Economics and Management Innovation
type: channel
description: 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.
publisher: George Brown Press
language: en
publication name: Economics and Management Innovation
issn: 3078-7645
items: Conceptual Modeling and Semantic Relations in the Construction of Financial Knowledge Graphs
items: Research on Cross-Regional Collaborative Service Mechanisms in Rural Public Management
items: CRG-Former: Early Identification of Securities Violations via Causal Relational Graph Transformers across Multi-Market Data
items: AI-Assisted Budgeting and Cost Control: A Practical Model for Financial Decision-Making in SMEs
items: Graph-Based Deep Dive on AI Startup Revenue Composition and Venture Capital Network Effect
items: The Application of Data-Driven Financial Risk Management in Multinational Enterprises
items: Business Model Innovation in China's Cultural Heritage Sector: An Empirical Study of Intellectual Property Commercialization and Organizational Governance
items: A FEDformer-Based Hybrid Framework for Anomaly Detection and Risk Forecasting in Financial Time Series
items: A Study on the Influence of Continuous Purchase Intention of Users of Paid Courses of Sports App in the Perspective of Coi Framework
items: Supply Chain Restructuring and Competitive Strategies of Chinese Window and Door Brands in the North American Market
items: Research on Innovation of Ai-Driven Investment Decision-Making Paradigm in Primary Market
items: Using Big Data Analysis to Optimize the Financing Structure and Capital Allocation of Energy Enterprises
items: SeqUDA-Rec: Sequential User Behavior Enhanced Recommendation via Global Unsupervised Data Augmentation for Personalized Content Marketing
items: Digital Transformation Strategy and Platform Capability Building: Evidence from the Healthcare and Financial Industries
items: Does Artificial Intelligence Promote the Business Performance of Agricultural Enterprises
items: Changes in China's Winter Sports Tourism Industry After the Beijing Winter Olympics: A Big Data Perspective
items: Evaluating the Impact of Financial Policy News on Market Responses Using Natural Language Processing
items: Prediction Framework for E-Commerce Platform Sales Data Based on Informer: A Study on Furniture Sales on Amazon E-Commerce Platform
items: Research on Digital Overall Solution and Transformation Path of Fine Chemical Industry
items: User-Profile-Driven Optimization of Marketing Services for Biohealth Products and B2B Customer Relationship Management Strategies
items: Media Exposure and Corporate Financing Performance of FinTech Firms: An Empirical Study
items: Synergistic Premium Effects of Creativity and Sustainability in E-Commerce: Evidence from Consumer Willingness to Pay
items: Designing the Future of Shopping: An Immersive Simulation Framework for Digital Commerce Strategy and Retail Transformation
items: Automated Reasoning and Technological Innovation in Cloud Computing Security
items: Research on the Application of AI in Enterprise Financial Risk Management and Its Optimization Strategy
items: Research and Strategy Optimization of Quantitative Trading Live Trading Based on Index Reconstruction
items: Emotional Analysis and Strategy Optimization of Live Streaming E-Commerce Users Under the Framework of Causal Inference
items: Research on Architecture Optimization of Intelligent Cloud Platform and Performance Enhancement of MicroServices
items: The Enabling Path of Private Equity Funds in the Growth Process of Emerging Market Enterprises
items: Construction and Practice of a Sustainable Protection Model for the Commercialization of Cultural Heritage

Conceptual Modeling and Semantic Relations in the Construction of Financial Knowledge Graphs
type: item
description: Financial Knowledge Graphs (FKGs) are increasingly recognized as essential tools for managing and leveraging financial data. This review paper explores the conceptual modeling approaches and semantic relations crucial for their construction. We begin by providing a historical overview of knowledge representation in finance, tracing the evolution from early expert systems to contemporary FKGs. The core of the paper delves into two critical themes: (A) conceptual modeling techniques, including ontologies, entity-relationship diagrams, and semantic networks, and their application to financial data; and (B) the types and roles of semantic relations (e.g., is-a, part-of, influences) in connecting financial entities and concepts. We then present a comparative analysis of these modeling techniques, highlighting their strengths, weaknesses, and challenges in capturing the complexities of financial information. Furthermore, we address the unique challenges of constructing FKGs, such as data heterogeneity, ambiguity, and the dynamic nature of financial markets. The review concludes by discussing promising future research directions, including the integration of machine learning techniques for automated knowledge graph construction and the development of novel semantic relation discovery methods. This paper serves as a comprehensive guide for researchers and practitioners interested in the design and implementation of effective FKGs for various financial applications.
subject: Article
  topic: section
subject: financial knowledge graph
  topic: keywords
subject: conceptual modeling
  topic: keywords
subject: semantic relations
  topic: keywords
subject: ontology
  topic: keywords
subject: financial data
  topic: keywords
subject: knowledge representation
  topic: keywords
creator: Shuai Yuan
rights: Copyright (c) 2026 Shuai Yuan (Author)
date: 2026-02-02
publication date: 2026-02-02
starting page: 64
ending page: 70
doi: 10.71222/evj1tt66

Research on Cross-Regional Collaborative Service Mechanisms in Rural Public Management
type: item
description: In response to the growing demands for governance coordination prompted by the cross-regional flow of rural public affairs, this study systematically explores the operational logic and essential technical elements of collaborative service mechanisms. The research adopts a multi-faceted approach, integrating subject system analysis, process structure modeling, the design of data interconnection mechanisms, and the evaluation of institutional compatibility. The findings reveal that establishing unified process chains, implementing controllable and secure data exchange protocols, and constructing a hierarchical institutional coordination framework collectively contribute to significant improvements in collaborative efficiency, resource allocation, and service responsiveness. Furthermore, these elements support the development of a quantifiable supervision and evaluation system, enabling effective monitoring of inter-regional administrative operations and facilitating adaptive adjustments to policy implementation. By emphasizing both technical integration and institutional harmonization, the proposed framework provides a practical reference for enhancing the systematic and coordinated management of rural public affairs. Overall, the study demonstrates that combining structured process design, robust data connectivity, and compatible institutional arrangements forms a foundation for sustained improvements in cross-regional governance and the equitable distribution of public resources.
subject: Article
  topic: section
subject: cross-regional collaborative services
  topic: keywords
subject: rural public management
  topic: keywords
subject: data interconnection
  topic: keywords
subject: process modeling
  topic: keywords
subject: institutional coordination
  topic: keywords
creator: Huanxi Zhu
rights: Copyright (c) 2026 Huanxi Zhu (Author)
date: 2026-01-30
publication date: 2026-01-30
starting page: 56
ending page: 63
doi: 10.71222/efr1t975

CRG-Former: Early Identification of Securities Violations via Causal Relational Graph Transformers across Multi-Market Data
type: item
description: The increasing interconnection of modern financial markets has led securities violations-such as insider trading, market manipulation, and disclosure misconduct-to exhibit cross-market, multi-entity, and temporally progressive characteristics, posing significant challenges to traditional rule-based and post-event regulatory frameworks. In response to the growing demand for proactive and risk-oriented supervision, this paper addresses the task of early identification of securities violations using AI-driven analysis of cross-market multi-source data, with particular relevance to regulatory authorities, brokerage compliance departments, and merger and acquisition funds. We propose CRG-Former (Causal Relational Graph Transformer), a deep learning framework that integrates cross-market financial time-series data, heterogeneous relational graphs among market participants, and causality-aware attention mechanisms to detect potential violations at an early stage. The model employs Transformer-based temporal encoders to capture evolving abnormal trading patterns, heterogeneous graph attention networks to model complex relational dependencies, and causal attention constraints to align model inference with legal notions of behavioral causation. To enhance regulatory usability, CRG-Former further incorporates uncertainty-aware risk prediction, enabling probabilistic early warning rather than deterministic judgments. Experiments on a multi-market dataset integrating equity transactions, derivatives activity, corporate disclosures, and regulatory enforcement records show that CRG-Former achieves an AUC of 0.912, outperforming strong baseline models by over 6%. Moreover, the proposed framework provides an average early warning lead time of 18 trading days before confirmed violations, demonstrating its effectiveness in delivering timely, risk-based, and operationally meaningful signals for AI-empowered securities supervision.
subject: Article
  topic: section
subject: securities regulation
  topic: keywords
subject: early violation detection
  topic: keywords
subject: graph neural networks
  topic: keywords
subject: Transformer
  topic: keywords
subject: RegTech
  topic: keywords
subject: causal learning
  topic: keywords
subject: multi-market data
  topic: keywords
creator: Xiaolei Wang
creator: Shuhan Liao
rights: Copyright (c) 2026 Xiaolei Wang, Shuhan Liao (Author)
date: 2026-01-27
publication date: 2026-01-27
starting page: 46
ending page: 55
doi: 10.71222/kjtvwj93

AI-Assisted Budgeting and Cost Control: A Practical Model for Financial Decision-Making in SMEs
type: item
description: In an increasingly uncertain and competitive business environment, effective budgeting and cost control are critical to the financial sustainability of small and medium-sized enterprises (SMEs). However, traditional budgeting practices in SMEs are often constrained by static planning methods, fragmented data, and heavy reliance on managerial intuition. The rapid development of artificial intelligence (AI) offers new opportunities to enhance financial decision-making through data-driven analysis and predictive capabilities. This paper proposes a practical model for AI-assisted budgeting and cost control tailored to the organizational characteristics and resource constraints of SMEs. Adopting a conceptual and application-oriented approach, the study analyzes how AI can support key financial management processes, including budget forecasting, cost monitoring, and decision support, without focusing on technical algorithms or empirical testing. The proposed model emphasizes data integration, continuous analysis, and human-AI collaboration, positioning AI as a tool that augments managerial judgment rather than replacing it. By illustrating key application scenarios and discussing implementation conditions and potential risks, this paper demonstrates how AI-assisted budgeting and cost control can improve flexibility, transparency, and decision quality in SMEs. The study contributes to the understanding of AI-enabled financial management by offering a scalable and managerially relevant framework that supports more adaptive and informed financial decision-making.
subject: Article
  topic: section
subject: artificial intelligence
  topic: keywords
subject: budgeting
  topic: keywords
subject: cost control
  topic: keywords
subject: financial decision-making
  topic: keywords
subject: small and medium-sized enterprises
  topic: keywords
creator: Zhijun Liu
rights: Copyright (c) 2026 Zhijun Liu (Author)
date: 2026-01-17
publication date: 2026-01-17
starting page: 37
ending page: 45
doi: 10.71222/jkbht737

Graph-Based Deep Dive on AI Startup Revenue Composition and Venture Capital Network Effect
type: item
description: Recent years have witnessed unprecedented valuations for AI startups, driven by investor confidence in technological potential rather than established revenue streams. This article provides a conceptual overview of the revenue structures of venture-backed AI startups, focusing on network effects and reciprocal revenue relationships. It also proposes a graph-based deep dive of such a network of N=50 startups and their key investors, showcasing the concrete connectivity through visualization. Network-driven revenue enables rapid early-stage growth by leveraging investor connections and portfolio ecosystems, while reciprocal revenue, in which startups act as each other’s clients, accelerates ARR and strengthens ecosystem cohesion. Despite these advantages, both revenue models carry risks, including customer concentration, revenue volatility, and limited market validation, which may affect long-term valuation sustainability. The discussion highlights the importance for entrepreneurs and investors of assessing revenue composition, quality, and scalability, rather than relying solely on headline metrics such as ARR. By balancing early gains from network effects and reciprocal arrangements with broader market adoption, AI startups can enhance growth resilience, and investors can make more informed decisions regarding valuation and long-term potential.
subject: Article
  topic: section
subject: AI startups
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: venture capital
  topic: keywords
subject: network effects
  topic: keywords
subject: reciprocal revenue
  topic: keywords
subject: revenue diversification
  topic: keywords
subject: valuation sustainability
  topic: keywords
subject: enterprise sales
  topic: keywords
subject: graph theory
  topic: keywords
creator: Jingyao (Lux) Zhao
rights: Copyright (c) 2026 Jingyao (Lux) Zhao (Author)
date: 2026-01-17
publication date: 2026-01-17
starting page: 27
ending page: 36
doi: 10.71222/4vg9t911

The Application of Data-Driven Financial Risk Management in Multinational Enterprises
type: item
description: With the intensification of global economic integration and market complexity, the financial risks of multinational enterprises will face severe challenges. The financial risk management system based on big data helps multinational enterprises track, predict and handle financial risks in advance. This article mainly introduces the basic concepts and core technical system of data-driven financial risk management, and elaborates on the specific implementation of key technologies such as data collection, risk prediction and assessment, and data visualization in multinational enterprises. The utilization of advanced big data technologies such as machine learning and statistics helps multinational enterprises identify risk points more accurately and improve decision-making efficiency. The main purpose of this article is to propose a scientific and comprehensive financial risk management system to multinational enterprises, helping them manage financial risks better in the complex and changeable global market and promoting their sustainable and stable development.
subject: Article
  topic: section
subject: multinational enterprises
  topic: keywords
subject: financial risk management
  topic: keywords
subject: big data technology
  topic: keywords
subject: risk prediction
  topic: keywords
subject: data-driven decision-making
  topic: keywords
creator: Yuchen Liu
rights: Copyright (c) 2026 Yuchen Liu (Author)
date: 2026-01-14
publication date: 2026-01-14
starting page: 20
ending page: 26
doi: 10.71222/v5k71w76

Business Model Innovation in China's Cultural Heritage Sector: An Empirical Study of Intellectual Property Commercialization and Organizational Governance
type: item
description: This study investigates business model innovation in China's cultural heritage sector, with a particular focus on the commercialization of intellectual property (IP) and organizational governance. Over recent decades, the integration of cultural heritage into market-oriented practices has created both opportunities and challenges for cultural organizations seeking to balance preservation with economic value creation. By examining emerging business models, current approaches to IP commercialization, and supporting governance mechanisms, this research provides insights into the effective management of cultural assets in a complex legal and regulatory environment. The study highlights how strategic IP management and organizational innovation can enhance the economic potential of cultural heritage resources while ensuring their long-term protection, offering both theoretical and practical guidance for stakeholders in the industry.
subject: Article
  topic: section
subject: cultural heritage
  topic: keywords
subject: intellectual property commercialization
  topic: keywords
subject: business model innovation
  topic: keywords
subject: organizational governance
  topic: keywords
subject: China
  topic: keywords
creator: Yaoting Li
rights: Copyright (c) 2026 Yaoting Li (Author)
date: 2026-01-14
publication date: 2026-01-14
starting page: 9
ending page: 19
doi: 10.71222/dmqyvc69

A FEDformer-Based Hybrid Framework for Anomaly Detection and Risk Forecasting in Financial Time Series
type: item
description: Financial markets are inherently volatile and prone to sudden disruptions such as market crashes, flash collapses, and liquidity crises. Accurate anomaly detection and early risk forecasting in financial time series are therefore crucial for preventing systemic instability and supporting informed investment decisions. Traditional deep learning models, such as LSTM and GRU, often fail to capture long-term dependencies and complex periodic patterns in highly non-stationary financial data. To address this limitation, this study proposes a FEDformer-Based Hybrid Framework for Anomaly Detection and Risk Forecasting in Financial Time Series, which integrates the Frequency Enhanced Decomposed Transformer (FEDformer) with a residual-based anomaly detector and a risk forecasting head. The FEDformer module models temporal dynamics in both time and frequency domains, decomposing signals into trend and seasonal components for improved interpretability. The residual-based detector identifies abnormal fluctuations by analyzing prediction errors, while the risk head predicts potential financial distress using learned latent embeddings. Experiments conducted on the S&P 500, NASDAQ Composite, and Brent Crude Oil datasets (2000-2024) demonstrate the superiority of the proposed model over benchmark methods, achieving an 15.7% reduction in RMSE and a 11.5% improvement in F1-score for anomaly detection. These results confirm the model's effectiveness in capturing financial volatility, enabling reliable early-warning systems for market crash prediction and risk management.
subject: Article
  topic: section
subject: FEDformer
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: risk forecasting
  topic: keywords
subject: financial time series
  topic: keywords
subject: deep learning
  topic: keywords
subject: Transformer
  topic: keywords
subject: frequency domain
  topic: keywords
creator: Ziling Fan
creator: Ruijia Liang
creator: Yiwen Hu
rights: Copyright (c) 2026 Ziling Fan, Ruijia Liang, Yiwen Hu (Author)
date: 2026-01-05
publication date: 2026-01-05
starting page: 1
ending page: 8
doi: 10.71222/dymz5n98

A Study on the Influence of Continuous Purchase Intention of Users of Paid Courses of Sports App in the Perspective of Coi Framework
type: item
description: The rise of online information technology has spurred the swift advancement of sports app technology in China. Due to various factors, such as the epidemic, more people are gradually opting for sports app courses. Despite the increasing number of registrants every year, the percentage of paying customers does not grow, and the retention rate of paying customers is low. The presence of a sense of community is a crucial factor that affects the effectiveness of paid sports app courses. To investigate the impact of CoI presence on the willingness to continue learning through sports app paid courses, the study utilized the ACSI model and ECM model. The research team distributed online questionnaires to the top three paid sports app user groups in China, receiving 353 valid responses. They employed structural equation modeling and mediating effect analysis to determine the structural and quantitative relationships between variables. The findings of the study reveal that the intention to continue purchasing sports app paid courses is significantly influenced by perceived quality and CoI satisfaction. Expectation confirmation, perceived quality, and perceived value significantly affect the CoI satisfaction of sports app paid courses and indirectly impact the intention to continue purchasing, with CoI satisfaction acting as the mediating variable. Additionally, expectation confirmation has a significant impact on perceived quality and perceived value. Based on these results, the study provides recommendations to foster the continuous purchase intention of users of paid sports app courses.
subject: Article
  topic: section
subject: CoI framework
  topic: keywords
subject: continuous purchase
  topic: keywords
subject: app paid courses
  topic: keywords
subject: ACSI model
  topic: keywords
subject: ECM model
  topic: keywords
creator: Lin Shen
creator: Mingming Li
rights: Copyright (c) 2025 Lin Shen, Mingming Li (Author)
date: 2025-12-27
publication date: 2025-12-27
starting page: 32
ending page: 43
doi: 10.71222/9hkjbj47

Supply Chain Restructuring and Competitive Strategies of Chinese Window and Door Brands in the North American Market
type: item
description: This study investigates how global supply chain restructuring reshapes the competitive strategies of Chinese window and door brands seeking sustainable development in the North American market. As geopolitical tensions, regionalization trends, sustainability requirements, and technological transformation intensify, Chinese firms face both opportunities and constraints in overseas expansion. Guided by supply chain resilience theory, international business strategy, and branding frameworks, this paper analyzes strategic pathways that can strengthen competitiveness under the emerging global supply chain landscape. The research reveals that competitive advantages increasingly depend on diversified supply chain layouts, localization-oriented operational adjustments, compliance with regulatory standards, and higher-value brand positioning. The findings highlight that Chinese window and door firms must move beyond cost-driven models and adopt integrated strategies that emphasize technological upgrading, digital coordination, and sustainable value creation. The study contributes to ongoing discussions on the upgrading trajectory of Chinese manufacturing and offers strategic recommendations for enhancing long-term competitiveness in North America.
subject: Article
  topic: section
subject: supply chain restructuring
  topic: keywords
subject: Chinese window and door brands
  topic: keywords
subject: North American market
  topic: keywords
subject: competitive strategies
  topic: keywords
subject: localization
  topic: keywords
subject: branding strategy
  topic: keywords
creator: Haiwen Yao
rights: Copyright (c) 2025 Haiwen Yao (Author)
date: 2025-12-18
publication date: 2025-12-18
starting page: 25
ending page: 31
doi: 10.71222/bvw8we74

Research on Innovation of Ai-Driven Investment Decision-Making Paradigm in Primary Market
type: item
description: The primary market plays a critical role in allocating capital to emerging enterprises and innovative startups, yet traditional investment decision-making faces challenges such as information asymmetry, subjective bias, and inefficiency. This review explores the transformative potential of artificial intelligence (AI) in primary market investment, examining applications in venture capital and private equity across deal sourcing, due diligence, portfolio management, and exit planning. It presents a comprehensive AI-driven investment decision framework that integrates multi-source data acquisition, machine learning, deep learning, natural language processing, and graph-based modeling to enhance predictive accuracy and decision support. Key challenges, including data quality, privacy, interpretability, talent gaps, and ethical considerations, are discussed alongside future directions such as multi-modal data fusion, reinforcement learning, explainable AI, and human-AI collaborative strategies. The review highlights AI’s capacity to systematize, optimize, and intelligentize investment processes, offering new pathways for efficiency, risk management, and strategic value creation in primary market activities.
subject: Article
  topic: section
subject: artificial intelligence
  topic: keywords
subject: primary market investment
  topic: keywords
subject: venture capital
  topic: keywords
subject: private equity
  topic: keywords
subject: data-driven decision-making
  topic: keywords
creator: Yifeng Li
rights: Copyright (c) 2025 Yifeng Li (Author)
date: 2025-12-14
publication date: 2025-12-14
starting page: 16
ending page: 24
doi: 10.71222/ed2tw043

Using Big Data Analysis to Optimize the Financing Structure and Capital Allocation of Energy Enterprises
type: item
description: With the gradual development of big data technology, the application of big data technology has a certain impact on the financing structure and capital allocation of energy enterprises. Through data integration, the information big data platform establishes an intelligent data analysis model, which can more effectively optimize financing methods, accurately position investment directions, and promptly determine the location of investment risks. This paper aims at the problems of concentrated funds, investment deviation and low risk awareness of current energy enterprises, and proposes a big data aggregation approach to solve them, in order to improve the effectiveness of the company's financial management, achieve the goal of structural adjustment.
subject: Article
  topic: section
subject: big data analysis
  topic: keywords
subject: energy enterprise
  topic: keywords
subject: financing structure
  topic: keywords
subject: capital allocation
  topic: keywords
subject: risk early warning system
  topic: keywords
creator: Yuhan Zhou
rights: Copyright (c) 2025 Yuhan Zhou (Author)
date: 2025-12-14
publication date: 2025-12-14
starting page: 8
ending page: 15
doi: 10.71222/95bjdv97

SeqUDA-Rec: Sequential User Behavior Enhanced Recommendation via Global Unsupervised Data Augmentation for Personalized Content Marketing
type: item
description: Personalized content marketing has become a central strategy for digital platforms seeking to deliver highly relevant advertisements and recommendations. However, conventional recommendation systems often rely heavily on sparse supervised signals derived from explicit user feedback and are easily affected by noisy or unintentional interactions, resulting in unstable predictions. To overcome these limitations, we propose SeqUDA-Rec, a unified deep learning framework that incorporates sequential user behavior modeling with global unsupervised data augmentation. The framework begins by constructing a Global User-Item Interaction Graph (GUIG) from all historical behavior sequences, enabling the extraction of both local item transitions and global cross-user relational structures. A graph contrastive learning module is introduced to learn noise-resistant representations by maximizing agreement across multiple graph views. Meanwhile, a Transformer-based sequential encoder captures users' evolving interests and long-term dependencies within interaction trajectories. To further mitigate the challenges of limited labeled data and behavior sparsity, SeqUDA-Rec integrates a GAN-based augmentation module, which generates realistic synthetic sub-sequences to enrich training diversity and improve model generalization. We evaluate SeqUDA-Rec on two large-scale advertising datasets-Amazon Ads and TikTok Ad Clicks-covering both stable e-commerce environments and fast-changing short-video scenarios. Experimental results show that our model consistently outperforms strong baselines including SASRec, BERT4Rec, and GCL4SR, achieving 6.7% improvement in NDCG@10 and 11.3% improvement in HR@10. These results demonstrate that SeqUDA-Rec effectively enhances recommendation robustness, alleviates noise sensitivity, and provides a powerful solution for real-world personalized content marketing.
subject: Article
  topic: section
subject: sequential recommendation
  topic: keywords
subject: user behavior analysis
  topic: keywords
subject: graph contrastive learning
  topic: keywords
subject: GAN-based augmentation
  topic: keywords
subject: personalized content marketing
  topic: keywords
creator: Ruihan Luo
creator: Xuanjing Chen
creator: Ziyang Ding
rights: Copyright (c) 2025 Ruihan Luo, Xuanjing Chen, Ziyang Ding (Author)
date: 2025-12-11
publication date: 2025-12-11
starting page: 1
ending page: 7
doi: 10.71222/1r79aa27

Digital Transformation Strategy and Platform Capability Building: Evidence from the Healthcare and Financial Industries
type: item
description: Successful digital transformation is a strategic imperative across all industries and hasreshapedhow organizations create value, govern data, and engage with ecosystems. This paper provides a comprehensive review of digital transformation strategies and platform capability building in the healthcare and financial sectors. By analyzing drivers, challenges, and emerging technologies, the study highlights the critical role of platform architecture, governance, and ecosystem integration in enabling sustainable innovation. Comparative analysis reveals key commonalities, such as the centrality of trust and regulatory compliance, as well as distinctions, including the public-interest-driven nature of healthcare versus the market-oriented dynamics of finance. The findings offer theoretical insights into dynamic capabilities and ecosystem orchestration, as well as practical guidance for managers and policymakers seeking to align strategy, capability, and governance in digital transformation initiatives. Future research directions emphasize quantitative modeling, longitudinal studies, and cross-sector policy analysis to deepen understanding of digital maturity and platform evolution.
subject: Article
  topic: section
subject: digital transformation
  topic: keywords
subject: platform capability
  topic: keywords
subject: healthcare
  topic: keywords
subject: financial services
  topic: keywords
subject: ecosystem integration
  topic: keywords
subject: governance
  topic: keywords
creator: Yutian (Summer) Cai
rights: Copyright (c) 2025 Yutian (Summer) Cai (Author)
date: 2025-12-09
publication date: 2025-12-09
starting page: 119
ending page: 130
doi: 10.71222/dfr5xw46

Does Artificial Intelligence Promote the Business Performance of Agricultural Enterprises
type: item
description: Artificial intelligence (AI) plays a pivotal role in advancing the development of agricultural enterprises and is essential for enhancing their business performance. This study empirically investigates the influence of AI on the operational performance of agricultural firms in China, drawing on data from A-share listed agricultural companies on the Shanghai and Shenzhen stock exchanges from 2003 to 2023. A fixed-effects model is employed to examine both the effects of AI and the underlying mechanisms. The empirical results show that AI significantly boosts the business performance of agricultural enterprises, with particularly pronounced effects among non-high-tech firms, non-heavy-pollution enterprises, and asset-intensive industries. AI also exerts a stronger positive impact on firms in their growth stage, whereas its effect on mature enterprises is not statistically significant. Mechanism analysis reveals that AI enhances performance by easing financing constraints, strengthening organizational resilience, and promoting collaborative R&D. Moreover, policy measures-such as government innovation subsidies-substantially amplify the positive impact of AI on firm performance. Based on these findings, the study recommends strengthening AI-driven technological innovation and application, designing differentiated policy measures tailored to the needs of various types of agricultural enterprises, enhancing complementary capabilities for smart transformation, and optimizing organizational structures to further unlock AI's potential in improving business performance.
subject: Article
  topic: section
subject: artificial intelligence
  topic: keywords
subject: agriculture
  topic: keywords
subject: business performance
  topic: keywords
subject: innovation
  topic: keywords
creator: Yutao Liang
rights: Copyright (c) 2025 Yutao Liang (Author)
date: 2025-12-08
publication date: 2025-12-08
starting page: 109
ending page: 118
doi: 10.71222/ze0nf816

Changes in China's Winter Sports Tourism Industry After the Beijing Winter Olympics: A Big Data Perspective
type: item
description: With the successful hosting of the 2022 Beijing Winter Olympics, China's winter sports tourism industry has entered a period of accelerated development. This study uses big data analytics to examine the long-term impacts of the Winter Olympics on market demand, industrial structure, and cultural integration. Data related to the keyword "winter sports tourism industry" were collected from Baidu, Google, and Sina Weibo from February 21, 2022, to November 4, 2024. Using Textom, keyword frequency analysis, TF-IDF weighting, and CONCOR clustering, the study identifies core themes and development patterns in the post-Olympic era. Results show that "economy," "market," "sustainability," and "culture" are key topics driving industry discourse. Four clusters were identified: market expansion, cultural tourism integration, infrastructure development, and international cooperation. Findings indicate that the Beijing Winter Olympics significantly enhanced public attention and stimulated new business opportunities in winter tourism. This research provides empirical evidence to support policy formulation, industrial planning, and sustainable development in China's winter sports tourism sector.
subject: Article
  topic: section
subject: winter sports tourism
  topic: keywords
subject: Beijing Winter Olympics
  topic: keywords
subject: big data analysis
  topic: keywords
subject: industry development
  topic: keywords
subject: TF-IDF
  topic: keywords
subject: CONCOR
  topic: keywords
creator: Jin Peng
rights: Copyright (c) 2025 Jin Peng (Author)
date: 2025-12-05
publication date: 2025-12-05
starting page: 101
ending page: 108
doi: 10.71222/ch6ea355

Evaluating the Impact of Financial Policy News on Market Responses Using Natural Language Processing
type: item
description: This study presents a practical and replicable framework for analyzing the impact of financial policy news on market behavior by integrating natural language processing (NLP) techniques with market prediction models. By extracting sentiment scores, sentiment volatility, news volume, topic intensity, and policy uncertainty indicators from policy texts, the research transforms unstructured information into quantitative features that help explain market returns and volatility. Using simulated yet realistic data, models such as linear regression, random forest, and LSTM are employed to evaluate the predictive value of these features, showing that NLP-derived indicators enhance the forecasting of market responses. While the study confirms the usefulness of textual features in capturing policy-driven market dynamics, it also acknowledges limitations related to data scale and model generalizability. The findings offer methodological insights that may support investors, financial institutions, and policymakers in interpreting and anticipating market behavior under policy influence.
subject: Article
  topic: section
subject: natural language processing
  topic: keywords
subject: financial policy
  topic: keywords
subject: sentiment analysis
  topic: keywords
subject: market prediction
  topic: keywords
subject: textual features
  topic: keywords
subject: policy uncertainty
  topic: keywords
creator: Junchi Tian
rights: Copyright (c) 2025 Junchi Tian (Author)
date: 2025-11-27
publication date: 2025-11-27
starting page: 88
ending page: 100
doi: 10.71222/dr58vx16

Prediction Framework for E-Commerce Platform Sales Data Based on Informer: A Study on Furniture Sales on Amazon E-Commerce Platform
type: item
description: Accurate sales forecasting is a critical requirement for e-commerce platforms, as it supports efficient inventory management, dynamic pricing, and targeted marketing strategies. Nonetheless, forecasting is inherently challenging due to the volatility of consumer demand, the impact of external factors such as economic indicators and weather conditions, and the occurrence of promotional events. To address these complexities, this study proposes a prediction framework based on the Informer model, a Transformer-based architecture specifically designed for long-sequence time series forecasting. The framework leverages weekly sales data of five furniture categories from the Amazon e-commerce platform spanning 2015 to 2020, combined with auxiliary features including price, discount rate, consumer price index (CPI), and weather-related variables. By integrating both temporal dependencies and exogenous influences, the model captures intricate patterns in sales dynamics. Experimental results demonstrate that the Informer model significantly outperforms traditional baselines, including LSTM, GRU, and Prophet, achieving a mean absolute error (MAE) of 37.3 and a root mean square error (RMSE) of 52.7. These findings highlight the model’s effectiveness in enhancing e-commerce sales forecasting and underscore the potential of advanced deep learning techniques to improve operational decision-making for digital retail platforms.
subject: Article
  topic: section
subject: e-commerce
  topic: keywords
subject: sales prediction
  topic: keywords
subject: Informer
  topic: keywords
subject: Amazon
  topic: keywords
subject: time series forecasting
  topic: keywords
subject: deep learning
  topic: keywords
creator: Lianzhen Wang
creator: Xi Zhang
creator: Lu Jiang
rights: Copyright (c) 2025 Lianzhen Wang, Xi Zhang, Lu Jiang (Author)
date: 2025-11-26
publication date: 2025-11-26
starting page: 80
ending page: 87
doi: 10.71222/tnb55s75

Research on Digital Overall Solution and Transformation Path of Fine Chemical Industry
type: item
description: The fine chemical industry, characterized by complex multi-step processes and high-value products, faces challenges in efficiency, quality, and sustainability under traditional production methods. This review examines the application of digital technologies—including Industrial Internet of Things (IIoT), big data analytics, artificial intelligence, cloud and edge computing, and automation and robotics—in addressing these challenges. It highlights the benefits of digital transformation for process optimization, quality enhancement, cost and energy reduction, safety improvement, and environmental sustainability. A stepwise transformation roadmap, from basic automation and data collection to intelligent optimization and predictive operations, is proposed to guide enterprises through effective digitalization. Case studies and quantitative data illustrate the tangible outcomes of digital adoption, while future trends emphasize AI-driven autonomous production, integration with green chemistry, cross-industry collaboration, and platform economies. This review provides actionable insights for policymakers, managers, and engineers aiming to achieve sustainable growth and competitive advantage in the fine chemical industry through comprehensive digital solutions.
subject: Article
  topic: section
subject: digital transformation
  topic: keywords
subject: fine chemical industry
  topic: keywords
subject: IIoT
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: process optimization
  topic: keywords
creator: Dajiang Guo
rights: Copyright (c) 2025 Dajiang Guo (Author)
date: 2025-11-24
publication date: 2025-11-24
starting page: 71
ending page: 79
doi: 10.71222/6na51q97

User-Profile-Driven Optimization of Marketing Services for Biohealth Products and B2B Customer Relationship Management Strategies
type: item
description: The Bio-Health Product sector, characterized by rapid technological advancements and intense market competition, faces unique challenges in formulating marketing strategies and managing B2B customer relationships. Traditional, generalized marketing models are no longer sufficient to meet the precise and diverse needs of B-end clients, including hospitals, professional clinics, retail pharmacy chains, and corporate wellness centers. This paper argues that a shift toward strategic, data-driven user profiling is essential for enhancing marketing effectiveness and fostering long-term B-client partnerships. By moving beyond basic firmographic information and developing dynamic, multi-dimensional profiles of client institutions and end-users, companies can achieve unprecedented precision in their marketing initiatives. This study outlines a comprehensive approach to creating B-client profiles, incorporating critical dimensions such as organizational operations, clinical performance, economic drivers, and end-user demographics. These dimensions directly inform the optimization of the key components of the marketing mix-product, price, place, and promotion-enabling the transformation of standard offerings into tailored service solutions. Furthermore, the paper highlights the intrinsic link between user-profile-based marketing and B-client retention. This approach redefines the vendor-client relationship: rather than simply exchanging products, vendors and clients engage as collaborative partners who co-create value through effective cooperation. In the knowledge-intensive bio-health field, sustainable competitive advantage arises not merely from superior products, but from delivering actionable insights and personalized services derived from a deep understanding of clients.
subject: Article
  topic: section
subject: user profiling
  topic: keywords
subject: B2B marketing
  topic: keywords
subject: bio-health products
  topic: keywords
subject: CRM (Customer Relationship Management)
  topic: keywords
subject: marketing optimization
  topic: keywords
subject: servitization
  topic: keywords
subject: value co-creation
  topic: keywords
creator: Yan Sun
rights: Copyright (c) 2025 Yan Sun (Author)
date: 2025-11-17
publication date: 2025-11-17
starting page: 63
ending page: 70
doi: 10.71222/vdt4a695

Media Exposure and Corporate Financing Performance of FinTech Firms: An Empirical Study
type: item
description: The rapid expansion of financial-technology (fintech) firms has reshaped global finance, yet the influence of media exposure on their corporate financing performance remains insufficiently explored. Studies on traditional firms indicate that favorable media coverage can reduce information asymmetry and lower financing costs, but there is limited understanding of how tone, timing, and regulatory narratives affect outcomes in the fintech context. To address this gap, this study develops a Media-Signal-Finance (MSF) framework, integrating media-signaling theory with mechanisms of information-asymmetry reduction, and applies a comparative case-study approach to Ant Group (China) and Revolut (United Kingdom). Media data from 2018 to 2024 were coded for tone, intensity, and timing, and matched with financing indicators such as valuation and fundraising success rate. The results reveal that positive, well-timed coverage strengthens investor confidence and enhances fundraising outcomes, whereas negative or regulatory-critical exposure undermines credibility and delays financing processes. This study advances theoretical understanding by linking media signaling with behavioral and institutional factors and provides practical insights for managers, investors, and regulators to strategically manage media visibility during fintech financing activities.
subject: Article
  topic: section
subject: fintech firms
  topic: keywords
subject: media exposure
  topic: keywords
subject: corporate financing performance
  topic: keywords
subject: signalling theory
  topic: keywords
subject: comparative case study
  topic: keywords
creator: Ziyin Zhou
rights: Copyright (c) 2025 Ziyin Zhou (Author)
date: 2025-11-09
publication date: 2025-11-09
starting page: 53
ending page: 62
doi: 10.71222/7jzr4z35

Synergistic Premium Effects of Creativity and Sustainability in E-Commerce: Evidence from Consumer Willingness to Pay
type: item
description: The increasing emphasis on sustainable consumption and the widespread application of creative design in e-commerce underscore the need to understand how these two elements jointly affect consumers' willingness to pay (WTP). Although previous research has explored creativity and sustainability as independent drivers of consumer behavior, little attention has been paid to their interactive effects or the psychological pathways through which they generate premium value. This study bridges this gap by adopting a multi-method approach that integrates cross-platform analyses of Etsy, Amazon Handmade, and Tmall Green with a survey experiment involving 600 participants. The results indicate that creativity enhances both the hedonic and symbolic value of products, while sustainability strengthens their ethical and environmental appeal. When combined, these two attributes create a synergistic premium effect that exceeds the sum of their individual influences. Mediation analysis further reveals that perceptions of authenticity and feelings of moral satisfaction partially account for this effect. Overall, the study enriches value perception theory by linking hedonic and ethical dimensions and offers practical guidance for e-commerce platforms and sellers aiming to increase product premiums through the integration of creative design and credible sustainability initiatives.
subject: Article
  topic: section
subject: creativity
  topic: keywords
subject: sustainability
  topic: keywords
subject: willingness to pay (WTP)
  topic: keywords
subject: authenticity
  topic: keywords
subject: e-commerce platforms
  topic: keywords
creator: Wanyi Zhang
rights: Copyright (c) 2025 Wanyi Zhang (Author)
date: 2025-11-08
publication date: 2025-11-08
starting page: 43
ending page: 52
doi: 10.71222/xwanf219

Designing the Future of Shopping: An Immersive Simulation Framework for Digital Commerce Strategy and Retail Transformation
type: item
description: This paper proposes an immersive simulation framework for digital business strategy and retail transformation. The framework integrates digital twins, scenario factories, and VR/AR/WebXR front ends with in-situ analytics and computational capabilities. Built on a hyperconverged, on-demand, low-latency infrastructure, it supports multiple users while maintaining restricted telemetry. A KPI ontology unifies four critical dimensions: business, operations, experience, and governance. A traceable experimental process-including process traceability, versioned scenarios, and pre-registered metrics-ensures auditability and reproducibility. The framework combines enterprise-level platform practices with evaluation methods from design science research, such as analysis demonstrations, scenario walkthroughs, and Delphi expert reviews, to validate its feasibility. Results demonstrate that the framework achieves orders-of-magnitude acceleration with minimal data movement, significantly reducing offline testing costs and shifting decision-making from traditional "offline A/B testing with local BI" to a "simulation-first approach with closed-loop measurement."
subject: Article
  topic: section
subject: immersive simulation framework
  topic: keywords
subject: digital twin
  topic: keywords
subject: phygital retail
  topic: keywords
subject: Design Science Research (DSR)
  topic: keywords
creator: Jing Yang
rights: Copyright (c) 2025 Jing Yang (Author)
date: 2025-11-08
publication date: 2025-11-08
starting page: 33
ending page: 42
doi: 10.71222/nxccya55

Automated Reasoning and Technological Innovation in Cloud Computing Security
type: item
description: 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.
subject: Article
  topic: section
subject: cloud computing security
  topic: keywords
subject: automated reasoning
  topic: keywords
subject: knowledge graph
  topic: keywords
subject: technological innovation
  topic: keywords
subject: privacy protection
  topic: keywords
creator: Yihong Zou
rights: Copyright (c) 2025 Yihong Zou (Author)
date: 2025-11-02
publication date: 2025-11-02
starting page: 25
ending page: 32
doi: 10.71222/mkf1wm07

Research on the Application of AI in Enterprise Financial Risk Management and Its Optimization Strategy
type: item
description: 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.
subject: Article
  topic: section
subject: artificial intelligence
  topic: keywords
subject: financial risk
  topic: keywords
subject: risk identification
  topic: keywords
subject: data governance
  topic: keywords
creator: Cheng Sheng
rights: Copyright (c) 2025 Cheng Sheng (Author)
date: 2025-10-25
publication date: 2025-10-25
starting page: 18
ending page: 24
doi: 10.71222/26p0dk79

Research and Strategy Optimization of Quantitative Trading Live Trading Based on Index Reconstruction
type: item
description: 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.
subject: Article
  topic: section
subject: index reconstruction
  topic: keywords
subject: quantitative trading
  topic: keywords
subject: strategy optimization
  topic: keywords
subject: real time backtesting
  topic: keywords
creator: Minghao Chi
rights: Copyright (c) 2025 Minghao Chi (Author)
date: 2025-10-22
publication date: 2025-10-22
starting page: 9
ending page: 17
doi: 10.71222/61mrfr64

Emotional Analysis and Strategy Optimization of Live Streaming E-Commerce Users Under the Framework of Causal Inference
type: item
description: 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.
subject: Article
  topic: section
subject: causal inference
  topic: keywords
subject: counterfactual reasoning
  topic: keywords
subject: graph neural network
  topic: keywords
subject: multi modal sentiment analysis
  topic: keywords
subject: live streaming e-commerce
  topic: keywords
creator: Xiao Liu
rights: Copyright (c) 2025 Xiao Liu (Author)
date: 2025-10-22
publication date: 2025-10-22
starting page: 1
ending page: 8
doi: 10.71222/30ewjs62

Research on Architecture Optimization of Intelligent Cloud Platform and Performance Enhancement of MicroServices
type: item
description: 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.
subject: Article
  topic: section
subject: intelligent cloud platform
  topic: keywords
subject: microservice architecture
  topic: keywords
subject: performance optimization
  topic: keywords
creator: Xiang Chen
rights: Copyright (c) 2025 Xiang Chen (Author)
date: 2025-10-21
publication date: 2025-10-21
starting page: 103
ending page: 111
doi: 10.71222/hcak5w83

The Enabling Path of Private Equity Funds in the Growth Process of Emerging Market Enterprises
type: item
description: 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.
subject: Article
  topic: section
subject: private equity funds
  topic: keywords
subject: emerging markets
  topic: keywords
subject: enterprise growth
  topic: keywords
subject: resource-based theory
  topic: keywords
subject: empowerment pathways
  topic: keywords
creator: Xiaoyi Zhang
rights: Copyright (c) 2025 Xiaoyi Zhang (Author)
date: 2025-10-17
publication date: 2025-10-17
starting page: 94
ending page: 102
doi: 10.71222/511cxp26

Construction and Practice of a Sustainable Protection Model for the Commercialization of Cultural Heritage
type: item
description: 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.
subject: Article
  topic: section
subject: cultural heritage
  topic: keywords
subject: commercialization
  topic: keywords
subject: sustainable protection
  topic: keywords
subject: cultural economy
  topic: keywords
subject: digital empowerment
  topic: keywords
subject: heritage management
  topic: keywords
creator: Liyan Zhai
rights: Copyright (c) 2025 Liyan Zhai (Author)
date: 2025-10-09
publication date: 2025-10-09
starting page: 86
ending page: 93
doi: 10.71222/pjh1fg47