European Journal of Business, Economics & Management
European Journal of Business, Economics & Management (EJBEM) is an international, refereed, double-blind peer-reviewed, open-access journal covering business, economics, and management. It publishes high-quality research papers that advance both theory and practice, focusing on topics such as corporate management, market economics, financial analysis, and innovation strategies.
AI-Driven ESG Analytics for Sustainable Investment in U.S. Non-Profits: Integrating LLMs and Causal Modeling for Policy-Enhanced Resilience
This study develops an integrated analytical framework that combines large language models with causal inference methods to strengthen sustainable investment decision-making in U.S. non-profit organizations. Drawing on advances in natural language processing, the proposed system applies an LLM-based architecture capable of interpreting regulatory texts, extracting domain-specific ESG signals, and synthesizing policy-relevant insights. Complementing this linguistic capability, the framework incorporates causal modeling-particularly Difference-in-Differences estimation-to identify the impact of policy changes on environmental, social, and governance performance factors. Together, these tools provide a structured foundation for supporting responsible investment strategies that align with mission-driven objectives. The model design also includes a multilayered feedback mechanism for continuous refinement, multilingual accessibility, and multi-format output generation, enabling diverse nonprofit stakeholders to access interpretable ESG results. The findings suggest that the integration of LLM-driven analytics with empirical causal evaluation enhances transparency, improves resilience in policy-sensitive contexts, and supports equitable governance practices. This research contributes to emerging scholarship on AI-enabled sustainability systems while offering practical implications for organizational strategy in the nonprofit sector.
Copyright (c) 2026 Zaolin Zhang (Author) https://creativecommons.org/licenses/by/4.0
AI-Enabled Predictive Analytics for U.S. Small-Business Resilience: A Policy-Neutral, Data-Driven Assessment
This research investigates the role of AI-enabled predictive analytics in strengthening the resilience of small businesses within the United States through a policy-neutral, evidence-driven lens. The study identifies the core dimensions of small-business vulnerability, examines the capacities and constraints of contemporary predictive models, and assesses how machine-learning techniques can support anticipatory decision-making without embedding normative or ideological assumptions. By synthesizing multi-source economic indicators, firm-level operational data, and market-volatility metrics, the paper constructs an integrated analytical framework for forecasting financial distress, operational disruptions, and adaptive recovery potential. Findings demonstrate that AI-driven forecasting methods significantly enhance early-warning accuracy, reduce information asymmetry, and improve managerial responsiveness when compared with traditional heuristic or intuition-based approaches. However, the effectiveness of these tools ultimately depends on data representativeness, interpretability safeguards, and alignment with small-business resource environments. The study concludes by outlining a neutral, scalable adoption model that supports resilience building while avoiding prescriptive policy judgments.
Copyright (c) 2026 Jinyuan Li (Author) https://creativecommons.org/licenses/by/4.0
Navigating the New Normal: COVID-19's Impact on Local Communities and Sustainable Tourism in Phuket
The COVID-19 pandemic profoundly disrupted the tourism-dependent economy of Phuket, exposing the inherent vulnerabilities of local communities. This paper explores the impact of the new normal on Phuket's local residents by examining four key dimensions: employment vulnerability, risk perception of COVID-19 infection, sustainability awareness, and pro-tourism attitudes. Adopting a qualitative research approach, data were collected through semi-structured, face-to-face interviews with residents from diverse socio-economic backgrounds. The findings indicate that employment vulnerability represented a primary challenge, characterized by widespread job losses and significant barriers to re-employment resulting from reduced hiring and more stringent professional requirements. Furthermore, residents expressed heightened concerns regarding health risks, particularly concerning visitors who bypassed safety protocols, which led to an increase in self-protective behaviors. Simultaneously, the pandemic fostered a greater sense of sustainability awareness, as communities recognized the necessity of balancing economic recovery with environmental and cultural preservation. Despite these multifaceted challenges, pro-tourism attitudes remained resilient, with many residents acknowledging the indispensable role of the tourism sector in maintaining economic stability and driving future recovery. By integrating stakeholder theory and focusing on local community perspectives, this study fills a critical gap in the existing literature regarding island tourism during global health crises. These findings offer practical recommendations for policymakers and stakeholders to enhance sustainable and responsible tourism development in Phuket. Future research may benefit from adopting broader, more diverse samples and incorporating quantitative methods to validate and extend the exploratory insights generated by this qualitative study.
Copyright (c) 2026 Xu Tang, Ilian Assenov (Author) https://creativecommons.org/licenses/by/4.0
The Role of AI-Driven Automation in Enhancing Administrative Efficiency for U.S. Small Businesses: A Conceptual Design of the AdminAI Pro System
U.S. small businesses face mounting administrative burdens due to intensifying regulatory requirements, fragmented documentation flows, and increasingly complex communication channels. Existing automation solutions are often either overly simple or built for enterprise-scale organizations, leaving small businesses with limited feasible options. This paper presents an expanded conceptual design for AdminAI Pro, a unified AI-driven automation system tailored to the structural, economic, and organizational realities of U.S. small enterprises. Rather than offering empirical findings, this paper develops a comprehensive theoretical model describing the system's architecture, workflow logic, intelligent components, and anticipated organizational impacts. The goal is to offer a deep, integrative conceptual framework that may guide future prototype development and empirical evaluation.
Copyright (c) 2026 Siqi You (Author) https://creativecommons.org/licenses/by/4.0
Building an AI-Driven Wellness Platform for the Aging Population: A Case Study of ElderAI Wellness Hub
The United States faces an unprecedented demographic and fiscal challenge as its population rapidly ages. By 2060, individuals aged 65 and above will surpass 94 million, representing nearly a quarter of the total population. This demographic shift exerts immense pressure on the Medicare system, which already allocates over 80% of its expenditures to older adults with chronic conditions. To address this dual crisis of health and finance, this study presents ElderAI Wellness Hub, a non-profit, AI-driven digital platform designed to enhance preventive health management, optimize Medicare spending, support caregivers, and integrate telehealth services for aging Americans. Grounded in gerontechnology and AI-based behavioral health management, ElderAI seeks to create a sustainable, equitable model that aligns with Healthy People 2030 and the One Big Beautiful Bill Act (OBBBA) priorities. Employing a mixed-method case study and policy analysis, this paper evaluates ElderAI's technical architecture, operational model, and societal impact. The findings indicate that AI-assisted predictive analytics can reduce hospitalization risks by up to 30%, enhance caregiving efficiency, and yield potential national Medicare savings of $10-20 billion over a decade. The research demonstrates both substantial merit and national importance, fulfilling the U.S. National Interest Waiver (NIW) criteria through innovation, fiscal responsibility, and measurable public health outcomes.
Copyright (c) 2025 Jiyao Yang (Author) https://creativecommons.org/licenses/by/4.0
Optimization Suggestions for Shandong Province's Digital Government Public Service Platform
This study examines the performance of Shandong Province's Digital Government Public Service Platform and proposes targeted optimization strategies based on survey data from 300 users. The findings indicate moderate satisfaction, with process efficiency and system stability performing better than data sharing and personalization. No significant differences were observed across demographic variables, demonstrating a consistent and inclusive user experience. Correlation analysis shows that improved usability, stability, and cross-department integration are associated with fewer reported issues. Overall, the platform is functional and widely accessible but still requires enhancements in intelligent service response, unified data sharing, interface usability, and feedback mechanisms. Strengthening interoperability and user-centered design will support the development of a more efficient, responsive, and citizen-oriented digital governance model in Shandong Province.
Copyright (c) 2025 Hehang Yin (Author) https://creativecommons.org/licenses/by/4.0
Investor Sentiment and Its Mechanisms in Financial Markets: A Comprehensive Review
Investor sentiment significantly shapes stock market behavior, influencing asset prices, trading activity, market risk, and portfolio allocation. This review synthesizes conceptual frameworks, empirical evidence, and mechanisms through which sentiment affects financial markets, covering both developed and emerging markets. Direct and indirect sentiment measures, including surveys, market-based indicators, and text-based analyses, are evaluated. Findings underscore that optimism and pessimism drive overreaction, herding, and volatility, while also affecting asset allocation decisions. The review discusses practical implications for investors, regulators, and researchers, and identifies future research directions, including multi-modal sentiment analysis and theinteractions among behavioral biases.
Copyright (c) 2025 Zhenqi Guo, Yingqi Luo, Qianwen Wei, Yiling Huang, Kaiyu Luo, Rongzhen Lu, Qiyan Ke, Nianjiao Peng (Author) https://creativecommons.org/licenses/by/4.0
Research on the Incentive Effect of Government Tax Preference on Independent Innovation of Emerging Enterprises
Currently, emerging industries are gradually becoming the main drivers of the transformation of China's industrial structure, and independent innovation serves as the core force behind the development of these industries. For the government, stimulating independent innovation among emerging enterprises has become a key policy objective. Appropriate financial and tax incentives can partially offset the losses caused by the externalities of independent innovation, while also reducing the risks and costs associated with innovation activities. However, questions remain regarding whether such tax incentives can effectively promote independent innovation and which types of preferential policies yield the strongest incentives. To address these issues, this paper employs an empirical analysis approach to construct a relationship model that examines the impact of tax incentives on enterprise independent innovation, providing a reference for the formulation of government incentive policies. The regression analysis indicates that tax incentives not only increase the innovation investment of strategic emerging enterprises but also encourage the generation of more innovative outcomes and enhance overall innovation efficiency.
Copyright (c) 2025 Furui Pang (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Factors Influencing Chinese Consumers' Online Consumption Psychology and Behavior
As China's technological capabilities continue to advance and the scale of online shopping steadily expands, e-commerce has become one of the primary modes of daily consumption for the public. This trend not only provides convenience and satisfies diverse purchasing needs but also gradually reshapes consumer behavior. Some individuals may develop tendencies toward premature or impulsive consumption, which can disrupt their normal financial routines. Therefore, it is essential for consumers to clearly understand the key factors influencing their purchasing decisions, cultivate a rational consumption mindset, maintain a balanced relationship between income and expenditure, and strengthen the protection of their personal information in online environments.
Copyright (c) 2025 Mei Yu, Yan Ma (Author) https://creativecommons.org/licenses/by/4.0
Fraud Detection and Risk Assessment of Online Payment Transactions on E-Commerce Platforms Based on LLM and GCN Frameworks
With the rapid expansion of e-commerce, online payment fraud has become increasingly sophisticated, posing significant challenges to financial security and undermining consumer trust. Traditional detection methods often struggle to capture the intricate relational and behavioral patterns inherent in transactional data, limiting their effectiveness in highly imbalanced scenarios. This study introduces a novel fraud detection framework that integrates Large Language Models (LLMs) with Graph Convolutional Networks (GCNs) to enhance the identification of fraudulent activities in online payment systems. A dataset comprising 2,840,000 transactions was collected over a 14-day period from major e-commerce platforms, including Amazon, involving approximately 2,000 U.S.-based consumers and 30 merchants. Among these transactions, fewer than 6,000 were fraudulent, presenting a highly skewed class distribution that reflects real-world conditions. In this framework, consumers and merchants were represented as nodes, and transactions as edges, forming a heterogeneous graph that captures both direct and indirect interactions. A GCN was applied to this graph to learn complex structural and relational patterns, effectively modeling the interdependencies among participants. In parallel, semantic features were extracted from transaction metadata using GPT-4o and Tabformer, capturing textual and categorical information that complements the structural graph features. By fusing these semantic representations with graph-based features, the model can identify subtle and context-dependent indicators of fraudulent behavior that traditional methods may overlook. Experimental results demonstrate that the proposed framework achieves an overall accuracy of 0.98, with a balanced performance in terms of precision and sensitivity, highlighting its robustness in detecting rare fraudulent instances within a massive dataset. The integration of LLM-derived semantic features with graph structural information significantly improves detection efficacy compared with models relying solely on either approach. This hybrid approach offers a scalable, real-time solution for enhancing the security of online payment environments and provides a promising avenue for applying graph-based deep learning techniques in financial fraud prevention.
Copyright (c) 2025 Ruihan Luo, Nanxi Wang, Xiaotong Zhu (Author) https://creativecommons.org/licenses/by/4.0
Adaptive Intelligence in Robo-Advisory: A Framework for Risk Control and Return Optimization
The rapid integration of artificial intelligence (AI) into financial services has transformed investment management. However, existing robo-advisory systems remain limited by static optimization, constrained risk adaptability, and opaque decision-making processes. To overcome these challenges, this study introduces an Adaptive Intelligence Framework (AIF) that integrates reinforcement learning, modern portfolio theory, and explainable AI (XAI) to enable dynamic risk control and transparent return optimization. Employing a mixed-method approach that combines theoretical modeling, comparative case studies (BlackRock Aladdin and Betterment), and empirical simulations on verified global market data from 2018 to 2024, the framework demonstrated superior performance, achieving an 11.6% increase in cumulative return, a 17.3% reduction in volatility, and a high interpretability score of 0.82. These findings indicate that adaptive algorithms can simultaneously enhance stability and transparency under non-stationary market conditions. The study advances financial AI research by linking quantitative finance with algorithmic accountability and provides a practical blueprint for developing trustworthy, regulation-aligned robo-advisory systems capable of balancing efficiency, explainability, and resilience in capital markets.
Copyright (c) 2025 Tianyi Wang (Author) https://creativecommons.org/licenses/by/4.0
Analysis of the Impact of off Exchange Derivatives Trading on Capital Market Efficiency Improvement
In the modern financial system, over-the-counter (OTC) derivative trading plays a crucial role, with its flexibility and diversity significantly enhancing the operational efficiency of capital markets. OTC derivative trading improves market efficiency by facilitating information integration, increasing transparency, expanding the variety of trading instruments, and promoting the rational allocation of resources. This article examines the impact of information transparency, innovative trading instruments, and optimized resource allocation on capital market efficiency, alongside the distinct characteristics of OTC derivatives. Emerging technologies, particularly blockchain, offer new technical support for the development of OTC derivative markets, helping to balance efficiency gains with effective risk control. Further advancements in policy frameworks and technological innovation are expected to deepen the contribution of OTC derivative trading to the overall functioning of capital markets.
Copyright (c) 2025 Zhengle Wei (Author) https://creativecommons.org/licenses/by/4.0
Reinforcement Learning with Reward Shaping for Last-Mile Delivery Dispatch Efficiency
As the final and most labor-intensive segment of the logistics chain, last-mile delivery grapples with inherent challenges: dynamic traffic conditions, fluctuating order volumes, and the conflicting demands of timeliness, cost control, and resource efficiency. Conventional dispatch approaches-such as heuristic algorithms and static optimization models-exhibit limited adaptability to real-time fluctuations, often resulting in suboptimal resource utilization and elevated operational costs. To address these gaps, this study proposes a reinforcement learning (RL) framework integrated with multi-dimensional reward shaping (RS) to enhance dynamic last-mile delivery dispatch efficiency. First, we formalize the dispatch problem as a Markov Decision Process (MDP) that explicitly incorporates real-time factors (e.g., traffic congestion, order urgency, and vehicle status) into the state space. Second, we design a domain-specific RS function that introduces intermediate rewards (e.g., on-time arrival bonuses, empty-running penalties) to mitigate the sparsity of traditional terminal rewards and accelerate RL agent convergence. Experiments were conducted on a real-world dataset from a logistics enterprise in Chengdu (June-August 2024), comparing the proposed RS-PPO framework against two baselines: the classic Savings Algorithm (SA) and standard PPO without reward shaping (PPO-noRS). Results demonstrate that RS-PPO improves the on-time delivery rate (OTR) by 18.2% (vs. SA) and 9.5% (vs. PPO-noRS), reduces the average delivery cost (ADC) by 12.7% (vs. SA) and 7.3% (vs. PPO-noRS), and shortens convergence time by 40.3% (vs. PPO-noRS). Additionally, RS-PPO boosts vehicle utilization rate (VUR) by 29.8% (vs. SA) and 13.4% (vs. PPO-noRS). This framework provides a practical, data-driven solution for logistics enterprises seeking to balance service quality, cost efficiency, and sustainability-aligning with global last-mile optimization trends.
Copyright (c) 2025 Sichong Huang (Author) https://creativecommons.org/licenses/by/4.0
The Push of Financial Technology Innovation on Derivatives Trading Strategy Optimization
The innovation of financial technology has significantly advanced the optimization of derivatives trading strategies. By leveraging cutting-edge tools such as quantitative trading algorithms, big data analytics, and blockchain technology, fintech enhances the speed, precision, and reliability of financial transactions. These technologies not only strengthen risk management capabilities but also improve market transparency, thereby fostering a more informed and efficient trading environment. In addition, fintech facilitates real-time monitoring of market conditions, enabling traders and institutions to respond swiftly to fluctuations and potential anomalies, which contributes to reducing market volatility. Moreover, the integration of automated and intelligent systems supports fairer market practices by minimizing human errors and information asymmetries. This paper aims to comprehensively examine the practical applications of fintech in derivatives trading, exploring how its deployment improves operational efficiency, strengthens market integrity, and stimulates innovation and growth within the derivatives market. Through detailed analysis of technological frameworks, trading mechanisms, and performance outcomes, the study provides insights into the transformative role of fintech in shaping modern derivatives markets and highlights potential avenues for further technological advancement and strategic adoption.
Copyright (c) 2025 Yilin Fu (Author) https://creativecommons.org/licenses/by/4.0
Cross-Border Industrial Collaboration: Interactive Influences and Development Insights between European and Chinese Fertilizer Industries
This article traces the historical development of the fertilizer industry in major European countries, including Germany, the United Kingdom, and France, and examines its multifaceted influence on the formation of China's fertilizer industry. Specifically, it explores four key dimensions of this influence: the establishment of technical foundations, the construction of an industrial system, the shaping of international cooperation models, and the introduction of sustainable development concepts. The study finds that Europe-particularly Germany through the invention of the Haber-Bosch ammonia synthesis process-provided the crucial technological groundwork for China's fertilizer industry. Moreover, the evolution of Europe's fertilizer sector not only supplied the technical impetus for China's industrial modernization but also offered valuable experience and strategic guidance for building an independent, innovative, and sustainable fertilizer industry system.
Copyright (c) 2025 Xianhan Qin (Author) https://creativecommons.org/licenses/by/4.0
Causal Modeling for Fraud Detection: Enhancing Financial Security with Interpretable AI
Financial fraud poses a significant threat to the stability of modern economic systems. However, traditional machine learning approaches to fraud detection-primarily correlation-based-remain limited in precision, interpretability, and adaptability when confronting the constantly evolving strategies of fraudsters. This study introduces a causal inference framework for fraud detection, leveraging recent advancements in causal analysis to identify and quantify the underlying causal relationships among transaction attributes, user behaviors, and fraudulent outcomes. The framework incorporates three key components: causal discovery algorithms (PC and FCI), robust effect estimation techniques (e.g., PSM and DML), and an interpretable rule-extraction module that translates causal patterns into actionable insights. Experiments were conducted on two real-world datasets: a credit card transaction dataset (284,807 records, 32% fraud rate) and an insurance claims dataset (350,000 cases, 8% fraud rate). Results show that the proposed model consistently outperforms leading correlation-based methods-including AdaBoost, GBDT, XGBoost, and LightGBM-achieving notable performance improvements: an average 9-percentage-point gain in overall accuracy, a 2% increase in F1 score (up to 11%), a 5% boost in AUPRC, and a 13.3% improvement in MCC. A key finding highlights a 47% higher fraud risk associated with atypical location changes combined with large-value transactions, directly addressing the "black-box" limitations of conventional models. Robustness analyses further confirm the model's resilience against confounding influences such as seasonal fluctuations and demographic shifts, underscoring its adaptability to emerging fraud patterns. By integrating causal inference with interpretable artificial intelligence, this research advances fraud detection toward more precise, transparent, and regulatory-compliant financial risk management.
Copyright (c) 2025 Luqing Ren (Author) https://creativecommons.org/licenses/by/4.0
The Synergy of Cross-Platform Content Creation in Employer Brand Communication
With the proliferation of social media, recruitment platforms, and corporate websites, cross-platform content creation has emerged as a key strategy for enhancing employer brand promotion. This study investigates the synergistic effects of cross-platform content creation in employer brand communication, examining how collaboration across multiple platforms can expand brand awareness, strengthen brand value, and improve employee recognition. A synergy model of cross-platform communication is developed, elucidating the interaction mechanisms among diverse platforms, and its effectiveness is validated through case analyses. The findings indicate that cross-platform content creation not only optimizes brand communication strategies but also facilitates comprehensive diffusion of the employer brand image, offering both theoretical insights and practical guidance for enterprises seeking to enhance their employer branding initiatives.
Copyright (c) 2025 Runqi Lin (Author) https://creativecommons.org/licenses/by/4.0
The Development Trends and Future Challenges of Cross-Border E-Commerce: From a Global Perspective
Cross-border e-commerce has emerged as a vital driver of global trade and economic growth. Fueled by technological advancements and evolving consumer demand, the sector has witnessed rapid expansion and exhibits considerable potential worldwide. This paper investigates the key development trends, major challenges, and future opportunities in cross-border e-commerce from a global perspective. It first reviews the current state of the global market and highlights regional disparities in development. It then examines major trends, including digital transformation, the interplay between globalization and localization, the rise of mobile commerce, and innovations in logistics and payment systems. The study further analyzes critical challenges such as legal and policy barriers, cultural differences, and payment security risks. In addition, it explores the transformative role of emerging technologies-such as artificial intelligence, big data, and blockchain-in shaping the sector's future. Finally, the paper identifies potential growth avenues in areas such as social media integration, sustainability, and personalized services, while offering policy recommendations to support the healthy and sustainable development of global cross-border e-commerce. The findings provide valuable insights for academia, industry practitioners, and policymakers alike.
Copyright (c) 2025 Zixuan Zhang (Author) https://creativecommons.org/licenses/by/4.0
Cost Control and Stability Improvement in Enterprise Level Infrastructure Optimization
Optimizing enterprise-level infrastructure plays a key role in enhancing operational efficiency and saving costs. This article deeply analyzes the key factors and implementation strategies of infrastructure from the aspects of hardware resource optimization, automated operation and maintenance, and intelligent resource scheduling. With the deepening application of virtualization technology and cloud computing, enterprises can greatly improve resource utilization efficiency and stable system operation, while reducing maintenance costs and energy consumption. By implementing centralized monitoring and intelligent warning mechanisms, system issues can be quickly identified and effectively prevented, thereby enhancing the stability and reliability of infrastructure. With the rapid progress of cloud native architecture, artificial intelligence, edge computing, and green IT, the optimization of future infrastructure has more innovative possibilities. These strategies and Q trends provide a reference for efficient management of enterprises in the process of informatization and digitization, promoting the achievement of sustainable development goals.
Copyright (c) 2025 Yixin Zhou (Author) https://creativecommons.org/licenses/by/4.0
Time Series Analysis for Stock Price Forecasting: Evidence from Kansai Design Co., Ltd. (603458)
Stock price forecasting plays a critical role in investment decision-making, particularly in emerging sectors such as engineering consulting. Kansai Design Co., Ltd. (603458), a state-controlled listed firm, has drawn growing attention with China's ongoing infrastructure expansion. However, few studies have systematically examined its stock price dynamics using rigorous time series methods, leaving a gap in empirical evidence for short-term predictability. This study employs time series analysis to investigate the company's daily stock prices from October 10, 2022, to September 28, 2023. Following the established paradigm of stationarity testing, model identification, parameter estimation, and diagnostic checking, an Augmented Dickey-Fuller test confirmed series stationarity. Autocorrelation and partial autocorrelation analyses indicated the suitability of an autoregressive model. Comparative evaluation of AR (1), AR (2), and AR (3) models demonstrated that AR (1) provided the best fit, consistent with Occam's Razor. Residual diagnostics verified white noise properties, while predictive performance metrics showed high accuracy (RMSE = 0.0519; MAPE = 0.414%). The AR (1) model closely tracked actual prices, with errors mainly driven by random fluctuations rather than systematic bias. These findings highlight the short-term continuity of stock prices and demonstrate the model's practical value as a decision-support tool. The study contributes methodological insights and offers investors evidence-based guidance while emphasizing the importance of integrating quantitative models with fundamental market analysis.
Copyright (c) 2025 Yijing Wang (Author) https://creativecommons.org/licenses/by/4.0
The Influence of ESG Factors on Corporate Financing Costs in Emerging Markets
Emerging-market firms often face elevated financing costs due to macroeconomic volatility, institutional weaknesses, and environmental risks. Yet most existing studies focus on developed economies, leaving limited evidence on how environmental, social, and governance (ESG) factors affect debt pricing in emerging contexts. This study addresses this gap by adopting a mixed-methods design, combining panel regression analysis of firm-level ESG ratings, financial indicators, and macroeconomic data from 2023 to 2025 with comparative case studies of firms implementing substantial ESG reforms. The econometric results show that superior ESG performance is significantly associated with lower financing costs, with governance and environmental pillars exerting the strongest influence, while social factors have weaker and statistically insignificant effects. Case evidence further illustrates how governance reforms and environmental commitments lead to favorable refinancing terms, whereas social initiatives yield mainly reputational benefits. These findings extend ESG-financing research to emerging markets and provide practical insights for managers, investors, and policymakers seeking to lower capital costs and strengthen sustainable finance.
Copyright (c) 2025 Wenbo Zhang (Author) https://creativecommons.org/licenses/by/4.0
An Analysis of Management Strategies in Emerging Startups
Emerging Asian startups have become influential actors in the global economy, yet existing research has primarily emphasized Western firms, leaving gaps in understanding their unique strategies. This study addresses that gap by examining SHEIN, a China-founded digital-first fast-fashion company that has rapidly expanded to more than 150 countries. Using a qualitative case study method supported by comparative analysis with ByteDance and Grab, the research draws on secondary data, industry reports, and sustainability disclosures from 2021 to 2025. The findings show that SHEIN's growth is driven by an agile supply chain capable of releasing over 1.5 million new items annually, a platform-based model with more than 250 million monthly active users by 2024, and a flexible workforce exceeding 10,000 employees worldwide. Revenue increased from USD 15.7 billion in 2021 to an estimated USD 56 billion in 2025, while its U.S. market share peaked at 50 percent in 2022 before stabilizing above 40 percent in 2025. These results illustrate how digital integration, small-batch production, and global-local balance allow emerging startups to sustain international competitiveness. The study contributes to scholarship on entrepreneurship and offers practical lessons for managing agility, digital platforms, and global expansion in resource-constrained contexts.
Copyright (c) 2025 Jiarui Luo (Author) https://creativecommons.org/licenses/by/4.0
Cost Control and Risk Management in the Logistics and Transportation Segment of the Pulp Supply Chain
This review examines cost control and risk management strategies in the logistics and transportation segment of the pulp supply chain. The study first analyzes the structure and key characteristics of pulp logistics, highlighting the critical nodes and cost drivers that influence operational efficiency. It then explores major cost components, including transportation, handling, storage, and inventory, and evaluates strategies such as route optimization, multimodal transport, automation, and collaborative logistics to reduce expenses. Key risks—including operational, financial, environmental, geopolitical, and market-related factors—are identified and assessed for likelihood and impact, with examples illustrating potential disruptions. The review further discusses mitigation approaches, including insurance, contingency planning, contract management, and the adoption of digital technologies like IoT and AI for real-time monitoring and predictive decision-making. Finally, emerging trends, such as digitalization, AI-driven logistics, and sustainable transport solutions, are examined, emphasizing the integration of cost and risk management for resilient and efficient supply chains. Recommendations for practitioners and future research directions are provided to enhance supply chain performance, sustainability, and risk resilience.
Copyright (c) 2025 Ronglin Wang (Author) https://creativecommons.org/licenses/by/4.0
Opportunities, Challenges, and Strategic Responses for International Agricultural Trade in the Digital Era
The digitalization of international agricultural trade has emerged as a transformative force, reshaping production, distribution, and market dynamics in the global agri-food sector. This review examines the current status of digital adoption in agriculture, highlighting key technologies such as blockchain, e-trading platforms, Internet of Things (IoT), and big data analytics, and analyzes their impacts on trade efficiency, transparency, and market access. The study further explores opportunities created by digitalization, including reduced transaction costs, enhanced supply chain traceability, and innovative financial instruments that support risk management and inclusive participation for smallholder farmers and SMEs. Concurrently, the review identifies critical challenges and risks, encompassing technological barriers, regulatory inconsistencies, market inequality, and social-environmental implications. Strategic responses and policy recommendations are proposed, emphasizing investment in digital infrastructure, promotion of inclusive adoption, international cooperation, and robust risk mitigation strategies. Finally, emerging trends such as AI-driven trade analytics, smart contracts, and blockchain-based traceability are discussed, providing directions for future research. The findings underscore that, while digitalization presents significant opportunities, coordinated efforts among policymakers, businesses, and researchers are essential to ensure equitable, resilient, and sustainable international agricultural trade.
Copyright (c) 2025 Jian Wang (Author) https://creativecommons.org/licenses/by/4.0
Research on Market Expansion Strategies under the New Pattern of International Agricultural Trade
This review examines the evolving patterns of international agricultural trade and the strategies employed by firms to expand their markets in a rapidly changing global environment. Drawing on literature from the past decade, the study highlights three dominant trends: market diversification, technological digitalization, and sustainability-oriented trade practices. Case studies from China's Belt and Road Initiative, Latin America's soybean sector, the European Union's organic agriculture, and Africa's emerging markets illustrate how infrastructure development, policy frameworks, value-added processing, and digital supply chain integration shape trade outcomes. While these strategies enhance market access and competitiveness, challenges persist, including institutional disparities, environmental sustainability concerns, and uneven technological adoption. The review synthesizes these insights to identify research gaps and proposes future directions, emphasizing comparative studies, data-driven analytics, and the interplay between policy support and firm-level strategies. The findings provide a comprehensive framework for scholars, policymakers, and practitioners seeking to navigate contemporary agricultural trade dynamics effectively.
Copyright (c) 2025 Jie Chen (Author) https://creativecommons.org/licenses/by/4.0
Digital Credit Risk Management Systems and Solutions
Digital transformation has fundamentally revolutionized credit risk management practices across financial institutions, introducing sophisticated technological solutions that enhance accuracy, efficiency, and decision-making capabilities in lending processes. This comprehensive study examines the evolution of digital credit risk management systems, analyzing the implementation of advanced machine learning algorithms, artificial intelligence technologies, and data analytics platforms that have transformed traditional credit assessment methodologies. Through systematic evaluation of contemporary digital solutions, this research reveals significant improvements in risk prediction accuracy, processing efficiency, and portfolio management effectiveness compared to conventional approaches. Digital credit risk systems demonstrate superior performance in detecting fraudulent activities, assessing borrower creditworthiness, and optimizing lending decisions through real-time data processing and predictive modeling capabilities. The study investigates various technological implementations including ensemble learning methods, deep learning architectures, and alternative data sources that enhance credit scoring precision while reducing operational costs by 35-50% and improving approval times by 70-85%. Furthermore, the research examines challenges associated with digital system implementation including data privacy concerns, regulatory compliance requirements, and model interpretability issues that influence adoption strategies. The findings demonstrate that organizations implementing comprehensive digital credit risk management solutions achieve substantial improvements in portfolio performance, risk mitigation, and operational efficiency while maintaining regulatory compliance and customer satisfaction. This analysis provides evidence-based insights for financial institutions considering digital transformation initiatives and offers practical recommendations for optimizing credit risk management through technology integration.
Copyright (c) 2025 Rui Xu (Author) https://creativecommons.org/licenses/by/4.0
Optimization of Packaging Procurement and Supplier Strategy in Global Supply Chain
The complexity of the global supply chain requires refined management of each link. Packaging material procurement, as the core link of supply chain management, has a direct impact on cost control, resource allocation, and supply chain adaptability. The role of packaging is not only for product protection and appearance design, but also plays an important role in cost control, logistics efficiency, and environmental sustainability. This article analyzes the path of optimizing packaging procurement in the context of globalization, focusing on the balance strategy between centralized and decentralized procurement, the application of information technology and big data, and the flexible selection of various packaging materials. It also emphasized the importance of supplier management strategies, including selecting suitable suppliers, implementing a grading system, and risk control. By optimizing packaging procurement and supplier management in the supply chain, the operational efficiency of the entire supply chain can be improved, operating costs can be reduced, and the competitiveness of the enterprise in the market can be enhanced.
Copyright (c) 2025 Hao Xu (Author) https://creativecommons.org/licenses/by/4.0
Research on Cloud Computing Resource Scheduling Strategy Based on Big Data and Machine Learning
Optimizing the efficient scheduling of cloud platforms is crucial for enhancing the performance and resource utilization of these platforms. The intelligent scheduling optimization framework proposed in this article combines big data and deep learning technologies, which can effectively solve a series of severe challenges in cloud platform resource scheduling, such as redundant resource preparation, inaccurate capacity estimation, and high scheduling costs. Suggest building a multidimensional feature model that combines historical and real-time monitoring information, and using LSTM for accurate resource prediction. Based on this result, a reinforcement learning based automatic adjustment hybrid scheduling algorithm was designed to achieve intelligent dynamic scheduling of different resources. Meanwhile, a graph partitioning mechanism is adopted to reduce scheduling complexity and improve system scalability. On the Google Cluster Trace dataset, the proposed solution can significantly improve resource utilization, increasing it from raw utilization to 21.5%, reducing the average waiting time of tasks by 18.3%, and lowering SLA default rates by 13%. The deployment experiment in the Kubernetes environment further validated the feasibility and effectiveness of the proposed solution. The research results provide evidence and understanding for the application of learning based intelligent scheduling technology in cloud infrastructure.
Copyright (c) 2025 Jiaying Huang (Author) https://creativecommons.org/licenses/by/4.0
Research on New Drivers of Event Consumption and Strategies for High-Quality Development
This study examines the new drivers of event consumption and strategies for high-quality development in the context of Wenzhou's evolving event economy. Through a mixed-methods approach combining quantitative analysis and case studies, the research identifies five key drivers propelling event consumption: the experience economy, digital transformation, social-cultural engagement, sustainability and health consciousness, and policy-infrastructure support. The findings reveal how Wenzhou has successfully leveraged its unique economic characteristics and cultural assets to develop a robust event ecosystem that generates significant economic benefits while addressing urban development goals. The study further identifies major challenges including event homogenization, intellectual property protection, talent shortages, and environmental sustainability concerns. Based on these findings, the research proposes a comprehensive framework and tailored strategies for event organizers, government bodies, and destination marketing organizations to optimize event economy development. The Wenzhou case offers valuable insights for other cities seeking to harness the power of event economy for sustainable urban transformation, demonstrating how strategic integration of local advantages with modern consumption trends can create synergistic effects for high-quality development.
Copyright (c) 2025 Xueyi Lin (Author) https://creativecommons.org/licenses/by/4.0
Strategic Realignment: Post-Brexit Economic Relations between the UK and Japan
This paper examines the strategic realignment of UK-Japan economic relations in the post-Brexit era, focusing on the evolving geopolitical context, trade and investment trends, and the significance of the UK-Japan Comprehensive Economic Partnership Agreement (CEPA). Following Brexit in 2020, the “Global Britain” strategy has sought to expand trade relations beyond Europe, positioning Japan as a strategic anchor in the Indo-Pacific. Japan’s role as a major economic power, its leadership in high-value manufacturing and global finance, and its stewardship of the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) make it a natural partner for the UK. The paper outlines bilateral trade and investment patterns, the development of political and economic cooperation mechanisms-including the Reciprocal Access Agreement and the economic “2+2” dialogue in 2025, and their implications for security and market integration. A detailed analysis of CEPA highlights its negotiation background, key provisions, and departures from the EU-Japan Economic Partnership Agreement. While CEPA ensures trade continuity and introduces sector-specific enhancements, its symbolic significance may outweigh its immediate economic impact, given the structural constraints and continued dependence on EU market access.
Copyright (c) 2025 Siying Chen (Author) https://creativecommons.org/licenses/by/4.0