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.
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.
Challenges and Future Directions in the Institutionalization of Private Equity Fund Management in China
This paper examines the evolution and current status of private equity (PE) fund management in China, highlighting the industry's transition from informal, fragmented practices to a partially institutionalized system. Key challenges in this process include fragmented regulatory oversight, inconsistent policies, weak corporate governance, limited investor protection, talent shortages, and structural market inefficiencies. Drawing on international experiences from the United States and Europe, the study identifies lessons in regulatory frameworks, governance standards, professionalization, and market development that are applicable to China. The paper further outlines future directions for the institutionalization of Chinese PE funds, emphasizing improvements in legal and regulatory systems, enhanced governance and transparency, long-term capital mobilization, talent development, and sustainable market practices, including ESG integration. Strengthening institutional frameworks is essential not only for mitigating risks and protecting investors but also for fostering the long-term stability, efficiency, and competitiveness of China's private equity industry.
Administrative Distance Primacy in Huawei's Strategic Adaptation across Contrasting Markets
This study examines how institutional distance influences multinational enterprises’ (MNEs) selection of foreign operation modes. We conduct a comparative analysis of Huawei’s international expansion strategies in Russia and Japan. Drawing on institutional theory and the Uppsala internationalization model, we analyze Huawei’s different foreign operation modes: international joint ventures (IJVs) in Russia and foreign direct investment (FDI) with licensing in Japan. Our findings reveal that cultural-administrative distance significantly influences operation mode choices, with higher institutional distance favoring collaborative modes while lower distance enables more committed modes. The study contributes to international business literature by providing empirical evidence on how Chinese MNEs navigate institutional distances during internationalization. It also offers practical insights for emerging market MNEs pursuing global expansion strategies.
Vietnam's Agricultural Export Competitiveness in the Global Market: A Panel Data Analysis
This paper presents an in-depth analysis of the competitiveness of Vietnam's agricultural exports in the global market using panel data analysis over the period from 2010 to 2023. The research integrates various economic indicators, such as export volume, value, and production data, both from Vietnam and its key trading partners. The core of the analysis involves the application of two key indices—the Revealed Comparative Advantage (RCA) Index and the Trade Specialization Coefficient (TSC)—to evaluate Vietnam's relative standing in agricultural exports. The results of the study reveal that Vietnam maintains a competitive edge in certain agricultural products, most notably rice, coffee, and cashew nuts. These sectors have historically contributed significantly to the country's agricultural export growth. However, despite these strengths, the study highlights several persistent challenges. These include concerns over the consistency and quality of agricultural products, which hinder Vietnam's ability to access high-value markets. Additionally, Vietnam faces growing competition from other emerging agricultural exporters, particularly in regions such as South America and Africa, which have gained prominence in key agricultural export categories. The study also underscores the importance of factors such as market diversification, technological innovation, and sustainable farming practices in improving competitiveness. The research suggests that Vietnam's agricultural sector must transition from a reliance on traditional export products to incorporating more diversified, high-quality products that meet the standards of global markets. Furthermore, addressing logistical inefficiencies and investing in infrastructure are essential steps to reduce export costs and increase market reach. In light of these findings, the paper proposes several targeted policy recommendations to enhance Vietnam's agricultural export competitiveness. These include promoting research and development in agricultural technologies, strengthening international trade agreements, supporting smallholder farmers in improving their production techniques, and enhancing brand recognition for Vietnamese agricultural products. Additionally, fostering collaboration between the government, private sector, and educational institutions will be crucial in creating a more resilient agricultural export framework. These efforts will not only strengthen Vietnam's position in the global agricultural market but also contribute to the country's long-term economic development.
Copyright (c) 2025 Doan Ba Toai (Author)
The Role of FDI in Shaping Vietnam's High-Tech Export Growth: Evidence from Sectoral Data
This paper empirically investigates the pivotal role of Foreign Direct Investment (FDI) in fostering the growth of Vietnam's high-tech export sector by leveraging comprehensive sectoral data spanning from 2010 to 2023. Utilizing authoritative sources such as the General Statistics Office of Vietnam, the World Bank, and industry-specific reports, this study employs a combination of descriptive statistics, correlation analysis, and advanced panel data regression models to rigorously analyze the relationship between FDI inflows and high-tech export performance across multiple sectors. The findings demonstrate that FDI not only substantially expands the volume and value of Vietnam's high-tech exports but also facilitates significant technological spillovers and industrial upgrading, thereby enhancing the overall competitiveness of the domestic high-tech industry. Despite these positive impacts, the study identifies critical challenges including excessive reliance on foreign-owned enterprises, sectoral disparities in development, and limitations in domestic technological absorption capacity. In response, the paper offers targeted policy recommendations aimed at strengthening domestic innovation capabilities, promoting balanced sectoral growth, improving infrastructure, and encouraging greater international cooperation. These strategies are essential to sustainably harness the full potential of FDI and secure Vietnam's position in the global high-tech export market.
Implementation and Management of a Cross-Border Tax System Oriented Towards Global Tax Administration Informatization
Against the backdrop of accelerated development of the digital economy and globalization, traditional tax administration faces multiple challenges such as complex cross-border transactions, tax base erosion, and information barriers. The informatization construction of cross-border tax systems has become an important direction for improving management efficiency and governance capabilities. This article systematically reviews the theoretical basis of global tax management informatization, proposes an implementation path centered on system architecture optimization, information sharing mechanism, and data quality governance, and analyzes collaborative management and compliance risk prevention mechanisms. By analyzing typical cases such as the EU VIES, OECD CRS, and the Estonian e-tax platform, this study summarizes their systematic design and international collaboration experience, and provides relevant paths and practical references for building an efficient, secure, and sustainable global tax governance system.
informatization of tax collection and management
Spatiotemporal Arbitrage: How Digital Nomads Leverage Global Mobility for Financial Optimization
The emergence of digital nomadism has transformed traditional notions of work and mobility, enabling individuals to leverage global geographic and temporal disparities for financial and lifestyle optimization. This paper introduces the concept of Spatiotemporal Arbitrage, defined as the strategic exploitation of differences in living costs, tax regimes, currency fluctuations, and regulatory environments across time and space. By migrating between countries and cities, digital nomads maximize their economic benefits while enhancing quality of life. This review synthesizes interdisciplinary insights from human geography, financial decision-making, and mobility studies to articulate the mechanisms, patterns, and implications of spatiotemporal arbitrage. Understanding this phenomenon not only enriches theoretical frameworks but also informs policy debates on visa regulation, taxation, and digital labor governance in an increasingly mobile world.
Copyright (c) 2025 K.-K. Sun, S.-H. Deng (Author)
Strategic Management—Ferrari Strategy Report
Ferrari's "Green Scarcity" strategy addresses EU emissions regulations (2035 ICE ban) and Gen Z sustainability demands by integrating exclusivity with carbon neutrality. The approach features limited-edition BEVs (≤500 units/year from 2025) with carbon-fiber integration (<1.5t, >600km range), priced 20% above ICE models with carbon-neutral optional packages (+5%). It establishes a closed-loop supply chain via a "Zero-Carbon Tech Alliance" mandating 100% core supplier ISO 14064 certification by 2026, while enhancing customer value through carbon-footprint tracking. Leveraging F1 lightweight technology and projected €500M annual revenue, Ferrari targets an ESG upgrade (BBB→A by 2027) to balance heritage with sustainable innovation.
Discussion on Using Blockchain Technology to Improve Audit Efficiency and Financial Transparency
With the acceleration of enterprise informatization, traditional audit methods face many challenges in verifying the authenticity of enterprise information, real-time control, and transparency of audit information. The blockchain technology, with its decentralized control, traceability, and tamper proof features, provides new ideas to enhance audit efficiency and financial transparency. This article summarizes the relationship between blockchain technology and financial auditing, and constructs a blockchain based auditing application framework. Specific implementation paths are proposed from three aspects: referencing blockchain real-time auditing systems, automated auditing processes, and real-time multi node data sharing. Specific measures are also proposed to enhance financial transparency through the use of blockchain based financial records, hierarchical control information authorization, and unified standardized disclosure principles. This article analyzes that the application of blockchain will shift financial auditing from traditional "post verification" to "real-time monitoring", thereby achieving a win-win situation of real-time auditing and transparency before, during, and after the event.
Social Media Marketing Strategies for Employer Brand Building of Small and Medium-Sized Manufacturing Enterprises
In the increasingly competitive talent market, small and medium-sized manufacturing enterprises need to build their employer brand in order to enhance their attractiveness to talent. By leveraging the advantages of efficient dissemination and extensive reach through social media, enterprises have gained new channels for brand promotion. This article focuses on the role of social media marketing in helping employers build their brand, analyzing the shortcomings in content publishing, audience engagement, and platform integration, and explores feasible improvement paths. The aim is to enhance the brand awareness and brand conversion rate of enterprises through content planning, interactive mechanism innovation, and platform operation integration, and provide marketing support for the development and growth of small to medium-sized manufacturing enterprises.
manufacturing small and medium-sized enterprises
Consumer Behavior in the Age of Algorithmic Marketing: Insights from Interaction with Social Commerce Platforms
With the rapid advancement of data-driven technologies, algorithmic marketing has become a pivotal force reshaping consumer behavior in the digital age. This review explores how algorithmic systems embedded within social commerce platforms—such as TikTok, Instagram Shops, and Rednote—transform traditional consumer decision-making by delivering hyper-personalized content and facilitating social influence. Key mechanisms including recommendation engines, real-time content curation, and influencer endorsements are analyzed to understand their impact on consumer heuristics, emotional engagement, and social proof dynamics. The review also addresses the critical tension between personalization and privacy, highlighting consumer dilemmas in balancing relevance with data surveillance concerns. Emerging phenomena such as algorithm-driven micro-trends and the importance of ethical, transparent platform design are discussed. Finally, the paper identifies research gaps related to cultural variation, long-term psychological effects, and calls for interdisciplinary approaches integrating behavioral science and AI ethics. The findings underscore the necessity of human-centered, responsible algorithmic marketing to foster trust and autonomy in digital marketplaces.
Copyright (c) 2025 Jingping Xie (Author)
Research on the Application of Robo-Advisory Systems in Personal Wealth Management: A Case Study of Middle-Class Households in the United States
With the rapid development of artificial intelligence and big data technologies, robo-advisory systems have become an increasingly popular solution for personal wealth management, especially among middle-class households in the United States. This study combines questionnaire-based estimates, platform case analysis, and literature synthesis to examine how robo-advisors support asset allocation, risk management, and goal-based financial planning for middle-income families. Findings indicate that while robo-advisors offer clear benefits in terms of automation, cost efficiency, and improved investment discipline, trust issues, limited algorithmic transparency, and gaps in financial literacy remain key barriers to wider adoption. The paper further identifies emerging technological trends—such as AI integration with natural language processing and Open Banking—and proposes policy recommendations to enhance algorithm accountability, expand financial education, and promote inclusive access. By addressing these challenges and leveraging technological innovations, robo-advisory systems have the potential to democratize professional wealth management and strengthen the financial security of middle-class households.
Balance Model of Resource Management and Customer Service Availability in Cloud Computing Platform
In response to the real-time requirements of resource allocation and service availability dynamic balance in cloud service scheduling, this paper constructs an integrated Markov Decision Process (MDP) scheduling optimization model, comprehensively analyzes the impact of scheduling delay, load balancing efficiency, and virtual machine migration cost on service quality (SLA completion rate and average response time). At the same time, the XGBoost algorithm is used to further mine previous business data and improve the accuracy of business service availability prediction. The model was tested on scheduling and monitoring data for the fourth quarter of operation on a large public cloud platform. The results showed that within a 95% confidence interval, the prediction accuracy reached 93.7% and the resource utilization rate increased by 17.4%. The performance of this scheduling method was evaluated and validated in high concurrency scenarios, and it was found that it can effectively improve the system's availability and scheduling efficiency. This study provides theoretical and engineering practical basis for implementing an intelligent resource scheduling mechanism based on learning and modeling integration.
Copyright (c) 2025 Jiaying Huang (Author)
Practical Application of Data Analysis Technology in Startup Company Investment Evaluation
With the rise of startups in the global economy, accurate investment evaluation is especially important. Data analysis provides reasonable support for evaluation by analyzing market potential, financial condition, and team management capabilities. This paper discusses the application of data analysis technology in investment evaluation, analyzes the challenges of data collection, cost control, and technical capabilities, and proposes optimization paths such as strengthening data accumulation, reducing acquisition costs, improving the reliability of data interpretation, and enhancing technical support. These measures not only help investors make more accurate decisions but also improve the accuracy of investment evaluation and provide strong support for investment decisions.
Promoting Cross-field E-Commerce Development by Combining Educational Background and Technology
With the rapid advancement of technological innovation, the educational background increasingly plays a prominent role in supporting and promoting cross-domain e-commerce development. This paper explores the supporting role of education systems in nurturing talent and fostering technological innovation, and delves into the application of big data, artificial intelligence, and blockchain technologies to optimize e-commerce marketing, predict consumer behavior, and improve supply chain efficiency. At the same time, it proposes strategies that combine education and technological innovation to promote the development of cross-domain e-commerce, including suggestions for industry-university-research cooperation, interdisciplinary education, and policy support. It aims to inject innovative vitality into cross-field e-commerce.
Copyright (c) 2025 Jiangnan Huang (Author)