Economics and Management Innovation (EMI) is an international, refereed, double-blind peer-reviewed, open-access 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.
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.
Changes in China's Winter Sports Tourism Industry After the Beijing Winter Olympics: A Big Data Perspective
With the successful hosting of the 2022 Beijing Winter Olympics, China's winter sports tourism industry has entered a period of accelerated development. This study uses big data analytics to examine the long-term impacts of the Winter Olympics on market demand, industrial structure, and cultural integration. Data related to the keyword "winter sports tourism industry" were collected from Baidu, Google, and Sina Weibo from February 21, 2022, to November 4, 2024. Using Textom, keyword frequency analysis, TF-IDF weighting, and CONCOR clustering, the study identifies core themes and development patterns in the post-Olympic era. Results show that "economy," "market," "sustainability," and "culture" are key topics driving industry discourse. Four clusters were identified: market expansion, cultural tourism integration, infrastructure development, and international cooperation. Findings indicate that the Beijing Winter Olympics significantly enhanced public attention and stimulated new business opportunities in winter tourism. This research provides empirical evidence to support policy formulation, industrial planning, and sustainable development in China's winter sports tourism sector.
Evaluating the Impact of Financial Policy News on Market Responses Using Natural Language Processing
This study presents a practical and replicable framework for analyzing the impact of financial policy news on market behavior by integrating natural language processing (NLP) techniques with market prediction models. By extracting sentiment scores, sentiment volatility, news volume, topic intensity, and policy uncertainty indicators from policy texts, the research transforms unstructured information into quantitative features that help explain market returns and volatility. Using simulated yet realistic data, models such as linear regression, random forest, and LSTM are employed to evaluate the predictive value of these features, showing that NLP-derived indicators enhance the forecasting of market responses. While the study confirms the usefulness of textual features in capturing policy-driven market dynamics, it also acknowledges limitations related to data scale and model generalizability. The findings offer methodological insights that may support investors, financial institutions, and policymakers in interpreting and anticipating market behavior under policy influence.
Prediction Framework for E-Commerce Platform Sales Data Based on Informer: A Study on Furniture Sales on Amazon E-Commerce Platform
Accurate sales forecasting is a critical requirement for e-commerce platforms, as it supports efficient inventory management, dynamic pricing, and targeted marketing strategies. Nonetheless, forecasting is inherently challenging due to the volatility of consumer demand, the impact of external factors such as economic indicators and weather conditions, and the occurrence of promotional events. To address these complexities, this study proposes a prediction framework based on the Informer model, a Transformer-based architecture specifically designed for long-sequence time series forecasting. The framework leverages weekly sales data of five furniture categories from the Amazon e-commerce platform spanning 2015 to 2020, combined with auxiliary features including price, discount rate, consumer price index (CPI), and weather-related variables. By integrating both temporal dependencies and exogenous influences, the model captures intricate patterns in sales dynamics. Experimental results demonstrate that the Informer model significantly outperforms traditional baselines, including LSTM, GRU, and Prophet, achieving a mean absolute error (MAE) of 37.3 and a root mean square error (RMSE) of 52.7. These findings highlight the model’s effectiveness in enhancing e-commerce sales forecasting and underscore the potential of advanced deep learning techniques to improve operational decision-making for digital retail platforms.
Copyright (c) 2025 Lianzhen Wang, Xi Zhang, Lu Jiang (Author)
Research on Digital Overall Solution and Transformation Path of Fine Chemical Industry
The fine chemical industry, characterized by complex multi-step processes and high-value products, faces challenges in efficiency, quality, and sustainability under traditional production methods. This review examines the application of digital technologies—including Industrial Internet of Things (IIoT), big data analytics, artificial intelligence, cloud and edge computing, and automation and robotics—in addressing these challenges. It highlights the benefits of digital transformation for process optimization, quality enhancement, cost and energy reduction, safety improvement, and environmental sustainability. A stepwise transformation roadmap, from basic automation and data collection to intelligent optimization and predictive operations, is proposed to guide enterprises through effective digitalization. Case studies and quantitative data illustrate the tangible outcomes of digital adoption, while future trends emphasize AI-driven autonomous production, integration with green chemistry, cross-industry collaboration, and platform economies. This review provides actionable insights for policymakers, managers, and engineers aiming to achieve sustainable growth and competitive advantage in the fine chemical industry through comprehensive digital solutions.
User-Profile-Driven Optimization of Marketing Services for Biohealth Products and B2B Customer Relationship Management Strategies
The Bio-Health Product sector, characterized by rapid technological advancements and intense market competition, faces unique challenges in formulating marketing strategies and managing B2B customer relationships. Traditional, generalized marketing models are no longer sufficient to meet the precise and diverse needs of B-end clients, including hospitals, professional clinics, retail pharmacy chains, and corporate wellness centers. This paper argues that a shift toward strategic, data-driven user profiling is essential for enhancing marketing effectiveness and fostering long-term B-client partnerships. By moving beyond basic firmographic information and developing dynamic, multi-dimensional profiles of client institutions and end-users, companies can achieve unprecedented precision in their marketing initiatives. This study outlines a comprehensive approach to creating B-client profiles, incorporating critical dimensions such as organizational operations, clinical performance, economic drivers, and end-user demographics. These dimensions directly inform the optimization of the key components of the marketing mix-product, price, place, and promotion-enabling the transformation of standard offerings into tailored service solutions. Furthermore, the paper highlights the intrinsic link between user-profile-based marketing and B-client retention. This approach redefines the vendor-client relationship: rather than simply exchanging products, vendors and clients engage as collaborative partners who co-create value through effective cooperation. In the knowledge-intensive bio-health field, sustainable competitive advantage arises not merely from superior products, but from delivering actionable insights and personalized services derived from a deep understanding of clients.
Media Exposure and Corporate Financing Performance of FinTech Firms: An Empirical Study
The rapid expansion of financial-technology (fintech) firms has reshaped global finance, yet the influence of media exposure on their corporate financing performance remains insufficiently explored. Studies on traditional firms indicate that favorable media coverage can reduce information asymmetry and lower financing costs, but there is limited understanding of how tone, timing, and regulatory narratives affect outcomes in the fintech context. To address this gap, this study develops a Media-Signal-Finance (MSF) framework, integrating media-signaling theory with mechanisms of information-asymmetry reduction, and applies a comparative case-study approach to Ant Group (China) and Revolut (United Kingdom). Media data from 2018 to 2024 were coded for tone, intensity, and timing, and matched with financing indicators such as valuation and fundraising success rate. The results reveal that positive, well-timed coverage strengthens investor confidence and enhances fundraising outcomes, whereas negative or regulatory-critical exposure undermines credibility and delays financing processes. This study advances theoretical understanding by linking media signaling with behavioral and institutional factors and provides practical insights for managers, investors, and regulators to strategically manage media visibility during fintech financing activities.
Synergistic Premium Effects of Creativity and Sustainability in E-Commerce: Evidence from Consumer Willingness to Pay
The increasing emphasis on sustainable consumption and the widespread application of creative design in e-commerce underscore the need to understand how these two elements jointly affect consumers' willingness to pay (WTP). Although previous research has explored creativity and sustainability as independent drivers of consumer behavior, little attention has been paid to their interactive effects or the psychological pathways through which they generate premium value. This study bridges this gap by adopting a multi-method approach that integrates cross-platform analyses of Etsy, Amazon Handmade, and Tmall Green with a survey experiment involving 600 participants. The results indicate that creativity enhances both the hedonic and symbolic value of products, while sustainability strengthens their ethical and environmental appeal. When combined, these two attributes create a synergistic premium effect that exceeds the sum of their individual influences. Mediation analysis further reveals that perceptions of authenticity and feelings of moral satisfaction partially account for this effect. Overall, the study enriches value perception theory by linking hedonic and ethical dimensions and offers practical guidance for e-commerce platforms and sellers aiming to increase product premiums through the integration of creative design and credible sustainability initiatives.
Designing the Future of Shopping: An Immersive Simulation Framework for Digital Commerce Strategy and Retail Transformation
This paper proposes an immersive simulation framework for digital business strategy and retail transformation. The framework integrates digital twins, scenario factories, and VR/AR/WebXR front ends with in-situ analytics and computational capabilities. Built on a hyperconverged, on-demand, low-latency infrastructure, it supports multiple users while maintaining restricted telemetry. A KPI ontology unifies four critical dimensions: business, operations, experience, and governance. A traceable experimental process-including process traceability, versioned scenarios, and pre-registered metrics-ensures auditability and reproducibility. The framework combines enterprise-level platform practices with evaluation methods from design science research, such as analysis demonstrations, scenario walkthroughs, and Delphi expert reviews, to validate its feasibility. Results demonstrate that the framework achieves orders-of-magnitude acceleration with minimal data movement, significantly reducing offline testing costs and shifting decision-making from traditional "offline A/B testing with local BI" to a "simulation-first approach with closed-loop measurement."
Automated Reasoning and Technological Innovation in Cloud Computing Security
Cloud technology, as a major advancement in the field of information technology, has gained widespread adoption due to its real-time capabilities and flexibility. However, security concerns remain a key factor limiting its further development. Automated reasoning, leveraging advanced graph construction, rule-based management, and pattern recognition, provides a robust foundation for safeguarding cloud computing systems. In addition, the integration of big data has significantly enhanced the speed and accuracy of detecting abnormal behaviors. Technological innovations, including the application of deep learning for threat detection, multi-source data fusion, and dynamic access control, have substantially strengthened the security and stability of cloud computing infrastructures. Moreover, innovative privacy-preserving computing models offer transformative solutions for the secure processing and sharing of sensitive data. This article provides a comprehensive analysis of the latest trends and practical applications of automated reasoning technologies in cloud computing security, focusing on three aspects: theoretical foundations, key technologies, and avenues for innovation.
Research on the Application of AI in Enterprise Financial Risk Management and Its Optimization Strategy
With the rapid advancement of information technology, artificial intelligence (AI) has increasingly become a pivotal tool in enterprise financial risk management, offering the potential to enhance both efficiency and decision-making capabilities. This paper examines the application of AI in key areas such as risk identification, data processing, and predictive evaluation, highlighting its role in transforming traditional risk management practices. Despite its promising potential, enterprises often face practical challenges, including insufficient adaptability of AI models to dynamic financial environments, inconsistent quality and integration of data management processes, and limited institutional support for comprehensive AI deployment. To address these challenges, this study proposes targeted improvement strategies, including the development of tailored AI models that align with specific organizational contexts, the optimization of data processing workflows to ensure reliability and completeness, and the establishment of standardized management systems to facilitate scalable and sustainable AI integration. By providing both theoretical insights and practical implementation guidelines, this paper aims to support enterprises in constructing a robust AI-driven financial risk management framework that effectively balances predictive accuracy, operational efficiency, and strategic decision-making.
Research and Strategy Optimization of Quantitative Trading Live Trading Based on Index Reconstruction
In recent years, index-enhanced investment strategies have grown increasingly sophisticated and complex, with the application of index reconstruction techniques becoming more widespread in quantitative trading. Nevertheless, the effectiveness of these strategies in real-world trading can be constrained by factors such as index fitting bias, model overfitting, and transaction costs. This article builds on the theoretical foundation of integrating index reconstruction with quantitative strategies and provides a comprehensive analysis of the challenges encountered in actual market operations. To address these issues, corresponding optimization methods are proposed, including the development of a dynamic index reconstruction mechanism, the use of regularization techniques to enhance model stability, and the refinement of execution path management to reduce costs. Empirical research is conducted to verify the effectiveness of these strategies, offering both theoretical insights and practical guidance for optimizing index-enhanced strategies in real-market environments.
Emotional Analysis and Strategy Optimization of Live Streaming E-Commerce Users Under the Framework of Causal Inference
Current emotion recognition models largely rely on similarity-based approaches, which limits their ability to explain the underlying causes of emotional changes and reduces the reliability of subsequent strategy adjustments. To address these limitations, this study proposes a unified framework that integrates multiple forms of emotion modeling with dynamic strategy regulation. Within this framework, novel methods such as reverse reasoning and causal embedding are introduced within graph neural networks to explicitly capture the relationships between intervention variables and emotional states. By incorporating causal control attention mechanisms and the NOTEARS algorithm, the framework enhances the fusion of heterogeneous information and improves the discrimination between critical influencing factors. Furthermore, a causal evaluation and cyclic regulation mechanism is constructed to enable continuous assessment and adjustment of emotional interventions. This comprehensive approach not only provides a robust computational foundation for emotion modeling but also offers practical guidance for developing intelligent, controllable, and adaptive emotion management systems. The proposed framework demonstrates potential for applications in personalized mental health support, human-computer interaction, and affective computing systems, where understanding the causal dynamics of emotion is essential for effective intervention.
Research on Architecture Optimization of Intelligent Cloud Platform and Performance Enhancement of MicroServices
Intelligent cloud platforms have become a cornerstone in managing and operating complex business process systems. However, the microservice architectures underlying these platforms often face significant challenges in real-world applications, including tight coupling between services, intricate inter-service communication, inefficient resource scheduling, and difficulties in tracing and diagnosing service calls. To address these issues, this study proposes a comprehensive framework that integrates multiple key strategies: architectural decoupling to reduce interdependencies, advanced service governance for standardized and reliable service management, optimized resource scheduling to improve system efficiency, and end-to-end call chain diagnosis to enhance fault detection and performance monitoring. Based on these strategies, a high-performance intelligent cloud platform model is established, capable of achieving both operational efficiency and system reliability. The findings of this research not only provide a solid theoretical foundation but also offer practical guidelines for ensuring the stable operation, proactive maintenance, and intelligent management of microservice-based cloud platforms, ultimately supporting scalable and resilient enterprise IT infrastructures.
The Enabling Path of Private Equity Funds in the Growth Process of Emerging Market Enterprises
This study investigates how private equity (PE) funds enable the growth of emerging market enterprises through multiple empowerment pathways. It first develops a theoretical framework by outlining the PE fund operation model-capital raising, project screening, post-investment management, and exit-and by interpreting their role in bridging resource gaps faced by emerging market firms through the lens of resource-based theory. Considering the distinctive characteristics of emerging markets, such as underdeveloped financial systems and limited human capital, the study identifies five enabling pathways: financing empowerment (long-term capital and fintech-enabled channels), financial empowerment (standardized accounting and internal control systems), managerial empowerment (governance optimization and strategic goal-setting), human capital empowerment (talent referral and training), and resource integration empowerment (industry-based cooperation networks). It further highlights three key challenges-macroeconomic volatility, weak enterprise foundations, and limited fund management capabilities-and proposes corresponding countermeasures. The findings suggest that PE funds play a crucial role in alleviating resource constraints and promoting sustainable enterprise growth in emerging markets; however, achieving long-term effectiveness requires multi-stakeholder collaboration. Limitations include the absence of regional case studies, and future research is encouraged to explore industry-specific variations in empowerment mechanisms.
Copyright (c) 2025 Xiaoyi Zhang (Author)
Construction and Practice of a Sustainable Protection Model for the Commercialization of Cultural Heritage
The commercialization of cultural heritage has become an inevitable trend in the context of globalization and market-driven cultural economies. While commercialization presents opportunities to revitalize heritage sites, enhance cultural dissemination, and stimulate local economic growth, it also entails risks such as overexploitation, loss of authenticity, and cultural homogenization. This paper develops a sustainable protection model for the commercialization of cultural heritage through a combination of theoretical exploration and empirical analysis. The study examines the delicate balance between economic value extraction and heritage preservation by investigating stakeholder coordination, value chain integration, ecological sustainability, and digital empowerment. Two data tables are presented to categorize heritage commercialization types and to provide comparative assessments of sustainable practices. Findings indicate that effective and sustainable commercialization requires a multi-dimensional approach that integrates robust regulatory frameworks, cultural innovation, community participation, and advanced technological tools. By offering a structured model and practical strategies, this research contributes both theoretically and operationally, providing actionable guidance for cultural heritage institutions, policymakers, and practitioners.
A Hybrid DNN-Transformer-AE Framework for Corporate Tax Risk Supervision and Risk Level Assessment
Tax risk supervision has become a critical component of modern financial governance, as irregular tax behaviors and hidden compliance risks pose significant challenges to regulatory authorities and enterprises alike. Traditional rule-based methods often struggle to capture complex and dynamic tax-related anomalies in large-scale enterprise data. To address this issue, this paper proposes a hybrid deep learning framework (DNN-Transformer-Autoencoder) for corporate tax risk supervision and risk level assessment. The framework integrates three complementary modules: a Deep Neural Network (DNN) for modeling static enterprise attributes, a Transformer-based architecture for capturing long-term dependencies in historical financial time series, and an Autoencoder (AE) for unsupervised detection of anomalous tax behaviors. The outputs of these modules are fused to generate a comprehensive risk score, which is further mapped into discrete risk levels (high, medium, low). Experimental evaluations on a real-world enterprise tax dataset demonstrate the effectiveness of the proposed framework, achieving an accuracy of 0.91 and a Macro F1-score of 0.88. These results indicate that the hybrid model not only improves classification performance but also enhances interpretability and applicability in practical tax regulation scenarios. This study provides both methodological innovation and regulatory implications for intelligent tax risk management.
Copyright (c) 2025 Zhenzhen Song, Nanxi Wang, Hongji Li (Author)
Cost-Optimal Safety-Stock Levels for U.S. SME Importers under Tariff-Rate Volatility: A Distributionally Robust Optimization Approach
This study addresses the inventory management problem for U.S. small and medium-sized enterprises (SMEs) under U.S. import tariff policies. A distributionally robust optimization (DRO) approach is proposed to determine safety stock decisions. Considering that the tariff rate θ fluctuates within the interval the model aims to minimize the total cost, including tariff costs, inventory holding costs, and stockout costs, thereby establishing an (R, Q) inventory strategy framework. The study utilizes data from 3,400 HS-8 coded products in the 2018–2024 Section 301 tariff lists, complemented by a survey of 30 SMEs in New York and California. Numerical experiments indicate that the DRO method is more robust than stochastic programming (SP) in coping with tariff uncertainty, while being more flexible than robust optimization (RO), providing SMEs with actionable inventory decisions that balance cost and risk. The results also generate a downloadable "Tariff–Inventory Decision Table" for practical application and quantify the marginal impact of tariff uncertainty on SME cash flows, offering empirical evidence to inform policy making. This study provides both methodological and practical guidance for inventory management of SMEs under policy-induced uncertainty.
small and medium-sized enterprises (SMEs)
distributionally robust optimization (DRO)
Copyright (c) 2025 Xiangying Chen (Author)
Consumption Behaviors of Generation Z in the Global Creative Industries: Reflections from Sociological and Business Perspectives
The rapid growth of global creative industries has made Generation Z a central group of consumers. They are digital natives who value identity, inclusivity, and sustainability, and their choices influence both cultural and economic trends. While many studies examine Gen Z from either a sociological or a business angle, few combine these perspectives. This paper addresses that gap by using an interdisciplinary approach that links sociological theories of identity and cultural capital with business research on platforms, branding, and consumer strategies. The study reviews recent literature and develops comparative case studies across music streaming, fashion, gaming, and digital art, including NFTs. Cross-regional analysis highlights both global patterns and local adaptations in markets such as North America, East Asia, and China. Results show that Gen Z are not passive buyers but active participants who act as "prosumers," creating cultural meaning while driving new business models. They show strong commitments to authenticity and sustainability, though contradictions such as the fast-fashion "attitude-behavior gap" remain. The findings demonstrate the need for an integrated view of creative industries. They contribute to theory by linking culture and commerce, and to practice by offering insights for businesses and policymakers who must adapt to this generation's transformative role.
Cost Optimization in Construction SMEs: An AI-Based Approach to Procurement and Budget Control
In construction project management, small and medium-sized enterprises (SMEs) face significant financial risks due to volatile material prices, limited bargaining power, and weak cost-control mechanisms. This study proposes an AI-based integrated framework for cost and contract management. In the pre-construction phase, it evaluates contractors’ bills of quantities (BOQs) using AI-driven analysis to guide contractor selection. Once a contractor is chosen, the system integrates with change order and invoicing processes to track amounts, commitments, and estimates in real time while proactively flagging risks. Post-construction, it monitors invoices against actual progress, preventing duplicates and ensuring compliance with AIA invoice requirements, sworn statements, and conditional waivers, thereby reducing cash flow disruptions and project delays. The framework also includes an AI-driven cost optimization engine that leverages regression and clustering techniques to detect procurement inefficiencies, benchmark suppliers, and recommend corrective strategies. Validation on real project datasets demonstrates measurable reductions in budget variance - the difference between the approved budget and estimated project cost at completion - and overall project expenditures. Compared with manual estimation, the AI-driven approach delivers higher accuracy, actionable insights, and significant cost savings. Overall, this framework enhances SMEs’ efficiency in cost and invoice management, strengthens competitiveness, reduces financial risks, and supports broader participation in infrastructure projects.
Copyright (c) 2025 Yuanfeng Liang (Author)
Research on Quality Preservation Management and Operational Efficiency Optimization of Cold Chain Enterprises
This study investigates strategies to achieve synergy between quality preservation management and operational efficiency optimization in cold chain enterprises. Drawing upon supply chain management, total quality management, operations research, and lean management theories, the research develops a comprehensive framework to enhance both product integrity and cost-effectiveness. A mixed-methods approach is employed, including literature review, theoretical modeling, data envelopment analysis (DEA), Malmquist productivity index, structural equation modeling (SEM), and case studies of representative enterprises such as JD Cold Chain, SF Express, and DHL Supply Chain. The results demonstrate that advanced monitoring technologies, blockchain-based traceability, standardized operations, intelligent transportation scheduling, and digital transformation significantly improve freshness retention, transportation efficiency, and cost management while supporting sustainable development. The study validates the proposed integration model and provides practical guidance for managers and policymakers to promote technological adoption, process standardization, and green logistics. Future research should focus on emerging technologies and regulatory interactions to further optimize quality-efficiency integration in the cold chain sector.
Disruption and Reconstruction: A Data-Driven Marketing Paradigm Shift in China's B2B Management Training Industry
The rapid evolution of digital technologies and the increasing complexity of business ecosystems have reshaped the landscape of China's B2B management training industry. Traditional marketing models that relied on personal networks, offline promotion, and word-of-mouth have gradually lost their dominance, while data-driven approaches have emerged as a transformative force. This paper aims to explore how disruptive changes in data utilization, digital platforms, and algorithmic insights are reconstructing the paradigm of marketing in the management training sector. Drawing on policy analysis, industry observation, and theoretical frameworks of digital transformation, the study identifies the structural challenges, strategic adaptations, and performance outcomes associated with the integration of data-driven practices. The findings suggest that data analytics not only enhances customer acquisition and segmentation but also enables continuous optimization of content, channels, and pricing strategies. Furthermore, the shift to data-centric models improves organizational agility, customer engagement, and market competitiveness. The study concludes that the disruption is not merely technological but also organizational, requiring firms to reconstruct their marketing philosophy and operational logic. Practical recommendations are provided to guide training institutions in achieving sustainable growth, while implications for future research highlight the intersection of digital marketing, organizational learning, and industry regulation.
Research Question: To What Extent Does Digitalization Bring Opportunities to China in Global Economic Governance?
This article explores how digitalization creates opportunities for China in the field of global economic governance. By examining China's digital economy development and its engagement in international cooperation frameworks, the study highlights three major dimensions: global trade governance, financial and monetary governance, and sustainable development governance. It argues that digital technologies—such as big data, cloud computing, artificial intelligence, and blockchain—are reshaping global governance mechanism's, facilitating international data sharing, enhancing crisis management, and improving policy evaluation. China's active role in multilateral initiatives, including G20, APEC, BRICS, and the WTO, demonstrates its efforts to contribute to global digital governance while addressing challenges like the digital divide and standardization. The research concludes that digitalization not only enriches China's domestic economic growth but also provides new pathways for China to engage in and influence global governance.
Copyright (c) 2025 Yongshi Zheng (Author)
Strategies to Overcome Green Barriers in International Agricultural Trade
Green barriers, including Sanitary and Phytosanitary (SPS) standards, eco-certifications, green tariffs, and eco-label requirements, have become critical factors influencing international agricultural trade, posing challenges such as increased compliance costs, restricted market access, and altered trade patterns. This review examines strategies to overcome these barriers by integrating technological innovation, compliance with international standards, policy coordination, trade diversification, and market adaptation. Technological approaches, such as precision agriculture, IoT-enabled monitoring, and blockchain-based traceability, enhance efficiency and regulatory compliance, while certifications like GLOBALG.A.P., ISO 14001, and organic labels provide market credibility and access to high-value segments. Case studies from countries including the Netherlands, Chile, and New Zealand, as well as corporate examples from Olam International and Dole Food Company, demonstrate the effectiveness of combining technology, standards, and strategic market engagement. The review highlights that a multi-dimensional strategy can transform green barriers from obstacles into opportunities for innovation, sustainability, and competitiveness, and emphasizes the need for continued research on cost-benefit analyses, policy effectiveness, and emerging technologies to support sustainable agricultural trade.
The Importance of Cross-Departmental Collaboration Driven by Technology in the Compliance of Financial Institutions
Against the backdrop of increasing compliance pressure in the financial industry, the importance of cross-departmental collaboration is becoming increasingly prominent as business development progresses. Through new technologies, banks and other financial institutions have made significant improvements in information integration, risk warning, and compliance response. However, due to issues such as system fragmentation, data silos, and overlapping responsibilities, technology-driven team collaboration still faces various challenges. This article focuses on the analysis of cross-departmental collaboration in business driven by information technology, which effectively leverages the connection between various links through resource sharing and process linkage. It also identifies the substantive issues faced and proposes strategies such as system architecture optimization, process responsibility refinement, and data intelligence upgrading. The aim is to provide a structural reference path for compliance management and strengthen the overall management level of financial institutions.