Frontiers in Business, Economics and Management
Frontiers in Business, Economics and Management (FBEM) is a monthly, peer-reviewed journal published by Darcy & Roy Press. The mission of the journal is to provide an academic forum for discussing the issues of international business, economics and management across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Research on Financial Flexibility, R&D Investment and Corporate Performance
Amid the global wave of globalization, innovation has become the core driving force for corporate development, with the strength of innovation capability serving as the decisive factor for enterprises to stand out. Based on uncertainty theory, financing constraint theory, and flexible portfolio theory, this paper employs empirical research methods to deeply analyze how financial flexibility impacts corporate performance, with a focus on the mediating role of R&D investment. The study selects A-share listed companies on the main board of the Shanghai and Shenzhen stock exchanges from 2019 to 2024, screening a total of 6,354 sample data. Through empirical research and analysis, the following conclusions are drawn: (1) Enterprises can enhance financial performance by improving financial flexibility; (2) Higher financial flexibility promotes corporate R&D investment activities; (3) R&D investment acts as a mediator between financial flexibility and corporate performance. Finally, the paper proposes relevant recommendations based on the experimental results.
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Construction and Verification of a Scoring System for Chinese Export-Oriented Households/SMEs' Overseas Investment Decisions in Southeast Asian Countries
This study addresses the decision-making needs of Chinese export-oriented households and SMEs in their overseas expansion in Southeast Asia. A scoring system was constructed and verified through research. First, based on authoritative data sources such as the World Bank, the WGI (Global Governance Indicator), and the Doing Business Index, key dimensions such as political stability, production costs, and tariffs (for exports to Europe and the US) were selected to quantitatively assess and compare the scores of key Southeast Asian countries undergoing industrial relocation. Second, in-depth interviews were conducted with entrepreneurs with actual overseas export experience to cross-validate the effectiveness of the scoring system and analyze the consistency and differences between the actual decision-making focus of enterprises and the model indicators. The research results will help reveal the factors most valued by Chinese SMEs when choosing overseas locations and their degree of matching with the objective indicator system, thus providing quantitative support for enterprises to formulate overseas investment strategies.
A Study on the Geopolitical Risks and Countermeasures of Chinese Mining Enterprises' Expansion into Africa
Mineral resource security is crucial to national security and energy transition. Africa, with its abundant reserves of key minerals, has become a strategic location in the global mineral supply chain. In recent years, Chinese mining companies have leveraged the Belt and Road Initiative to increase their investment in African mining, gaining a significant development advantage. However, influenced by internal and external factors, geopolitical risks continue to rise. This paper analyzes the three core risks faced by Chinese mining companies venturing into Africa: rising economic nationalism leading to policy tightening, political instability in Africa causing a deterioration in the investment environment, and intensified competition among major powers impacting cooperation. Based on practical cases, it proposes targeted countermeasures and suggestions from three dimensions: mitigating nationalist sentiments, responding to political changes, and enhancing competitive advantages, providing a reference for Chinese mining companies to avoid risks and achieve sustainable development.
The Dilemmas and Coping Strategies of Disabled People Becoming Ride-Hailing Drivers in China under the Platform Economy Background
With the rapid development of the platform economy, the ride-hailing industry has ushered in a booming wave, which has provided new opportunities for promoting employment for disabled people. Currently, many disabled people in China have gradually participated in the ride-hailing industry and achieved effective employment. However, in reality, disabled people in China still face certain employment difficulties as ride-hailing drivers, mainly manifested in low public awareness of relevant regulations, implicit discrimination against disabled people, and intense competition in the ride-hailing industry. To effectively break this predicament, the Chinese government should increase publicity on relevant regulations, address implicit employment discrimination, and increase preferential support for disabled ride-hailing drivers, thereby effectively solving the employment difficulties faced by disabled people as ride-hailing drivers in China.
Data-Driven Mobile Network User Satisfaction Analysis: An Optimization Method Based on Multi-Model Fusion
With the rapid development of mobile communication technology, improving user satisfaction has become a core focus for mobile network operators. In an era of ubiquitous connectivity and homogeneous services, understanding and enhancing user experience is crucial for maintaining competitive advantages. Traditional methods to improve user satisfaction, such as responding to complaints and resolving specific issues, have become insufficient with the expansion of user bases and the diversification of mobile services. This study proposes a data-driven approach to analyze the factors influencing mobile user satisfaction in Beijing, focusing on voice and internet services. We combine feature engineering, decision tree models, and ensemble learning techniques to predict and quantify user satisfaction. Specifically, we use the CART (Classification and Regression Tree) model to extract feature importance, and integrate Random Forest and XGBoost models to further improve prediction accuracy through hyperparameter optimization and model fusion. This method combines structured data with unstructured text data, including user descriptions and service notes, to deeply explore the core factors of user experience. Experimental results show that multi-model fusion significantly improves prediction accuracy, with factors such as GPRS traffic, monthly usage, and network issues identified as the main drivers affecting user satisfaction. This study provides valuable insights for mobile network operators to optimize services and enhance customer experience. Additionally, the proposed method can be extended to other regions and applied to multiple industries where customer satisfaction is critical.
Online Health Care Consumerism of Shanghai College Students
With the rapid development of e-commerce, online consumption is increasingly popular as an emerging way of consumption. As the main group of online consumption, college students have become the most important part of Internet consumption, but the existing problems among them are also becoming increasingly prominent.This paper analyzes the problems in college students, especially physical and mental health, and proposes corresponding countermeasures. It mainly includes the analysis of the current situation of college students' online consumption, the existing problems and reasons, combined with e-commerce, marketing, psychology and other aspects of the content. Shanghai is one of the most developed cities in China, so the problems of college students in Shanghai in online shopping will be more representative and obvious, which will serve as a warning. Through the analysis of Shanghai college students' online consumption behavior and psychological performance, this paper understands individual differences and group characteristics, and puts forward corresponding countermeasures according to relevant theories, so as to guide college students to consume rationally and healthily.
State Evaluation of HVDC Core Equipment Based on BP Neural Network
The damage of core power equipment will cause serious losses to the power grid, and the evaluation and diagnosis of power equipment status can effectively avoid the economic loss of the power grid. In this paper, combining the operating characteristics of the DC transmission system and the impact of related core equipment on the stability of the system, the set value iteration method and BP neural network are used to determine the time-varying weight of the evaluation index of the core equipment of the DC transmission. Ideal samples and negative ideal samples, calculate the gray correlation degree between each sample and the positive and negative ideal samples and calculate the closeness of the core equipment status to realize the real-time status evaluation of the core equipment. Finally, take the voltage source converter as an example to establish an evaluation index system for real-time evaluation of equipment status.
Optimization of Tax Business Environment and Breakthrough in Enterprises' Key Technologies
At present, China's economy has shifted from a high-speed growth stage to a high-quality development stage. The achievement of breakthroughs in key technologies by enterprises is not only related to the improvement of their own core competitiveness, but also a strategic fulcrum for building a modern industrial system and consolidating the foundation of national security and development. Based on the data of A-share listed companies from 2015 to 2023, this paper takes the pilot policy of tax "streamlining administration, optimizing services and delegating power" as a quasi-natural experiment, constructs a multi-period difference-in-differences model, and conducts empirical analysis to examine the impact and mechanism of the optimization of the tax business environment on enterprises' breakthroughs in key technologies. The above conclusions reveal the economic benefits brought by the optimization of the tax business environment, and provide empirical evidence and useful enlightenment for China to continuously promote and deepen the optimization of the tax business environment and advance high-quality economic development.
Optimization of tax business environment
Enterprises' key technology breakthroughs
Challenges and Efficiency Improvement of Digital Empowerment at the Grassroots Level of Enterprises
Digital technology is bringing significant changes to grassroots management within enterprises. This study employs questionnaire surveys to deeply examine the core dimensions and its interrelationships of digital empowerment at the grassroots level, aiming to uncover the potential connections among system construction, user experience, organizational support, information efficiency, and application effectiveness. The results indicate that system construction forms the foundation, user experience directly influences continued usage intention, strong organizational support is a critical driving force, and information efficiency serves as a pivotal link, ultimately maximizing application effectiveness. Furthermore, this study identifies a practical pathway, "organizational support → information efficiency → application effectiveness," demonstrating that organizational support significantly enhances information efficiency, thereby improving application effectiveness. These findings offer valuable theoretical guidance and practical insights for enterprises seeking to undergo digital transformation.
Practical Application of Dynamic Risk Budgeting Models in Hybrid Asset Allocation
China's hybrid asset market exhibits dynamic volatility, rendering traditional asset allocation methods ill-suited to its rapidly shifting risk structure. This paper constructs a dual-layer dynamic risk budgeting model that innovatively integrates economic cycle identification with market state classification. It employs risk budgeting matrix mapping to achieve dynamic risk allocation adjustments. The model employs an improved alternating direction method of multipliers to solve non-convex optimization problems, with dual trigger conditions designed to reduce adjustment frequency and transaction costs. Empirical research demonstrates the model's robust performance under extreme market conditions, delivering significantly superior risk-adjusted returns compared to traditional approaches. This provides Chinese investors with an effective tool for navigating complex market environments.
A Game-Theoretic Analysis of Unauthorized Surcharging Behavior in Taxi and Ride-Hailing Markets
Against the backdrop of the long-term coexistence of street-hailing taxis and ride-hailing services, unauthorized surcharging, fare bargaining, and detours that effectively inflate passengers’ payments occur from time to time. Such practices may increase drivers’ revenues in the short run, but they can also trigger complaints, erode service trust, and raise regulatory enforcement costs. This paper abstracts the taxi and ride-hailing sectors as two competing players and develops a static complete-information game-theoretic model that incorporates regulatory penalties and reputational losses, in order to characterize the Nash equilibria under two strategies: compliant pricing and unauthorized surcharging. The analysis shows that whether unauthorized surcharging becomes prevalent in the industry hinges on the gap between the incremental gains from surcharging and the expected costs of punishment. When the additional payoff from surcharging exceeds the sum of the expected penalty loss upon detection and the reputational loss, both parties tend to adopt unauthorized surcharging, yielding the (unauthorized ,unauthorized ) equilibrium; otherwise, the (compliant ,compliant ) equilibrium emerges. Based on the derived threshold condition, the paper proposes policy implications including increasing the probability of detection, strengthening penalty severity, improving credit-based evaluation mechanisms, and enhancing platform–regulator collaborative governance.
Exploration of the Path for Low-Altitude Economy to Empower the High-Quality Development of Cultural Tourism
Against the dual background of the low-altitude economy emerging as a national strategic emerging industry and the continuous upgrading of cultural tourism demands, Yan'an, as a core national cultural tourism destination, holds significant demonstration value for the integrated development of its low-altitude economy and cultural tourism. Combining the theoretical logic of the integration of the low-altitude economy and the cultural tourism industry, this paper sorts out Yan'an's cultural tourism resource endowments and the development foundation of the low-altitude economy, analyzes the current practical dilemmas faced by Yan'an's low-altitude tourism, such as rigid airspace management mechanisms, insufficient industrial format innovation, and weak infrastructure. It further proposes a path for the integrated development of "low-altitude + cultural tourism" from four dimensions: policy coordination, industrial format innovation, infrastructure improvement, and market cultivation. This study aims to provide a reference for Yan'an to build a new growth pole of red cultural tourism and promote the transformation and upgrading of the regional cultural tourism industry.
National Audit, Digital Inclusive Finance, And High-Quality Economic Development
As a cornerstone of governance, national audit injects new momentum into high-quality economic development by promoting the development of digital inclusive finance. Based on data from 30 provinces in China from 2012 to 2021, this paper examines the impact of national audit on high-quality economic development. The study finds that the functioning of national audit can significantly enhance high-quality economic development, and digital inclusive finance plays a partial mediating role in this enhancement. Further analysis from the perspective of regional heterogeneity reveals that the promotional effects of national audit and digital inclusive finance on high-quality economic development are more pronounced in the eastern region. National audit, digital inclusive finance, and high-quality economic development.
Analysis of the Spatial and Temporal Distribution Char-acteristics and Influenc-ing Factors of Urban Taxi Empty Load Rate: A Case Study of the Central Urban Area of Chengdu City
This paper analyzes the spatial and temporal relationships between cab driver behavior-al factors and external environmental factors and cab unoccupancy rate based on the GPS trajectory data and POI data of cabs in Chengdu, using a geographically weighted regression (GWR) model. The results show that: (1) The average empty rate of Chengdu cabs on weekdays and non-weekdays is 53.9% and 56.7% respectively, and its empty rate on weekdays and non-weekdays as a whole shows the spatial distribution charac-teristics of clustering towards the city center. (2) The low empty rate of urban cabs is mainly dominated by the morning and evening peaks on weekdays and non-workdays. The external environmental factors can significantly reduce the cab idling time in most areas.
A Review of the Relationship between Third-Party Payment and College Students' Consumption Behavior
The widespread adoption and inherent convenience of third-party payments have significantly reshaped the consumption habits and structure of college students. This kind of social influence, manifested as imitating others and following group norms, is a key factor influencing college students' consumption choices. This study, through the method of literature review, referred to relevant research on this topic both domestically and internationally, clarified the key concepts, examined the impact of such payments on the consumption of higher education students, and explored the effect of social influence in it. This study underscores that in the digital economy, understanding the interaction between payment platforms and social influence is crucial for promoting responsible consumption among college students.
The Implementation of GBT30512 Standard and Its Implications for the Sustainable Development Strategy of Automotive Enterprises
Under the background of the dual-carbon goal and the accelerated evolution of global green supply chain, mandatory technical standards are increasingly becoming the key institutional tools to guide the strategic transformation of manufacturing industry. Focusing on "GBT30512-2014 Requirements for Prohibited Substances in Automobile", this paper systematically analyzes how this standard goes beyond the scope of technical compliance and deeply participates in the shaping process of sustainable development strategy of automobile enterprises from the perspective of institutional theory and strategic management. The study first deconstructs the logic and technical connotation of standard preventive governance, and then reveals its role in reconstructing the traditional strategic dimension, pointing out that environmental compliance, resource recycling, technical foresight and institutional synergy have become the core pillars of the new strategic framework. Further analysis shows that the standard guides enterprises to adjust their strategic objectives and reshape their internal governance structure through the mechanisms of objectification, power and responsibility reconfiguration and process embedding. This paper points out that the full release of standard efficiency depends on the multi-dimensional policy coordination with carbon management, green finance and international rules, and calls for promoting its evolution to an intelligent and life-cycle oriented institutional form.
Can the Opening of Government Data Improve the Level of Enterprise Innovation?
In the era of big data, the massive amount of government data, as an important national strategic resource, has a significant role in improving governance efficiency and promoting economic growth. However, whether the opening of government data can gather innovation factors, improve the efficiency of enterprise resource utilization, and thus improve the level of enterprise innovation needs further in-depth argumentation. Therefore, this paper establishes a dynamic difference-in-differences model and uses the enterprise sample data from 2011 to 2021 to study whether the opening of government data can improve the level of enterprise innovation. Based on the results of benchmark regression and various robustness tests, the empirical results show that the construction of government data open platforms can significantly and continuously improve the level of enterprise innovation, and the opening of government data can promote enterprise innovation in non-state-owned enterprises. This paper reveals the value of government data opening from the perspective of enterprise innovation value creation and provides an important decision-making reference for promoting the high-quality opening of government data and the innovation-driven development strategy.
An Economic-Ecological Modeling Framework for Agroecosystems: Integrating Dynamic Food Webs and Multi-Criteria Assessment
This paper proposes an intelligent algorithmic system for analyzing farmland ecosystems, focusing on computerized modeling and evaluation mechanisms that integrate multi-species interactions, agricultural intervention strategies, and system stability. First, an ecological computation pipeline centered on continuous-time dynamics is constructed. Producers, consumers, decomposers, and environmental factors are uniformly abstracted as computable state variables. Pulsed functions and decay mechanisms enable algorithmic representation of agricultural cycles and external inputs. Second, within this framework, multi-predator coordination and species recovery modules are introduced to conduct scenario simulations and comparative analyses of different regulatory strategies. Combined with a multi-indicator weighted algorithm, this enables automatic assessment of ecological status. Finally, ecological constraints are embedded into the profit optimization process, forming a decision support algorithm that balances system stability and output efficiency. This intelligent algorithmic system features a modular structure, excellent scalability, and scenario generalization capabilities. It can characterize complex ecological evolution processes with low computational complexity, providing an algorithmic paradigm with practical application value for intelligent agricultural management and ecological decision-making.
The Impact of Short-Video KOL Marketing Transparency on User Trust: A Case Study of Brand Collaboration Promotion
With the rapid popularity of KOL marketing on short-video platforms, it has become an important means of brand communication. However, issues such as insufficient content transparency and ambiguous advertising disclosures have led to a decline in users' trust in the creators and brands. This study explores how "content transparency" affects users' perception of authenticity and trust, and subsequently influences their behavioral intentions such as purchase intention or continued attention. The research will employ methods such as literature analysis, small-scale questionnaire surveys, and platform content observation to reveal the intrinsic relationship between transparency, authenticity, and trust. This will be achieved by comparing KOL brand cooperation cases with different levels of transparency (high, medium, and low). The research results provide references for brands and creators in their practices of content presentation and information disclosure, and also help deepen the understanding of the video influencer economy.
The Relationship Between Short Selling Mechanisms and Corporate Audit Fees
Against the backdrop of China's introduction of the margin trading system in 2010, this paper delves into the impact of short-selling mechanisms on corporate audit fees and their underlying mechanisms. Utilising annual data from non-financial listed companies in China's A-share market between 2007 and 2022, empirical analysis is conducted using a two-way fixed-effects panel model. Findings reveal that the implementation of short-selling mechanisms significantly reduces corporate audit fees, a conclusion that remains robust after controlling for year and industry fixed effects. The research indicates that short-selling mechanisms may lower auditors' audit risks and operational costs by enhancing corporate governance standards, improving disclosure transparency, and refining the market information environment, thereby leading to decreased audit fees. This study challenges the prevailing expectation that short selling increases audit costs, offering a fresh perspective on understanding the economic consequences of short-selling mechanisms. It provides insights for regulators to refine short-selling systems, listed companies to optimise governance practices, and the auditing industry to assess risk pricing.
A Stock Market Prediction Model Based on Linear Regression and Matrix Method
Stock market predictions, being one of the most important areas in financial studies, have for long been sought after by many researchers in these areas, with their applications being of utmost importance in studies on theory. Here, in an endeavor to bring about an efficient yet interpretative model for stock market predictions, there has been an impressive amalgamation of approaches based on linear regression models, matrix theory, and error correction mechanisms. Here, in particular, while model parameters have been identified with utmost precision based on least squares, there have been improvements with regard to computational speeds based on matrix calculation, with improved market versatility for resistance to dynamic markets based on error correction mechanisms. On testing applications, there has been an analysis based on historical Google stock market performances. It has been determined in these studies, based on their applications, that there has indeed been remarkable stability in performance levels in identifying stock performance based on its predictive nature, while particularly emphasizing their high flexibility levels in real-time applications based on its localization levels in its sliding window-based method. Though there have been limitations with regard to response levels based on short-term market changes, there have indeed been many advantageous levels in computational simplicity, interpretability, and versatility based on its applications in predictive conclusions for financial dealers. These studies have indeed paved new ways in achieving efficient fusion in financial predictive studies, with appropriate applications in their development in computational finance based on innovations brought about in finance theory based on modern computing innovations.
Analysis of the Legal Regulation of Financial Derivatives Trading Based on The Development of Fintech
The rapid development of fintech has brought about profound changes to financial markets. As financial derivatives, which have become an important pillar of modern financial systems, are constantly innovated with the support of new technologies. Inherent financial risks and high quality productive forces of innovation have brought complex situations. Frenzied innovation constantly evades the supervision during the process of technological development, which not only increases the transactional risks dramatically but also may cause the failure of financial system. This paper discusses the legal issues in fintech derivatives trading by reviewing the related researches from all over the world, and puts forward corresponding regulatory measures.
Research on the Impact of Climate Policy Uncertainty on Corporate Carbon Performance
In response to the increasingly severe issue of global climate change, countries around the world have formulated climate policies to drive climate governance initiatives. However, frequent policy changes can exacerbate the policy uncertainty faced by enterprises, thereby affecting their carbon performance. Based on this, this paper takes Shanghai and Shenzhen A-share listed companies from 2007 to 2023 as the research sample to examine the impact of climate policy uncertainty on corporate carbon performance. The research results indicate that climate policy uncertainty has a significant negative impact on corporate carbon performance. This result remains robust after a series of tests, including replacing core explanatory variables, excluding sample data from special years, and altering the clustering level of standard errors. Further analysis reveals that an increase in climate policy uncertainty inhibits corporate carbon performance by weakening the quality of the external financial environment, suppressing corporate green innovation, and hindering corporate digital transformation. Moreover, the inhibitory effect of climate policy uncertainty on corporate carbon performance is more pronounced for enterprises with weaker green awareness among executives, lower levels of policy environmental regulation, lower market competition intensity, and those that are capital- and technology-intensive. This study not only enriches the theoretical discussion on the relationship between climate policy uncertainty and corporate carbon performance but also provides important theoretical foundations and practical guidance for relevant departments in formulating climate policies and for enterprises in improving their carbon performance.
The Development of International Tourism Industry and Innovative Path of Hotel Management from the Perspective of Resource Integration
Under the dual drive of globalization and digital technology iteration, the international tourism industry is transforming from a decentralized model to a resource integration model. Resource optimization and allocation have become the key to breaking through the bottleneck of industry recovery and enhancing core competitiveness. As the core carrier of the industry, hotel management is deeply coupled with the integration of innovation and tourism resources. This article is based on the logic of resource integration, combined with the current development status of the industry, analyzing the dimensions and difficulties of resource integration, clarifying the inherent relationship between the two, and proposing innovative directions for hotel management from five aspects: product, service, operation, marketing, and governance. It constructs an implementation path of "resource collaboration technology empowerment standard connection talent support". Research has shown that effective resource integration can break down barriers to the circulation of industry factors and empower innovation in hotel management; Hotel management innovation activates the value of tourism resources in reverse, forming a virtuous cycle of "resource integration management innovation industrial upgrading", providing practical reference for the sustainable development of international tourism industry in the post epidemic era.
Research on the Optimization of Vanke Group's Capital Structure
The issue of capital is a core concern for the survival and development of modern enterprises. Optimizing a company's capital structure can not only reduce its costs and increase its profits but also standardize its operations, thereby enhancing its competitiveness, promoting its development, and ultimately realizing the maximization of its value. Therefore, capital structure optimization is a key issue in modern corporate decision-making, directly affecting the survival and growth of enterprises. Currently, the real estate industry generally faces problems such as high asset-liability ratios, limited financing channels, and excessive concentration of equity. This paper takes Vanke Group as the research subject and, based on relevant domestic and international theories on capital structure.
Modeling Carbon Emission Allowance Prices Using Multi-Scale Decomposition and Integrated Deep Learning Approaches
Carbon trading, as the critical market mechanism to achieve greenhouse gas emotion reduction, have received more attention around the world. The target that China establish and improve Carbon Emissions Trading Exchange (CCET) in recent year is to help the achievement of “peak carbon dioxide emissions”, “carbon neutrality”. However, Considering the characteristics of nonlinearness, strong fluctuation of carbon price, the accuracy of data prediction reduced significantly. Based on China Carbon Emissions Trading Exchange (CCET) data from July 2021 to March 2025, this paper build a multi-factor hybrid forecasting model that integrates VMD-CEEMDAN decomposition with GARCH-MIDAS and CNN-BiLSTM-Attention model throughout the ARCH and Sample Entropy as testing methods. Meanwhile, this model introduce influence factor including Macro Economy, Similar Products, Energy Structure and Climate Environment. Combine the random forest method with the MIDAS approach to select and integrate multi-frequency influencing factors, and evaluate model performance using indicators such as MAE, MSE, and RMSE. The experimental results show that the proposed model is better than the other model in accuracy and stability. The indicators including MAE, MSE and RMSE all are less than other comparison models.
A Preliminary Exploration of Mandatory Human Rights Due Diligence Law from the Perspective of Labor Protection
In response to the recurring violations of labor rights within global value chains, the international community has witnessed the emergence of a new legal governance mechanism exemplified by “mandatory human-rights due-diligence law”. The hallmark of such legislation is the creation of a statutory “due-diligence” obligation for companies, seeking to recast multinational enterprises as pivotal nodes in the governance of labor standards across their supply chains and to leverage their commercial influence to transmit compliance pressure throughout the entire value chain. Focusing on mandatory human-rights due-diligence law, this article analyzes how they achieve extensive extraterritorial reach over global value chains through distinctive ways of delineating the duty-bearing subjects and through the dual pathways of public regulation and private governance. By innovatively expanding the circle of legal duty bearers and by simultaneously deploying public-regulation channel and private governance channel, mandatory human-rights due-diligence law is transforming labor-rights protection from a traditional jurisdictional framework into a new model of global value-chain governance in which multinational corporations act as the primary enforcement intermediaries.
Mandatory human-rights due-diligence law
The Impact of Agricultural Insurance and Agricultural Loans on Farmers' Income
Based on the practical need for financial support to increase farmers' income within the context of rural revitalization, this paper explores the impact of agricultural insurance and agricultural loans on farmers' income and their synergistic mechanism. The study aims to clarify the directional effects, regional disparities, and interactive outcomes of these two factors on sustainable farmers' income, providing empirical evidence and policy references for improving the rural financial service system. Utilizing panel data from 31 provinces in China from 2012 to 2022, the research conducts an empirical analysis using the SYS-GMM model and panel threshold model. The results indicate that both agricultural insurance and agricultural loans exhibit a significant inhibitory effect on the growth of farmers' income, suggesting that the current synergistic mechanism between agricultural insurance and agricultural loans fails to effectively promote income growth and even demonstrates a "synergy failure" phenomenon. Further analysis reveals that this negative impact shows distinct regional heterogeneity, being particularly pronounced in eastern China, while relatively moderate in central and western regions. The conclusion posits that the current development and linkage model of agricultural insurance and agricultural loans in China have not yet fully realized their income-enhancing effects, and their efficacy is influenced by regional economic conditions and development stages.
Research on the Construction Mechanism of Ambidexterous Entrepreneurship Capability of Chinese Private Enterprises from the Perspective of Social Entrepreneurship: A Literature Review
The dual value creation of social enterprises is the core to solving social problems and achieving their own sustainable development, and how to balance "commercial-social" values through ambidextrous entrepreneurial capabilities under resource constraints has become a current research focus. Based on the logical framework of "resource constraints - ambidextrous entrepreneurial capabilities - dual value creation", this paper systematically reviews relevant literature. Firstly, it defines the core connotations and research contexts of dual value creation, resource constraints, and ambidextrous entrepreneurial capabilities of social enterprises; secondly, it analyzes the interaction mechanism among the three, clarifying the mediating role of ambidextrous entrepreneurial capabilities between resource constraints and dual value creation; finally, it identifies research gaps in existing studies regarding the balance mechanism of dual values, the dynamic evolution of resource constraints, the uniqueness of ambidextrous entrepreneurial capabilities, and research methods and contexts, and prospects future research directions. This review integrates the theoretical achievements of social entrepreneurship and ambidexterity theory, clarifies the current research status and development trends in the field, and provides a systematic reference for subsequent theoretical research and corporate practice.
Can Digital Inclusive Finance Narrow the Urban-Rural Income Gap?
This study utilizes panel data from 280 prefecture-level and above cities between 2011 and 2022, along with three waves of the China Household Finance Survey (CHFS), to examine the role and mechanisms of digital inclusive finance in narrowing urban-rural income disparities. It further analyzes synergistic effects with talent agglomeration. Findings reveal that digital inclusive finance significantly narrows urban-rural income disparities, a conclusion that holds up after rigorous robustness tests. Heterogeneity analysis indicates the most pronounced impact occurs in western regions. Considering its pro-poor characteristics, digital inclusive finance reduces income gaps by increasing rural households' output distribution share and financial asset proportion.