European Journal of Business, Economics & Management
type: channel
description: European Journal of Business, Economics & Management (EJBEM) is an international, refereed, double-blind peer-reviewed, open-access journal covering business, economics, and management. It publishes high-quality research papers that advance both theory and practice, focusing on topics such as corporate management, market economics, financial analysis, and innovation strategies.
publisher: Pinnacle Academic Press
language: en
publication name: European Journal of Business, Economics & Management
issn: 3079-8655
items: Research on the Incentive Effect of Government Tax Preference on Independent Innovation of Emerging Enterprises
items: Analysis of Factors Influencing Chinese Consumers' Online Consumption Psychology and Behavior
items: Fraud Detection and Risk Assessment of Online Payment Transactions on E-Commerce Platforms Based on LLM and GCN Frameworks
items: Adaptive Intelligence in Robo-Advisory: A Framework for Risk Control and Return Optimization
items: Analysis of the Impact of off Exchange Derivatives Trading on Capital Market Efficiency Improvement
items: Reinforcement Learning with Reward Shaping for Last-Mile Delivery Dispatch Efficiency
items: The Push of Financial Technology Innovation on Derivatives Trading Strategy Optimization
items: Cross-Border Industrial Collaboration: Interactive Influences and Development Insights between European and Chinese Fertilizer Industries
items: Causal Modeling for Fraud Detection: Enhancing Financial Security with Interpretable AI
items: The Synergy of Cross-Platform Content Creation in Employer Brand Communication
items: The Development Trends and Future Challenges of Cross-Border E-Commerce: From a Global Perspective
items: Cost Control and Stability Improvement in Enterprise Level Infrastructure Optimization
items: Time Series Analysis for Stock Price Forecasting: Evidence from Kansai Design Co., Ltd. (603458)
items: The Influence of ESG Factors on Corporate Financing Costs in Emerging Markets
items: An Analysis of Management Strategies in Emerging Startups
items: Cost Control and Risk Management in the Logistics and Transportation Segment of the Pulp Supply Chain
items: Opportunities, Challenges, and Strategic Responses for International Agricultural Trade in the Digital Era
items: Research on Market Expansion Strategies under the New Pattern of International Agricultural Trade
items: Digital Credit Risk Management Systems and Solutions
items: Optimization of Packaging Procurement and Supplier Strategy in Global Supply Chain
items: Research on Cloud Computing Resource Scheduling Strategy Based on Big Data and Machine Learning
items: Research on New Drivers of Event Consumption and Strategies for High-Quality Development
items: Strategic Realignment: Post-Brexit Economic Relations between the UK and Japan
items: Digital Brand Management in Social Media: A Case Study of Want Want's Consumer Engagement Strategy on Chinese Platforms
items: The Impact of Trade Agreements on Vietnam's Export Performance: A Data-Driven Analysis (2005–2024)
items: Export Diversification and Its Role in Vietnam's Economic Resilience: A Quantitative Study
items: International Management Report—An Analysis Report for Boost Juice: Entering China's Market
items: Academic and Practical Cases: From TFP Variables in DEA-Malmquist Model to the Conduction of County-Level Digital Agriculture
items: Integration of Market-Oriented Development Models and Marketing Strategies in Real Estate
items: The Commercialization Path of Large Language Models in Start-Ups

Research on the Incentive Effect of Government Tax Preference on Independent Innovation of Emerging Enterprises
type: item
description: Currently, emerging industries are gradually becoming the main drivers of the transformation of China's industrial structure, and independent innovation serves as the core force behind the development of these industries. For the government, stimulating independent innovation among emerging enterprises has become a key policy objective. Appropriate financial and tax incentives can partially offset the losses caused by the externalities of independent innovation, while also reducing the risks and costs associated with innovation activities. However, questions remain regarding whether such tax incentives can effectively promote independent innovation and which types of preferential policies yield the strongest incentives. To address these issues, this paper employs an empirical analysis approach to construct a relationship model that examines the impact of tax incentives on enterprise independent innovation, providing a reference for the formulation of government incentive policies. The regression analysis indicates that tax incentives not only increase the innovation investment of strategic emerging enterprises but also encourage the generation of more innovative outcomes and enhance overall innovation efficiency.
subject: Article
  topic: section
subject: tax preference
  topic: keywords
subject: incentive effect
  topic: keywords
subject: independent innovation
  topic: keywords
subject: emerging enterprise
  topic: keywords
creator: Furui Pang
rights: Copyright (c) 2025 Furui Pang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-21
publication date: 2025-11-21
starting page: 27
ending page: 34
doi: 10.71222/m0zcaq83

Analysis of Factors Influencing Chinese Consumers' Online Consumption Psychology and Behavior
type: item
description: As China's technological capabilities continue to advance and the scale of online shopping steadily expands, e-commerce has become one of the primary modes of daily consumption for the public. This trend not only provides convenience and satisfies diverse purchasing needs but also gradually reshapes consumer behavior. Some individuals may develop tendencies toward premature or impulsive consumption, which can disrupt their normal financial routines. Therefore, it is essential for consumers to clearly understand the key factors influencing their purchasing decisions, cultivate a rational consumption mindset, maintain a balanced relationship between income and expenditure, and strengthen the protection of their personal information in online environments.
subject: Article
  topic: section
subject: consumers
  topic: keywords
subject: online shopping behavior
  topic: keywords
subject: attitudes towards online shopping
  topic: keywords
subject: willingness to shop online
  topic: keywords
creator: Mei Yu
creator: Yan Ma
rights: Copyright (c) 2025 Mei Yu, Yan Ma (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-21
publication date: 2025-11-21
starting page: 10
ending page: 26
doi: 10.71222/649nfy78

Fraud Detection and Risk Assessment of Online Payment Transactions on E-Commerce Platforms Based on LLM and GCN Frameworks
type: item
description: With the rapid expansion of e-commerce, online payment fraud has become increasingly sophisticated, posing significant challenges to financial security and undermining consumer trust. Traditional detection methods often struggle to capture the intricate relational and behavioral patterns inherent in transactional data, limiting their effectiveness in highly imbalanced scenarios. This study introduces a novel fraud detection framework that integrates Large Language Models (LLMs) with Graph Convolutional Networks (GCNs) to enhance the identification of fraudulent activities in online payment systems. A dataset comprising 2,840,000 transactions was collected over a 14-day period from major e-commerce platforms, including Amazon, involving approximately 2,000 U.S.-based consumers and 30 merchants. Among these transactions, fewer than 6,000 were fraudulent, presenting a highly skewed class distribution that reflects real-world conditions. In this framework, consumers and merchants were represented as nodes, and transactions as edges, forming a heterogeneous graph that captures both direct and indirect interactions. A GCN was applied to this graph to learn complex structural and relational patterns, effectively modeling the interdependencies among participants. In parallel, semantic features were extracted from transaction metadata using GPT-4o and Tabformer, capturing textual and categorical information that complements the structural graph features. By fusing these semantic representations with graph-based features, the model can identify subtle and context-dependent indicators of fraudulent behavior that traditional methods may overlook. Experimental results demonstrate that the proposed framework achieves an overall accuracy of 0.98, with a balanced performance in terms of precision and sensitivity, highlighting its robustness in detecting rare fraudulent instances within a massive dataset. The integration of LLM-derived semantic features with graph structural informa…
subject: Article
  topic: section
subject: fraud detection
  topic: keywords
subject: GPT-4o
  topic: keywords
subject: GCN
  topic: keywords
subject: LLM
  topic: keywords
subject: unbalanced data
  topic: keywords
creator: Ruihan Luo
creator: Nanxi Wang
creator: Xiaotong Zhu
rights: Copyright (c) 2025 Ruihan Luo, Nanxi Wang, Xiaotong Zhu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-12
publication date: 2025-11-12
starting page: 1
ending page: 9
doi: 10.71222/15dz2d22

Adaptive Intelligence in Robo-Advisory: A Framework for Risk Control and Return Optimization
type: item
description: The rapid integration of artificial intelligence (AI) into financial services has transformed investment management. However, existing robo-advisory systems remain limited by static optimization, constrained risk adaptability, and opaque decision-making processes. To overcome these challenges, this study introduces an Adaptive Intelligence Framework (AIF) that integrates reinforcement learning, modern portfolio theory, and explainable AI (XAI) to enable dynamic risk control and transparent return optimization. Employing a mixed-method approach that combines theoretical modeling, comparative case studies (BlackRock Aladdin and Betterment), and empirical simulations on verified global market data from 2018 to 2024, the framework demonstrated superior performance, achieving an 11.6% increase in cumulative return, a 17.3% reduction in volatility, and a high interpretability score of 0.82. These findings indicate that adaptive algorithms can simultaneously enhance stability and transparency under non-stationary market conditions. The study advances financial AI research by linking quantitative finance with algorithmic accountability and provides a practical blueprint for developing trustworthy, regulation-aligned robo-advisory systems capable of balancing efficiency, explainability, and resilience in capital markets.
subject: Article
  topic: section
subject: adaptive intelligence
  topic: keywords
subject: robo-advisory
  topic: keywords
subject: risk control
  topic: keywords
subject: explainable AI
  topic: keywords
subject: capital markets
  topic: keywords
creator: Tianyi Wang
rights: Copyright (c) 2025 Tianyi Wang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-11-08
publication date: 2025-11-08
starting page: 139
ending page: 149
doi: 10.71222/x5xsg430

Analysis of the Impact of off Exchange Derivatives Trading on Capital Market Efficiency Improvement
type: item
description: In the modern financial system, over-the-counter (OTC) derivative trading plays a crucial role, with its flexibility and diversity significantly enhancing the operational efficiency of capital markets. OTC derivative trading improves market efficiency by facilitating information integration, increasing transparency, expanding the variety of trading instruments, and promoting the rational allocation of resources. This article examines the impact of information transparency, innovative trading instruments, and optimized resource allocation on capital market efficiency, alongside the distinct characteristics of OTC derivatives. Emerging technologies, particularly blockchain, offer new technical support for the development of OTC derivative markets, helping to balance efficiency gains with effective risk control. Further advancements in policy frameworks and technological innovation are expected to deepen the contribution of OTC derivative trading to the overall functioning of capital markets.
subject: Article
  topic: section
subject: over-the-counter derivative trading
  topic: keywords
subject: capital market efficiency
  topic: keywords
subject: information transparency
  topic: keywords
subject: resource allocation
  topic: keywords
subject: blockchain technology
  topic: keywords
creator: Zhengle Wei
rights: Copyright (c) 2025 Zhengle Wei (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-31
publication date: 2025-10-31
starting page: 131
ending page: 138
doi: 10.71222/554vfe24

Reinforcement Learning with Reward Shaping for Last-Mile Delivery Dispatch Efficiency
type: item
description: As the final and most labor-intensive segment of the logistics chain, last-mile delivery grapples with inherent challenges: dynamic traffic conditions, fluctuating order volumes, and the conflicting demands of timeliness, cost control, and resource efficiency. Conventional dispatch approaches-such as heuristic algorithms and static optimization models-exhibit limited adaptability to real-time fluctuations, often resulting in suboptimal resource utilization and elevated operational costs. To address these gaps, this study proposes a reinforcement learning (RL) framework integrated with multi-dimensional reward shaping (RS) to enhance dynamic last-mile delivery dispatch efficiency. First, we formalize the dispatch problem as a Markov Decision Process (MDP) that explicitly incorporates real-time factors (e.g., traffic congestion, order urgency, and vehicle status) into the state space. Second, we design a domain-specific RS function that introduces intermediate rewards (e.g., on-time arrival bonuses, empty-running penalties) to mitigate the sparsity of traditional terminal rewards and accelerate RL agent convergence. Experiments were conducted on a real-world dataset from a logistics enterprise in Chengdu (June-August 2024), comparing the proposed RS-PPO framework against two baselines: the classic Savings Algorithm (SA) and standard PPO without reward shaping (PPO-noRS). Results demonstrate that RS-PPO improves the on-time delivery rate (OTR) by 18.2% (vs. SA) and 9.5% (vs. PPO-noRS), reduces the average delivery cost (ADC) by 12.7% (vs. SA) and 7.3% (vs. PPO-noRS), and shortens convergence time by 40.3% (vs. PPO-noRS). Additionally, RS-PPO boosts vehicle utilization rate (VUR) by 29.8% (vs. SA) and 13.4% (vs. PPO-noRS). This framework provides a practical, data-driven solution for logistics enterprises seeking to balance service quality, cost efficiency, and sustainability-aligning with global last-mile optimization trends.
subject: Article
  topic: section
subject: last-mile delivery
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: multi-dimensional reward shaping
  topic: keywords
subject: dynamic dispatch
  topic: keywords
subject: Markov decision process
  topic: keywords
creator: Sichong Huang
rights: Copyright (c) 2025 Sichong Huang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-31
publication date: 2025-10-31
starting page: 122
ending page: 130
doi: 10.71222/ejnna704

The Push of Financial Technology Innovation on Derivatives Trading Strategy Optimization
type: item
description: The innovation of financial technology has significantly advanced the optimization of derivatives trading strategies. By leveraging cutting-edge tools such as quantitative trading algorithms, big data analytics, and blockchain technology, fintech enhances the speed, precision, and reliability of financial transactions. These technologies not only strengthen risk management capabilities but also improve market transparency, thereby fostering a more informed and efficient trading environment. In addition, fintech facilitates real-time monitoring of market conditions, enabling traders and institutions to respond swiftly to fluctuations and potential anomalies, which contributes to reducing market volatility. Moreover, the integration of automated and intelligent systems supports fairer market practices by minimizing human errors and information asymmetries. This paper aims to comprehensively examine the practical applications of fintech in derivatives trading, exploring how its deployment improves operational efficiency, strengthens market integrity, and stimulates innovation and growth within the derivatives market. Through detailed analysis of technological frameworks, trading mechanisms, and performance outcomes, the study provides insights into the transformative role of fintech in shaping modern derivatives markets and highlights potential avenues for further technological advancement and strategic adoption.
subject: Article
  topic: section
subject: financial technology
  topic: keywords
subject: derivatives
  topic: keywords
subject: trading strategy
  topic: keywords
subject: big data
  topic: keywords
subject: blockchain
  topic: keywords
creator: Yilin Fu
rights: Copyright (c) 2025 Yilin Fu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-25
publication date: 2025-10-25
starting page: 114
ending page: 121
doi: 10.71222/5mdpn225

Cross-Border Industrial Collaboration: Interactive Influences and Development Insights between European and Chinese Fertilizer Industries
type: item
description: This article traces the historical development of the fertilizer industry in major European countries, including Germany, the United Kingdom, and France, and examines its multifaceted influence on the formation of China's fertilizer industry. Specifically, it explores four key dimensions of this influence: the establishment of technical foundations, the construction of an industrial system, the shaping of international cooperation models, and the introduction of sustainable development concepts. The study finds that Europe-particularly Germany through the invention of the Haber-Bosch ammonia synthesis process-provided the crucial technological groundwork for China's fertilizer industry. Moreover, the evolution of Europe's fertilizer sector not only supplied the technical impetus for China's industrial modernization but also offered valuable experience and strategic guidance for building an independent, innovative, and sustainable fertilizer industry system.
subject: Article
  topic: section
subject: European fertilizer industry
  topic: keywords
subject: technology transfer
  topic: keywords
subject: Haber-Bosch process
  topic: keywords
subject: China's fertilizer industry
  topic: keywords
creator: Xianhan Qin
rights: Copyright (c) 2025 Xianhan Qin (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-18
publication date: 2025-10-18
starting page: 105
ending page: 113
doi: 10.71222/s1ycn703

Causal Modeling for Fraud Detection: Enhancing Financial Security with Interpretable AI
type: item
description: Financial fraud poses a significant threat to the stability of modern economic systems. However, traditional machine learning approaches to fraud detection-primarily correlation-based-remain limited in precision, interpretability, and adaptability when confronting the constantly evolving strategies of fraudsters. This study introduces a causal inference framework for fraud detection, leveraging recent advancements in causal analysis to identify and quantify the underlying causal relationships among transaction attributes, user behaviors, and fraudulent outcomes. The framework incorporates three key components: causal discovery algorithms (PC and FCI), robust effect estimation techniques (e.g., PSM and DML), and an interpretable rule-extraction module that translates causal patterns into actionable insights. Experiments were conducted on two real-world datasets: a credit card transaction dataset (284,807 records, 32% fraud rate) and an insurance claims dataset (350,000 cases, 8% fraud rate). Results show that the proposed model consistently outperforms leading correlation-based methods-including AdaBoost, GBDT, XGBoost, and LightGBM-achieving notable performance improvements: an average 9-percentage-point gain in overall accuracy, a 2% increase in F1 score (up to 11%), a 5% boost in AUPRC, and a 13.3% improvement in MCC. A key finding highlights a 47% higher fraud risk associated with atypical location changes combined with large-value transactions, directly addressing the "black-box" limitations of conventional models. Robustness analyses further confirm the model's resilience against confounding influences such as seasonal fluctuations and demographic shifts, underscoring its adaptability to emerging fraud patterns. By integrating causal inference with interpretable artificial intelligence, this research advances fraud detection toward more precise, transparent, and regulatory-compliant financial risk management.
subject: Article
  topic: section
subject: causal inference
  topic: keywords
subject: fraud detection
  topic: keywords
subject: explainable AI
  topic: keywords
subject: financial security
  topic: keywords
subject: propensity score matching
  topic: keywords
subject: causal discovery
  topic: keywords
creator: Luqing Ren
rights: Copyright (c) 2025 Luqing Ren (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-16
publication date: 2025-10-16
starting page: 94
ending page: 104
doi: 10.71222/jaw0kn39

The Synergy of Cross-Platform Content Creation in Employer Brand Communication
type: item
description: With the proliferation of social media, recruitment platforms, and corporate websites, cross-platform content creation has emerged as a key strategy for enhancing employer brand promotion. This study investigates the synergistic effects of cross-platform content creation in employer brand communication, examining how collaboration across multiple platforms can expand brand awareness, strengthen brand value, and improve employee recognition. A synergy model of cross-platform communication is developed, elucidating the interaction mechanisms among diverse platforms, and its effectiveness is validated through case analyses. The findings indicate that cross-platform content creation not only optimizes brand communication strategies but also facilitates comprehensive diffusion of the employer brand image, offering both theoretical insights and practical guidance for enterprises seeking to enhance their employer branding initiatives.
subject: Article
  topic: section
subject: cross-platform content creation
  topic: keywords
subject: employer brand communication
  topic: keywords
subject: synergistic effect
  topic: keywords
subject: brand awareness
  topic: keywords
subject: brand appeal
  topic: keywords
creator: Runqi Lin
rights: Copyright (c) 2025 Runqi Lin (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-14
publication date: 2025-10-14
starting page: 85
ending page: 93
doi: 10.71222/9cve1k40

The Development Trends and Future Challenges of Cross-Border E-Commerce: From a Global Perspective
type: item
description: Cross-border e-commerce has emerged as a vital driver of global trade and economic growth. Fueled by technological advancements and evolving consumer demand, the sector has witnessed rapid expansion and exhibits considerable potential worldwide. This paper investigates the key development trends, major challenges, and future opportunities in cross-border e-commerce from a global perspective. It first reviews the current state of the global market and highlights regional disparities in development. It then examines major trends, including digital transformation, the interplay between globalization and localization, the rise of mobile commerce, and innovations in logistics and payment systems. The study further analyzes critical challenges such as legal and policy barriers, cultural differences, and payment security risks. In addition, it explores the transformative role of emerging technologies-such as artificial intelligence, big data, and blockchain-in shaping the sector's future. Finally, the paper identifies potential growth avenues in areas such as social media integration, sustainability, and personalized services, while offering policy recommendations to support the healthy and sustainable development of global cross-border e-commerce. The findings provide valuable insights for academia, industry practitioners, and policymakers alike.
subject: Article
  topic: section
subject: cross-border e-commerce
  topic: keywords
subject: global perspective
  topic: keywords
subject: development trends
  topic: keywords
subject: technological innovation
  topic: keywords
subject: policy challenges
  topic: keywords
creator: Zixuan Zhang
rights: Copyright (c) 2025 Zixuan Zhang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-09
publication date: 2025-10-09
starting page: 77
ending page: 84
doi: 10.71222/gr58ph65

Cost Control and Stability Improvement in Enterprise Level Infrastructure Optimization
type: item
description: Optimizing enterprise-level infrastructure plays a key role in enhancing operational efficiency and saving costs. This article deeply analyzes the key factors and implementation strategies of infrastructure from the aspects of hardware resource optimization, automated operation and maintenance, and intelligent resource scheduling. With the deepening application of virtualization technology and cloud computing, enterprises can greatly improve resource utilization efficiency and stable system operation, while reducing maintenance costs and energy consumption. By implementing centralized monitoring and intelligent warning mechanisms, system issues can be quickly identified and effectively prevented, thereby enhancing the stability and reliability of infrastructure. With the rapid progress of cloud native architecture, artificial intelligence, edge computing, and green IT, the optimization of future infrastructure has more innovative possibilities. These strategies and Q trends provide a reference for efficient management of enterprises in the process of informatization and digitization, promoting the achievement of sustainable development goals.
subject: Article
  topic: section
subject: infrastructure optimization
  topic: keywords
subject: cost control
  topic: keywords
subject: system stability
  topic: keywords
subject: virtualization technology
  topic: keywords
subject: cloud native architecture
  topic: keywords
creator: Yixin Zhou
rights: Copyright (c) 2025 Yixin Zhou (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-01
publication date: 2025-10-01
starting page: 70
ending page: 76
doi: 10.71222/46k0nw07

Time Series Analysis for Stock Price Forecasting: Evidence from Kansai Design Co., Ltd. (603458)
type: item
description: Stock price forecasting plays a critical role in investment decision-making, particularly in emerging sectors such as engineering consulting. Kansai Design Co., Ltd. (603458), a state-controlled listed firm, has drawn growing attention with China's ongoing infrastructure expansion. However, few studies have systematically examined its stock price dynamics using rigorous time series methods, leaving a gap in empirical evidence for short-term predictability. This study employs time series analysis to investigate the company's daily stock prices from October 10, 2022, to September 28, 2023. Following the established paradigm of stationarity testing, model identification, parameter estimation, and diagnostic checking, an Augmented Dickey-Fuller test confirmed series stationarity. Autocorrelation and partial autocorrelation analyses indicated the suitability of an autoregressive model. Comparative evaluation of AR (1), AR (2), and AR (3) models demonstrated that AR (1) provided the best fit, consistent with Occam's Razor. Residual diagnostics verified white noise properties, while predictive performance metrics showed high accuracy (RMSE = 0.0519; MAPE = 0.414%). The AR (1) model closely tracked actual prices, with errors mainly driven by random fluctuations rather than systematic bias. These findings highlight the short-term continuity of stock prices and demonstrate the model's practical value as a decision-support tool. The study contributes methodological insights and offers investors evidence-based guidance while emphasizing the importance of integrating quantitative models with fundamental market analysis.
subject: Article
  topic: section
subject: stock price forecasting
  topic: keywords
subject: time series analysis
  topic: keywords
subject: autoregressive model (AR)
  topic: keywords
subject: stationarity
  topic: keywords
subject: investment decision-making
  topic: keywords
creator: Yijing Wang
rights: Copyright (c) 2025 Yijing Wang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-01
publication date: 2025-10-01
starting page: 61
ending page: 69
doi: 10.71222/mqnkg107

The Influence of ESG Factors on Corporate Financing Costs in Emerging Markets
type: item
description: Emerging-market firms often face elevated financing costs due to macroeconomic volatility, institutional weaknesses, and environmental risks. Yet most existing studies focus on developed economies, leaving limited evidence on how environmental, social, and governance (ESG) factors affect debt pricing in emerging contexts. This study addresses this gap by adopting a mixed-methods design, combining panel regression analysis of firm-level ESG ratings, financial indicators, and macroeconomic data from 2023 to 2025 with comparative case studies of firms implementing substantial ESG reforms. The econometric results show that superior ESG performance is significantly associated with lower financing costs, with governance and environmental pillars exerting the strongest influence, while social factors have weaker and statistically insignificant effects. Case evidence further illustrates how governance reforms and environmental commitments lead to favorable refinancing terms, whereas social initiatives yield mainly reputational benefits. These findings extend ESG-financing research to emerging markets and provide practical insights for managers, investors, and policymakers seeking to lower capital costs and strengthen sustainable finance.
subject: Article
  topic: section
subject: emerging markets
  topic: keywords
subject: financing costs
  topic: keywords
subject: ESG performance
  topic: keywords
subject: governance
  topic: keywords
subject: environmental
  topic: keywords
creator: Wenbo Zhang
rights: Copyright (c) 2025 Wenbo Zhang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-25
publication date: 2025-09-25
starting page: 50
ending page: 60
doi: 10.71222/13w2mg46

An Analysis of Management Strategies in Emerging Startups
type: item
description: Emerging Asian startups have become influential actors in the global economy, yet existing research has primarily emphasized Western firms, leaving gaps in understanding their unique strategies. This study addresses that gap by examining SHEIN, a China-founded digital-first fast-fashion company that has rapidly expanded to more than 150 countries. Using a qualitative case study method supported by comparative analysis with ByteDance and Grab, the research draws on secondary data, industry reports, and sustainability disclosures from 2021 to 2025. The findings show that SHEIN's growth is driven by an agile supply chain capable of releasing over 1.5 million new items annually, a platform-based model with more than 250 million monthly active users by 2024, and a flexible workforce exceeding 10,000 employees worldwide. Revenue increased from USD 15.7 billion in 2021 to an estimated USD 56 billion in 2025, while its U.S. market share peaked at 50 percent in 2022 before stabilizing above 40 percent in 2025. These results illustrate how digital integration, small-batch production, and global-local balance allow emerging startups to sustain international competitiveness. The study contributes to scholarship on entrepreneurship and offers practical lessons for managing agility, digital platforms, and global expansion in resource-constrained contexts.
subject: Article
  topic: section
subject: SHEIN
  topic: keywords
subject: agile supply chain
  topic: keywords
subject: platform-based model
  topic: keywords
subject: international competitiveness
  topic: keywords
subject: emerging startups
  topic: keywords
creator: Jiarui Luo
rights: Copyright (c) 2025 Jiarui Luo (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-25
publication date: 2025-09-25
starting page: 41
ending page: 49
doi: 10.71222/9amz3j53

Cost Control and Risk Management in the Logistics and Transportation Segment of the Pulp Supply Chain
type: item
description: This review examines cost control and risk management strategies in the logistics and transportation segment of the pulp supply chain. The study first analyzes the structure and key characteristics of pulp logistics, highlighting the critical nodes and cost drivers that influence operational efficiency. It then explores major cost components, including transportation, handling, storage, and inventory, and evaluates strategies such as route optimization, multimodal transport, automation, and collaborative logistics to reduce expenses. Key risks—including operational, financial, environmental, geopolitical, and market-related factors—are identified and assessed for likelihood and impact, with examples illustrating potential disruptions. The review further discusses mitigation approaches, including insurance, contingency planning, contract management, and the adoption of digital technologies like IoT and AI for real-time monitoring and predictive decision-making. Finally, emerging trends, such as digitalization, AI-driven logistics, and sustainable transport solutions, are examined, emphasizing the integration of cost and risk management for resilient and efficient supply chains. Recommendations for practitioners and future research directions are provided to enhance supply chain performance, sustainability, and risk resilience.
subject: Article
  topic: section
subject: pulp supply chain
  topic: keywords
subject: logistics cost
  topic: keywords
subject: risk management
  topic: keywords
subject: transportation
  topic: keywords
subject: digitalization
  topic: keywords
creator: Ronglin Wang
rights: Copyright (c) 2025 Ronglin Wang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 30
ending page: 40
doi: 10.71222/nvmgpf46

Opportunities, Challenges, and Strategic Responses for International Agricultural Trade in the Digital Era
type: item
description: The digitalization of international agricultural trade has emerged as a transformative force, reshaping production, distribution, and market dynamics in the global agri-food sector. This review examines the current status of digital adoption in agriculture, highlighting key technologies such as blockchain, e-trading platforms, Internet of Things (IoT), and big data analytics, and analyzes their impacts on trade efficiency, transparency, and market access. The study further explores opportunities created by digitalization, including reduced transaction costs, enhanced supply chain traceability, and innovative financial instruments that support risk management and inclusive participation for smallholder farmers and SMEs. Concurrently, the review identifies critical challenges and risks, encompassing technological barriers, regulatory inconsistencies, market inequality, and social-environmental implications. Strategic responses and policy recommendations are proposed, emphasizing investment in digital infrastructure, promotion of inclusive adoption, international cooperation, and robust risk mitigation strategies. Finally, emerging trends such as AI-driven trade analytics, smart contracts, and blockchain-based traceability are discussed, providing directions for future research. The findings underscore that, while digitalization presents significant opportunities, coordinated efforts among policymakers, businesses, and researchers are essential to ensure equitable, resilient, and sustainable international agricultural trade.
subject: Article
  topic: section
subject: digitalization
  topic: keywords
subject: international agricultural trade
  topic: keywords
subject: blockchain
  topic: keywords
subject: e-trading platforms
  topic: keywords
subject: supply chain transparency
  topic: keywords
creator: Jian Wang
rights: Copyright (c) 2025 Jian Wang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 21
ending page: 29
doi: 10.71222/ycvm1g76

Research on Market Expansion Strategies under the New Pattern of International Agricultural Trade
type: item
description: This review examines the evolving patterns of international agricultural trade and the strategies employed by firms to expand their markets in a rapidly changing global environment. Drawing on literature from the past decade, the study highlights three dominant trends: market diversification, technological digitalization, and sustainability-oriented trade practices. Case studies from China's Belt and Road Initiative, Latin America's soybean sector, the European Union's organic agriculture, and Africa's emerging markets illustrate how infrastructure development, policy frameworks, value-added processing, and digital supply chain integration shape trade outcomes. While these strategies enhance market access and competitiveness, challenges persist, including institutional disparities, environmental sustainability concerns, and uneven technological adoption. The review synthesizes these insights to identify research gaps and proposes future directions, emphasizing comparative studies, data-driven analytics, and the interplay between policy support and firm-level strategies. The findings provide a comprehensive framework for scholars, policymakers, and practitioners seeking to navigate contemporary agricultural trade dynamics effectively.
subject: Article
  topic: section
subject: international agricultural trade
  topic: keywords
subject: market expansion strategies
  topic: keywords
subject: sustainability
  topic: keywords
subject: digital transformation
  topic: keywords
subject: belt and road initiative
  topic: keywords
creator: Jie Chen
rights: Copyright (c) 2025 Jie Chen (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-23
publication date: 2025-09-23
starting page: 11
ending page: 20
doi: 10.71222/hawr5b41

Digital Credit Risk Management Systems and Solutions
type: item
description: Digital transformation has fundamentally revolutionized credit risk management practices across financial institutions, introducing sophisticated technological solutions that enhance accuracy, efficiency, and decision-making capabilities in lending processes. This comprehensive study examines the evolution of digital credit risk management systems, analyzing the implementation of advanced machine learning algorithms, artificial intelligence technologies, and data analytics platforms that have transformed traditional credit assessment methodologies. Through systematic evaluation of contemporary digital solutions, this research reveals significant improvements in risk prediction accuracy, processing efficiency, and portfolio management effectiveness compared to conventional approaches. Digital credit risk systems demonstrate superior performance in detecting fraudulent activities, assessing borrower creditworthiness, and optimizing lending decisions through real-time data processing and predictive modeling capabilities. The study investigates various technological implementations including ensemble learning methods, deep learning architectures, and alternative data sources that enhance credit scoring precision while reducing operational costs by 35-50% and improving approval times by 70-85%. Furthermore, the research examines challenges associated with digital system implementation including data privacy concerns, regulatory compliance requirements, and model interpretability issues that influence adoption strategies. The findings demonstrate that organizations implementing comprehensive digital credit risk management solutions achieve substantial improvements in portfolio performance, risk mitigation, and operational efficiency while maintaining regulatory compliance and customer satisfaction. This analysis provides evidence-based insights for financial institutions considering digital transformation initiatives and offers practical recommendations for optimizing cre…
subject: Article
  topic: section
subject: digital credit risk
  topic: keywords
subject: machine learning
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: credit scoring
  topic: keywords
subject: financial technology
  topic: keywords
subject: risk assessment
  topic: keywords
creator: Rui Xu
rights: Copyright (c) 2025 Rui Xu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-20
publication date: 2025-09-20
starting page: 1
ending page: 10
doi: 10.71222/yzs0f406

Optimization of Packaging Procurement and Supplier Strategy in Global Supply Chain
type: item
description: The complexity of the global supply chain requires refined management of each link. Packaging material procurement, as the core link of supply chain management, has a direct impact on cost control, resource allocation, and supply chain adaptability. The role of packaging is not only for product protection and appearance design, but also plays an important role in cost control, logistics efficiency, and environmental sustainability. This article analyzes the path of optimizing packaging procurement in the context of globalization, focusing on the balance strategy between centralized and decentralized procurement, the application of information technology and big data, and the flexible selection of various packaging materials. It also emphasized the importance of supplier management strategies, including selecting suitable suppliers, implementing a grading system, and risk control. By optimizing packaging procurement and supplier management in the supply chain, the operational efficiency of the entire supply chain can be improved, operating costs can be reduced, and the competitiveness of the enterprise in the market can be enhanced.
subject: Article
  topic: section
subject: global supply chain
  topic: keywords
subject: packaging procurement
  topic: keywords
subject: supplier management
  topic: keywords
subject: optimization strategy
  topic: keywords
subject: synergistic effect
  topic: keywords
creator: Hao Xu
rights: Copyright (c) 2025 Hao Xu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-15
publication date: 2025-09-15
starting page: 111
ending page: 117
doi: 10.71222/ha6yhd21

Research on Cloud Computing Resource Scheduling Strategy Based on Big Data and Machine Learning
type: item
description: Optimizing the efficient scheduling of cloud platforms is crucial for enhancing the performance and resource utilization of these platforms. The intelligent scheduling optimization framework proposed in this article combines big data and deep learning technologies, which can effectively solve a series of severe challenges in cloud platform resource scheduling, such as redundant resource preparation, inaccurate capacity estimation, and high scheduling costs. Suggest building a multidimensional feature model that combines historical and real-time monitoring information, and using LSTM for accurate resource prediction. Based on this result, a reinforcement learning based automatic adjustment hybrid scheduling algorithm was designed to achieve intelligent dynamic scheduling of different resources. Meanwhile, a graph partitioning mechanism is adopted to reduce scheduling complexity and improve system scalability. On the Google Cluster Trace dataset, the proposed solution can significantly improve resource utilization, increasing it from raw utilization to 21.5%, reducing the average waiting time of tasks by 18.3%, and lowering SLA default rates by 13%. The deployment experiment in the Kubernetes environment further validated the feasibility and effectiveness of the proposed solution. The research results provide evidence and understanding for the application of learning based intelligent scheduling technology in cloud infrastructure.
subject: Article
  topic: section
subject: cloud computing
  topic: keywords
subject: resource scheduling
  topic: keywords
subject: machine learning
  topic: keywords
creator: Jiaying Huang
rights: Copyright (c) 2025 Jiaying Huang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-13
publication date: 2025-09-13
starting page: 104
ending page: 110
doi: 10.71222/wrn0b009

Research on New Drivers of Event Consumption and Strategies for High-Quality Development
type: item
description: This study examines the new drivers of event consumption and strategies for high-quality development in the context of Wenzhou's evolving event economy. Through a mixed-methods approach combining quantitative analysis and case studies, the research identifies five key drivers propelling event consumption: the experience economy, digital transformation, social-cultural engagement, sustainability and health consciousness, and policy-infrastructure support. The findings reveal how Wenzhou has successfully leveraged its unique economic characteristics and cultural assets to develop a robust event ecosystem that generates significant economic benefits while addressing urban development goals. The study further identifies major challenges including event homogenization, intellectual property protection, talent shortages, and environmental sustainability concerns. Based on these findings, the research proposes a comprehensive framework and tailored strategies for event organizers, government bodies, and destination marketing organizations to optimize event economy development. The Wenzhou case offers valuable insights for other cities seeking to harness the power of event economy for sustainable urban transformation, demonstrating how strategic integration of local advantages with modern consumption trends can create synergistic effects for high-quality development.
subject: Article
  topic: section
subject: event economy
  topic: keywords
subject: high-quality development
  topic: keywords
subject: consumption drivers
  topic: keywords
subject: urban transformation
  topic: keywords
subject: sustainable development
  topic: keywords
creator: Xueyi Lin
rights: Copyright (c) 2025 Xueyi Lin (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-05
publication date: 2025-09-05
starting page: 96
ending page: 103
doi: 10.71222/0mqerf49

Strategic Realignment: Post-Brexit Economic Relations between the UK and Japan
type: item
description: This paper examines the strategic realignment of UK-Japan economic relations in the post-Brexit era, focusing on the evolving geopolitical context, trade and investment trends, and the significance of the UK-Japan Comprehensive Economic Partnership Agreement (CEPA). Following Brexit in 2020, the “Global Britain” strategy has sought to expand trade relations beyond Europe, positioning Japan as a strategic anchor in the Indo-Pacific. Japan’s role as a major economic power, its leadership in high-value manufacturing and global finance, and its stewardship of the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) make it a natural partner for the UK. The paper outlines bilateral trade and investment patterns, the development of political and economic cooperation mechanisms-including the Reciprocal Access Agreement and the economic “2+2” dialogue in 2025, and their implications for security and market integration. A detailed analysis of CEPA highlights its negotiation background, key provisions, and departures from the EU-Japan Economic Partnership Agreement. While CEPA ensures trade continuity and introduces sector-specific enhancements, its symbolic significance may outweigh its immediate economic impact, given the structural constraints and continued dependence on EU market access.
subject: Article
  topic: section
subject: UK-Japan relations
  topic: keywords
subject: CEPA
  topic: keywords
subject: CPTPP
  topic: keywords
subject: Brexit
  topic: keywords
creator: Siying Chen
rights: Copyright (c) 2025 Siying Chen (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-27
publication date: 2025-08-27
starting page: 88
ending page: 95
doi: 10.71222/2f4ce357

Digital Brand Management in Social Media: A Case Study of Want Want's Consumer Engagement Strategy on Chinese Platforms
type: item
description: This study investigates the digital brand management strategies of Want Want Holdings Limited on Chinese social media, with a specific focus on anthropomorphic branding and consumer engagement. Employing a single case study methodology, the research analyzes the brand's official Douyin presence alongside user-generated content over one year period, utilizing content analysis of posts, engagement metrics, and brand consistency evaluation. Findings indicate that Want Want effectively utilizes anthropomorphic characteristics to foster emotional connections across diverse consumer demographics and maintains coherence between its official messaging and user interpretations, demonstrating successful digital brand identity management. However, challenges to brand authenticity arise from unofficial content creators imitating brand elements. The study yields insights for international brands operating within China's digital ecosystem, emphasizing the strategic imperative to balance brand control with organic user engagement. It contributes to understanding brand management in emerging digital markets, specifically elucidating the interplay between anthropomorphic branding and social media engagement in the Chinese context.
subject: Article
  topic: section
subject: digital brand management
  topic: keywords
subject: social media marketing
  topic: keywords
subject: anthropomorphic branding
  topic: keywords
subject: consumer engagement
  topic: keywords
subject: Chinese market
  topic: keywords
subject: Douyin
  topic: keywords
creator: Zhe Chen
rights: Copyright (c) 2025 Zhe Chen (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-26
publication date: 2025-08-26
starting page: 81
ending page: 87
doi: 10.71222/m15vwn38

The Impact of Trade Agreements on Vietnam's Export Performance: A Data-Driven Analysis (2005–2024)
type: item
description: This paper provides an in-depth, data-driven analysis of the impact of trade agreements on Vietnam's export performance spanning from 2005 to 2024. By examining the evolution of Vietnam's trade agreement network and analyzing key export indicators such as volume, value, and product-market structure changes, the study aims to develop a holistic understanding of how trade agreements have shaped Vietnam's export landscape over the past two decades. The research incorporates an analysis of various bilateral, multilateral, and regional trade agreements Vietnam has entered into, and assesses their direct and indirect effects on export performance. Through statistical and econometric techniques, the paper explores both the positive and negative outcomes of these agreements, highlighting the nuances of their impact on different sectors and markets. The results indicate that while trade agreements have had a broadly positive effect on Vietnam's export performance, contributing to increased market access, diversification of export goods, and enhanced competitiveness, the benefits have not been evenly distributed across all sectors. Several trade agreements, such as those within the Asia-Pacific region and with major trading partners, have significantly boosted the country's export volume and value, particularly in electronics, textiles, and agricultural products. However, the study also reveals that challenges remain in the implementation and optimization of trade agreements, with issues such as non-tariff barriers, regulatory discrepancies, and inadequate infrastructure hindering the full potential of these deals. Furthermore, the paper explores how Vietnam's trade agreements have influenced its export strategies, particularly in terms of product diversification, market access, and the competitiveness of domestic industries. It suggests that, despite the positive outcomes, Vietnam's export policies require refinement to address structural challenges and ensure sustainable gr…
subject: Article
  topic: section
subject: Vietnam
  topic: keywords
subject: trade agreements
  topic: keywords
subject: export performance
  topic: keywords
subject: data analysis
  topic: keywords
creator: Doan Ba Toai
rights: Copyright (c) 2025 Doan Ba Toai (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-16
publication date: 2025-08-16
starting page: 73
ending page: 80
doi: 10.71222/dwq22q38

Export Diversification and Its Role in Vietnam's Economic Resilience: A Quantitative Study
type: item
description: This quantitative study investigates the intricate relationship between export diversification and Vietnam's economic resilience, focusing on the period from 2010 to 2023. Through an in-depth analysis of export data, the study constructs several indices to assess the degree of export diversification in the country. The primary objective of the research is to examine how different levels of export diversification have contributed to Vietnam's economic stability and resilience in the face of various challenges, such as economic downturns, trade disputes, and global economic crises. Vietnam, a developing economy with a strong dependence on specific sectors and markets, faces constant exposure to external economic shocks. The paper delves into how export diversification, by spreading risks across multiple sectors and geographical regions, can mitigate these risks and offer a buffer against volatility. The study also considers how external factors, including shifts in global trade patterns, geopolitical tensions, and the COVID-19 pandemic, have underscored the importance of a diversified export portfolio for sustaining economic growth. The empirical analysis highlights key trends and correlations, demonstrating that export diversification is not only a tool for reducing Vietnam's vulnerability to external economic pressures, but also an essential factor for fostering long-term economic growth and stability. Furthermore, the paper suggests that a diversified export base can contribute to improving the country's competitiveness in global markets, enabling it to better navigate fluctuations in international demand and commodity prices. Based on these findings, the study offers policy recommendations for Vietnam, urging the government to implement strategies aimed at diversifying export markets and products. This would involve encouraging the development of new industries, expanding trade agreements with emerging economies, and enhancing the competitiveness of existing secto…
subject: Article
  topic: section
subject: Vietnam
  topic: keywords
subject: export diversification
  topic: keywords
subject: economic resilience
  topic: keywords
subject: quantitative analysis
  topic: keywords
subject: export data
  topic: keywords
creator: Doan Ba Toai
rights: Copyright (c) 2025 Doan Ba Toai (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-14
publication date: 2025-08-14
starting page: 66
ending page: 72
doi: 10.71222/5vteka28

International Management Report—An Analysis Report for Boost Juice: Entering China's Market
type: item
description: This report evaluates Boost Juice's potential entry into China's beverage market, highlighting significant opportunities driven by China's large population (over 1.4 billion), rising middle-class disposable income, and growing health consciousness. Analysis of China's business environment reveals strict food safety regulations (e.g., GACC Decree 248) as key barriers but also trust-building mechanisms for imported health brands. Regional taste preferences and competitive local markets necessitate localized marketing strategies. Recommendations include establishing flagship stores in tier-1 cities (Beijing, Shanghai, Guangzhou) with local sourcing, leveraging cross-border e-commerce, and gradual expansion via franchising to mitigate risks while capitalizing on China's projected 7% to 9% annual growth in healthy beverages.
subject: Article
  topic: section
subject: market entry strategy
  topic: keywords
subject: healthy beverage market
  topic: keywords
subject: localization
  topic: keywords
creator: Rui Jiang
rights: Copyright (c) 2025 Rui Jiang (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-02
publication date: 2025-08-02
starting page: 60
ending page: 65
doi: 10.71222/9hg1fm18

Academic and Practical Cases: From TFP Variables in DEA-Malmquist Model to the Conduction of County-Level Digital Agriculture
type: item
description: This paper investigates the construction and practical implementation of digital agriculture at the county level in China by integrating academic theories and real-world cases. Utilizing the DEA-Malmquist index model, key input variables such as labor, land, machinery, chemical fertilizers, and irrigation are analyzed to measure total factor productivity (TFP) growth in digital agriculture. The study highlights the critical role of smart sensors, big data, artificial intelligence, and Internet of Things technologies in enhancing agricultural productivity, resource optimization, and sustainable development. The paper further explores digital agriculture management systems, including production management, product traceability, and integrated management platforms, which collectively facilitate precision farming, traceability, and intelligent decision-making. The construction path emphasizes government-enterprise cooperation, data integration, and technological innovation, driving transformation from traditional experience-based agriculture to data-driven precision agriculture. The practical value lies in ensuring food security, promoting industrial upgrading, increasing farmers' income, and supporting the global green transformation. Finally, the paper provides policy recommendations focusing on technological advancement, infrastructure improvement, and talent cultivation to foster the sustainable development of digital agriculture in China.
subject: Article
  topic: section
subject: digital agriculture
  topic: keywords
subject: total factor productivity
  topic: keywords
subject: DEA-malmquist index
  topic: keywords
subject: smart sensors
  topic: keywords
subject: precision agriculture
  topic: keywords
creator: Chenxi Hu
rights: Copyright (c) 2025 Chenxi Hu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-02
publication date: 2025-08-02
starting page: 53
ending page: 59
doi: 10.71222/wtt83c67

Integration of Market-Oriented Development Models and Marketing Strategies in Real Estate
type: item
description: The real estate industry faces increasing challenges due to dynamic market conditions, evolving customer demands, and rapid technological advancements. This paper explores the integration of market-oriented development models with innovative digital marketing strategies to enhance competitive advantage. It analyzes core principles such as customer focus, competitor awareness, and responsiveness, alongside the adoption of digital tools like BIM, intelligent scheduling, and data analytics. The study highlights the transformative role of digital platforms, immersive VR/AR experiences, and agile development methodologies in reshaping development and marketing practices. Furthermore, it discusses emerging risks including data privacy and financial uncertainties, proposing strategic recommendations for sustainable growth. This integrated approach aims to align real estate development with contemporary market needs and technological trends, fostering resilience and efficiency.
subject: Article
  topic: section
subject: market orientation
  topic: keywords
subject: real estate development
  topic: keywords
subject: digital marketing
  topic: keywords
subject: BIM
  topic: keywords
subject: data analytics
  topic: keywords
subject: VR/AR
  topic: keywords
subject: agile methods
  topic: keywords
creator: Wei Sun
rights: Copyright (c) 2025 Wei Sun (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-07-29
publication date: 2025-07-29
starting page: 45
ending page: 52
doi: 10.71222/gth5zb26

The Commercialization Path of Large Language Models in Start-Ups
type: item
description: Recently, large language models (LLMs) have demonstrated strong performance in large-scale natural language processing tasks and have been widely adopted by many companies in the market. But in reality, for most start-ups, how to get their commercialization right is still a problem. This paper will analyze in detail the challenges posed to start-ups during the commercialization process of large language models, such as cost, economic constraints, compliance with relevant data protection laws, and adaptability to market demands, and provide solutions for technology integration, industry personalized design, and multiple commercialization models to help them successfully complete the commercialization process. In addition, it presents the benefits for start-ups in exploring new markets and globalization strategies. Finally, the paper summarizes the current commercialization status and future development trends of large language models and puts forward some perspectives and suggestions on the rational use of large language models to facilitate commercialization in start-ups.
subject: Article
  topic: section
subject: large language model
  topic: keywords
subject: start-ups
  topic: keywords
subject: commercialization path
  topic: keywords
creator: Hongjun Wu
rights: Copyright (c) 2025 Hongjun Wu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-07-23
publication date: 2025-07-23
starting page: 38
ending page: 44
doi: 10.71222/s628ke93