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.
The Impact of Central Bank Digital Currencies (CBDCs) on Monetary Policy Transmission: Evidence from the Eurozone
This research investigates the profound implications of Central Bank Digital Currencies (CBDCs) on the effectiveness and dynamics of monetary policy transmission within the Eurozone. As digital financial innovations rapidly reshape the global economic landscape, the European Central Bank faces critical decisions regarding the implementation of a digital euro. By systematically analyzing both established theoretical frameworks and recent empirical evidence, this study explores how the introduction of CBDCs could fundamentally alter traditional monetary mechanisms. Specifically, we examine the potential transformations in primary transmission pathways, such as interest rate channels, bank lending behaviors, and broader liquidity management strategies. The findings highlight a spectrum of potential macroeconomic benefits, including significantly enhanced policy precision, accelerated transaction speeds, and broader financial inclusion for underbanked populations. Conversely, the study rigorously addresses the inherent challenges and vulnerabilities associated with digital currency adoption. These include heightened privacy concerns among consumers, the risk of commercial bank disintermediation, and the potential amplification of systemic risks during periods of financial stress. Furthermore, we evaluate the cross-border implications of a digital euro on international trade and exchange rate volatility. Ultimately, the research concludes with a comprehensive set of strategic policy recommendations tailored for central banks and regulatory authorities. These actionable insights aim to facilitate the seamless integration of CBDCs into existing monetary systems while safeguarding financial stability, ensuring regulatory compliance, and maximizing the socioeconomic benefits of digital financial innovation across the Eurozone.
Copyright (c) 2026 Julien Mercier (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence Integration in Recruitment: Enhancing Organizational Attractiveness and Job Seeker Experience
This research article explores the integration of artificial intelligence (AI) in contemporary recruitment processes, focusing specifically on its profound impact on organizational attractiveness and the overall job seeker experience. As the global labor market becomes increasingly competitive, human resource departments are rapidly adopting advanced technological solutions to streamline talent acquisition. By examining the complex interplay between emerging AI-driven tools—such as automated resume screening, predictive analytics, and conversational chatbots—and traditional recruitment methods, this study provides critical insights into how organizations can effectively leverage AI to enhance their appeal to prospective candidates. Furthermore, the research investigates the delicate balance required to maintain a human-centric approach while ensuring a seamless, efficient, and highly engaging application process. Through a comprehensive analysis of candidate feedback and organizational performance metrics, we identify key drivers of applicant satisfaction and potential pitfalls associated with algorithmic bias or perceived impersonality. The findings significantly contribute to the broader academic understanding of AI's transformative role in shaping modern recruitment strategies. Ultimately, this paper highlights the dual implications of AI adoption for both employers seeking top-tier talent and job seekers navigating an increasingly digitized landscape, offering actionable recommendations for optimizing human resource technology deployments to foster mutual value creation and sustained organizational success.
Copyright (c) 2026 Sarah J. Harrington (Author) https://creativecommons.org/licenses/by/4.0
ESG Performance and Corporate Financial Distress Risk: The Moderating Role of Board Diversity in European Firms
This research investigates the complex relationship between Environmental, Social, and Governance (ESG) performance and corporate financial distress risk, with a specific focus on the moderating role of board diversity within the context of European firms. In recent years, the integration of sustainability metrics into corporate strategy has become a critical determinant of long-term viability and resilience. However, the extent to which ESG initiatives actively shield companies from financial instability remains a subject of ongoing academic debate. By systematically analyzing key ESG metrics alongside comprehensive board diversity indicators—encompassing gender, age, educational background, and professional expertise—this study explores how heterogeneous governance structures influence a firm's capacity to mitigate financial distress risks associated with varying levels of ESG performance. Utilizing a robust dataset of publicly listed European enterprises over a multi-year period, the empirical analysis reveals that higher ESG performance is generally associated with a reduced probability of financial distress. More importantly, the results demonstrate that board diversity significantly positively moderates this relationship. Diverse boards appear better equipped to navigate the multifaceted challenges of sustainability implementation, thereby enhancing the protective effects of ESG practices against economic downturns and operational vulnerabilities. The findings provide profound insights into the intricate interplay between proactive sustainability practices and robust governance structures. Ultimately, this study offers valuable implications for corporate strategy, investor decision-making, and regulatory policymaking, highlighting the necessity of fostering inclusive leadership to maximize the financial benefits of corporate social responsibility initiatives.
Copyright (c) 2026 Sophia Bouchard (Author) https://creativecommons.org/licenses/by/4.0
Digital Transformation and Business Model Innovation in European SMEs: A Dynamic Capabilities Perspective
This research investigates the complex interplay between digital transformation and business model innovation within European small and medium-sized enterprises (SMEs) through the theoretical lens of the dynamic capabilities framework. In an increasingly volatile global market, the imperative for SMEs to integrate advanced digital technologies has become paramount for sustained survival and growth. By systematically examining how these enterprises adapt to rapid technological disruptions and strategically leverage digital tools to fundamentally innovate their existing business models, this study identifies the critical key enablers and structural barriers to successful organizational transformation. A robust mixed-methods approach, combining in-depth qualitative interviews with industry leaders and rigorous quantitative analysis of survey data from a diverse cross-section of European firms, is meticulously employed to provide a comprehensive, multi-dimensional understanding of the phenomenon. The empirical results highlight that dynamic capabilities—specifically sensing, seizing, and transforming—are indispensable for navigating the complexities of digital integration. Furthermore, the research reveals that resource constraints and organizational inertia frequently impede progress, whereas agile leadership and a culture of continuous learning significantly accelerate digital maturity. Ultimately, the findings contribute substantially to the growing academic discourse on digital transformation and strategic management. The study offers highly actionable insights and practical frameworks for SME managers, stakeholders, and regional policymakers who are actively aiming to foster sustainable innovation, enhance operational resilience, and drive long-term economic competitiveness in the modern digital era.
Copyright (c) 2026 Emily Thornton (Author) https://creativecommons.org/licenses/by/4.0
Building Supply Chain Resilience against Geopolitical Disruptions: Strategies for European Manufacturing Sectors
The European manufacturing sector currently faces unprecedented and increasing vulnerabilities due to a complex landscape of geopolitical disruptions, necessitating the development and implementation of robust strategies to enhance overall supply chain resilience. In recent years, global trade dynamics have been significantly altered by trade wars, regional conflicts, and shifting alliances, which have collectively exposed the fragility of traditional, highly optimized supply networks. This research article explores the multifaceted challenges posed by such unpredictable geopolitical disruptions and proposes actionable, theoretically grounded frameworks for effectively mitigating associated risks. By systematically examining key resilience-building strategies—such as supplier diversification, nearshoring, advanced inventory management, and the integration of digital twin technologies—this study provides a comprehensive analysis of adaptive mechanisms. We specifically focus on their practical applicability, economic feasibility, and long-term effectiveness within the unique regulatory and economic environment of the European context. Furthermore, the research evaluates how European Union policies and cross-border collaborative initiatives can either facilitate or hinder the adoption of these resilience measures. Through empirical case studies and theoretical modeling, we demonstrate that proactive adaptation is critical for maintaining operational continuity and competitive advantage. Ultimately, the findings aim to guide policymakers, supply chain managers, and industry leaders in fortifying manufacturing supply chains against future uncertainties, ensuring sustainable economic growth and industrial stability across the European continent in an era of continuous global volatility.
Copyright (c) 2026 David Harrison (Author) https://creativecommons.org/licenses/by/4.0
Research on the Construction of Intelligent Decision Support System for SMEs Under Multi-Source Financial Data Fusion
This research article explores the construction of an intelligent decision support system tailored for small and medium-sized enterprises (SMEs) by leveraging multi-source financial data fusion. The study introduces a systematic framework to integrate diverse financial data sources, optimize decision-making processes, and enhance operational efficiency. By employing advanced computational methods and data fusion techniques, the proposed system addresses the unique challenges faced by SMEs in financial decision-making. The findings highlight the potential of intelligent systems in transforming SME financial management and provide actionable insights for future implementations.
Copyright (c) 2026 Zhijun Liu (Author) https://creativecommons.org/licenses/by/4.0
Research on the Development Countermeasures in Educational Tourism of Chengdu City from the Perspective of PESTEL Model
The comprehensive promotion of quality-oriented education is the requirement of China's current economic and social development for primary and secondary education. In the critical period of China's economic development in the next ten years, it is of special significance to vigorously promote the "Study + Travel" model of educational tourism, both from the theoretical and practical level. The paper selects Chengdu of Sichuan province as a "research object" in educational tourism. Through the analysis of a large number of studies related theory both at home and abroad, this paper introduces the basic situation of Chengdu educational tourism research development. This paper expounds and reflects on the current situation of the development of educational tourism in the city, takes "PESTEL" theory from the angle of marketing model, finds out various problems existing in the development of educational tourism, and finally puts forward corresponding suggestions. It is expected to play a certain guiding role in the development of educational tourism in the whole country.
Copyright (c) 2026 Baoliang Dong (Author) https://creativecommons.org/licenses/by/4.0
Empowering Traditional Tourism through the Activation and Utilization of Intangible Cultural Heritage: New Trends, Mechanisms, and Optimization Paths
As a living carrier of China's excellent traditional culture, Intangible Cultural Heritage (hereinafter referred to as "ICH") embodies the historical memory, cultural genes, and spiritual codes of the nation. Its activation and utilization are deeply intertwined with tourism development, creating a relationship of mutual empowerment. With the deepening integration of culture and tourism and the iterative upgrading of consumer demands, the traditional tourism industry is undergoing a profound transformation: from "sightseeing-oriented" to "experience-oriented," from "singular" to "diversified," and from "traffic-oriented" to "value-oriented." As a core resource possessing cultural, experiential, and unique characteristics, ICH not only injects vibrant cultural connotations into traditional tourism-resolving the dilemma of "homogenized landscapes"-but also drives innovation in business formats, quality improvement, and value reconstruction. Based on the core connotation of ICH activation, this paper systematically analyzes the new changes and characteristics of tourism development in the new era. It focuses on elaborating the empowering role of ICH in traditional tourism, analyzes existing problems in current integrated development through practical cases, and proposes targeted optimization paths. This study aims to provide theoretical references and practical insights for promoting the high-quality development of both ICH activation and the tourism industry.
Copyright (c) 2026 Sihai Wu, Qiong Wu (Author) https://creativecommons.org/licenses/by/4.0
Cross Examining Consolidation Between the LLM Market and US Electricity Market Dynamics
Model (LLM) market and the U.S. electricity market. The increasing computational demands of training and deploying LLMs necessitate significant energy consumption, primarily fulfilled by electricity. This creates a vertical integration scenario where the LLM industry becomes increasingly intertwined with electricity generation, transmission, and distribution. We analyze the historical context of both industries, focusing on the technological advancements, market structures, and regulatory landscapes that have shaped their current states. Core themes explored include the energy intensity of LLMs, the potential for LLMs to optimize electricity grid management, and the economic and environmental implications of this convergence. We compare and contrast the differing operational paradigms and assess the challenges of integrating these disparate sectors. Finally, we consider future perspectives, including the role of renewable energy sources, advancements in energy-efficient computing, and the potential for new business models that leverage the synergy between LLMs and the electricity market. This review synthesizes interdisciplinary research to provide a comprehensive understanding of the complex relationship between these rapidly evolving sectors.
Copyright (c) 2026 Jingyao (Lux) Zhao (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven MCP Service Automation: A Framework for SMBs to Achieve Zero-Code Integration and High Efficiency
This research proposes an AI-driven, zero-code integration framework to automate Managed Cloud Provider (MCP) services for Small and Medium-sized Businesses (SMBs). SMBs often lack the resources and technical expertise for complex cloud management, hindering their adoption of cloud technologies. Our framework leverages AI to streamline MCP service provisioning, configuration, and monitoring, enabling SMBs to achieve significant efficiency gains without requiring coding or extensive IT infrastructure. The framework incorporates machine learning models for automated resource allocation, anomaly detection, and predictive maintenance, optimizing performance and minimizing downtime. Zero-code integration is achieved through a drag-and-drop interface and pre-built connectors, simplifying the deployment and management of cloud services. The research includes a case study demonstrating the framework’s effectiveness in improving the operational efficiency and reducing the operational costs for SMBs. Case-based evaluations demonstrate practical efficiency improvements in representative SMB deployments. The framework also enhances scalability and security in cloud environments. We evaluate the performance of our framework using key performance indicators (KPIs) such as service deployment time, resource utilization, and system uptime, showing significant improvements compared to traditional methods. The framework’s adaptability to diverse SMB requirements and its ease of use positions it as a valuable tool for promoting widespread cloud adoption among SMBs.
Small and Medium-sized Businesses (SMBs)
Copyright (c) 2026 Zhenyuan He (Author) https://creativecommons.org/licenses/by/4.0
Explainable Remote Sensing Image Captioning with Uncertainty-Aware Vision–Language Feature Fusion for SMB Decision Support
The democratization of remote sensing data presents a transformative opportunity for Small and Medium Businesses (SMBs), yet the adoption of automated interpretation tools is hindered by the "black box" nature of current Vision-Language Models (VLMs). Standard models frequently exhibit overconfidence in ambiguous scenarios, posing financial risks for applications in precision agriculture and logistics. This paper introduces SentiMap, an uncertainty-aware image captioning framework that disentangles aleatoric and epistemic uncertainty through a dual-stream Bayesian architecture. We propose a novel Adaptive Fusion Mechanism that dynamically re-weights visual representations based on spatial variance maps, prioritizing semantic priors when image quality degrades. Extensive experiments on the RSICD dataset and a curated "SMB-Risk" benchmark demonstrate that SentiMap achieves state-of-the-art calibration (ECE: 0.05) without compromising captioning accuracy. User studies confirm that providing interpretable "Trust Scores" and uncertainty heatmaps significantly enhances human decision confidence, bridging the gap between raw pixel data and actionable business intelligence.
Copyright (c) 2026 Qikun Zuo (Author) https://creativecommons.org/licenses/by/4.0
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