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.
Financial Transformation Strategies of High-Energy-Consumption Enterprises under the Background of Green Finance
Green finance has emerged as a driving force in the restructuring of energy-intensive industries by integrating ecological objectives into financial decision-making. This paper explores the significance and strategic pathways of financial transformation for high-energy-consumption enterprises under the guidance of green finance. The discussion emphasizes the necessity of industrial upgrading, diversification of financing channels, improvement of environmental risk management, and reinforcement of sustainable competitiveness. Four major strategies are examined: enhancing green accounting information disclosure, innovating green investment instruments, strengthening environmental cost and performance management, and constructing green risk control systems. These approaches illustrate how enterprises can achieve financial resilience and ecological accountability simultaneously, thereby securing long-term growth within increasingly stringent environmental and financial frameworks.
Green finance; High-energy-consumption enterprises; Financial transformation; Environmental management; Sustainable development.
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The Impact of Green Finance Development on Industrial Structure Upgrading—— Evidence from 244 Prefecture-Level Cities in China
In the alteration of China's industrial structure, it is crucial to promote the green upgrading of traditional industries and the cultivation of new green industries. Therefore, on the basis of panel data from 244 prefecture-level cities in China from 2016 to 2023, The effect of green finance development on industrial structure upgrading is empirically analyzed in this paper using a two-way fixed effects model (TFE). The results indicates that green financial development significantly promotes industrial structure upgrading. Heterogeneity analysis shows that the promoting effect is more pronounced in underdeveloped regions and eastern regions. The innovative points of this paper lie in breaking through the traditional perspective of financial development, focusing on the impact of green finance development instead of finance development on industrial structure upgrading, and creatively employing prefecture-level city data for research. This study provides important references for formulating green financial development plans scientifically, clarifying its development direction, and boosting the systematic upgrading of the industrial structure in an orderly manner.
Green Finance Development, Industrial Structure Upgrading, Prefecture-Level Cities, China.
The Impact of Green Credit on Industrial Structure Upgrading: Evidence from Chinese Prefecture-Level Cities
This study utilizes 2005-2022 panel data from 225 prefecture-level cities in China to construct a two-way fixed effects model. The empirical results show that green credit significantly advances industrial structure. Heterogeneity analysis indicates its effect is stronger in more developed regions than in less developed ones, and more pronounced in the eastern region than in the central and western regions. These findings offer guidance for crafting targeted policies and driving green transformation in developing economies.
Green Credit, Industrial Structure Upgrading, Heterogeneity, Prefecture-Level City, Fixed-Effects Model.
Fintech Enablement of Corporate Digital Transformation
Amidst the accelerated reorganization of the global digital economy landscape and China's rapid FinTech advancement, this study employs empirical methods to investigate FinTech's influence on corporate digital transformation dynamics and its underlying mechanisms. Utilizing data from Shanghai and Shenzhen-listed A-share companies spanning 2003 to 2023, findings indicate that an incremental rise in the FinTech development index correlates with a significant enhancement in firms' digital transformation levels. Analysis of the causal pathways reveals that FinTech facilitates this transformation primarily through two synergistic channels: mitigating corporate financing difficulties and reducing internal transaction costs. The exploration of heterogeneity reveals that firms with specific characteristics experience a more pronounced transformation impetus. Non-state-owned entities, coastal enterprises, and those within high-tech sectors effectively leverage institutional frameworks and infrastructure endowments to achieve stronger enabling effects. Conversely, state-owned enterprises exhibit relatively muted responsiveness, hindered by institutional rigidities and technological access barriers. Critically, a firm's technological innovation capability exerts a significant positive moderating influence on FinTech's transformation-enabling effect, underscoring the pivotal role of technological adaptation and assimilation. Collectively, these results provide empirical evidence for understanding FinTech's role as a catalyst for corporate digital transformation, thereby informing the design of targeted policy interventions, including geographically tailored support strategies.
Fintech, Corporate digital transformation, Financing constraints, Internal transaction costs.
Stock Market Price Prediction Based on Attention-LSTM Model with Multi-Feature Bayesian Optimization
Stock price prediction plays a critical role in investment decision-making and financial regulation. However, traditional time series models and early neural networks are limited either by restrictive assumptions or by their inability to effectively handle long sequences, resulting in suboptimal prediction performance. This paper proposes a hybrid predictive model that integrates multi-feature fusion, the attention mechanism, and Bayesian optimization into a Long Short-Term Memory (LSTM) framework to enhance prediction accuracy and stability. Using daily data from the S&P 500 Index from 2020 to 2022, the study employs LSTM to capture long-term temporal dependencies, introduces an attention mechanism to highlight key sequential features, and utilizes Bayesian optimization for adaptive hyperparameter tuning. Empirical results demonstrate that compared with conventional LSTM, attention-enhanced LSTM, and Bayesian-optimized LSTM models, the proposed Multi-Feature Bayesian Optimized Attention-LSTM achieves significantly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), reaching 25.51, 18.35, and 0.66%, respectively. Even during periods of extreme market volatility such as the Russia–Ukraine conflict and the U.S. Federal Reserve’s interest rate hikes in 2022, the MAPE remained below 0.70%. These findings validate the synergistic effect of multi-feature fusion, the attention mechanism, and Bayesian optimization, providing more reliable decision support for financial market participants.
LSTM model; attention mechanism; Bayesian optimization; stock price prediction.
The Impact of Major Shareholders' Equity Pledges on Audit Pricing: Empirical Evidence from China's Private Listed Companies
Compared to financing options like bank loans, equity pledge financing is widely recognized among shareholders of listed companies due to its convenience, ability to raise funds at a low cost while retaining control. However, when a company's stock price declines to the liquidation threshold, pledged shareholders face the risk of losing control over their shares, which heightens their motivation to appropriate corporate assets or engage in earnings management to stabilize stock prices. State-owned enterprises benefit from natural political connections with the government, granting them advantages in financing and tax policies, as well as superior margin-call capabilities. Moreover, due to stricter and more complex conditions for equity transfers in state-owned enterprises, pledgees are more inclined to use private negotiation to cover loans. In contrast, private enterprises lack these policy and negotiation advantages, making their controlling shareholders more likely to resort to equity pledges to meet financial needs. However, in the event of a stock price drop, they often lack the capital to adequately cover margin calls, leading them to focus on controlling stock price fluctuations through earnings management. Thus, after pledging equity, private enterprises face intensified risks of asset appropriation and earnings management, prompting auditors to establish higher audit pricing to compensate for additional audit resources and hedge against increased audit risks.This study analyzes the relationship between controlling shareholders' equity pledges and audit pricing using data from China's A-share private listed companies between 2015 and 2020. The findings indicate a significant positive relationship between equity pledges by controlling shareholders and audit pricing, with higher pledging levels leading to increased audit fees. Further analysis reveals that this significant positive relationship only exists in regions with higher marketization and among firms audited by the "Big Ten" accounting firms. The conclusions enrich the literature regarding the economic consequences of equity pledges and factors influencing audit pricing, providing valuable insights for corporate governance and market regulation.
Major shareholders' equity pledges; audit pricing; firm size; marketization level.
Validation on Talent Selection Criteria for High School Football Central Defenders Based on A Multidimensional Evaluation System
This study further validates the previously established selection indicator system for high school soccer central defenders. For each evaluation indicator, a corresponding test was designed; for example, the Illinois Agility Test was used for the testing agility. An online research method was employed to reference authoritative scoring standards, such as those in Physical Education and the Study of Sport. The percentage conversion method was used to convert raw scores into a percentage scale. The expert scoring method involved inviting professional coaches to evaluate players during seven-a-side matches. Three high school central defenders were selected: Student Wang achieved a final score of 72.2102 points, Student Xue obtained 75.2710 points, and Student Zhao scored 78.5893 points. The players with the higher final scores, Student Zhao and Student Xue, were starters on their team in reality; the player with the lower final score, Student Wang, was a substitute on his team. This finding confirms the validity and reliability of the model.
Football central defender; Talent selection; Web Searching Method; Percentage Conversion Method; Expert Scoring Method.
A Study on the Influence of TMT Stability on Enterprise Information Disclosure Quality
Information disclosure is the cornerstone of high-quality development in capital markets, as it effectively mitigates information asymmetry between enterprises and stakeholders and improves resource allocation efficiency. However, violations by listed companies—such as financial fraud and delayed disclosure—remain frequent, severely undermining market stability. As the core of corporate decision-making, the stability of the top management team may exert an impact on corporate information disclosure quality. Based on this, this study selects A-share listed companies on the Shenzhen Stock Exchange in China from 2007 to 2023 as the research sample to explore the relationship between TMT stability and corporate information disclosure quality. The results show that TMT stability significantly improves corporate information disclosure quality, and this effect is achieved by reducing operational risk. Further analysis reveals that the positive impact of TMT stability on information disclosure quality is more pronounced in non-state-owned enterprises. This study expands the research on the economic consequences of TMT stability and the influencing factors of information disclosure quality, providing references for enterprises to maintain TMT stability and for regulatory authorities to formulate relevant policies.
TMT Stability; Quality of Information Disclosure; Operational Risk.
Research on the Influence of User Acceptance Willingness for AI-Generated Ads: A Perspective Based on Perceived Eeriness
Based on the uncanny valley theory, this study uses the scenario experiment method to explore user acceptance willingness toward AI-generated advertisements and its influence mechanism, and analyzes the roles of perceived eeriness, perceived trust, and perceived effort in this process. The study finds that perceived eeriness has a significant negative impact on user acceptance willingness toward AI-generated advertisements, while perceived trust and perceived effort play a mediating role between perceived eeriness and user acceptance willingness. This study provides a theoretical basis and practical implications for advertising practitioners and technology developers to optimize advertising design.
AI-generated advertisements, user acceptance willingness, perceived eeriness, perceive trust, perceived effort.
The Impact of Emerging Digital Solutions on the Improvement of ESG Evaluations
With the growing emphasis on sustainable and green finance, ESG ratings have become more important for investors and regulators, yet their effectiveness remains a significant concern. This study systematically reviews some critical problems of ESG rating in sustainable investment, such as methodological divergences and consistency challenges among major rating agencies. On this basis, it further synthesizes current progress and practical exploration of digital technologies including blockchain, big data and artificial intelligence in improving transparency and disclosure efficiency for ESG assessments. Specifically, blockchain can enhance the traceability and authenticity of data, AI can improve the scoring consistency through better data processing, and big data can provide more information inputs and dynamic dimensions for evaluation. The conclusion calls for integrating these technologies to foster a more transparent and dynamic ESG rating ecosystem for better reliability, which requires supportive policy frameworks in area such as data governance and cross-sector collaboration to ensure effective implementation.
ESG Evaluations, Digital Solutions, blockchain, AI, big data.
Impact of the Digital Economy on Cultural -Tourism Integration
In recent years, the digital economy has become a key force driving the in-depth integration of culture and tourism, but existing studies still lack sufficient empirical discussion on its impact mechanism. Using panel data from 31 Chinese provinces from 2012 to 2023 as samples, this study measured the level of cultural-tourism integration using a coupling coordination degree model and empirically tested the impact of the digital economy on cultural-tourism integration with multiple econometric models.The results show that: (1) The digital economy has a significant positive effect on cultural-tourism integration, which remains reliable, and digital infrastructure plays the strongest driving role; (2) The digital economy exerts an indirect influence through industrial structure optimization and technological innovation; (3) Its impact has a single threshold effect, and the promoting effect significantly enhances after crossing the threshold values; (4) The digital economy has a spatial spillover effect on cultural-tourism integration.
Digital Economy; Cultural-Tourism Integration; Influence Mechanism; Spatial Spillover Effect.
Xiaomi And ESG: Sustainable Development Practices and Challenges of Tech Enterprises
This study analyzes Xiaomi's ESG practices and challenges, reflecting broader issues for Chinese tech firms. Facing ESG investment trends and industry pressures like high energy consumption and data privacy, Xiaomi pursues sustainability. Environmentally, it targets 2040 carbon neutrality. Smart factories saved 23% energy, and bio-based materials cut product carbon footprints by 18%. However, supply chain emissions dominate (99.2% of total), and green products like premium-priced (40%) solar panels have low market penetration (5%), highlighting economic-environmental tensions. Socially, its "Elderly Mode" (120 million devices) aids digital inclusion, but Indian labor disputes and a €6.5 million EU data fine reveal cross-border governance gaps. In governance, only 30% of ecosystem partners have full-time ESG roles. The findings indicate that Chinese tech companies must deeply embed ESG into core strategy, shifting from compliance to strategic drivers via supply chain decarbonization, cross-border compliance integration, and quantitative tool development.
Xiaomi, ESG, high energy consumption, data privacy, supply chain decarbonization, cross-border compliance integration.
The Impact of ESG Rating Discrepancies on Investor Decision-Making: A Comprehensive Analysis Based on Behavioral Finance Theory
Prompted by the rapid development of ESG (including environmental, Social and Governance) investment, ESG ratings are coming into sight as an indispensable reference for investment decision-making. But the rating of the same firm by different rating agencies can differ a lot, which aggravate the adverse selection problems of investors. This paper examines how ESG rating deviation affects investor decision making behavior and changes the investment decision making logic through investor risk perception, sentiment swing, decision heterogeneity and market performance. Empirical study proves that the rating differences increase the stock market fluctuation, and has a certain impact on the market stability and the management's strategic adjustment. Lastly, this paper concludes with future research directions and recommends further investigating the responses of various investors for better transparency and uniformity of the rating model.
ESG Rating Discrepancies, risk perception, sentiment swing, decision heterogeneity, market performance.
Research on Green Gross Domestic Product Accounting Based on a Comprehensive Evaluation Model
Traditional gross domestic product ignores environmental and resource costs, making it difficult to reflect the sustainability of economic activities. This study constructs a green GDP accounting model based on the United Nations System of Environmental-Economic Accounting, integrating resource consumption, environmental degradation losses, and pollution control costs. It quantifies the economic impact on the environment through 12 indicators. Using a green restoration rate model and the analytic hierarchy process, it assesses the potential for global climate mitigation using rainfall, greenhouse gas emissions, sea level, and biodiversity as indicators. Weighted analysis shows that biodiversity and greenhouse gas emissions have the greatest impact. Based on data from 2015 to 2023, the study uses China as a case study to combine climate impact models and entropy analysis in examining the multidimensional impacts of green GDP, with results showing that the environmental dimension holds the highest weighting at 39.2% and a projected 4.3% reduction in fossil fuel consumption over the next five years.. The study validates the application value of green GDP in global and Chinese climate mitigation efforts, providing scientific basis for optimizing resource allocation and formulating environmental policies, thereby contributing to the achievement of sustainable development goals.
Green GDP, SEEA framework, Green Remedy Rate, Analytic Hierarchy Process, Sustainable development.
Construction of environmental performance evaluation system for iron and steel enterprises under digital background
Against the backdrop of global climate warming and China's "dual carbon" goals, the steel industry, as a representative of high energy consumption and high emissions, has drawn widespread attention regarding its environmental performance. This paper takes steel enterprises as the research subject, analyzes the connotation of digital-green coordinated development, and constructs an environmental performance evaluation system for steel enterprises in the digital context by integrating policy requirements for the metallurgical industry outlined in the Implementation Guidelines for Digital-Green Coordinated Transformation Development. The paper briefly introduces data processing methods for evaluation indicators and subsequent scoring calculation approaches, providing concrete ideas on how to incorporate digital indicators into the environmental performance evaluation of steel enterprises.
digitalization, steel enterprises, environmental performance, double transformation coordination.
Farming the Green Future economically based on Lotka-Volterra model and firefly algorithm
With the global agricultural environment changes, agroecosystems face the challenges of inefficient resource utilization and ecological imbalance. This paper explores the relationship between agricultural activities and species dynamics by constructing an agroecosystem model. It analyzes the potential of organic agriculture in enhancing ecosystem stability and other aspects. We proved the stability of the emerging agroecosystem under the model with the Stability index and Shannon’s diversity index. The results showed that the crop yield increased after the return of the two species, up to 6.05t/ha per year, and that the agroecosystem, in general, was able to maintain a relatively balanced cyclic steady state.
Logistic growth model, Lotka-Volterra model, 4th Runger-Kuta, Cellular Automaton Mode, Firefly algorithm.
Research on the Impact of Green Credit on the Resilience of Agricultural Industry Chain Based on Panel Data
Based on panel data from 30 Chinese provinces spanning 2010-2021, this paper constructs a resilience index system for the agricultural industry chain, encompassing three dimensions: resistance, recovery and innovation, using the entropy weight method. A fixed-effects panel model is employed to investigate the direct impact, mediating pathways, and moderating mechanisms of green credit on agricultural industry chain resilience. The empirical findings reveal that green credit significantly enhances resilience, particularly in non-major grain-producing regions. Technological innovation and industrial integration serve as important mediators, while agricultural digitalization plays a significant moderating role. These results suggest that optimizing green credit policies by improving financial support systems, strengthening the synergy between technological innovation and industrial integration, and accelerating digital transformation that can effectively foster a greener and more resilient agricultural industry chain.
Green Credit, Agricultural Industry Chain Resilience, Panel Data.
The Welfare Impact of Intermediate Goods Tariffs in the US-China Trade War
This paper examines the welfare impact of intermediate goods tariffs imposed during the U.S.-China trade war (2018-2020) on consumers, producers, and employment in both economies. Combining qualitative and quantitative analysis, we utilize government reports, corporate case studies (e.g., Apple, BYD, Whirlpool), and industry data (washing machine prices, soybean exports, employment trends) to trace tariff transmission through global value chains (GVCs). U.S. consumers bore 108–225% tariff pass-through costs, with washing machine prices rising 12% (86/unit),reducingrealincomesby1.4B monthly. U.S. producers saw short-term gains in protected sectors (e.g., steel output ↑1.9%), but net losses prevailed due to input cost surges (3.5Bdownstreamdamage)and inefficientreshoring(820K per job created). Chinese SMEs suffered profit margin declines (0.35% per 1% tariff rise), triggering 10–20% workforce reductions and supply chain relocations to Southeast Asia, exacerbating hidden unemployment. Tariffs targeting intermediate goods—deeply embedded in GVCs—impose multi-stakeholder welfare losses. Policymakers should prioritize supply chain diversification and critical intermediate goods tariff exemptions over decoupling. By focusing on intermediate goods as the core transmission channel, this study pioneers a systematic analysis of their multi-dimensional welfare impacts across economies, revealing the trade war’s true costs beyond gross trade statistics.
Intermediate Goods Trade; U.S.-China Trade War; Welfare Effects; Global Value Chains (GVCs).
The Truth behind ESG Disclosures: Detecting Greenwashing through Text Readability
As the concept of sustainable development advances, ESG disclosure has become a key component of corporate non-financial reporting. However, some firms engage in strategic obfuscation by complicating the language of ESG reports to exaggerate their actual performance, resulting in greenwashing. This study examines 1133 non-financial A-share listed firms in China from 2019 to 2023, extracting ESG sections from annual reports to construct a text readability indicator. Empirical findings suggest that lower readability is significantly associated with a higher degree of greenwashing. This effect is mitigated in larger firms, where reputation and regulatory pressure impose constraints. Further heterogeneity analysis reveals that such textual manipulation is more prevalent in high-pollution sectors. The study sheds light on the “soft manipulation” path of ESG disclosures, enriching the literature on greenwashing and offering language-based warning indicators for regulators.
ESG Disclosures, Greenwashing, Text Readability, non-financial A-share listed firms.
A Study on the Impact of ESG Disclosure Policies on Corporate Stock Returns Based on the Difference-in-Differences (DID) Method
Under the background of "dual carbon" goal and the deepening of ecological environment governance, implementation of "Measures for the Management of Enterprise Environmental Information Disclosure according to Law" (2022) requires key polluters to disclose environmental information and promotes the capital market to pay attention to the environmental behavior of enterprises. This study takes the A-share heavy polluters from 2019 to 2023 as the sample, sets 412 enterprises that had been included in the key polluters before the policy as the experimental group, and matches 824 non-key polluters as the control group. Results show that after the implementation of the policy, the abnormal return rate of the experimental group is significantly higher than that of the control group (coefficient is 0.003, p<0.01), and the effect is more prominent in the enterprises with high quality of environmental information disclosure. Event study further showed that the cumulative abnormal return of the experimental group within the policy announcement window was 1.2% (p<0.05). Conclusion shows that the environmental information disclosure policy reduces information asymmetry by enhancing corporate environmental transparency, effectively improves the market evaluation of heavy polluting enterprises, verifies the capital market incentive effect of the policy, and provides empirical evidence for the synergy between environmental regulation and corporate environmental responsibility.
Stock Returns, Difference-in-Differences Method, Disclosure Policies, ecological environment governance, environmental transparency.
Triggers and Marketing Strategies of Corporate Brand Crisis in the New Media Environment
Brand is the intangible asset of an enterprise. In the new media environment, the social market environment closely related to the survival of enterprises is more complex under the influence of many uncertain factors, which increases the probability of brand crisis. Only by using marketing strategies that are suitable for the new media era can enterprises turn crisis into opportunity. This paper mainly uses the literature research method and case analysis method, and on the basis of briefly expounding the characteristics of new media marketing, analyzes the various reasons for the outbreak of brand crisis in the new media environment, and puts forward new media marketing methods to deal with the brand crisis of enterprises, hoping to provide useful reference and suggestions for the long-term development of enterprise marketing and brand in the new media environment.
Enterprise crisis, new media marketing, brand, brand management.
Climate Change: Impacts on the Global Economy
Climate change significantly threatens the global economy by influencing various sectors through rising temperatures, extreme weather events, and shifting ecosystems. This paper explores economic consequences of climate change, which include reduced agricultural productivity, damage to infrastructure, supply chain disruptions, and increased costs related to disaster recovery and adaptation. Additionally, the transition to a low or zero-carbon economy presents both challenges and opportunities. Economic fields such as renewable energy and sustainable technology are experiencing growth, while fossil fuel-dependent sectors are in decline. The financial sectors of nations must also adapt to climate-related risks, including asset devaluation and insurance losses. This study highlights the need for proactive policies, investment in sustainable infrastructure, and international cooperation to mitigate economic vulnerabilities. By addressing climate change through innovation and policy reform, one can ensure long-term economic stability. These measures are essential to building a more resilient and sustainable global economy in the face of ongoing climate impacts.
Climate Change, Economic Impact, Green Energy.
Research on the Theoretical Construction of Enterprise Innovation Culture and the Integration Model of Incentive Mechanism
The core driving force for sustainable development of enterprises stems from an innovative corporate culture, and the key to promoting the formation and development of this culture lies in effective incentive mechanisms. This paper aims to explore the theoretical construction of corporate innovation culture and its integration model with incentive mechanisms. At the beginning of the study, the concept, characteristics, and constituent elements of corporate innovation culture are sorted out through literature review to construct its theoretical framework, and the role and influence of incentive mechanisms in corporate innovation culture are analyzed, and various incentive methods such as material incentives, spiritual incentives, and institutional incentives are explored. Then an integrated model of corporate innovation culture and incentive mechanism is proposed, covering three dimensions: culture-oriented incentive design, incentive-driven cultural shaping, and culture-incentive synergy. The feasibility and effectiveness of this integrated model are verified through case studies and empirical research. The results show that the organic integration of innovation culture and incentive mechanism can significantly enhance the innovation ability and performance of enterprises. Finally, this paper puts forward specific suggestions for enterprises to build an innovation culture and design a matching incentive mechanism, providing theoretical guidance and implementation paths for enterprise practice.
Enterprise innovation culture; Incentive mechanism; Theoretical construction; Integration mode; Innovation performance.
Research on the Theoretical Connotation and Formation Mechanism of Enterprise Core Competitiveness
The theoretical content and formation mechanism of the core competitiveness of enterprises are discussed in this paper. First, the evolution process of the concept of core competitiveness is sorted out through literature review to clarify its important position in the field of strategic management. Then, the essential characteristics of core competitiveness such as value, scarcity, difficulty to imitate and irreplaceability are analyzed in depth from the perspective of resource-based view and dynamic capability theory. Based on this, construct a model of the formation mechanism of enterprise core competitiveness that includes three key links: resource accumulation, capability integration, and continuous innovation, and explore the commonalities and differences in the process of cultivating core competitiveness in different types of enterprises through case analysis. The results show that the formation of core competitiveness is a dynamic and complex system process influenced by multiple internal and external factors. Therefore, enterprises need to select and cultivate core competitiveness in a targeted manner based on their own characteristics and the external environment. Finally, strategic suggestions for enterprises to build and enhance core competitiveness are proposed, covering aspects such as strengthening strategic orientation, optimizing resource allocation, promoting organizational learning, and strengthening innovation-driven. This study has significant theoretical guidance and practical reference value for enterprises to maintain a sustained competitive advantage in a highly competitive environment.
Core Competitiveness; Theoretical content; Formation mechanism; Resource-based view; Dynamic capabilities.
The Impact of Attention to Financial Information on Households' Commercial Endowment Insurance Purchase Decisions — Based on CHFS Data
In the context of China's increasingly aging population, commercial endowment insurance has become an important support for multi-level endowment security system, but its family participation rate has been low for a long time.Based on the mixed cross-section data of CHFS from 2015 to 2019, this paper adopts two-way fixed effect model to systematically examine the impact of financial literacy represented by attention to financial information on whether households purchase commercial endowment insurance. The empirical results show that attention to financial information significantly improves the probability of household insurance participation, and it is still robust after controlling the characteristics of household head, household economic status and two-way fixed effects. After multiple robustness tests such as shrinking the sample, replacing the fixed effect and changing the estimation model, the conclusions remain unchanged. Heterogeneity results show that the positive effect is more significant in rural and eastern regions, but relatively limited in urban and central and western regions. Risk preference is a significantly positive moderating effect on the insurance participation decision. The mechanism analysis further reveals that attention to financial information promotes households to make insurance participation decisions by improving financial knowledge level and risk identification ability. Therefore, the article suggests building the differentiated financial education system, strengthening the construction of financial infrastructure, and improving the institutional environment to enhance residents' long-term confidence in participation.
Attention to financial information, Commercial endowment insurance, Family insurance decision, Two-way fixed effects.
Research on Defective Rate Control in Production Processes Based on Hypothesis Testing and Cost Optimization
In modern manufacturing, delivering high-quality products at minimal cost is essential for maintaining competitive advantage. This study combines statistical hypothesis testing with cost-strategy optimization to design a low-cost sampling and testing scheme that effectively controls defect rates at multiple production stages. First, under the null hypothesis that the defect rate does not exceed the nominal value, In this article,calculate required sample sizes of 98 at 95 % confidence and 59 at 90 % confidence. In this article,then apply normal distribution theory to determine appropriate sampling frequencies, ensuring test accuracy across confidence levels. Second, by constructing a cost matrix and sampling-quantity vector, In this article,evaluate inspection strategies at both the procurement (spare parts) and production (finished product) stages. In this article,find that foregoing spare-parts inspection in favor of finished-product testing significantly reduces inspection costs and market-exchange losses while maintaining quality standards. Specifically, this approach minimizes the number of original-parts inspections yet preserves finished-product integrity, yielding substantial cost savings and lower risk exposure. Overall, our framework offers manufacturers a theoretically grounded, practically feasible method for defect-rate control and inspection-cost optimization.
Sample Testing, Hypothesis Testing, Cost Optimisation, Cost Strategy.
The Impact of Green Finance Reform and Innovation Pilot Zone Policy on Firm-Level Total Factor Productivity
Against global climate change and China’s “Dual Carbon” targets, green finance has evolved from pilot to institutionalized frameworks. While China leads in green finance market size via policy innovation, the micro-mechanisms driving firm transformation and policy efficacy remain under-researched. Using the green finance reform and innovation pilot zone policy as a quasi-natural experiment, this research employs a multi-time point difference in difference model with 2010–2022 A-share listed firm data to examine its impact on total factor productivity. Results show the policy significantly suppresses firm total factor productivity, with robustness confirmed via parallel trends and multiple robustness tests. Structural heterogeneity exists: larger firms and those with higher institutional ownership experience stronger total factor productivity declines, reflecting asymmetric transition costs. Firms partially offset these effects through adaptive strategies like increased R&D and improved carbon performance, revealing a “constraint-innovation” dynamic. Finally, based on the empirical results, this research puts forward relevant policy suggestions.
Green finance, Total factor productivity, R&D investment, Carbon performance.
Study on Production Decision Making of Electronic Products-Based on Multi-stage Optimisation Model
With the progress of science and technology, how to optimise the production of electronic products has become a very important issue, because it can control the quality of parts and reduce the cost. This paper investigates the production optimization of electronic products and analyzes the defective parts rate and finished product rate of spare parts through Monte Carlo, binomial distribution and multi-objective optimization models. A hierarchical decision-making model is established to optimize the production stages, and the total cost and net profit for six cases are calculated by combining heuristic decision-making and iterative optimization. Finally, decision trees and multi-objective optimization were used to determine the optimal failure rate. It was found that spare parts should be rejected at 95% confidence level and acceptable at 90% confidence level, with case 5 having the highest net profit and the optimal failure rate threshold of 0.01, which helps to improve the quality of parts and reduce costs.
Production Decision-making; Multi-stage Optimisation; Monte Carlo model; Binomial Distribution model; Multi-objective Optimisation model.
Research on e-commerce user stratification and coupon intelligent placement optimisation based on multi-behavioural RFM and deep reinforcement learning
With the ongoing accumulation of user behavioral data on e-commerce platforms, the precision of coupon allocation based on individual characteristics has emerged as a pivotal challenge for enhancing both resource allocation efficiency and marketing performance. Addressing the limitations of the traditional RFM model—particularly its reliance on single-dimensional user stratification and inability to capture complex interactive behaviors—this study introduces a novel customer segmentation approach that integrates a multi-behavioral RFM model with an enhanced self-organizing map (SOM) algorithm. This integrated method enables a more comprehensive identification of user value and facilitates fine-grained segmentation. Building on this stratification, an intelligent coupon delivery optimization framework powered by deep reinforcement learning (DRL) is further developed. In this framework, user stratification outcomes are embedded into the state space alongside real-time behavioral data, allowing dynamic learning of individual response patterns through a state-action-reward mechanism. Consequently, the system autonomously generates personalized and optimized coupon distribution strategies. Experimental results demonstrate that, under budgetary constraints, the proposed approach significantly increases the activity levels and repurchase frequencies of key users. Furthermore, it substantially outperforms static and random delivery strategies across critical metrics, including coupon conversion rate, delivery efficiency, and overall marketing effectiveness. These findings not only confirm the method’s flexibility and adaptability in practical e-commerce settings but also offer a viable pathway and robust theoretical support for advancing precision marketing and optimizing resource allocation.
E-commerce platform, Customer segmentation, Multi-behavior RFM, Deep reinforcement learning, Intelligent coupon delivery.
Crop planting strategy by robust particle swarm optimized-algorithm
With the rapid transformation of rural economies, optimizing crop planting structures has become a key strategy to address limited resources, market volatility, and environmental risks. This issue is especially critical in the mountainous regions of North China, where arable land is scarce and topographic constraints pose serious challenges to traditional agriculture. This study develops a multi-objective mathematical model that comprehensively considers key uncertain factors, including yield per mu, planting costs, and sales volume. By integrating Particle Swarm Optimization (PSO) with Robust Optimization techniques, the model identifies an optimal crop planting scheme for the period 2025–2030. The proposed solution remains stable under uncertainty and meets practical requirements for adaptability and feasibility. The results provide valuable theoretical and decision-making support for improving land use efficiency, reducing planting risks, and promoting sustainable agricultural development in resource-constrained mountainous areas. This research offers practical guidance for policymakers and agricultural planners seeking data-driven strategies in the context of rural economic transformation.
Robust Optimization, Particle Swarm Optimization, Crop Planting.