”—experienced as meaningless by those responsible for it—even CST’s principles may struggle to take hold. To address this, we propose Scientific Self-Management (SCSM), a systems approach inspired by Community Operational Research (COR), which promotes individual exploration and learning as sources of meaning and improvement. A public sector case study illustrates how OD methodologies can lose purpose in resistant or hostile cultures, but also how personal initiative and self-directed engagement can reintroduce value and satisfaction. Our findings suggest that success depends less on the OD methodology itself and more on employing approaches like SCSM to address cultural and contextual conditions from the bottom up.

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: 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 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: 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: Purpose: Accurate credit default prediction is essential for sustainable risk management in peer-to-peer (P2P) lending platforms. However, the presence of class imbalance, high-dimensional borrower information, and complex nonlinear relationships among financial variables continues to limit the effectiveness of conventional prediction models. This study proposes a hybrid deep learning framework that transforms borrower attributes into structured grayscale images, enabling convolutional feature extraction for enhanced credit risk assessment. Material and Methods: A LendingClub dataset containing 115,487 completed loan records was analyzed. Data preprocessing involved feature cleaning, transformation, and statistical feature selection, reducing 151 original variables to 64 informative predictors through a composite scoring framework integrating five complementary statistical tests. The selected features were encoded into 64×64 grayscale images and processed using a Convolutional Neural Network (CNN). Seven predictive models were evaluated under five-fold stratified cross-validation across three dataset configurations: the original imbalanced dataset, SMOTE-balanced training data, and randomly undersampled training data. The evaluated models included conventional machine learning classifiers (Support Vector Machine, Random Forest, and Decision Tree), a Deep Neural Network (DNN), a standalone CNN, and three hybrid architectures combining CNN-based feature extraction with downstream classifiers (CNN+SVM, CNN+RF, and CNN+DT). Statistical significance was assessed using the Friedman test and Wilcoxon signed-rank tests at α = 0.05. Results: The CNN+RF model consistently achieved the best performance across all experimental settings. On the original dataset, it attained an AUC-ROC of 1.000, Accuracy of 0.987, F1-Score of 0.991, and MCC of 0.977, significantly outperforming all competing approaches (p < 0.05). The standalone CNN also demonstrated strong predictive capability, supporting the effectiveness of the proposed feature-to-image representation strategy. Furthermore, SMOTE-based balancing yielded superior performance compared with random undersampling across all models. Among conventional classifiers, SVM exhibited the greatest sensitivity to class imbalance, whereas DNN produced comparatively balanced class-level predictions. Conclusion: The proposed CNN–Random Forest hybrid framework provides a highly effective and statistically validated solution for P2P credit default prediction. By converting borrower information into image-based representations and leveraging convolutional feature learning, the model achieves superior predictive accuracy, robustness, and stability compared with both traditional machine learning and standalone deep learning methods. The framework offers practical value for loan approval decisions, borrower screening, and portfolio risk management in digital lending environments.

Abstract: Universities play a pivotal role in preparing future leaders capable of addressing complex sustainability challenges. However, the development of sustainable entrepreneurial intentions requires more than technical competence; it involves emotional resilience and higher-order cognitive capacity. This study examines how mental wellbeing and academic burnout affect sustainable entrepreneurship attitudes among university students, with systems thinking acting as a mediating competency. Drawing on a sample of 367 students from three universities in northeastern Mexico, Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to test a six-hypothesis conceptual model. Results confirm that mental wellbeing positively influences both systems thinking (β = 0.31, p &lt; 0.001) and sustainable entrepreneurial orientation (β = 0.28, p &lt; 0.001), while burnout exerts a detrimental effect on both dimensions. Systems thinking partially mediates these relationships, underscoring its role as a cognitive lever for sustainability-oriented action. The model explains 48% of the variance in sustainable entrepreneurship attitude. These findings reinforce the importance of integrating psychological support and cognitive training into higher education programs that aim to promote sustainable entrepreneurship, with direct implications for SDG 4 (Quality Education) and SDG 8 (Decent Work and Economic Growth).

Abstract: Live-streaming commerce has become a critical information channel through which consumers evaluate products and make purchase decisions, yet brand manufacturers face substantial uncertainty when selecting live-streaming partners. This study investigates how streamer influence and consumer sensitivity to live-streaming service quality jointly shape optimal cooperation structures in platform-based commerce. We develop a game-theoretic decision framework comparing three cooperation modes—Nash negotiation, manufacturer-led, and streamer-led, and derive closed-form equilibria for pricing, service quality and profit allocation. The results show that manufacturer-led cooperation consistently maximizes brand profit by preserving incentives for service quality provision, while streamer-led cooperation can reduce overall efficiency when dominant streamers lack motivation to improve service quality. Using live-stream sales data from the Douyin (TikTok) platform covering multiple streamer tiers and two cosmetic brands, we empirically validate the model's predictions. The findings contribute to research on information processing and incentive alignment in platform-based live-streaming commerce.

Abstract: This study analyses governance tensions among Google, OpenAI, Meta, and Microsoft to explain how ethics suppression may emerge in competitive AI development environments. While responsible AI frameworks, ethical principles, safety initiatives, and governance mechanisms have expanded rapidly across the technology sector, considerably less attention has been paid to how ethical concerns function under conditions of accelerated development and strategic rivalry. The paper introduces the concept of ethics suppression in AI development to explain how ethical oversight may remain formally present even as its practical authority over development and deployment decisions weakens. Unlike existing literature on ethics washing, which focuses primarily on symbolic external signalling, the paper examines the internal organisational dynamics through which ethical authority may weaken despite continued institutional visibility. Drawing on corporate governance documents, public statements, investigative reporting, and documented governance controversies, the analysis identifies recurring patterns involving constrained escalation authority, temporal compression, deployment urgency, and governance fragmentation. The findings suggest that intensified competition may reduce the practical impact of ethical oversight, even within organisations with mature responsible AI structures. The paper contributes to responsible AI governance literature by introducing the concept of ethics suppression and an operational-suppression lens to examine how ethical authority is preserved or diminished under competitive pressure. The study argues that the central challenge of responsible AI governance may no longer lie primarily in establishing ethical frameworks, but in maintaining ethical influence when deployment incentives intensify.

Abstract: This study examines how investment priorities for sustainable rural development are shaped when financial, environmental, social, and institutional criteria are evaluated simultaneously. Using the Analytic Hierarchy Process (AHP), the study assesses six investment alternatives: eco-tourism, agro-tourism, renewable energy, digital tourism, sustainable agriculture, and cultural tourism. The results reveal the dominance of financial performance and risk considerations, which together account for more than two-thirds of total decision weight. Renewable energy emerges as the highest-ranked investment alternative, whereas agro-tourism and sustainable agriculture remain under-prioritized despite their environmental and social benefits. A comparative scenario analysis demonstrates that policy-oriented weighting structures substantially alter investment rankings, increasing the attractiveness of locally embedded and sustainability-oriented activities. The findings suggest a structural divergence between market-driven capital allocation and broader rural development objectives. By integrating sustainable finance and rural development within a multi-criteria decision-making framework, the study provides practical insights for investors and policymakers seeking to align investment decisions with long-term sustainability goals.

Abstract: Sustainable agri-food value chains increasingly depend on smallholders' capacity to participate in markets in stable, remunerative and upgrade-oriented ways. Yet in conflict-affected and politically constrained settings, market integration is weakened by mobility restrictions, insecure logistics, payment disruptions, volatile input systems, thin market information and uncertain enforcement. This integrative review synthesizes peer-reviewed evidence on how these constraints reshape smallholder integration across agricultural value chains and how governance and institutional arrangements can reduce, or sometimes reproduce, market exclusion. A structured narrative search of Scopus, Web of Science and Google Scholar was organized around smallholders, market integration, agricultural value chains and conflict or political constraint. Evidence was coded across seven diagnostic dimensions: inputs and supplies, production capacity and technology, end-markets and trade, value chain governance, sustainable production and energy use, value chain finance, and the enabling business and socio-political environment. The synthesis shows that governance and institutions operate as a conditional bridge: cooperatives, contracting, trader coordination, market-support institutions and public enabling arrangements can lower transaction costs and stabilize exchange, but only where transparency, credible enforcement, fair risk-sharing and protection against opportunism are present. The review concludes with a measurement-focused research agenda for resilient and sustainable value-chain integration under constraint.