Business, Economics and Management

Abstract: In this article, the review and critical evaluation of the proximity-based urbanism will be provided with an explicit focus on the 15-minute city and similar X-minute city ideas. The key point is that proximity is a necessary but not sufficient condition for achieving spatial equity because proximate facilities might still be either too expensive, dangerous, difficult for usage by disabled people and elderly people, temporal unavailable, low-quality, or not appropriate for local conditions. The review will be based on the peer-reviewed and landmark literature available until June 2026 and will cover four main themes including proximity-based urban planning, accessibility and spatial equity, open geospatial data, and AI urban analytics. In particular, the research will be conducted not as PRISMA review, systematic review, bibliometric analysis, or empirical validation but as conceptual review and framework paper.The article provides several contributions including clarification of the conceptual differences between proximity, accessibility, and spatial equity, organization of the evidence base via the use of the transparent corpus and the evidence role framework, and development of the Open-Data Explainable Proximity Planning Framework. The framework includes nine interconnected layers including (1) proximity planning goals, (2) open geospatial data infrastructure, (3) accessibility measurement, (4) spatial-equity diagnosis, (5) AI/urban analytics, (6) explainability, (7) governance safeguards, (8) planning decision support, and (9) policy intervention and monitoring. The paper promotes the idea of the planning led with the assistance of AI where the open data and explainable analytics can facilitate the diagnosis, scenario comparison, and public justification of decisions.

Abstract: 4Cast Lab is a free, open-source application for machine learning, econometrics and statistical analysis that runs on the user's own computer, with no installation, no programming and no data leaving the machine. It was developed at the E|M|E|F Lab, Department of Economics, Democritus University of Thrace, for researchers, analysts, instructors and students in any field. Its Forecasting mode trains eight learning algorithms (with three kernels for SVM/SVR), implemented from scratch, under leakage-safe validation for time-ordered data; it selects models by cross-validation, compares the selected model with every alternative and with naive benchmarks through formal tests of predictive accuracy, and documents each run in a methods report and a Python script that the user can export. Its Estimation mode covers the core of applied econometrics, from OLS with robust inference to ARIMA, ARDL, VAR/VECM and GARCH. This paper describes the design, the interface and the workflow of version 4.6.2, reports a validation of 1,234 numerical results against statsmodels, linearmodels, arch, scikit-learn and NIST certified values, illustrates the workflow with three examples, and discusses current limitations.

Abstract: Investors in cultured meat face technical, regulatory and market uncertainties that compound across development stages, while the evidence they rely on is largely modelled, small-scale or company reported. We present CM-IDSS, a stand-alone decision support system that combines stochastic multicriteria screening (SMAA-2) of nine investment archetypes, a stochastic techno-economic cost trajectory, correlated jurisdictional approval gating and stage-gate Monte Carlo valuation with global sensitivity analysis. Inputs were parameterized from a preliminary review of the literature published between 2000 and 2026 and tagged by provenance. In a baseline analysis (20,000 draws, 12% discount rate), no archetype had a positive expected net present value (NPV): values ranged from USD −93 million (whole-cut producer) to −5 million (media and growth-factor supply). Media and growth-factor supply and analytics and quality assurance held the top two screening ranks under four investor profiles, and the whole-cut producer ranked last under all of them. Continuing after technical proof destroyed value for eight of nine archetypes. Expected NPV turned positive only if the 2032 market was 1.2 to 15.6 times the forecast range, depending on archetype, or if technology followed an optimistic trajectory. Rankings were stable in 15 of 17 parameter perturbations but inverted when optimistic technology and a fourfold larger market were combined. The model is a transparent framework for expert calibration, not a forecast.

Abstract: Maritime disruptions increasingly redirect vessels and cargo towards alternative routes and ports. Although rerouting can preserve connectivity, its effectiveness can-not be assessed through route availability or port capacity alone, as redirected flows may generate new constraints and dependencies within the resulting logistics config-uration. This study examines how maritime rerouting reconfigures logistics dependen-cies and under which conditions alternative ports provide effective rather than merely nominal substitutability. A comparative case-study approach is applied to five Euro-pean ports—Algeciras, Valencia, Barcelona, Rotterdam and Sines—following the Red Sea crisis. The study develops a Post-Rerouting Sequential Stress Test (PR-SST) com-bining maritime connectivity and port capacity with function-specific gateway and transhipment pathways, while incorporating sustainability as a cross-cutting dimen-sion. The analysis distinguishes nominal capacity from effective substitutability and introduces post-rerouting dependency to capture dependencies that emerge or inten-sify following adaptation. The comparative assessment shows that the effectiveness of an alternative port depends not only on its capacity to receive redirected flows, but al-so on its functional role and the continuity of the subsequent logistics pathway. The PR-SST therefore extends resilience assessment beyond maritime rerouting and indi-vidual port capacity towards the integrated logistics configuration through which re-directed flows continue.

Abstract: Against the background of China's Rural Revitalisation Strategy, agricultural livestreaming has become an important link between the production and marketing of agricultural products. Drawing on the stimulus-organism-response (S-O-R) framework, this study examines consumers who watch livestreams supporting farmers. Data were collected from 482 valid questionnaire responses, and confirmatory factor analysis, regression analysis, and bootstrap mediation analysis were used to examine the mechanisms underlying consumers' purchase intentions. The results show that agricultural livestreaming characteristics have a significant positive effect on purchase intention. Perceived value and perceived trust both mediate this relationship, with perceived trust accounting for the stronger indirect effect. These findings suggest that livestreams supporting farmers operate not only through immediate promotional stimulation but also through an ongoing communication process that shapes consumers' evaluations and judgements. The study extends understanding of the psychological mechanisms underlying agricultural livestreaming and provides practical implications for improving content presentation and strengthening consumer trust.

Abstract: Artificial intelligence (AI) and digital technologies can facilitate sport organizations’ forward-looking strategies, administration, accountability, and stakeholder communication. However, there is a need to consider leadership competencies, data utility, implementation infrastructure, and governance mechanisms. The purpose of this review article is to provide an Africa-centric analytical synthesis of AI-enabled leadership and digital governance in sport institutions. The review focuses on digital transformation, organizational dynamic capabilities, stakeholder governance, and responsible AI. Five conceptual areas were considered, including strategic leadership, data-driven decision-making, transparency and accountability, organizational readiness, and ethical and inclusive implementation. AI can enable financial audits, administrative reporting, resource allocation, performance evaluations, and stakeholder communications in sport organizations. However, there are risks associated with data privacy, algorithmic biases, cyber security, x- ai, digital divides, and reliance on external technologies. These factors could challenge the performance of sport organizations, particularly those with limited financial and infrastructural capabilities. The article provides a conceptual framework that connects institutional environments, AI-enabled leadership capabilities, digital governance mechanisms, and institutional transformation outcomes. Research gaps and implications were identified, including the need to study African sport-sponsoring entities, digital governance maturity models, financial accountability, and player data privacy. Since this is a review article, the proposed research strategy and thematic analysis need to go through a verifiable database search and screening process before the finalizing of the completed systematic review.

Abstract: The first industrial robot, the PUMA (Programmable Universal Manipulator Arm) was built by a company called animation in 1961. According to history, 50 years ago robots were viewed as creations designed to exterminate the human race as portrayed in science fictions. To help resolve this assumption, Isaac Asimov compounded the three laws of robotics to protect humans from interactions with robots (Asimov 1984). The laws states that: (1) a robot may not injure a human being or, through inaction, allow a human being to come to harm. (2) a robot must obey the orders given it by human beings except where such orders would conflict with the First Law. (3) a robot must protect its own existence as long as such protection does not conflict with the First or Second Laws. (1). Asimov’s law are considered unscientific by researchers yet it unarguably forms the basis of various machine learning activities used in many manufacturing sectors. Furthermore, the field of robotics has highly evolved by producing a wide variety of objects from autonomous machines to human collaborative robots through the use of artificial intelligence and machine learning. By monitoring the ever increasing research activities in machine learning, it cannot be denied that human robot collaborations is only expected to keep growing. In the logistics industry, collaborative robots are achieving human-like dexterity which allows for increased productivity. This work expands on the foundation of existing scientific research in the ergonomic aspect of human robot communication in warehouses which promotes seamless collaboration of robots and human workers.

Abstract: The present study aims to analyze the Effect of the political skill of employees, on Emotional Intelligence. Political skills may affect employees’ Mental Faculties as well as the close relationship between leaders and employees. In turn, the Qualities of the employee, Negotiation or manipulation will positively impact the organization as well as the Emotional Stance of the employee. This study used a questionnaire and was conducted in the Manufacturing sector of Gujarat India. questionnaires were properly filled and 40 respondents were considered fit to be used in the study. A level of significance was observed between the dimensions of political skills and dimensions of Emotional Intelligence. Regression equations were prepared to observe the impact of dimensions of political skills on the dimensions of Emotional Intelligence. The results showed that political skill positively affected dimensions of Emotional Intelligence.

Abstract: Project management challenges are mainly sociotechnical. This paper proposes a be-haviorist sociotechnical approach, an integrative framework combining Actor-Network Theory, artificial intelligence, and reflective practice to enhance project management performance. We alert to some current pitfalls in project management and intend to produce actionable value for practitioners and researchers rendering project management more effective. In fact, this paper argues that project management should be understood as the management of a temporary sociotechnical network, where continuous learning, AI-mediated support, and actor-network dynamics are more decisive than traditional planning and control mechanisms.

Abstract: Machine-learning performance depends not only on algorithm selection but also on whether the predictor set preserves information essential for reliable prediction. This study develops an econometric-guided cross-dataset predictor-necessity framework that distinguishes predictor importance from predictive necessity through controlled infor-mation removal. Three information environments are compared: a full-information benchmark, an ex ante information-removal specification, and an econometrically screened specification. Random Forest, XGBoost, and artificial neural networks are evalu-ated under common out-of-sample validation conditions across four public housing da-tasets. Econometric screening retained dataset-specific combinations of accessibility, spa-tial location, income, age, quality, living space, condition, and related structural attributes. Controlled removal identified accessibility information in UCI Real Estate Valuation, spa-tial-location information in California Housing, and property-quality and living-space information in Ames Housing and King County as consequential valuation channels. Across twelve dataset-algorithm combinations, the econometrically screened specification retained an average of 98.93% of full-information predictive R², whereas the controlled in-formation-removal specification retained 92.74%. Predictive deterioration therefore de-pended more on the substantive information removed than on the number of predictors excluded. The findings demonstrate that essential predictors are market-specific, whereas the procedure for identifying consequential information is transferable across datasets and learning architectures.

Abstract: Circular economy practices are increasingly integrated into supply chains through reuse, repair, remanufacturing, recycling, resource recovery, and reverse logistics. Although these practices are generally expected to improve sustainability, greater circular activity does not necessarily produce proportional environmental, economic, or social benefits. Circular interventions can increase transportation, sorting, processing, inventory, coordination, and energy requirements, while efficiency improvements and lower effective resource costs may stimulate additional consumption. Recovered materials may also lose value when recovery volumes exceed processing capability or secondary market demand. This study examines this problem as the circularity paradox and develops a qualitative system dynamics framework to explain the conditions under which circular supply chain initiatives create or erode sustainable value. The study synthesizes peer-reviewed literature and secondary evidence, with selected evidence from the United States and Bangladesh used to illustrate system relationships under different institutional and operational conditions. The framework integrates circular intervention intensity, reverse flow complexity, recovery efficiency, organizational capability, stakeholder coordination, secondary market absorptive capacity, rebound demand, value leakage, and sustainability performance. Reinforcing and balancing feedback loops are developed to show how circular interventions can generate sustained benefits, diminishing returns, capacity pressure, market constraints, and rebound effects. The study distinguishes circular activity from sustainable circular performance and argues that effective circular supply chains require alignment between the scale of circular intervention and the capabilities of the supporting logistics, organizational, processing, and market systems. The framework provides a theoretical foundation for future empirical validation and quantitative system dynamics simulation and offers implications for managers and policymakers seeking to expand circularity without shifting environmental and economic burdens elsewhere in the supply chain.

Abstract: Public financial support can be rapidly disbursed yet remain unevaluable when fiscal, beneficiary, exposure, and outcome records are not linked. This study audits Mongolia’s livestock-sector support using budget-execution and livestock-loss data for 2010–2024, along with aggregated records for 289 classified White Gold concessional-loan applications as of 20 June 2025. The analysis distinguishes implementation, delivery conversion, concentration, spatial traceability, and the evidentiary requirements for causal effectiveness. Support execution rose from MNT 10.9 billion in 2010 to MNT 316.1 billion in 2024; the mean execution rate was 87.55% and the minimum 45.62%. Livestock mortality was episodic, with 10.2 million losses in 2010 and 9.3 million in 2024, but aggregate fiscal data cannot separate prevention, compensation, or anticipatory financing. Of the classified applications, 145 were approved and 133 disbursed; MNT 411.4 billion was recorded as disbursed, equal to 90.8% of the approved value and 47.2% of the requested value. Disbursement was concentrated in working capital (81.4%), cashmere-related purposes (80.9%), Ulaanbaatar-registered firms (95.0%), and five banks (94.7%; HHI = 0.246). The program is measurable for implementation and concentration, but not for final regional incidence or causal effectiveness. Evaluation-ready policy requires province-level exposure, supplier and beneficiary tracing, and post-finance outcomes.

Abstract: The sports industry is undergoing a major transformation driven by technological progress, especially in artificial intelligence. Leveraging these capabilities, AI can serve as a strategic asset to enhance leadership capacity, cultivate a stronger organizational culture, and drive improved organizational performance. This review examines the dynamic relationship among artificial intelligence, leadership capabilities, and organizational culture within sports organizations. This paper explores the implications of classical leadership theories alongside recent AI literature, highlighting potential applications for leaders in decision-making, strategic planning, communication, talent management, and organizational transformation. AI systems can bolster leadership competencies such as visioning, achievement orientation, empowerment, networking, and communication by leveraging data-driven insights and predictive analysis. Furthermore, digitalization and the adoption of AI are transforming organizational culture, which encompasses shared values, beliefs, and behavioral norms. The review highlights the critical role of leaders in fostering adaptive cultures that champion innovation, creativity, collaboration, and the adoption of technology. The findings indicate that successfully integrating AI into sports organizations requires leadership capable of merging technological innovation with a human-centric management style. The paper suggests that leveraging AI to develop leadership skills yields positive outcomes for sports organizations' cultures and fosters sustainable competitive advantages in today's sports landscape.

Abstract: Based on bilateral trade panel data from the fifteen member states of the Regional Comprehensive Economic Partnership (RCEP) from 2002 to 2024, this paper employs an extended gravity model to systematically examine the impact effects and transmission mechanisms of exporting countries' environmental regulation depth on bilateral trade scale. The results indicate that the deepening of environmental regulations significantly suppresses bilateral export scale, a conclusion that remains robust after conducting multiple robustness checks. Mechanism tests reveal that environmental regulation deepening transmits to the export side through three channels: first, it compels high-carbon industries to reduce emissions, leading to a significant decline in per capita CO₂ emissions; second, compliance expenditures crowd out R&D resources in the short term, reducing green patent output and indicating that the innovation offset effect of the Porter Hypothesis is subject to a lag; third, it drives the elimination of backward production capacity and substitution by clean energy, reducing per capita energy consumption. Moderation effect analysis shows that countries with higher financial development levels experience greater short-term export declines under environmental regulation shocks, reflecting the accelerated capacity rationalization effect of financial systems. Heterogeneity analysis reveals pronounced structural differences: exporting countries with lower institutional quality and lower urbanization levels are more severely suppressed by environmental regulations, whereas transparent institutional environments and factor agglomeration levels effectively buffer the compliance shock. This paper supplements the empirical evidence on the trade effects of environmental regulations from the supply-side perspective of exporting countries, providing an experiential basis for coordinating green transition with stable foreign trade development.

Abstract: As the electric two-wheeler (E2W) sector rapidly proliferates in India, achieving genuine sustainability is strongly dependent upon upstream supply chain dynamics. Drawing on sustainable supply chain and partnership theories, this research investigates how strategic vendor partnerships (SVP) drive economic, environmental, and social sustainability within this emerging market. We deconstruct the SVP framework into three fundamental practices: outsourcing, vendor-managed inventory (VMI), and vendor selection tied to long-term alliances (VSLTA). For an evaluation of how these practices impact critical sustainability metrics like emission control, waste reduction, resource optimization, cost efficiency, and delivery reliability, we surveyed procurement and supply chain experts from major Indian E2W manufacturers and their tier-1 suppliers. The gathered data was empirically evaluated utilizing exploratory factor analysis and multiple regression. The analysis reveals that sustainable performance is significantly and positively enhanced by all three facets of SVP. Notably, establishing enduring relationships through VSLTA, proved to be the most impactful catalyst for sustainability, followed closely by VMI integration and outsourcing strategies. Finally, this research advances the academic discourse on sustainable e-mobility operations in developing economies and equips industry managers with evidence-based strategies to build supply chains that are both highly competitive and ecologically responsible.

Abstract: The Gulf Cooperation Council (GCC) states have to find a way to balance their reliance on hydrocarbon resources and their ambitious aims on moving to renewable energy (for instance, the Saudi Vision 2030.) Despite the policies in place, the implementation of renewable energy technologies in the region is relatively low indicating that more needs to be understood about the reasons behind the use of renewable energy. The present research will analyze the macroeconomic determinants of renewable energy consumption in the 6 GCC states for the periods 2000-2025 using a combination of panel econometric techniques and machine learning. Panel data were constructed from data from the World Bank and the International Energy Agency. The econometric results show that R&D expenditure and trade openness are the two most important determinants of renewable energy consumption, which jointly explain about 63% of the variation (adjusted R² = 0.629). The random forest model is consistent with these findings, with R&D expenditure as the most important predictor followed by trade openness. No statistically meaningful relationship was found between GDP, foreign direct investment, inflation and renewable energy consumption in the final models due to the structural characteristics of GCC economies. The random forest model also had a low RMSE value of 0.032 and an out-of-sample R-squared of 0.425, indicating satisfactory predictive performance for an initial model. Sub-period analysis also indicates that the relationships between R&D expenditure and trade openness became stronger after 2015, which coincides with the introduction of national energy transition strategies in the GCC. The study implies that innovation in terms of technology as well as the open nature of economy could promote energy transition in the region whereby high amount of investments in research and development are required as well as deeper international trade linkages might be supporting the acceptance of green energy approach.

Abstract: Criminal extortion and protection fees have emerged as significant threats to the sustainability of township businesses in South Africa, undermining entrepreneurship, formalisation, investment, and local economic development. While FinTech has transformed financial inclusion and digital transactions globally, its potential to disrupt cash-based extortion economies and strengthen the resilience of township enterprises remains largely unexplored. This paper investigates how FinTech innovations, including blockchain-enabled payment systems, mobile money, digital payment technologies, and complementary community-based digital platforms, can enhance economic resilience by reducing vulnerabilities associated with cash-based transactions and extortion. Drawing on the diffusion of innovation theory, this qualitative systematic literature review examined the opportunities, challenges, and institutional conditions influencing the adoption of these technologies within township economies. The findings suggest that FinTech innovations have the potential to reduce cash dependency, enhance transaction transparency, strengthen financial inclusion, improve collective reporting mechanisms, and build community resilience against extortion networks. However, their effectiveness depends on complementary investments in digital literacy, affordable digital infrastructure, institutional trust, enabling regulatory frameworks, and multi-stakeholder collaboration. The paper contributes to digital governance and FinTech scholarship by reframing technology adoption not only as a financial inclusion strategy but also as a governance mechanism that can enhance transparency, accountability, collective reporting, and economic security within vulnerable local economies. It further demonstrates how FinTech-enabled digital financial ecosystems can support the long-term sustainability of township economies through enhanced business resilience, secure digital transactions, and inclusive local economic development.

Abstract: (1) Background: Generative and, increasingly, agentic artificial intelligence (AI) are reshaping how firms create, deliver, and capture value. Yet business model innovation (BMI) research largely assumes that humans design business models and digital technologies support their decisions. The capacity of AI systems to generate value-bearing artifacts and undertake goal-directed action challenges this assumption. (2) Approach: Integrating research on business models, dynamic capabilities, and AI in management, this concept paper develops a framework that distinguishes three logics of AI-enabled BMI: a supporting logic (AI as instrument), a generative logic (AI as co-creator), and an agentic logic (AI as economic actor). It explains how these logics vary in the locus of agency and the tempo of innovation. (3) Contribution: The paper advances six propositions showing how generative and agentic AI redistribute agency in value creation, delivery, and capture, while shifting BMI from episodic redesign toward continuous and potentially autonomous adaptation. (4) Conclusions: It develops a research agenda spanning value architectures, human–AI configurations, governance and legitimacy, organizational capabilities, and measurement, thereby providing a theoretically grounded and empirically tractable foundation for research on AI-enabled BMI.

Abstract: Sustainable development of the agro-industrial complex (AIC) requires an integrated assessment of investment activity, production performance, financial returns, and resource provision at the district level. This study evaluates the production and economic performance of 13 districts of the North Kazakhstan Region using a composite scoring approach based on seven groups of indicators: investment activity, gross agricultural output, crop yields, livestock productivity, crop-production profitability, livestock-production profitability, and capital intensity. The information base includes official district-level statistics, materials of the regional agricultural authorities, data on fixed production assets for 2021–2023, and national land-use reporting. The results reveal substantial territorial differentiation. Kyzylzhar and Taiynsha districts form the high-performance group, Esil District is classified above average, whereas Ualikhanov District occupies the lowest position in the aggregate ranking. The analysis also identifies a scale-efficiency mismatch: large agricultural territories do not necessarily demonstrate high profitability or productivity, while several medium-sized districts achieve stronger returns on land and production resources. The findings provide a comparative basis for territorially differentiated agricultural policy, targeted investment support, technological modernization, and the development of processing infrastructure in the North Kazakhstan Region.

Abstract: Artificial intelligence is rapidly transforming education globally, yet the economic consequences of AI-enabled education remain insufficiently understood. Existing research has largely examined AI in relation to learning outcomes and instructional efficiency, with limited attention to its implications for human capital formation, productivity, and inclusive economic growth. This study develops and empirically examines a framework linking AI-enabled education with economic outcomes through the formation of human capital and the development of labour-market-relevant skills. Using OECD country-level panel data for 38 countries over the period 2015-2025 (N = 350 country-year observations), we construct a composite AI-Enabled Education Index (AIEI) using Principal Component Analysis. The index incorporates indicators of digital educational infrastructure, AI skills, educational technology adoption, teacher digital competence, and AI-related educational policies. We employ two-way fixed effects estimation with country and year fixed effects, mediation analysis, and robustness tests including system GMM and instrumental variables. The AIEI demonstrates a significant positive association with human capital formation (β = 0.28, p < 0.001). Human capital mediates the relationship between AI-enabled education and labour productivity, with the indirect effect accounting for 45.5% of the total effect (Sobel Z = 4.82, p < 0.001). AI-enabled education is also positively associated with inclusive economic growth (β = 0.28, p < 0.001). Heterogeneity analysis reveals that effects are larger in countries with higher digital infrastructure (difference = 0.20, p < 0.001) and stronger institutional quality (difference = 0.20, p < 0.001). AI-enabled education contributes to human capital formation, productivity, and inclusive economic growth, with human capital serving as a key mediating mechanism. Investments in digital infrastructure, teacher development, and institutional quality amplify these benefits. The findings provide evidence for policy interventions that leverage AI to enhance educational and economic outcomes.