Economics and Management Innovation (EMI) is an international, refereed, double-blind peer-reviewed, open-access, and indexed journal dedicated to advancing interdisciplinary research in economics and management. It publishes original studies on topics such as economic theory, business innovation, organizational behavior, entrepreneurship, and strategic management. The journal aims to foster scholarly dialogue and provide insights into the evolving challenges and opportunities in the fields of economics and management.
Proposed Business Model Innovations in Digitalized Education in Jiangxi Province China
This study comprehensively examined the extent of Business Model Innovation and Digitized Education within a higher education institution located in Jiangxi Province, China. It analyzed significant differences across selected demographic profiles and explored the underlying relationship between these two main variables. Utilizing a rigorous quantitative descriptive-comparative and correlational research design, empirical data were systematically collected from 230 diverse respondents representing varied age groups, sex, educational attainment levels, and lengths of service. Business Model Innovation was meticulously assessed in terms of value proposition innovation, infrastructure and capability reconfiguration, revenue model transformation, and stakeholder engagement mechanisms. Concurrently, Digitized Education was evaluated through the lenses of digital literacy, digital pedagogy, technological infrastructure, learner engagement, and institutional support and leadership. The empirical results demonstrate that both variables are generally rated as highly evident within the institution. Specifically, value proposition innovation, infrastructure configuration, and institutional support obtained notably higher scores, whereas revenue model transformation remained relatively weak. Significant demographic differences were observed exclusively in infrastructure and capability reconfiguration across age and length of service. Furthermore, correlation analysis revealed significantly positive relationships between all dimensions of the two variables. These findings strongly indicate that digital education is inextricably integrated with business model restructuring. Ultimately, aligning digital education strategies with comprehensive institutional innovation is crucial for improving sustainability, inclusiveness, and long-term competitiveness in the modern digital education ecosystem.
Analysis and Governance Research on the Motives and Governance of Cross-Border Financial Fraud by Listed Companies
This research article investigates the motives and governance mechanisms related to cross-border financial fraud perpetrated by listed companies. We analyze the contributing factors, including regulatory arbitrage, information asymmetry, and weakened internal controls, that enable such fraudulent activities. Furthermore, we examine the effectiveness of existing governance structures and propose enhanced oversight mechanisms to mitigate the risk of cross-border financial fraud. The study employs a multi-method approach, combining quantitative analysis of financial data with qualitative case studies of prominent instances of cross-border fraud. Our findings provide valuable insights for regulators, investors, and corporate boards seeking to strengthen governance practices and prevent future occurrences of cross-border financial misconduct. The policy implications of this research suggest the need for greater international cooperation, enhanced regulatory scrutiny, and improved corporate accountability in the global financial marketplace. The study also contributes to the academic literature on corporate governance and financial crime by providing an in-depth analysis of the specific challenges posed by cross-border fraud.
Copyright (c) 2026 Xuanming Zhang (Author)
Research on the Necessity of Sustainable Development of E-commerce Enterprise in Shandong from the Perspective of Green Ecology
As an important way to adapt to the national sustainable development strategy in the new era, the role of green enterprise development cannot be ignored. In recent years, the rapid expansion of the digital economy has brought unprecedented opportunities and environmental challenges, making the integration of ecological considerations into business operations essential. Exploring this mode has become one of the important directions of contemporary business research, particularly within the rapidly evolving e-commerce sector. This research begins with the fundamental concept of the green enterprise, systematically defining its core characteristics and operational mechanisms within the context of modern digital trade. Subsequently, it discusses the current situation and the critical necessity of fostering green e-commerce enterprises specifically in Shandong Province, a major economic hub facing significant transitional pressures. By conducting a comprehensive analysis of the regional market dynamics and environmental policies, this study identifies and evaluates the existing problems hindering sustainable transformation, such as high carbon footprints in logistics, excessive packaging waste, and a lack of green consumer awareness. Finally, the paper explores and proposes a robust future development strategy tailored for e-commerce enterprises in Shandong. These strategic recommendations emphasize the adoption of green supply chain management, the implementation of eco-friendly packaging solutions, and the enhancement of corporate environmental responsibility. Ultimately, this research provides valuable theoretical insights and practical guidelines for policymakers and business leaders aiming to achieve a harmonious balance between economic profitability and ecological sustainability in the e-commerce industry.
Cross-Cultural Management in Multinational Enterprises: A Case Study of the Coca-Cola Company
With the rapid acceleration of economic globalization, multinational enterprises increasingly operate across highly complex and culturally diverse environments, making effective cross-cultural management a decisive factor influencing overall organizational performance, competitive resilience, and long-term sustainability. Inherent cultural differences fundamentally shape employees' core values, interpersonal communication patterns, leadership expectations, and intrinsic motivational preferences. Consequently, these dynamics create multifaceted challenges for multinational enterprises seeking to seamlessly integrate globally dispersed workforces while maintaining operational coherence. This paper systematically explores how multinational enterprises can achieve superior cross-cultural management through the implementation of adaptive and culturally sensitive human resource management strategies. Particular emphasis is placed on the critical domains of talent recruitment, cross-cultural training programs, tailored motivation mechanisms, and comprehensive employee development frameworks. Drawing on established theoretical paradigms of cross-cultural management and international human resource management, this study adopts a rigorous qualitative, theory-informed case analysis approach, utilizing the Coca-Cola Company as a primary illustrative example. The in-depth analysis demonstrates that culturally responsive yet strategically integrated human resource practices empower multinational enterprises to effectively transform cultural diversity from a potential operational friction point into a robust source of organizational flexibility, heightened employee engagement, and sustained competitive advantage. Ultimately, this paper significantly contributes to the broader cross-cultural management literature by explicitly highlighting the strategic, indispensable role of sophisticated human resource systems in successfully balancing the dual imperatives of global integration and local responsiveness within modern multinational enterprises.
Autoformer-Based Sales and Inventory Forecasting for Cross-Border E-commerce: A Time Series Deep Learning Approach
Abstract. Accurate forecasting of product sales and inventory is a critical task for cross-border e-commerce platforms, where demand volatility, long logistics cycles, and dynamic pricing present significant challenges for efficient supply chain management. Traditional statistical and short-term machine learning models often fail to capture long-term dependencies and complex seasonal variations in sales data, leading to inaccurate demand planning and inefficient inventory allocation. To address this limitation, we propose a forecasting framework based on the Autoformer model, a deep learning architecture designed for long-sequence time series prediction. The model leverages series decomposition blocks to separate trend and seasonal components, and employs an auto-correlation mechanism to enhance the capture of periodic demand patterns in product sales and inventory turnover. We evaluate the framework on a real-world cross-border e-commerce dataset comprising transaction-level order volume, prices, inventory records, and external market indicators such as exchange rates and shipping costs. Experimental results show that the proposed Autoformer-based approach achieves superior forecasting accuracy compared with baseline models including LSTM, Transformer, and Informer. Specifically, our model reduces prediction error with a Mean Absolute Error (MAE) of 18.6 and a Root Mean Square Error (RMSE) of 25.4, representing a 17.3% improvement over the best-performing baseline. These findings highlight the potential of Autoformer for enhancing sales forecasting, reducing stockouts, and improving inventory turnover in cross-border e-commerce platforms, thereby supporting more effective logistics management and strategic decision-making.
Copyright (c) 2026 Tianran Li, Xinyu Li, Yuanliang Qu (Author)
Independent R&D of Digital Management Platforms for Real Estate: Innovation in Format Optimization, Investment Decision-making and Benefit Control Systems
This review paper synthesizes existing literature on the independent research and development (R&D) of digital management platforms for real estate, focusing on innovation across format optimization, investment decision-making, and benefit control systems. The real estate industry faces unique challenges in adapting to digital transformation, requiring platforms tailored to its specific needs. This paper investigates the historical evolution of these platforms, highlighting key milestones and paradigm shifts. Core themes explored include innovative format optimization techniques such as BIM integration and spatial data analytics, advancements in investment decision-making through AI-driven predictive models, and sophisticated benefit control systems incorporating blockchain and smart contracts. A comparative analysis of different platforms reveals their strengths, weaknesses, and the persistent challenges hindering widespread adoption. These include data security concerns, interoperability issues, and the need for skilled personnel. Finally, the paper presents future perspectives on the R&D of real estate digital management platforms, emphasizing the potential of edge computing, IoT integration, and personalized user experiences. This review aims to provide a comprehensive understanding of the current state-of-the-art and guide future research and development efforts in this rapidly evolving field. Ultimately, this paper facilitates the diffusion of essential knowledge on the applications of digital optimization, investment management, and smart benefit control in the real estate sector.
RAPS-Net: A Risk-Aware CNN-LSTM Framework for Cross-Domain Risk Prediction and Dynamic Security Control in Cloud Payment Supply Chains
Cloud payment systems are increasingly deployed on cloud platforms and tightly coupled with complex supply chain ecosystems, including suppliers, logistics services, and financial intermediaries. In such environments, security risks are not only driven by malicious payment behaviors, but are also significantly amplified by misconfigured Identity and Access Management (IAM) policies and supply chain disruptions. In particular, excessive authorization, permission drift, and abnormal access behaviors may propagate across payment supply chains under volatile operational conditions, posing substantial threats to payment availability and security. However, existing payment security mechanisms typically rely on static access control or isolated risk analysis, lacking predictive capability and adaptive security responses. To address these challenges, this paper proposes RAPS-Net, a risk-aware CNN-LSTM framework that integrates permission risk sensing, cross-domain risk prediction, and dynamic security control for cloud payment supply chains. RAPS-Net jointly models IAM permission risks, supply chain operational risks, and payment system states, and employs a hybrid CNN-LSTM architecture to capture short-term cross-domain risk coupling patterns as well as long-term risk evolution trends. Based on the predicted risk levels, a risk-aware access control mechanism is designed to dynamically adjust cloud permissions and proactively mitigate potential security threats. Experimental results on an integrated cloud payment supply chain dataset demonstrate that RAPS-Net consistently outperforms representative baseline models. Specifically, RAPS-Net reduces RMSE to 0.108, achieving approximately 19% improvement over LSTM and 11% over CNN-LSTM, while obtaining the highest F1-score of 0.87. These results validate the effectiveness of jointly modeling permission risks and supply chain dynamics for accurate risk prediction and adaptive security control in cloud payment environments.
Copyright (c) 2026 Yue Hao, Jiawei Xu, Shuhan Liao (Author)
Profit-Oriented Production and Pricing Optimization for Manufacturing Enterprises Using Proximal Policy Optimization
In modern intelligent manufacturing, enterprises face increasingly dynamic market environments where production costs, consumer demand, and pricing strategies interact in complex, nonlinear ways. Traditional static or rule-based decision models fail to capture these interdependencies, often leading to suboptimal profit margins and excessive inventory accumulation. To address this challenge, this study proposes a profit-oriented production and pricing optimization system for manufacturing enterprises based on Proximal Policy Optimization (PPO), an advanced reinforcement learning algorithm well-suited for continuous control and dynamic environments. The proposed system autonomously learns optimal production quantities and pricing strategies through interactions with a simulated economic environment characterized by stochastic demand, fluctuating raw material costs, and inventory constraints. By modeling the problem as a Markov Decision Process, the PPO agent optimizes a reward function that balances short-term profitability with long-term inventory stability. Experimental results on a simulated manufacturing dataset demonstrate that the proposed PPO-based optimization system achieves an 12.8% improvement in cumulative profit, a 16.4% reduction in inventory risk, and a 50.9% decrease in final loss compared with the Deep Q-Network (DQN) baseline. Moreover, the PPO-P³OS framework exhibits highly stable convergence and superior adaptability under dynamic market fluctuations, highlighting its effectiveness in real-time production and pricing decision-making for manufacturing enterprises. These results highlight the model's ability to dynamically adapt to market volatility and enhance decision-making efficiency. This research contributes to the integration of reinforcement learning and business analytics, offering a scalable, data-driven framework for real-time profit optimization in intelligent manufacturing systems.
Copyright (c) 2026 Pingmei Fan, Hanwu Li, Mengdie Hu (Author)
Research on the Collaborative Development of Rural Tourism and Landscape Architecture Development and Operation - Based on the Logical Analysis of Landscape Architecture Operation Boosting Rural Tourism
Against the backdrop of rural revitalization and the high-quality integration of culture and tourism, rural tourism has become a crucial carrier for activating endogenous rural dynamics and promoting the flow of urban-rural elements. Landscape architecture serves as the core support for enhancing landscape quality, ecological protection, and the inheritance of local culture in rural tourism. This paper takes the synergistic symbiotic relationship between the two as its entry point to elucidate the internal logic by which landscape architecture operations empower rural tourism development. It analyzes existing problems in their integrated development, such as disconnection in planning, singular operation models, and insufficient cultural excavation. Combining typical case studies, the paper proposes optimization strategies to provide theoretical and practical references for improving the quality and efficiency of rural tourism and supporting comprehensive rural revitalization.
Copyright (c) 2026 Sihai Wu, Qiong Wu (Author)
Academic Merit and Financial Philanthropy: A Study of Admissions Integrity and Institutional Autonomy at the University of Oxford
This study examines whether Oxford University should lower its admissions standards to attract the children of generous benefactors. The article first analyzes why this proposal may appear reasonable on the surface but would, in reality, severely damage Oxford's academic reputation, economic efficiency, and social fairness. By examining the financial status of Oxford, the author points out that the university's substantial funding does not depend on the enrollment of benefactors' children, but rather on its outstanding reputation and academic achievements. Further analysis shows that lowering admissions standards would lead to a decline in student quality, harm Oxford's global reputation, and even affect its research output and the employment prospects of its graduates. Additionally, the article highlights how such a practice could exacerbate social inequality, undermine educational fairness, and potentially lead to social unrest. Finally, the article emphasizes that Oxford, as a historic academic institution, must uphold its academic integrity and rigorous standards to maintain its leadership position in global higher education.
GenRiskNet: A GenAI-Driven Multi-Source Heterogeneous Data Fusion Framework for Financial Risk Prediction
Modern financial markets are increasingly shaped by fast-evolving information flows, ranging from market micro-structure signals to corporate disclosures, macroeconomic indicators, ESG assessments, and large volumes of high-frequency textual news. Traditional risk-prediction models struggle to jointly model these heterogeneous sources, limiting their ability to capture cross-modal causal drivers and abrupt risk dynamics. To address these challenges, this paper introduces GenRiskNet, a GenAI-driven heterogeneous data fusion framework that integrates large-language-model (LLM)-based event understanding with multi-branch time-series encoding and cross-modal multi-scale attention fusion. GenRiskNet jointly leverages (1) quantitative market features, (2) LLM-extracted financial textual events, (3) macroeconomic indicators, and (4) ESG corporate profiles to improve credit-risk and market-risk forecasting. Experiments conducted on the multi-source financial dataset show that GenRiskNet consistently outperforms LSTM, Temporal Fusion Transformer, and state-of-the-art multimodal fusion baselines across all tasks. The proposed framework achieves a 15.8% improvement in AUC for credit-risk prediction, reduces VaR forecasting error by 12.6%, and delivers a 19.7% gain in F1 score for default-event detection. These results closely align with the characteristics of the heterogeneous dataset and demonstrate the effectiveness of GenAI-driven cross-modal fusion in capturing complex financial risk patterns, confirming GenRiskNet as a robust and scalable framework for next-generation risk prediction.
Copyright (c) 2026 Yikun Zhang, Zishan Bai (Author)
Structural Inefficiencies in Cross-border Digital Payments: The Relationship Between Cost, Speed, Foreign Exchange Fluctuations, and Consumer Behavior
Cross-border digital payments have become an essential component of global commerce and personal remittances. Despite rapid technological adoption, these payment systems continue to exhibit persistent inefficiencies, including elevated transaction costs, delayed settlement, and exposure to foreign exchange fluctuations. Existing discussions often address these issues in isolation, providing limited insight into their structural interdependence. This paper develops a structural and mechanism-based analytical framework to examine the interaction among payment costs, settlement speed, and foreign exchange volatility within cross-border digital payment systems. By positioning settlement time as a central transmission channel, the analysis explains how foreign exchange risk is converted into effective payment costs under different institutional arrangements. The study further incorporates a consumer behavior perspective, demonstrating how cognitive perception, trust, and risk tolerance mediate payment method selection under structural constraints. The findings suggest that settlement speed plays a pivotal role in reinforcing the trade-off between cost efficiency and payment predictability, with implications for both system-level performance and user behavior. Based on this framework, the paper proposes system-level improvement directions aimed at enhancing transparency, reducing structural costs, and mitigating foreign exchange risk exposure. The analytical approach developed in this study provides practical insights for fintech product design, regulatory policy, and the broader evolution of cross-border payment infrastructure.
DFG-JointGNN: A Data-Flow Graph Neural Network for Unified Sales Order Fraud Detection and Sales Forecasting
Sales order systems in e-commerce platforms, enterprise information systems, and supply chain finance generate large-scale transactional data streams that exhibit strong process dependency and complex entity interactions. Fraudulent behaviors such as fake orders, brushing transactions, and refund abuse not only cause direct economic losses but also distort historical sales data, leading to biased and unreliable sales forecasting. Existing studies typically address sales order fraud detection and sales forecasting as independent tasks, overlooking the intrinsic coupling between fraud risk and sales demand in real transaction flows. In this paper, we propose DFG-JointGNN, a data-flow graph neural network that unifies sales order fraud detection and sales forecasting within a single learning framework. We model the full lifecycle of sales orders-including order creation, payment, logistics fulfillment, and settlement-as a temporal heterogeneous data-flow graph, where customers, orders, merchants, payment accounts, and logistics nodes are jointly represented. A relation-aware temporal graph attention network is employed to capture both structural dependencies and temporal dynamics of transaction flows. Fraud detection is performed at the order level, while sales forecasting is conducted at the merchant or product level through a fraud-aware aggregation mechanism that explicitly suppresses the influence of high-risk orders. Experiments on real-world and semi-synthetic sales order datasets show that DFG-JointGNN consistently outperforms state-of-the-art baselines. For fraud detection, it achieves an AUC of 0.956, improving approximately 6.3% over the strongest baseline (Temporal GAT, 0.893). For sales forecasting, it reduces RMSE to 78.4, a 15.2% improvement compared to the fraud-agnostic Temporal GAT model. These results confirm that jointly modeling fraud detection and sales forecasting with transaction data-flow graphs enhances both predictive accuracy and operational robustness.
Copyright (c) 2026 Yuxuan Liu, Shuhan Liu, Hongjing Shao (Author)
Operational Data Integration and Visual Analytics for Decision-Making in SMB Fleets
This research investigates the integration of operational data and visual analytics to enhance decision-making for small and medium-sized business (SMB) fleets. Effective fleet management is critical for SMBs, impacting costs, efficiency, and customer satisfaction. However, many SMBs lack the resources and expertise to fully leverage their operational data. This study explores how data from various sources, including vehicle telematics, maintenance records, fuel consumption, and driver behavior, can be integrated into a unified platform. We then examine how visual analytics techniques, such as interactive dashboards and geospatial visualizations, can be employed to extract actionable insights. The research focuses on developing a practical framework for SMBs to improve fleet performance, reduce operational costs, and enhance customer service through data-driven decision-making. A case study involving a regional delivery fleet is used to demonstrate the effectiveness of the proposed approach. The implementation led to measurable improvements, including an 8% increase in fuel efficiency (miles per gallon), a 15% reduction in unplanned vehicle downtime, and a 20% decrease in hard-braking incidents. This research provides valuable insights and guidance for SMBs seeking to optimize their fleet operations through data integration and visual analytics.
Small and Medium-Sized Businesses (Smbs)
A Literature Review of Enterprise Strategic Management in the Context of Digital Transformation
This literature review synthesizes existing research on enterprise strategic management in the context of digital transformation. It examines the historical evolution of strategic management theories, analyzes the core themes of digital capabilities and organizational agility, and compares different strategic approaches adopted by enterprises undergoing digital transformation. The review identifies challenges such as technological disruptions, talent acquisition, and cybersecurity risks. Finally, it explores future research directions, including the integration of artificial intelligence, blockchain, and the Internet of Things into enterprise strategic planning. This review aims to provide a comprehensive overview for researchers and practitioners interested in understanding and navigating the complexities of strategic management in the digital age.
Conceptual Modeling and Semantic Relations in the Construction of Financial Knowledge Graphs
Financial Knowledge Graphs (FKGs) are increasingly recognized as essential tools for managing and leveraging financial data. This review paper explores the conceptual modeling approaches and semantic relations crucial for their construction. We begin by providing a historical overview of knowledge representation in finance, tracing the evolution from early expert systems to contemporary FKGs. The core of the paper delves into two critical themes: (A) conceptual modeling techniques, including ontologies, entity-relationship diagrams, and semantic networks, and their application to financial data; and (B) the types and roles of semantic relations (e.g., is-a, part-of, influences) in connecting financial entities and concepts. We then present a comparative analysis of these modeling techniques, highlighting their strengths, weaknesses, and challenges in capturing the complexities of financial information. Furthermore, we address the unique challenges of constructing FKGs, such as data heterogeneity, ambiguity, and the dynamic nature of financial markets. The review concludes by discussing promising future research directions, including the integration of machine learning techniques for automated knowledge graph construction and the development of novel semantic relation discovery methods. This paper serves as a comprehensive guide for researchers and practitioners interested in the design and implementation of effective FKGs for various financial applications.
Research on Cross-Regional Collaborative Service Mechanisms in Rural Public Management
In response to the growing demands for governance coordination prompted by the cross-regional flow of rural public affairs, this study systematically explores the operational logic and essential technical elements of collaborative service mechanisms. The research adopts a multi-faceted approach, integrating subject system analysis, process structure modeling, the design of data interconnection mechanisms, and the evaluation of institutional compatibility. The findings reveal that establishing unified process chains, implementing controllable and secure data exchange protocols, and constructing a hierarchical institutional coordination framework collectively contribute to significant improvements in collaborative efficiency, resource allocation, and service responsiveness. Furthermore, these elements support the development of a quantifiable supervision and evaluation system, enabling effective monitoring of inter-regional administrative operations and facilitating adaptive adjustments to policy implementation. By emphasizing both technical integration and institutional harmonization, the proposed framework provides a practical reference for enhancing the systematic and coordinated management of rural public affairs. Overall, the study demonstrates that combining structured process design, robust data connectivity, and compatible institutional arrangements forms a foundation for sustained improvements in cross-regional governance and the equitable distribution of public resources.
CRG-Former: Early Identification of Securities Violations via Causal Relational Graph Transformers across Multi-Market Data
The increasing interconnection of modern financial markets has led securities violations-such as insider trading, market manipulation, and disclosure misconduct-to exhibit cross-market, multi-entity, and temporally progressive characteristics, posing significant challenges to traditional rule-based and post-event regulatory frameworks. In response to the growing demand for proactive and risk-oriented supervision, this paper addresses the task of early identification of securities violations using AI-driven analysis of cross-market multi-source data, with particular relevance to regulatory authorities, brokerage compliance departments, and merger and acquisition funds. We propose CRG-Former (Causal Relational Graph Transformer), a deep learning framework that integrates cross-market financial time-series data, heterogeneous relational graphs among market participants, and causality-aware attention mechanisms to detect potential violations at an early stage. The model employs Transformer-based temporal encoders to capture evolving abnormal trading patterns, heterogeneous graph attention networks to model complex relational dependencies, and causal attention constraints to align model inference with legal notions of behavioral causation. To enhance regulatory usability, CRG-Former further incorporates uncertainty-aware risk prediction, enabling probabilistic early warning rather than deterministic judgments. Experiments on a multi-market dataset integrating equity transactions, derivatives activity, corporate disclosures, and regulatory enforcement records show that CRG-Former achieves an AUC of 0.912, outperforming strong baseline models by over 6%. Moreover, the proposed framework provides an average early warning lead time of 18 trading days before confirmed violations, demonstrating its effectiveness in delivering timely, risk-based, and operationally meaningful signals for AI-empowered securities supervision.
Copyright (c) 2026 Xiaolei Wang, Shuhan Liao (Author)
AI-Assisted Budgeting and Cost Control: A Practical Model for Financial Decision-Making in SMEs
In an increasingly uncertain and competitive business environment, effective budgeting and cost control are critical to the financial sustainability of small and medium-sized enterprises (SMEs). However, traditional budgeting practices in SMEs are often constrained by static planning methods, fragmented data, and heavy reliance on managerial intuition. The rapid development of artificial intelligence (AI) offers new opportunities to enhance financial decision-making through data-driven analysis and predictive capabilities. This paper proposes a practical model for AI-assisted budgeting and cost control tailored to the organizational characteristics and resource constraints of SMEs. Adopting a conceptual and application-oriented approach, the study analyzes how AI can support key financial management processes, including budget forecasting, cost monitoring, and decision support, without focusing on technical algorithms or empirical testing. The proposed model emphasizes data integration, continuous analysis, and human-AI collaboration, positioning AI as a tool that augments managerial judgment rather than replacing it. By illustrating key application scenarios and discussing implementation conditions and potential risks, this paper demonstrates how AI-assisted budgeting and cost control can improve flexibility, transparency, and decision quality in SMEs. The study contributes to the understanding of AI-enabled financial management by offering a scalable and managerially relevant framework that supports more adaptive and informed financial decision-making.
Graph-Based Deep Dive on AI Startup Revenue Composition and Venture Capital Network Effect
Recent years have witnessed unprecedented valuations for AI startups, driven by investor confidence in technological potential rather than established revenue streams. This article provides a conceptual overview of the revenue structures of venture-backed AI startups, focusing on network effects and reciprocal revenue relationships. It also proposes a graph-based deep dive of such a network of N=50 startups and their key investors, showcasing the concrete connectivity through visualization. Network-driven revenue enables rapid early-stage growth by leveraging investor connections and portfolio ecosystems, while reciprocal revenue, in which startups act as each other’s clients, accelerates ARR and strengthens ecosystem cohesion. Despite these advantages, both revenue models carry risks, including customer concentration, revenue volatility, and limited market validation, which may affect long-term valuation sustainability. The discussion highlights the importance for entrepreneurs and investors of assessing revenue composition, quality, and scalability, rather than relying solely on headline metrics such as ARR. By balancing early gains from network effects and reciprocal arrangements with broader market adoption, AI startups can enhance growth resilience, and investors can make more informed decisions regarding valuation and long-term potential.
Copyright (c) 2026 Jingyao (Lux) Zhao (Author)
The Application of Data-Driven Financial Risk Management in Multinational Enterprises
With the intensification of global economic integration and market complexity, the financial risks of multinational enterprises will face severe challenges. The financial risk management system based on big data helps multinational enterprises track, predict and handle financial risks in advance. This article mainly introduces the basic concepts and core technical system of data-driven financial risk management, and elaborates on the specific implementation of key technologies such as data collection, risk prediction and assessment, and data visualization in multinational enterprises. The utilization of advanced big data technologies such as machine learning and statistics helps multinational enterprises identify risk points more accurately and improve decision-making efficiency. The main purpose of this article is to propose a scientific and comprehensive financial risk management system to multinational enterprises, helping them manage financial risks better in the complex and changeable global market and promoting their sustainable and stable development.
Business Model Innovation in China's Cultural Heritage Sector: An Empirical Study of Intellectual Property Commercialization and Organizational Governance
This study investigates business model innovation in China's cultural heritage sector, with a particular focus on the commercialization of intellectual property (IP) and organizational governance. Over recent decades, the integration of cultural heritage into market-oriented practices has created both opportunities and challenges for cultural organizations seeking to balance preservation with economic value creation. By examining emerging business models, current approaches to IP commercialization, and supporting governance mechanisms, this research provides insights into the effective management of cultural assets in a complex legal and regulatory environment. The study highlights how strategic IP management and organizational innovation can enhance the economic potential of cultural heritage resources while ensuring their long-term protection, offering both theoretical and practical guidance for stakeholders in the industry.
A FEDformer-Based Hybrid Framework for Anomaly Detection and Risk Forecasting in Financial Time Series
Financial markets are inherently volatile and prone to sudden disruptions such as market crashes, flash collapses, and liquidity crises. Accurate anomaly detection and early risk forecasting in financial time series are therefore crucial for preventing systemic instability and supporting informed investment decisions. Traditional deep learning models, such as LSTM and GRU, often fail to capture long-term dependencies and complex periodic patterns in highly non-stationary financial data. To address this limitation, this study proposes a FEDformer-Based Hybrid Framework for Anomaly Detection and Risk Forecasting in Financial Time Series, which integrates the Frequency Enhanced Decomposed Transformer (FEDformer) with a residual-based anomaly detector and a risk forecasting head. The FEDformer module models temporal dynamics in both time and frequency domains, decomposing signals into trend and seasonal components for improved interpretability. The residual-based detector identifies abnormal fluctuations by analyzing prediction errors, while the risk head predicts potential financial distress using learned latent embeddings. Experiments conducted on the S&P 500, NASDAQ Composite, and Brent Crude Oil datasets (2000-2024) demonstrate the superiority of the proposed model over benchmark methods, achieving an 15.7% reduction in RMSE and a 11.5% improvement in F1-score for anomaly detection. These results confirm the model's effectiveness in capturing financial volatility, enabling reliable early-warning systems for market crash prediction and risk management.
Copyright (c) 2026 Ziling Fan, Ruijia Liang, Yiwen Hu (Author)
A Study on the Influence of Continuous Purchase Intention of Users of Paid Courses of Sports App in the Perspective of Coi Framework
The rise of online information technology has spurred the swift advancement of sports app technology in China. Due to various factors, such as the epidemic, more people are gradually opting for sports app courses. Despite the increasing number of registrants every year, the percentage of paying customers does not grow, and the retention rate of paying customers is low. The presence of a sense of community is a crucial factor that affects the effectiveness of paid sports app courses. To investigate the impact of CoI presence on the willingness to continue learning through sports app paid courses, the study utilized the ACSI model and ECM model. The research team distributed online questionnaires to the top three paid sports app user groups in China, receiving 353 valid responses. They employed structural equation modeling and mediating effect analysis to determine the structural and quantitative relationships between variables. The findings of the study reveal that the intention to continue purchasing sports app paid courses is significantly influenced by perceived quality and CoI satisfaction. Expectation confirmation, perceived quality, and perceived value significantly affect the CoI satisfaction of sports app paid courses and indirectly impact the intention to continue purchasing, with CoI satisfaction acting as the mediating variable. Additionally, expectation confirmation has a significant impact on perceived quality and perceived value. Based on these results, the study provides recommendations to foster the continuous purchase intention of users of paid sports app courses.
Copyright (c) 2025 Lin Shen, Mingming Li (Author)
Supply Chain Restructuring and Competitive Strategies of Chinese Window and Door Brands in the North American Market
This study investigates how global supply chain restructuring reshapes the competitive strategies of Chinese window and door brands seeking sustainable development in the North American market. As geopolitical tensions, regionalization trends, sustainability requirements, and technological transformation intensify, Chinese firms face both opportunities and constraints in overseas expansion. Guided by supply chain resilience theory, international business strategy, and branding frameworks, this paper analyzes strategic pathways that can strengthen competitiveness under the emerging global supply chain landscape. The research reveals that competitive advantages increasingly depend on diversified supply chain layouts, localization-oriented operational adjustments, compliance with regulatory standards, and higher-value brand positioning. The findings highlight that Chinese window and door firms must move beyond cost-driven models and adopt integrated strategies that emphasize technological upgrading, digital coordination, and sustainable value creation. The study contributes to ongoing discussions on the upgrading trajectory of Chinese manufacturing and offers strategic recommendations for enhancing long-term competitiveness in North America.
Research on Innovation of Ai-Driven Investment Decision-Making Paradigm in Primary Market
The primary market plays a critical role in allocating capital to emerging enterprises and innovative startups, yet traditional investment decision-making faces challenges such as information asymmetry, subjective bias, and inefficiency. This review explores the transformative potential of artificial intelligence (AI) in primary market investment, examining applications in venture capital and private equity across deal sourcing, due diligence, portfolio management, and exit planning. It presents a comprehensive AI-driven investment decision framework that integrates multi-source data acquisition, machine learning, deep learning, natural language processing, and graph-based modeling to enhance predictive accuracy and decision support. Key challenges, including data quality, privacy, interpretability, talent gaps, and ethical considerations, are discussed alongside future directions such as multi-modal data fusion, reinforcement learning, explainable AI, and human-AI collaborative strategies. The review highlights AI’s capacity to systematize, optimize, and intelligentize investment processes, offering new pathways for efficiency, risk management, and strategic value creation in primary market activities.
Using Big Data Analysis to Optimize the Financing Structure and Capital Allocation of Energy Enterprises
With the gradual development of big data technology, the application of big data technology has a certain impact on the financing structure and capital allocation of energy enterprises. Through data integration, the information big data platform establishes an intelligent data analysis model, which can more effectively optimize financing methods, accurately position investment directions, and promptly determine the location of investment risks. This paper aims at the problems of concentrated funds, investment deviation and low risk awareness of current energy enterprises, and proposes a big data aggregation approach to solve them, in order to improve the effectiveness of the company's financial management, achieve the goal of structural adjustment.
SeqUDA-Rec: Sequential User Behavior Enhanced Recommendation via Global Unsupervised Data Augmentation for Personalized Content Marketing
Personalized content marketing has become a central strategy for digital platforms seeking to deliver highly relevant advertisements and recommendations. However, conventional recommendation systems often rely heavily on sparse supervised signals derived from explicit user feedback and are easily affected by noisy or unintentional interactions, resulting in unstable predictions. To overcome these limitations, we propose SeqUDA-Rec, a unified deep learning framework that incorporates sequential user behavior modeling with global unsupervised data augmentation. The framework begins by constructing a Global User-Item Interaction Graph (GUIG) from all historical behavior sequences, enabling the extraction of both local item transitions and global cross-user relational structures. A graph contrastive learning module is introduced to learn noise-resistant representations by maximizing agreement across multiple graph views. Meanwhile, a Transformer-based sequential encoder captures users' evolving interests and long-term dependencies within interaction trajectories. To further mitigate the challenges of limited labeled data and behavior sparsity, SeqUDA-Rec integrates a GAN-based augmentation module, which generates realistic synthetic sub-sequences to enrich training diversity and improve model generalization. We evaluate SeqUDA-Rec on two large-scale advertising datasets-Amazon Ads and TikTok Ad Clicks-covering both stable e-commerce environments and fast-changing short-video scenarios. Experimental results show that our model consistently outperforms strong baselines including SASRec, BERT4Rec, and GCL4SR, achieving 6.7% improvement in NDCG@10 and 11.3% improvement in HR@10. These results demonstrate that SeqUDA-Rec effectively enhances recommendation robustness, alleviates noise sensitivity, and provides a powerful solution for real-world personalized content marketing.
Copyright (c) 2025 Ruihan Luo, Xuanjing Chen, Ziyang Ding (Author)
Digital Transformation Strategy and Platform Capability Building: Evidence from the Healthcare and Financial Industries
Successful digital transformation is a strategic imperative across all industries and hasreshapedhow organizations create value, govern data, and engage with ecosystems. This paper provides a comprehensive review of digital transformation strategies and platform capability building in the healthcare and financial sectors. By analyzing drivers, challenges, and emerging technologies, the study highlights the critical role of platform architecture, governance, and ecosystem integration in enabling sustainable innovation. Comparative analysis reveals key commonalities, such as the centrality of trust and regulatory compliance, as well as distinctions, including the public-interest-driven nature of healthcare versus the market-oriented dynamics of finance. The findings offer theoretical insights into dynamic capabilities and ecosystem orchestration, as well as practical guidance for managers and policymakers seeking to align strategy, capability, and governance in digital transformation initiatives. Future research directions emphasize quantitative modeling, longitudinal studies, and cross-sector policy analysis to deepen understanding of digital maturity and platform evolution.
Copyright (c) 2025 Yutian (Summer) Cai (Author)
Does Artificial Intelligence Promote the Business Performance of Agricultural Enterprises
Artificial intelligence (AI) plays a pivotal role in advancing the development of agricultural enterprises and is essential for enhancing their business performance. This study empirically investigates the influence of AI on the operational performance of agricultural firms in China, drawing on data from A-share listed agricultural companies on the Shanghai and Shenzhen stock exchanges from 2003 to 2023. A fixed-effects model is employed to examine both the effects of AI and the underlying mechanisms. The empirical results show that AI significantly boosts the business performance of agricultural enterprises, with particularly pronounced effects among non-high-tech firms, non-heavy-pollution enterprises, and asset-intensive industries. AI also exerts a stronger positive impact on firms in their growth stage, whereas its effect on mature enterprises is not statistically significant. Mechanism analysis reveals that AI enhances performance by easing financing constraints, strengthening organizational resilience, and promoting collaborative R&D. Moreover, policy measures-such as government innovation subsidies-substantially amplify the positive impact of AI on firm performance. Based on these findings, the study recommends strengthening AI-driven technological innovation and application, designing differentiated policy measures tailored to the needs of various types of agricultural enterprises, enhancing complementary capabilities for smart transformation, and optimizing organizational structures to further unlock AI's potential in improving business performance.