Highlights in Business, Economics and Management
Highlights in Business, Economics and Management is an open access, peer-reviewed publication series published by Darcy & Roy Press. It aims to publish conference proceedings dealing with all areas of business, economics and management, etc.
Research on the Changes in Chinese Residents' Consumption Behavior in the Digital Economy Era
With the rapid growth of the digital economy across China, it has greatly changed the way people consume and go about their daily buys and living in general. In this paper, the changes in people’s buying behavior from different Chinese areas because of digitization are examined. It includes changes of shop places, choices method of consumption and new model of consuming, which is like live-streaming commercial and platform co-operation economy. Use some academic papers, Official macro economics Data, Can Digital-Instruments increase context of increased consumption and reduce information difference by creating a completely new demand in Rural areas + Secondary cities? And the pragmatics: There will be issues with promotional campaigns about algorithm, because they’re always related to your private data; also not everyone likes people who don’t use algorithm. And it is applying a supply-side Platform Operating On Demand-Side Customer Behaviour Evaluations Showing The Possible Future Traits In China Consumers With Strategies For Equilibrium And Eternal Development Of Digital Retalies. To know about changes will help with more studies on making rules in China's very quickly growing cyber-economy. As the digital economy rapidly spreads across all of China, it has made significant changes to people’s national consumption structure as well as their normal purchasing and daily habits. This study looks at the many changes happening in Chinese people’s buying behavior because of digitalization. It includes changes of shop places, choices method of consumption and new model of consuming, which is like live-streaming commercial and platform co-operation economy. Use some academic papers, Official macro economics Data, Can Digital-Instruments increase context of increased consumption and reduce information difference by creating a completely new demand in Rural areas and Secondary cities? And also consider practical troubles during this developing time–individual’s personal privacy threat is lopsided algorithm pushings plus digital split-offs might leave old folks and technophobes who dislike these kinds of tools. And it is applying a supply-side Platform Operating On Demand-Side Customer Behaviour Evaluations Showing The Possible Future Traits In China Consumers With Strategies For Equilibrium And Eternal Development Of Digital Retalies. It could assist me in doing my research on making regulation for China's booming digital world.
Digital economy; consumption behavior; platform economy; consumer decision-making; China.
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Localization and Global Standardization Strategies in Cross-Border E-Commerce: A Case Study of SHEIN’s Internationalization
SHEIN is a global online fashion and lifestyle retailer established in 2008, focusing on customer needs, providing products ranging from fast fashion women's wear to home. The company meets the needs of global consumers with data-driven design and personalized recommendation, and has entered many international markets. SHEIN's internationalization has developed rapidly. Instead of following the traditional progressive mode, it has adopted a leap-forward internationalization strategy, relying on China's supply chain foundation to rapidly grow into a large enterprise. The company adopts the dual-engine mode of "self-operated brand+platform" to provide different entry modes to meet the needs of various businesses. SHEIN implements localization strategy, emphasizes global localization, and adapts to and meets the diverse needs of the global market through global layout of independent stations, localization of logistics and supply chain, internationalization and localization of management team and localization of marketing. At the same time, SHEIN also achieved rapid market expansion and brand consistency through the global standardization strategy. In addition, the organizational structure and market entry of SHEIN are also discussed accordingly.
Localization, Global Standardization Strategies, E-Commerce, SHEIN.
Theoretical Research on the Impact of Investor Sentiment on Asset Pricing in Behavioral Finance
This paper examines the impact and reasons of investor sentiment on asset pricing from the perspective of behavioral finance. Traditional finance holds that investors are rational and markets are efficient, but in reality, investors are influenced by various psychological factors such as cognitive biases and mood swings, which cause asset prices to deviate from their true value. This article, through a review of the relevant literature in behavioral finance, focuses on analyzing the generation process of investor sentiment, the way it spreads, and its impact on asset pricing. The research shows that investor sentiment affects asset pricing mainly in the following ways: First, it directly influences investors' risk appetite and investment decisions, thereby causing systematic bias in asset prices. Secondly, due to the herd effect and the contagion of sentiment, market volatility is further expanded, making asset prices even more unstable. Finally, under certain arbitrage constraints, mispricing caused by sentiment can persist for a long time. This paper presents a theoretical framework of how investor sentiment affects asset pricing, analyzes the reasons why optimism leads to asset overvaluation and pessimism leads to asset undervaluation, and also discusses the role of sentiment factors in intertemporal asset pricing models. The results show that investor sentiment not only affects short-term fluctuations in asset prices, but is also an important factor in explaining long-term yield anomalies. Asset pricing models that incorporate sentiment factors can better explain market anomalies and are of great significance to investment practice and risk management. This paper enriches the theoretical framework of behavioral finance and helps to understand the complexity of financial markets and the irrational behavior of investors.
Behavioral Finance; Investor sentiment; Asset pricing; Market anomalies; Irrational behavior.
Theoretical Research on The Sustainable Transformation of Fast Fashion Business Models
As global environmental problems grow and consumers' environmental awareness increases, the fast fashion industry is also under great pressure for sustainable development. This paper explores the sustainable transformation of fast fashion business models from the perspectives of sustainable development theory, business model innovation theory, and stakeholder theory. The traditional fast fashion business model is characterized by being "fast, cheap, and abundant", launching new clothing styles in a short time and selling them to consumers at lower prices to make a lot of profits, but this approach causes a lot of problems such as resource waste, environmental pollution, and damage to the rights and interests of workers. By analyzing the transformation cases of typical fast fashion enterprises such as Zara, H&M, and Uniqlo, this paper proposes a theoretical framework for the sustainable transformation of fast fashion, namely "green supply chain reconstruction, circular economy model innovation, digital technology empowerment, and consumer education guidance". The study suggests that for fast fashion companies to achieve sustainable development, they must shift from the original linear model of "acquisition - manufacture - discard" to a circular model of "design - production - use - recycling", improve production methods while using sustainable materials, establish recycling systems, and develop new business forms such as leasing and sharing to achieve a win-win situation for economic and social benefits. The findings of this study provide a theoretical basis for how fast fashion enterprises can achieve sustainable development, have a certain reference value for how relevant government departments and industry associations can introduce relevant policies and standards, and also contribute to promoting the green development of the entire fashion industry.
Fast fashion; Business model; Sustainable transformation; Circular economy; Green supply chain.
Dynamic Projection of Federal Fiscal Revenue Under Reciprocal Tariffs and Macroeconomic Assessment of Manufacturing Reshoring Potential
In response to the U.S. proposed "reciprocal tariffs" policy scheduled for implementation in 2025, this study constructs a quantitative analytical framework integrating machine learning forecasting with macroeconomic comprehensive evaluation. First, utilizing approximately 70,000 trade and tariff data points from 2020 to 2024, the study employs a log-linear model to estimate the tariff-trade elasticity coefficient at approximately -0.75, revealing the significant suppressing effect of tariff changes on import volumes. Building upon this, an ensemble learning model combining gradient-boosted decision trees and random forests is introduced to conduct dynamic simulations of fiscal revenue across different time horizons. Findings indicate that raising the average effective tariff to 50% would cause tariff revenue to surge by 181% in the short term. However, due to trade diversion and contraction, long-term growth would narrow to 32%. Subsequently, the study establishes a macroeconomic impact assessment system encompassing dimensions such as trade balance, price levels, GDP, and employment, and derives a "manufacturing reshoring potential index." Empirical analysis indicates that while high tariffs could temporarily reduce the trade deficit by approximately $12.8 billion and yield a marginal positive GDP contribution, the resulting surge in household consumption expenditures, steep rise in strategic resource costs, and reciprocal countermeasures from partner nations limit the Manufacturing Reshoring Potential Index to 33.3/100—a modest level. This study confirms that tariff measures alone are insufficient to achieve large-scale, resilient manufacturing reshoring.
Tariff Revenue Forecasting; Manufacturing Reshoring Index; Ensemble Learning Model.
A Study on the Prediction of Stock Market Returns Based on Financial News Sentiment Combined with NLP and Deep Learning
The complexity and volatility of financial markets have made stock return prediction a research hotspot. Traditional prediction methods often rely on historical price data, neglecting the key nonlinear influencing factor of financial news sentiment. The high proportion of retail investors in my country's stock market and their susceptibility to sentiment further highlight the importance of this factor. This study aims to construct a financial news sentiment analysis framework integrating Natural Language Processing (NLP) and deep learning to accurately predict stock market return volatility trends. The study collected 150,000 financial news articles and corresponding stock trading data from 2018 to 2023. After preprocessing, sentiment features were extracted using a financial sentiment dictionary. Comparative experiments were conducted using logistic regression, random forest, and Long Short-Term Memory (LSTM) networks, and the model was optimized by incorporating temporal features. The results show that the LSTM model has the best prediction performance, with an F1 score of 0.82 and a root mean square error (RMSE) of 0.035, significantly outperforming traditional models. News sentiment and stock returns show a significant nonlinear correlation; negative sentiment has a stronger impact on market volatility than positive sentiment, and different types of stocks exhibit different sensitivities to sentiment. This study provides quantitative support for investors to formulate scientific investment strategies and for regulatory authorities to implement risk management, promoting the in-depth application of sentiment analysis technology in the capital market.
Financial News Sentiment; Stock Market; Revenue forecast; Natural language processing; deep learning.
Study on the Impact of ESG Rating Discrepancies on the Sustainability of Corporate Green Innovation
As ESG principles have become increasingly influential in guiding investment strategies and corporate decisions, notable variations in ESG evaluations across different rating organizations have become a frequent occurrence. Yet, the precise mechanisms through which these variations affect corporate efforts in sustainable innovation are not fully understood. This study focuses on Chinese A-share companies listed in Shanghai and Shenzhen between 2015 and 2022, analyzing how divergences in ESG ratings influence the long-term viability of corporate green innovation and the underlying dynamics. The findings reveal that discrepancies in ESG ratings undermine the enduring nature of corporate green innovation. Further analysis of mechanisms indicates that limited access to financing and heightened business risks serve as critical pathways through which ESG rating divergences impair the continuity of green innovation. Results from heterogeneity analysis suggest that, in contrast to non-heavy-polluting firms and state-owned entities, the adverse effects of ESG rating divergences on green innovation persistence are more pronounced in heavy-polluting firms and non-state-owned entities. This study sheds light on the consequences of ESG rating divergences for the persistence of corporate green innovation, broadens the scope of research on ESG rating variations and the durability of green innovation, and offers empirical support for enhancing China’s ESG rating framework and fostering the long-term viability of corporate green innovation.
ESG Rating Disagreement; Sustainability of Corporate Green Innovation; Financing Constraints.
The Impact of ESG Internal Development Balance on Corporate Financing Constraints
Under the dual carbon goals and the context of high-quality development, ESG performance has become an important measure for assessing corporate risk and value. However, the unbalanced development model of some companies that 'focus on the environment but neglect governance' can easily trigger accusations of 'greenwashing' and conceal potential financial risks. Based on this, this paper uses Chinese A-share listed companies from 2010 to 2024 as a sample and innovatively constructs an 'ESG Internal Development Balance' indicator to deeply examine its impact on corporate financing constraints and the transmission mechanism. The study finds: First, the ESG internal development balance can significantly alleviate corporate financing constraints. Coordinated ESG practices can convey highly reliable sustainability signals to the market, effectively reducing the information screening costs for financial institutions. Second, external audit certification and enhanced debt acquisition capability serve as dual transmission paths. Balanced ESG development not only encourages companies to engage high-quality audits to reduce information asymmetry but also aligns with green credit preferences, thereby significantly improving borrowing capacity and substantively enhancing liquidity. Third, heterogeneity analysis shows that this alleviation effect is more pronounced in non-state-owned enterprises, non-heavy-pollution enterprises, and high-tech companies. This paper provides practical insights for companies to optimize sustainable development resource allocation and break financing barriers.
Balance of internal ESG development, financing constraints, external audit certification, financial leverage, information asymmetry.
The Impact of ESG Investment Intensity on Mutual Funds’ Performance
Employing a two-way fixed-effects specification on a 2017–2021 panel of Chinese equity-oriented mutual funds, we examine whether ESG intensity influences CAPM Alpha. Huazheng ESG ratings serve as the key explanatory variable. ESG scores are positively and significantly related to one-month and six-month alphas, but the association attenuates at the twelve-month horizon. High-ESG funds exhibit a mid-term reversal followed by long-run outperformance, whereas low-ESG funds earn short-lived, risk-driven gains that subsequently erode. Fund size, age and volatility are additional, material determinants of excess returns.
Fund ESG Investment Level; Fund Performance; CAPM Model; Fund Alpha.
Enterprise New-Quality Productive Forces and the Improvement of Human Capital Level
The world has gradually entered the era of digital economy, and in this context, vigorously enhancing the new-quality productive forces of enterprises has become an important path to promote the high-quality development of enterprises. Based on the data of A-share listed companies from 2014 to 2023, this paper studies the influence mechanism of enterprise new-quality productive forces on the level of human capital. The study finds that enterprise new-quality productive forces have a positive impact on the level of human capital, and this conclusion still holds after a series of robustness tests. Heterogeneity analysis shows that the promoting effect of enterprise new-quality productive forces on the level of human capital is more significant in central region enterprises, state-owned enterprises and non-competitive industries. Mechanism analysis indicates that enterprise new-quality productive forces promote the improvement of human capital level by strengthening the resource integration capacity of enterprises and increasing the level of knowledge diversification. The above results are highly consistent with the internal action mechanism of empowering the upgrading of enterprise human capital through new-quality productive forces. This paper has certain significance for developing enterprise new-quality productive forces and formulating strategies to improve the level of enterprise human capital.
New Quality Productive Forces of Enterprises, Human Capital Level, Resource Integration Capability, Knowledge Diversification.
Pricing Decisions in Live Streaming E-Commerce Supply Chains Under the Scarcity Effect
Despite being a prevalent scarcity marketing strategy in live streaming e-commerce, limited-release sales still lack rigorous theoretical elucidation regarding their underlying supply chain coordination and pricing mechanisms. This paper constructs a three-echelon supply chain game model comprising a brand, a live streamer (either an in-house employee or an external influencer), and a live streaming platform to systematically analyze the impacts of scarcity strategies (supply ratio control) and traffic empowerment on operation mode selection. We identify three channel structures: the pure in-house streaming model (Model R), the pure influencer streaming model (Model A), and the dual-channel streaming model (Model D). Our findings reveal that the scarcity strategy affects the equilibrium through a dual pathway of "willingness-to-pay amplification" and "physical supply restriction." The centralization of pricing power with the influencer streamer mitigates coordination failure but exacerbates double marginalization, prompting the brand to implement aggressive supply rationing as a hedging mechanism. When dual channels coexist, a subtle trade-off emerges between the inter-channel demand spillover effect and the self-cannibalization effect, leading to pricing suppression and asymmetric scarcity allocation. Furthermore, platform traffic allocation exhibits a strong scarcity preference, creating a positive feedback loop with brand supply restrictions. The theoretical analysis provides a systematic decision-making framework for understanding operation mode selection in live streaming e-commerce.
Live streaming e-commerce; Supply chain management; Scarcity strategy; Traffic empowerment; Stackelberg game.
Research on the Influencing Factors of the Added Value of the Primary Industry
This paper takes the added value of China's primary industry from 1978 to 2021 as the research object, based on the Cobb-Douglas production function theory, using the cointegration analysis and Granger causality test method, systematically examines the impact of the number of employees in the primary industry, the total power of agricultural machinery and the total sown area of crops on the added value of the primary industry. The results show that the changes of the total power of agricultural machinery and the total sown area of crops have a significant positive impact on the added value of the primary industry, in which the added value of the primary industry changes 0.3922% in the same direction for every 1% change of the total power of agricultural machinery; For every 1% change in the total sown area of crops, the added value of the primary industry changes by 0.5869% in the same direction. However, the change of the number of employees in the primary industry has no significant impact on the added value of the primary industry, which reflects the increasing degree of agricultural mechanization in China and the realistic trend of labor transfer to the secondary and tertiary industries. Based on the empirical results, this paper puts forward some policy suggestions, such as increasing investment in agricultural mechanization, optimizing land use structure, strengthening agricultural infrastructure construction, deepening agricultural science and technology research and development and talent support, and formulating regional agricultural development strategies according to local conditions. This paper provides an empirical basis for understanding the growth mechanism of China's primary industry and provides a reference for formulating relevant agricultural policies.
Value Added of Primary Industry; Total Power of Agricultural Machinery; Sown Area of Crops; Employment Population; Cointegration Analysis; Granger Causality Test.
Artificial Intelligence-Assisted Fall Prevention in Older Adults - Risk Identification, Individualized Intervention, and Clinical Translation
Falls are a leading cause of disability, hospitalization, and mortality in later life. Although traditional fall-prevention assessment already includes relatively mature scales and physical examination, it is still largely based on one-time screening and may miss fluctuating risks in home and community settings. By reviewing published work, this paper discusses how AI may shift traditional fall prevention from post-event recognition to pre-event prediction and what applications this may enable in VR and AR training, robot rehabilitation, and closed-loop management across home, community, and hospital settings. Current clinical use still requires stronger external validation, better explainability, clearer privacy and bias safeguards, more persuasive cost-effectiveness evidence, and smoother workflow integration. AI has real potential for objective assessment and dynamic management, but its current clinical soundness remains uncertain.
Artificial intelligence; older adults; fall risk assessment; wearable sensors; virtual reality; clinical translation.
The Impact of Domestic Tourism Development on Economic Growth—An Empirical Study Based on Cross-Country Data
Domestic tourism is the foundation of tourism development in most countries, the main component of the global tourism market, and an important part of the global economy. Based on a systematic review of relevant domestic and international literature, this paper uses balanced panel data from 121 countries during 1996–2015 and applies static and dynamic panel models to empirically study the impact of domestic tourism development on economic growth. The results show that the level of domestic tourism development varies across countries; the Tourism-Led Growth Hypothesis applies to domestic tourism development globally, and according to dynamic model estimates, a 1% increase in domestic tourism development level raises economic growth rate by 0.00254%. Meanwhile, factors such as physical capital, government size, innovation capacity, human capital, industrial structure, passenger transport capacity, and urbanization level also have certain impacts on economic growth and should not be ignored.
Domestic tourism development; economic growth; balanced panel data.
Competitiveness Strategy of Education Technology Enterprises Based on Intelligent Hardware under the "Double Reduction" Policy-Taking Zuoyebang Learning Machine as an Example
China's "Double Reduction" policy was officially implemented in 2021, bringing profound changes to the K12 subject-based after-school training industry, and educational technology enterprises face an urgent need for transformation. As an important carrier of the "non-practical training" track, intelligent hardware has become a key factor for enterprises to find growth points. This paper takes the Zuoyebang learning machine as the research object, uses the PEST model to analyze the impact of the macro environment on the educational hardware industry, and employs the SWOT model to sort out the internal strengths and weaknesses as well as external opportunities and challenges of the Zuoyebang learning machine, focusing on exploring the core role of artificial intelligence technology in building product competitiveness. The study finds that the core competitiveness of the Zuoyebang learning machine stems from its deep question bank data and mature AI algorithms, but it faces challenges in the hardware ecosystem and channel penetration. The paper proposes strategic recommendations such as focusing on deep AI applications, building an integrated software-hardware ecosystem, and expanding compliant education application scenarios, providing references for the sustainable development of educational technology enterprises after the "Double Reduction" policy.
Double Reduction Policy, Edtech, Zuoyebang, Smart Hardware, Competitiveness.
Applications of Supervised Learning Methods in the Fields of Economics and Management
Based on a systematic literature review, this paper explores the current application status, effects, and challenges of supervised learning methods in the fields of economics and management. Grounded in literature from the past five years and utilizing multi-database retrieval and rigorous screening, the study analyzes specific applications of supervised learning across various domains. These include asset pricing and fraud detection in finance, financial fraud identification and audit automation in accounting, and corporate performance prediction and macroeconomic modeling in economics. The research finds that supervised learning, through its advantages of high-precision prediction and automated feature engineering, significantly enhances decision-making efficiency and risk management capabilities in economics and management. However, the field still faces challenges such as data privacy and model interpretability. Future research should focus on directions such as small-sample transfer learning, model transparency, and interdisciplinary integration to further promote development in this area.
Supervised learning, Economics and management, Big data analytics, Application effectiveness, Future directions.
Major Individual Shareholders and Stock Price Synchronicity
The phenomenon of”co-movement in rise and fall”of stock prices is prominent in China’s capital market.High stock price synchronicity is detrimental to the stable development of the capital market.Using Chinese A-share listed firms from 2007 to 2024 as the research sample,this paper empirically examines the impact of shareholding by major individual shareholders on stock price synchronicity.The results show that the shareholding ratio of major individual shareholders is negatively correlated with stock price synchronicity,and this conclusion remains robust after robustness and endogeneity tests.Heterogeneity analysis indicates that the inhibitory effect is stronger when internal power concentration is higher and shareholding changes are more volatile,while high audit quality and high analyst coverage weaken this effect.Mechanism analysis reveals that major individual shareholders improve information transparency by reducing agency costs and curbing earnings management,thereby lowering stock price synchronicity.This paper clarifies the influence path of major individual shareholders on stock price synchronicity,providing empirical evidence for optimizing ownership structure,enhancing information efficiency in the capital market,and supporting high-quality economic development.
Major Individual Shareholder, Stock Price Synchronicity, Information Transparency.
Information Disclosure Quality and Online News Media Coverage: Evidence from Chinese Listed Companies
Using 44,989 firm-year observations from Chinese A-share listed companies over the period 2009–2023, this paper examines how information disclosure quality affects online news media exposure from the perspectives of corporate disclosure and public opinion. We measure disclosure quality with the KV index proposed by Kim and Verrecchia, and capture media exposure through three metrics: headline mentions, body-text mentions, and the count of negative reports. Year, industry, and city fixed effects are included alongside a set of firm-level controls. The results show that firms with lower disclosure quality receive significantly greater headline and body-text coverage and attract more negative reports. These findings remain robust after using lagged explanatory variables and alternative measures of the dependent variable. Further analysis reveals that overall innovation quality significantly amplifies the media-exposure effect triggered by declining disclosure quality. Heterogeneity tests indicate that the effect is more pronounced among firms with rapid asset growth, those not audited by a Big Four firm, and those that are profitable. This study uncovers the social communication consequences of corporate disclosure and provides empirical evidence for risk-oriented disclosure regulation, corporate information-governance and reputation-management systems, and media-platform fact-checking and content governance.
Information disclosure quality; KV index; Online news media exposure; Negative coverage; Media sentiment; Innovation quality.
How Does Gendered Design Influence Consumer Behaviour and Brand Perception ?
Gendered design plays an increasingly significant role in product identification, identity construction, and brand communication. By strategically utilizing visual symbols such as color, shape, and typography, it activates identity-based responses, thereby influencing consumers’ purchasing decisions and their overall perception of brand personality. However, existing research continues to face several limitations, including a heavy reliance on binary gender frameworks, a Western-centric focus, and an insufficient exploration of gendered visual language within digital and social media contexts. This study aims to examine how gendered design influences consumer behavior and brand perception, with a particular emphasis on the critical roles played by visual communication and cultural context. The theoretical framework draws on Gender Performativity Theory and Brand Personality Theory to explain how brands encode and reproduce social gender norms, and how consumers utilize these symbols for self-identity confirmation and performance. Adopting a qualitative research design, this study conducted semi-structured interviews with 11 participants aged 18 to 25. The data underwent thematic analysis, and the findings indicate that consumers possess a high sensitivity to gendered visual cues when judging product suitability. Notably, Generation Z consumers demonstrate strong critical awareness toward traditional binary designs, favoring brands that embody authentic inclusivity, social responsibility, and consistent values. The significance of this study lies in revealing how design serves as a symbolic practice that shapes cognitive boundaries, providing strategic insights for brands to develop inclusive and culturally resonant visual strategies in an increasingly diverse market.
Gendered design, Consumer behavior, Brand perception, Visual communication.
Carbon Neutrality Management and Firm Risk
Under the backdrop of the 'dual carbon' goals, carbon neutrality has become a critical strategy for firms to address climate change and achieve sustainable development, with its impact on corporate risk attracting increasing attention. Using data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2018 to 2022, this paper empirically examines the impact of corporate carbon neutrality management on firm risk and its underlying mechanisms through multiple regression models. The findings reveal that corporate carbon neutrality management significantly reduces firm risk, and this conclusion remains robust across a series of robustness checks, including alternative risk measurement indicators, replacement of the core explanatory variable, and controlling for endogeneity issues. Heterogeneity analysis demonstrates that the risk-reducing effect of carbon neutrality management is more pronounced in firms with lower audit quality and lower institutional investor ownership, suggesting that carbon neutrality management plays a more critical role in signaling and governance in firms characterized by higher information asymmetry and relatively weaker external monitoring. Mechanism analysis indicates that corporate carbon neutrality management reduces firm risk by alleviating financing constraints. Specifically, the positive signals conveyed by carbon neutrality management reduce the degree of information asymmetry, enabling firms to obtain external funding support at lower costs, thereby enhancing resource allocation capabilities, improving operational efficiency, and ultimately reducing firm risk. These results are consistent with the theoretical expectation that carbon neutrality management reduces corporate information asymmetry, mitigates financing constraints, and consequently lowers firm risk. The findings of this paper not only enrich the literature on the economic consequences of carbon neutrality management but also provide important theoretical foundations and practical implications for firms implementing carbon neutrality strategies, enhancing risk management capabilities, and for governments refining green finance policies.
Carbon neutrality management, firm risk, financing constraints, information asymmetry.
Equity Incentives and Corporate Green Innovation: Mechanisms and Institutional Contingencies
Against the backdrop of escalating global environmental degradation and resource scarcity, green innovation has emerged as a cornerstone of corporate sustainable development. Beyond reducing energy consumption and enhancing resource utilization efficiency, it strengthens competitive advantage and long-term profitability. Governments worldwide have implemented stringent environmental protection policies, with China's "dual carbon" goals accelerating industrial green transformation and compelling enterprises to assume greater environmental stewardship. By aligning senior executives' interests with corporate long-term value, equity incentives mitigate principal-agent conflicts, a mechanism particularly congruent with green innovation's inherent characteristics of high investment intensity, long gestation periods, and strong positive externalities. Based on empirical analysis of Chinese A-share listed companies from 2010 to 2023, this study demonstrates that equity incentives significantly enhance enterprises' green innovation capabilities, with pronounced effects on energy efficiency improvement and waste emission reduction. The findings offer empirical insights for corporate decision-makers and policymakers.
Equity Incentives, Green Innovation, State-Owned Enterprises, Principal-Agent Theory.
Research on the Impact of Green Finance on Corporate Green Technology Innovation
The development of green finance has brought new opportunities and challenges to enterprises' green technology. However, whether green finance can positively promote enterprises' green innovation still needs further examination. Based on the panel data of A-share listed companies from 2008 to 2018, this paper constructs an empirical model by using dummy variables before and after the implementation of policies and the attributes of enterprises to analyze the impact of green finance on enterprises' green technology innovation. Through regression analysis, the establishment of intermediary effect model and placebo test, the relationship between the two is further explored. The research shows that green finance has a positive impact on enterprises' green technology innovation. When green finance rises by 1 unit, enterprises' green technology rises by 0.12. Therefore, we should actively promote green finance, strengthen the infrastructure construction of green finance, improve the coverage of green finance, and deepen the deep joint supervision of the government, banks and enterprises.
Green Finance, Enterprises' Green Technology, Virtual Variable, Mediation Effect Test.
The Impact of ESG Performance on Corporate Innovation Performance: A Study Based on Listed Manufacturing Companies in China
In recent years, with the increasing awareness of sustainable development, the environmental, social, and governance (ESG) performance of enterprises has gradually attracted widespread attention from investors and has had a continuous impact on the long-term operation of enterprises. Based on stakeholder theory and signal transmission theory, this paper analyzes the impact and mechanism of ESG performance on innovation in manufacturing enterprises. Using data from 2,380 A-share listed manufacturing companies from 2009 to 2024. the study finds that the improvement of ESG performance has a significant positive impact on innovation in manufacturing enterprises; and this promoting effect is more pronounced in enterprises with larger scale. Further mechanism analysis reveals that ESG performance primarily promotes innovation in manufacturing enterprises through mechanisms such as alleviating financing constraints, increasing attention from securities analysts, and increasing government subsidies.
ESG, Corporate Innovation, Manufacturing.
The Impact of Green Finance in Anhui Province on Rural Revitalization
In the landmark "No. 1 Document" issued by the central government in 2023, the Party Central Committee emphasized the "three rural issues" as the core task of the Party's work. On the road of rural revitalization and development, the role and influence mechanism of green finance provide practical theoretical support and practical guidance for the implementation of this strategy. This paper selects the indicators of green insurance, green investment and the development level of rural revitalization in 16 cities of Anhui Province from 2012 to 2021 for empirical analysis. The results show that both green investment and green insurance have a positive role in promoting the development level of rural revitalization, and green insurance is more efficient than green investment in promoting rural revitalization. The supporting role of green finance shows obvious regional heterogeneity. Compared with the central and western regions, the eastern region has received more significant support from green finance in the process of rural revitalization. Green investment and green insurance have no significant effect on the western region. Therefore, the western region should learn from the eastern and central regions to increase public expenditure on energy conservation and environmental protection and optimize the structure of agricultural insurance.
Green Finance, Rural Revitalization, Entropy Method.
From Price Competition to Mechanism Design: An Economic Analysis of Tendering Based on Auction Theory
The tendering and bidding system constitutes a core mechanism for public procurement and resource allocation, with its economic essence amenable to the analytical framework of auction theory. This paper systematically reviews the application logic of auction theory in tendering analysis, revealing the mechanisms through which different bidding formats influence bidder behavior, allocative efficiency, and procurement costs. The research indicates that the economic performance of bidding mechanisms depends not only on the intensity of competition but also on the institutional design of information structure, scoring rules, and budget constraints. Although first-price sealed bidding is theoretically equivalent to English auctions in terms of expected revenue, structural differences exist in anti-collusion capabilities. Two-stage bidding may inadvertently facilitate collusion by reducing coordination costs. Multi-attribute scoring auctions and budget-constrained auctions introduce new strategic dimensions that can suppress low-quality competition. The findings suggest that tendering reform should transcend traditional thinking centered on “highest price wins” or “lowest price wins”, moving toward mechanism design concepts grounded in incentive compatibility.
Auction theory, tendering and bidding, mechanism design, collusion, multi-attribute auctions.
The Compensation Mechanism of Employee Experience from the Perspective of Algorithmic Management -- Based on the Perspective of Internal Marketing Strategy
With the wide application of artificial intelligence technology in human resource management, algorithm management is gradually becoming an important factor affecting employees' work experience. However, the efficiency priority logic followed by the algorithm system often conflicts with the psychological needs of employees, which may lead to the "dehumanization" of employees' experience. This paper aims to explore how enterprises can alleviate this structural tension by constructing a compensation mechanism to optimize the employee experience under algorithmic management, and analyze this process in the theoretical framework of internal marketing. This paper first reviews the dual impact of algorithm management on employee experience, and then expounds the evolution logic of the compensation mechanism from "efficiency compensation" as the guidance to "meaning giving" as the core. On this basis, a four-dimensional strategy system including institutional compensation, relationship compensation, psychological compensation and development compensation is proposed. The research shows that the employee experience compensation mechanism under algorithm management essentially reflects the internal marketing process in which enterprises regard employees as internal customers and obtain employee commitment through value transfer.
Algorithm management, Employee experience, Compensation mechanism, Internal marketing, Human resource management.
Aggregation Rules and Dynamic Weight Optimization in Multi-Source Evaluation Mechanisms: An Institutional Analysis of Ranking Robustness in Competitive Markets
This paper addresses the issues of weight structure and aggregation rules in hybrid evaluation mechanisms by constructing an analytical framework that combines backward inference with dynamic optimization. Under conditions of incomplete observational data, the “minimum necessary support” model is introduced. Combined with Monte Carlo simulations to characterize the feasible space of unobservable variables, this approach reveals the structural amplification effect of evaluation source differences under percentage summation rules. Further comparative analysis demonstrates that ranking methods effectively constrain extreme concentration shocks, enhancing result robustness. Building upon this, a multi-objective dynamic weight optimization framework is proposed. It identifies balanced weight intervals via the Pareto frontier and designs a phased dynamic adjustment mechanism to harmonize early-stage responsiveness with late-stage stability. This research integrates reverse inference, distribution simulation, and optimization analysis to provide a universal quantitative basis for designing multi-source information weighting and decision aggregation mechanisms.
Evaluation Mechanism Design, Preference Concentration Effects, Weight Optimization.
Digital Economy Development, Corporate Investment Efficiency and Green Innovation Performance: Microscopic Evidence and Influence Mechanism
The digital economy has profoundly changed enterprises’ production and management. Based on A-share listed companies’ panel data, this paper evaluates the impact of prefecture-level city digital economy development on local enterprises’ green innovation performance. It finds that regional digital economy significantly improves enterprises’ green innovation performance, which is robust after tests. Mechanism analysis shows corporate investment efficiency mediates this effect: digital economy improves investment efficiency by alleviating over-investment, thus promoting green innovation. The results enrich relevant literature and have practical and policy significance.
Digital Innovation, Green Innovation, Investment Efficiency.
Fund Flows and Stock Returns: Uncovering the Transmission Mechanisms
Fund flows, directly reflecting capital movements in financial markets, are central to asset pricing research on their relationship with stock returns. Existing studies have predominantly examined either their direct price pressure effect or indirect influences from isolated perspectives, such as behavioral finance or liquidity, lacking a systematic integration of multiple transmission channels. To address this gap, this paper develops a comprehensive "quadruple parallel mediation model" to fully unveil the complex mechanisms through which fund flows affect stock returns. Based on China's A-share market data from 2008 to 2024, this study employs a standardized empirical approach. First, a standardized fund flow measure is constructed following Bennett and Sias [1]. Second, intermediary variables: investor sentiment, market illiquidity, and stock price non-synchronicity are developed using text analysis and market microstructure data. Finally, a parallel multiple mediation model is established to simultaneously test both the direct effect of fund flows on stock returns and their transmission through three key indirect channels. The empirical findings reveal that: (1) Fund flows exhibit a robust positive contemporaneous total effect on stock returns. (2) Fund flows affect returns simultaneously through three parallel channels: boosting investor sentiment, inducing a liquidity premium, and promoting information incorporation. These multiple transmission mechanisms coexist. (3) The mechanisms show significant market state heterogeneity, with their modes of action dynamically changing across bull and bear markets. This study transcends single-theory explanations by integrating price pressure, investor sentiment, liquidity premium, and information hypotheses into one analytical framework, offering comprehensive empirical evidence for investors and regulators.
Fund Flows, Stock Returns, Investor Sentiment, Market Liquidity, Stock Price Non-synchronicity.
Analysis of the Impact of Directional Convertible Bonds on Mergers and Acquisitions of Technology-Based Enterprises
Against the backdrop of intensifying global competition in the technology industry and accelerating industrial upgrading, mergers and acquisitions have become the core pathway for technology-based enterprises to integrate innovation resources, overcome technological bottlenecks, and expand market share. However, the inherent characteristics of technology-based enterprises lead them to face severe financing constraints over the long term. This paper constructs a theoretical analysis framework of “instrument design, signal transmission, performance improvement” and analyzes the case of 3PEAK’s acquisition of ICM-Semi, revealing the mechanisms through which directional convertible bonds reduce financing costs, alleviate equity dilution, and balance transaction risks, which provides an in-depth discussion on the impact path and effect of directional convertible bonds on merger and acquisition performance. The research in this paper holds significant theoretical value and policy implications.
Directional convertible bonds, technology-based enterprises, mergers and acquisitions.