This article systematically reviews the role and challenges of independent directors in curbing overinvestment in enterprises. Based on the agency theory framework, this article analyzes the theoretical path for independent directors to improve investment efficiency through supervision, consultation, and signal transmission mechanisms. Research has found that although independent directors theoretically have governance effectiveness, their actual effectiveness is significantly constrained by practical difficulties such as lack of independence, information barriers, and insufficient incentives. This article further reveals the situational dependence of independent director effectiveness and points out that its effectiveness is deeply influenced by institutional environment and equity structure. Finally, The research findings of this paper show that this article proposes policy recommendations for shifting from formal compliance to substantive effectiveness, including optimizing the selection mechanism and improving the incentive compatibility system. This study provides important insights into the effectiveness of corporate governance mechanisms and has reference value for governance practices in emerging markets.
This paper aims to address the research gap on evaluating the National Housing Accord’s effects post-implementation. It applies ARIMAX time series forecasting to the Monthly Building Approvals from the Australian Bureau of Statistics prior to the commencement of the National Housing Accord. The forecasted values are then compared to the actual values to assess the short-term impacts of the Accord to date. It was found that the actual values had an increasing trend that deviated from the flat forecasted trend, indicating a positive effect of the National Housing Accord on building approvals. However, it remained within the 80% confidence interval of the forecasting model, not allowing a definite conclusion to be drawn. The main limitations of the paper discussed included the short time frame and the selection of exogenous variables for the model. It suggested that future research should carry out a similar methodology, but at a later stage of the Accord and after revisiting the selection of exogenous variables. Overall, this paper represents an initial step toward quantitatively evaluating the effects of the NHA on housing supply.
Driven by technologies such as 5G networks and social media, new media has established a “many-to-many” interactive communication landscape, becoming a vital medium for brand dissemination. However, most timehonored brand narratives remain confined to the singular theme of “centuries-old heritage,” struggling to resonate emotionally with contemporary consumers and thus facing brand obsolescence. This study explores strategies for reimagining and preserving brand narratives of timehonored brands in the new media landscape. Employing literature review methods, it synthesizes theories on new media, brand storytelling, and heritage brands. Through case analysis of the Quanjude heritage brand, it examines how short-video storytelling, IP-based cartoon expressions, and multi-to-multi communication strategies effectively convey brand history and values. Based on this, three core strategies are distilled: content reimagining, narrative mode reimagining, and medium reimagining. This study fills a gap in the intersection of new media and brand storytelling, offering fresh strategies and perspectives for communicating the narratives of time-honored brands.
Driven by the “dual-carbon strategy” and healthy consumption trends, green labels and health claims have become common on product packages. However, existing researches focus only on the effects of individual labels, with limited exploration of their combined impact. Based on signaling theory, this study treats green labels as high-cost, observable signals and health claims as low-cost signals. It argues that green labels can provide credibility for health claims, thereby enhancing brand trust and increasing consumer purchase intention. A questionnaire survey was conducted using Telunsu dairy products, a premium brand under the Inner Mongolia Yili Group, as a case study. We received 153 valid answers. Results show that green labels directly enhance brand trust and indirectly promote purchase intention by making the health claim more believable. Brand trust significantly mediates the relationship between green labels and purchase intention. This study not only extends the application of signaling theory to green and health marketing but also provides practical guidance for businesses in label design and communication.
The rise of artificial intelligence (AI) technology is the core driving force leading a new round of industrial transformation, and the deep application in the field of financial accounting is gradually reshaping the traditional application mode. Traditional accounting relies on manual processing, which is inefficient and error-prone, and makes it difficult to meet the needs of big data and real-time decision-making. The development and introduction of AI improves efficiency and accuracy, but at the same time, it also brings problems such as data security and compliance. This study mainly discusses the application through accounting automation, intelligent analysis, and proactive risk management, the impact, and the future development trend of artificial intelligence technology in the field of financial accounting. It provides a systematic framework for the transformative impact of financial accounting and promotes the deep integration of AI and corporate financial accounting.
This paper examines the evolution of social-media influencer marketing and addresses two questions: the current state of online influencer marketing (OIM) and its impact on sales performance. Within eight years global expenditure rose from USD 1.7 billion to USD 16.4 billion, confirming the channel’s shift from peripheral experiment to mainstream platform. Influencers enhance sales through three levers—demand activation, fourdimensional efficiency (cost, speed, targeting, conversion) and person-to-person trust—yet simultaneously expose firms to four challenges: integrity gaps between online and offline messages, privacy leakage across platforms, absence of face-to-face emotional reassurance, and overconcentration of sales on single SKUs. The live-stream controversy involving Li Jiaqi and Huaxizi eyebrow pencils demonstrates that sales can plummet as rapidly as they rise, proving that advantages and risks amplify at the same speed. The existing evidence base is skewed toward young and middle-aged consumers, while behavioural patterns of older and cross-cultural populations remain under-explored. Future studies should broaden age and cultural coverage, integrate survey instruments with platform-side log data, and employ longitudinal designs to test whether the documented benefits and drawbacks apply universally, thereby closing the current knowledge gap and providing a robust foundation for scholars and practitioners in the expanding field of influencer-driven marketing.
How do business cycles leave their mark on the stock market? This paper tackles this question by weaving together three empirical threads: Markov Regime Switching (MRS) to identify economic states, Vector Autoregression (VAR) to trace shock transmission, and GARCH models to capture volatility dynamics. Our analysis of U.S. data (1990-2024) reveals a clear, state-dependent narrative. Expansions are not just periods of higher equity returns; they are also characterized by a calmer market environment. In contrast, recessions deliver a dual blow: significantly lower returns and volatility that is both higher and more stubborn. Perhaps most critically, we find that the stock market’s sensitivity to macroeconomic shocks intensifies during downturns—a negative output shock or a tightening of financial conditions packs a stronger and more prolonged punch. These core findings prove resilient across a battery of robustness checks, from swapping in alternative cycle indicators like the CFNAI to examining industry-level data. The practical implication is straightforward: ignoring the business cycle is a risky strategy for both portfolio management and macroeconomic stabilization.
With the advancement of modern society and the growing urgency of environmental protection issues, numerous countries are actively promoting sustainable and green development. As a country with a large population and relatively limited per capita resources, China has adopted green supply chain management (GSCM) to address resource constraints and environmental challenges. This approach transcends the limitations of traditional supply chain management, which primarily focuses on economic indicators like cost and quality. However, the implementation pathways for such transformation in leading manufacturing enterprises remain underexplored. To address this gap, this paper conducts a case study of Huawei Technologies Co., Ltd. Instead, it systematically optimizes processes such as design, procurement, processing, and transportation within the supply chain, achieving a dual enhancement of economic benefits and environmental protection. Eco-design requires optimizing resource allocation while minimizing environmental pollution impacts, embedding environmental principles throughout supply chain processes to achieve conservation and foster coordinated economic and environmental development. This paper analyzes Huawei Technologies Co., Ltd. as a case study, examining its entire supplier management model and development trends within the economic era of clean energy and low-carbon development. The study reveals that Huawei’s transformation is driven by integrating carbon reduction throughout its supply chain, leveraging digital technologies for transparency, and fostering collaborative partnerships with suppliers. These practices have resulted in significant carbon emission reductions and enhanced brand reputation.
Credit risk management is fundamental to the survival of financial institutions and the stability of the broader financial system. While traditional credit scoring models like FICO serve as industry standards in consumer credit, they have notable limitations: they exclude individuals without conventional credit histories and rely on static historical data, which fails to capture dynamic changes in borrowers’ financial behaviour and risk profiles. This study addresses two core questions: (1) How can alternative features, derived from borrowers’ controllable financial behaviour, be constructed to capture risk information missed by FICO? (2) How can the marginal contribution of these features to credit risk identification be quantified? Using the Lending Club loan dataset, we adopt a framework of “feature selection → model building → performance evaluation → robustness testing,” constructing a baseline model using only the FICO score and an extended model that incorporates alternative features via the XGBoost algorithm with 5-fold crossvalidation. Results indicate that the extended model achieves robust improvements in key metrics (AUC, KS, F1), effectively bridging gaps in dynamic risk detection. Alternative features supplement traditional models by identifying high-risk segments, particularly those with “high debt, low stability, and no assets.” Academically, this study advances credit risk identification methodologies and enriches the theoretical application of alternative data; practically, it offers financial institutions enhanced risk control tools to reduce nonperforming loans.
Income inequality is a persistent issue in economics and public policy, as it influences opportunities for individuals and affects broader patterns of social mobility and economic fairness. Understanding the factors that drive differences in earnings is therefore essential for both researchers and policymakers. This study uses data from the Panel Study of Income Dynamics (PSID) to build a multiple nonlinear regression model that explores how age, gender, and education affect income. To capture the possible nonlinear relationship between age and income, a quadratic term is added for age, and a log transformation is applied to income to reduce skewness. The regression results show that income generally follows an inverted U-shaped trend with age. While gender shows a borderline significant effect, education does not appear to be statistically significant in this model. After applying regression without education, the fit of the model did not improve noticeably. Although the overall explanatory power of the model is limited, it still reflects important ideas from human capital theory. Therefore, the study highlights several limitations, including the oversimplification of the model and the exclusion of relevant variables such as race and occupation. Taking these factors into account and drawing on the insights from previous research, the report concludes with policy implications and recommendations for future studies.
Involution—socially inefficient intensification of individual effort in competitive areas like work and education—arises from individually optimal arms-race dynamics that are collectively suboptimal. To explore the phenomenon, the current study sets up a dynamic model, thinking of involution as an evolutionary game under replicator dynamics. A two-strategy model—High Effort (H) and Low Effort (L)—is set up with payoff externalities reflecting congestion and relative-performance rewards. The model generates a high-effort dominant attractor or an interior evolutionarily stable state, depending on parameters. Closed-form solutions are derived for the selection gradient, interior equilibrium, comparative statics, local stability, and social optimum maximizing average payoff. The analysis connects the mathematics to real examples of schooling, firms, and industries and calculates shortrun benefits (peak performance, tournament selection) and major costs (welfare loss, stagnation of innovation, burnout, inequality). Policy and institutional recommendations are then transformed into a revision of the revenue matrix and information structure, so as to guide the system from the “prisoner’s dilemma” pattern to a coordinated game or a quality-based competition pattern. The study further proposed a formalized diagnostic framework to describe the “inner volume trap” and formulate corresponding policies to promote more sustainable competition.
Against the backdrop of global economic integration and volatile international trade regimes, multinational technology firms confront challenges such as tariff hikes, supply chain restructuring, and cross-border policy disparities. This study employs SWOT analysis to explore how Apple’s strengths mitigate trade barriers, how trade tensions exacerbate its weaknesses, and how globalization creates opportunities or poses threats. The reason why Apple was selected as the research object is because its representativeness in global value chains, distinct competitive advantages, and urgent strategic dilemmas. Findings indicate that Apple’s core competitiveness lies in its brand, supply chain, and iOS ecosystem, yet trade frictions amplify its operational and policydependent weaknesses; it needs to balance short-term supply chain resilience/policy compliance with long-term “premium+emerging” dual growth engines and sustainable ecosystems. Theoretically, this study extends the SWOT framework by embedding macro trade variables; practically, it offers strategic references for multinational enterprises and emerging market firms navigating volatile trade landscapes.
The Apple App Store has become the dominant platform for digital distribution on iOS, integrating diverse monetization models such as paid downloads, in-app purchases, subscriptions, and ad-based hyper-casual formats. However, Apple’s App Tracking Transparency (ATT) has reshaped advertising dynamics by reducing cross-app attribution and increasing customer acquisition costs. This study examines how advertising expenditures translate into sustainable market share across monetization models under ATT constraints. Using quarterly panel data from 2019 to 2024, the analysis applies voice share and market share metrics, supplemented by Herfindahl-Hirschman Index and Lorenz curve indicators. Results show that subscription and freemium/IAP models benefit from high retention and ranking persistence, allowing advertising to drive long-term share growth, whereas hyper-casual games experience short-lived acquisition effects. Apple Search Ads, as a first-party channel, helps stabilize baseline share compared to external paid media. Policy implications include building budgets based on lifecycle-specific share-of-voice targets, aligning media spend with content releases, and preventing “bubble share” through centralized monitoring. The study offers a unified framework linking short-term advertising elasticity to long-term market concentration, providing insights for advertisers, developers, and policymakers navigating the privacy-driven digital advertising environment.
The increasing complexity of construction projects has underscored persistent challenges in supply chain coordination and risk governance. Traditional management tools often fail to address fragmented communication, information silos, and limited predictive capacity in risk identification. Large language models (LLMs), with their advanced natural language processing and reasoning capabilities, offer a transformative potential to enhance collaboration and adaptive governance in such contexts. This paper conducts a systematic literature review of recent studies across IEEE, Springer, Elsevier, and arXiv, classifying existing research by application scenario, methodology, and outcomes. The findings reveal that LLMs are most effective in improving communication and standardizing information exchange, while their application to predictive risk governance remains largely conceptual and underexplored. This highlights an important disparity between the predictive and assistive capabilities of Large Language Models (LLMs) in this area. Integrating digital platforms, for example, Building Information Modeling (BIM) and blockchain technology, is suggested as an eventual enabler of building the resilience and openness of supply chains. Nonetheless, the review presents significant gaps, for example, the lack of domain-specific fine-tuning, the lack of empirical verification in construction contexts, and the less-than-adequate integration from coordinationand governance-based viewpoints. The paper concludes by positing that LLMs should serve as collaborative infrastructures in supply chains best as they are optimally accessed using hybrid governance structures infusing artificial intelligence-based processes and blockchain as well as statistical procedures. Future work should focus on empirical case studies, system integration, and capacity building for closing the gap between potential applications on paper and practical application.
Competition in the daily chemical industry is becoming increasingly fierce, and financial indicators are an important window to measure a company’s performance. There are three examples, they are P&G, Unilever and Loreal. The financial indicators of the three aspects, namely their operational efficiency, solvency, profitability and growth potential, have been compared among the three companies. Based on data from financial official websites from 2022 to 2024, and the analysis is conducted by citing the ratios calculated by these financial official websites. L’Oreal has made the fastest progress in terms of net profit margin, while Unilever has the highest asset-liability ratio. L’Oreal is at a clear disadvantage in inventory turnover. L’Oreal has the highest growth rate in operating income, but its decline is also the fastest. L’Oreal needs to speed up the sale of its inventory, which will also help improve its net profit margin. Overall, Procter & Gamble is performing the best in operations, while Unilever has shown some downward trends recently.
This study applies machine learning algorithms to the field of quantitative finance. By employing both Random Forest and Extreme Gradient Boosting (XGBoost) models to predict price movements of nine different Exchange-traded funds (ETFs) from the US, it assesses the practical performance of machine learning in ETFs’ price forecasting, thereby assisting investors and institutions in better evaluating future ETF trends. The ETFs’ price data used in this research are sourced from the US ETF Prices datasets on Kaggle. Technical indicators such as Bollinger Bands, Relative Strength Index (RSI), and Moving Average (MA) were incorporated into the models through feature engineering. The performance of both models was evaluated across different time windows and ETF products. Comparative analysis revealed that both Random Forest and XGBoost perform well within the 5 to 200-day forecasting horizon. The results indicate that larger sample sizes positively impact the goodness-of-fit of the Random Forest model, while excessively large samples may lead to degraded performance in XGBoost. In conclusion, while machine learning algorithms show strong promise in predicting ETF price movements, practitioners should still integrate market experience, sentiment analysis, and multi-factor evaluation to comprehensively assess ETF performance.
In an era of profound shifts in global trade patterns and heightened uncertainties, maritime supply chain risk management faces critical challenges that traditional mechanisms struggle to address. This study investigates the core impediments to resilient development in maritime supply chains for logistics enterprises. Through a systematic analysis, it identifies three systemic bottlenecks: (1) a governance gap where traditional risk management mechanisms fail to cope with novel risks; (2) a collaboration gap stemming from disparate data standards that hinder industry-wide synergy; and (3) an investment gap characterized by the high costs and uncertain returns of building resilience. These three types of issues impede the resilient development of the maritime supply chain and also prevent enterprises from responding to supply chain risks and disruptions. To address these gaps, this paper proposes a multi-faceted solution: integrating resilience metrics into strategic performance appraisal systems, constructing a cost-resilience curve model to identify optimal investment timing, promoting data-sharing platforms and industry partnerships, and innovating with rental service models to lower the barrier to entry for SMEs. Finally, it emphasizes the need for multi-party collaborative efforts among the government, associations, and enterprises to build a flexible, efficient, economically sustainable, and highly resilient supply chain system.
With the in-depth development of the digital economy, selfmedia has become a crucial platform for college students to access information, acquire skills, and engage in career planning. Based on simulated data from 99 employed college graduates, this study empirically examines the impact of self-media usage behavior on college students’ employment outcomes (starting salary and employment timeliness) using descriptive statistics, correlation analysis, and multiple linear regression models. The findings of this paper reveal that self-media skill level, content creation frequency, and the frequency of obtaining career information through self-media significantly positively impact starting salaries; self-media enhances employment quality via digital skills and career information capital yet requires caution against its “time black hole” effect. This study enriches empirical research on digital media and youth employment, provides practical insights for stakeholders (students, colleges, platforms) to optimize self-media’s role in promoting high-quality employment, and bridges the gap between academic exploration and real-world career guidance.
As a core model integrating railway, road, waterway, and air transportation, the multimodal transport supply chain plays a critical role in improving logistics efficiency, reducing costs, and enhancing supply chain resilience. Against the backdrop of China’s “domestic and international dual circulation” strategy and the deepening of the “Belt and Road Initiative”, this paper explores the development status, existing problems, and optimization paths of China’s multimodal transport supply chain. However, its development is constrained by several critical challenges that need to be addressed. Through a systematic analysis of policy documents, industry reports, and academic literature, this study finds that China has made phased progress in infrastructure construction, market scale expansion, and low-carbon exploration, but still faces three key dilemmas: market-legal-cost constraints, information-standard barriers, and regional imbalance-low-carbon obstacles. Corresponding suggestions are proposed: improving infrastructure and standard systems, and optimizing policy and legal mechanisms. The proposed framework and recommendations not only contribute to the theoretical discourse on integrated logistics management but also offer actionable insights for policymakers and logistics enterprises to enhance operational efficiency and competitiveness.
This study analyzes POP MART’s success in China’s blind box market, focusing on its IP collaboration, marketing strategies, and store design. Drawing on literature reviews, media reports, and financial data, this study also explores the socio-economic effects of the blind box business model, including the positive aspect of supporting young designers and strengthening industries, as well as the negative consequences of tensions between families and concerns about consumer spending. Despite challenges such as rising competition and uncertainty of product popularity, POP MART maintains strong financial performance and a high stock valuation compared to its competitors, reflecting investor confidence in the company’s growth potential. The study concludes that the company is likely to remain its leadership position in the blind box market. However, its long-term success will depend on how effectively it adapts to evolving consumer preferences, expands its range of derivative products, and leverages social media to increase brand influence.
