Exploring Science Academic Conference Series
The Proceedings Series publishes peer-reviewed papers presented at the Exploring Science conferences. These proceedings are a valuable resource for the scientific community, providing access to the latest research findings, innovative methodologies, and interdisciplinary studies.
A Review of Cost-Related Research Under the Omni-channel Integration Background of Beauty Enterprises
Against the backdrop of digital transformation, the online-offline omni-channel integration in the cosmetics industry has become an inevitable trend, and channel reform has profoundly reshaped enterprises’ cost structure. Taking cosmetics, omni-channel and cost as the core keywords, this paper systematically reviews relevant literature and analyzes the transmission mechanism of how omni-channel integration affects the costs of cosmetics enterprises. It focuses on four major scenarios — offline experience, live-streaming marketing, cross-channel returns, and efficacy R&D — to identify industry-specific pain points in cost control, and sorts out the corresponding cost optimization paths and empirical conclusions. The study finds that the cost logic of omni-channel operation in the cosmetics industry differs from that of general retail, and existing three-dimensional interdisciplinary research remains scarce with insufficient quantitative analysis. This paper provides theoretical reference and practical enlightenment for the cost management of cosmetics enterprises in their omni-channel transformation. Furthermore, it offers theoretical reference and practical enlightenment for the cosmetics industry to cut costs, boost efficiency across the whole industrial chain and achieve high-quality development.
cosmetics enterprises, omni-channel integration, cost control, transmission path, cost optimization
Copyright (c) 2026 Han Xu (Author) https://creativecommons.org/licenses/by/4.0
Research on Algorithmic Explainability and Legal Liability Framework under AI Audit Background
The deep integration of artificial intelligence (AI) in auditing has significantly enhanced audit efficiency and data processing capabilities. However, the inherent “black box” nature of algorithms poses severe challenges in determining liability for audit failures, which hinders further development of AI auditing technologies. This study aims to address liability attribution dilemmas caused by algorithmic opacity in AI auditing contexts. By integrating algorithm governance theory with extended audit accountability theory, we establish a bidirectional framework of “algorithmic explainability and legal liability.” The research first analyzes the real-world contradiction between explainability deficiencies and legal lag in current AI auditing practices, revealing Deloitte's liability determination challenges stemming from generative AI hallucinations. Solutions are then proposed from both technical and legal dimensions. Findings indicate that algorithmic explainability serves as a prerequisite for legal liability determination, while clear legal regulations provide institutional safeguards for technological transparency. This framework not only clarifies accountability chains in human-machine collaboration but also offers theoretical support and practical guidance for promoting compliance in AI auditing and maintaining capital market trust mechanisms.
AI auditing, algorithmic explainability, legal liability, framework research, explainable artificial intelligence
Copyright (c) 2026 Wendong Li (Author) https://creativecommons.org/licenses/by/4.0
Greenwashing Governance through ESG Disclosure: A Double Face for Artificial Intelligence
The growing use of artificial intelligence (AI) in producing, interpreting, and verifying information on the environmental, social, and governance (ESG) information. Companies are under increasing pressure to report on ESG information that is extensive, useful, credible, comparable, and decision-useful. However, the proliferation of ESG disclosure does not guarantee more transparency. Rather, it has increased the risk of symbolic disclosure, narrative inflation, and greenwashing. In this context, AI is emerging as an attractive governance technology but also as a new source for reporting risk. In this narrative review, we discuss how AI could influence ESG disclosure quality, or even the governance of greenwashing. Here, we argue that we need to understand AI beyond its use as a technological solution to process sustainability information, but also as a socio-technical form of governance that transforms the production, evaluation, and contestation of sustainability claims. Drawing from the literature on ESG disclosure, greenwashing, computational text analysis, sustainability reporting and ESG assurance, our contribution is to develop a three-level framework in which AI can be used both as a disclosure mechanism and as an audit instrument, with the potential of also promoting indirect normative behavior. The work presented here builds on this triple role of AI. We find AI can improve ESG disclosure through automated processing and anti-greenwashing monitoring, yet generative AI may enable sophisticated symbolic compliance. AI offers opportunities for ESG disclosure and governance, but its actual effects depend on organizational incentives, disclosure environments, and accountability mechanisms.
artificial intelligence, ESG disclosure, greenwashing, sustainability reporting, ESG assurance, governance
Copyright (c) 2026 Hanwen Zhang (Author) https://creativecommons.org/licenses/by/4.0
A Theoretical and Literature-Based Comparison of the Markowitz Model and the Index Model
This paper presents a comprehensive review of two foundational portfolio models in modern finance: the Markowitz mean-variance model and the Sharpe single-index model. The study systematically examines their theoretical frameworks, computational requirements, empirical performance, and practical applicability. The Markowitz model, as a model-free optimisation framework, aims to achieve theoretically optimal risk-return trade-offs by fully considering pairwise covariances among assets. However, its high sensitivity to estimation errors and intensive data requirements often limit its empirical usefulness, particularly for large portfolios. In contrast, the index model simplifies covariance estimation by attributing asset return variations to a common market factor, thereby reducing parameter dimensionality and enhancing computational efficiency. Multi-factor extensions further improve explanatory power while maintaining practical feasibility. By reviewing existing literature and empirical evidence, this paper highlights the strengths, limitations, and appropriate application scenarios of both models. The findings provide guidance for investors, portfolio managers, and researchers in selecting or integrating portfolio models to balance theoretical rigor with empirical robustness and operational efficiency.
markowitz model, index model, portfolio optimisation, mean-variance analysis, factor models
Copyright (c) 2026 Jingxuan Yan (Author) https://creativecommons.org/licenses/by/4.0
A Comparative Study of Traditional Statistical Models and Machine Learning Algorithms in Credit Risk Assessment
Credit risk assessment underpins lending decisions, pricing strategies, portfolio management, and regulatory capital allocation within modern financial systems. Logistic regression has historically served as the dominant modeling framework in credit scoring due to its probabilistic coherence and interpretability. In recent years, advances in machine learning—particularly tree-based ensemble methods such as Random Forest and Gradient Boosting—have demonstrated strong predictive performance and often outperform traditional approaches in discrimination metrics such as the area under the ROC curve (AUC). However, the adoption of machine learning in credit risk modeling remains debated due to concerns regarding probability calibration, temporal robustness, interpretability, and regulatory governance. This paper provides a comprehensive comparison of traditional statistical models and tree-based machine learning approaches in credit risk assessment. Rather than focusing exclusively on discriminatory performance, the analysis adopts a multidimensional evaluation framework incorporating calibration quality and temporal stability. Drawing on foundational theory and recent empirical evidence, the paper argues that model adequacy in credit risk is inherently context dependent. A three-pillar framework—discrimination, calibration, and temporal robustness—is proposed to guide academic research and practical model deployment. The findings suggest that superior ranking performance does not necessarily imply superior decision quality and that effective credit risk modeling requires balancing predictive flexibility with probabilistic reliability and governance stability.
credit risk assessment, traditional statistical models, machine learning, logistic regression, tree-based models, model performance evaluation
Copyright (c) 2026 Hanrun Jin (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Social Media Content Marketing on Consumer Purchase Behavior: A Comprehensive Literature Review
This study presents a comprehensive literature review investigating how social media content marketing influences consumer purchase behavior through a complex sequence of psychological and social mechanisms. These mechanisms operate within contemporary digital ecosystems that are increasingly characterized by algorithmic content distribution, influencer-driven communication, and the seamless integration of social commerce features. Unlike traditional advertising models that rely heavily on persuasive interruption, modern social media content marketing emphasizes value-based communication strategies. These strategies are designed to deliver informational, experiential, and relational benefits, thereby encouraging voluntary consumer engagement and fostering long-term brand relationships. By synthesizing fragmented findings from interdisciplinary research across marketing, consumer behavior, and information systems domains, this review develops a conceptual mechanism framework in which content quality stimulates consumer engagement, engagement facilitates electronic word-of-mouth diffusion, electronic word-of-mouth strengthens consumer trust, and trust ultimately drives purchase intention and purchasing behavior. Employing literature review involves structured database search, screening procedures, and thematic synthesis. This study integrates empirical evidence across multiple platforms, industries, and cultural contexts to identify consistent patterns and theoretical inconsistencies. Findings reveal that content credibility, authenticity, and informativeness function as primary antecedents of engagement and trust formation, while peer validation, influencer credibility, and community interaction operate as amplification mechanisms that reduce perceived risk and enhance consumer confidence in brand claims. The review further identifies research gaps related to platform-specific dynamics, cross-cultural variations, and methodological limitations, providing theoretical contributions and managerial implications that support the design of trust-oriented, engagement-driven content marketing strategies in increasingly competitive and socially mediated digital marketplaces.
social media content marketing, consumer engagement, electronic word-of-mouth, consumer trust, digital marketing strategy
Copyright (c) 2026 Yixin Weng (Author) https://creativecommons.org/licenses/by/4.0
Investor Sentiment Bias and the Persistence of the Spring Festival Anomaly in China’s A-Share Market: A Behavioral Finance Perspective
The Spring Festival anomaly, characterized by significantly higher stock returns around the Chinese Lunar New Year, has persisted for over 30 years in China's A-share market, directly challenging the core proposition of the Efficient Market Hypothesis. Classical finance theory fails to explain why this seasonal pricing deviation remains stable despite the rapid development of institutional trading and algorithmic trading. This paper adopts a systematic literature review method based on investor sentiment theory and limits of arbitrage theory to analyze the formation mechanism and persistence logic of the Spring Festival anomaly. The research identifies three cultural-psychological drivers of positive investor sentiment bias during the Spring Festival period: the year-end bonus effect, festive atmosphere effect, and lucky culture effect. Furthermore, it clarifies how three types of arbitrage constraints—fundamental risk caused by holiday market closure, noise trader risk resulting from retail investor dominance, and implementation costs including short-selling restrictions—jointly prevent rational arbitrageurs from eliminating abnormal returns. Finally, this paper constructs an integrated behavioral finance framework that explains both the formation and persistence of the Spring Festival anomaly, filling the research gap in the field of cultural behavioral finance. The findings provide empirical evidence and practical guidance for investors to formulate seasonal investment strategies and for regulators to improve market efficiency.
spring festival anomaly, investor sentiment, limits of arbitrage, behavioral finance, market efficiency
Copyright (c) 2026 Jianyin Zheng (Author) https://creativecommons.org/licenses/by/4.0
Research on the Disruptive Innovation of NEV to the Traditional Automotive Industry from the Perspective of Business Models
Since its formal proposal, disruptive innovation theory has undergone more than two decades of improvement and evolution, exerting a profound influence on academic fields such as business and enterprise management. As the market share of New Energy Vehicles (NEVs) has gradually increased in recent years, people have noticed that traditional fuel vehicle enterprises seem to be struggling to cope with the pressure from new car-making forces. To analyze the mechanism of the dual drive of “technology + business model” and break through the transformation predicament of traditional automakers, this paper, relying on the disruptive innovation theory refined and expounded by Christensen in 2015, adopts methods such as systematic literature retrieval, case analysis, and comparative study, and selects typical international automakers such as Tesla, BYD, and Volkswagen as cases. Through conducting specific case studies on traditional energy and NEV enterprises worldwide, the author has distilled the intrinsic mechanisms of disruptive innovation and the development of the automotive industry, and then put forward breakthrough viewpoints. Research shows that the emergence of NEV represents a disruptive innovation for traditional automakers not only in terms of products but also in the entire business system. This makes it difficult for the decades of business models and management experience of traditional automakers to match the development logic of the new energy vehicle industry, and their long-accumulated business model advantages and management experience have been gradually weakened.
business model, disruptive innovation, new energy vehicle, asymmetric motivation
Copyright (c) 2026 Yutong Huang (Author) https://creativecommons.org/licenses/by/4.0
The Interweaving of Cultural Logic and Capital Logic: A Study on the Construction Mechanism of Emotional Value in the Contemporary Consumer Market
The current study aimed to explore how emotional value is constructed in the consumer market and the roles of culture and capital in this process. Consumers of different age groups are investigated through questionnaires and offline interviews. The marketing content and methods currently in the market have been analyzed. It also includes the study of typical cases such as PopMart. The consumers' emphasis on emotional value has shaped the positioning and marketing strategies of both businesses and the market for products. Emotional value consumption is the result of the joint construction of cultural logic and capital logic. Cultural logic provides the foundation for demands and the discourse resources, while capital logic completes the transformation and amplification of commodities. The interaction of these two-dimensional logics not only meets some emotional needs but may also exacerbate emotional alienation. Consumers should enhance their judgment skills and consumer awareness to avoid excessive consumption and emotional exploitation. Businesses should create products based on genuine culture and conduct appropriate marketing to prevent excessive stereotyping.
emotional value, cultural logic, capital logic
Copyright (c) 2026 Yiqiu Xiang (Author) https://creativecommons.org/licenses/by/4.0
Low-End Disruptive Innovation and Market Share Growth: A Case Study of Xiaomi in the Global Smartphone Industry
The smartphone world has been getting more competitive, as the leading companies have traditionally been oriented towards innovation at the high end. Nevertheless, Xiaomi is disrupting this trend due to the implementation of low-end disruptive innovation. This paper discusses how the Xiaomi strategy, which involves cost-efficiency, online sales, and quick product development has led to its expansion in market share in the global smartphone market. The study uses a qualitative, theoretically informed analysis grounded on the paradigm of disruptive innovation, and how companies can initially serve underserved or price-sensitive customers and subsequently be able to grow into mainstream markets. The results indicate that Xiaomi could achieve its market penetration by offering both low prices and high perceived value, which allowed it to enter the new markets and develop over time to compete with the well-established global brands. Moreover, its online environment and community-based strategy reinforced customer interaction and decreased the operational expenses. The research paper adds to the knowledge of how emerging-market companies can adopt disruptive strategies to realise rapid global growth. It also highlights the continued relevance of disruptive innovation theory in explaining competitive dynamics in modern technology markets.
low-end disruptive innovation, Xiaomi, smartphone industry, market share growth
Copyright (c) 2026 Xinmei Chen (Author) https://creativecommons.org/licenses/by/4.0
A Review of the Impact of Capability Illusions Induced by Generative Artificial Intelligence on Individual Investors' Overconfidence: From the Perspective of Heterogeneity in Investment Decision Efficiency and Risk Preferences
With the rapid commercialisation of Generative Artificial Intelligence (GenAI) in the field of financial investment, the illusion and overconfidence of individual investors' skills have become an important factor affecting the efficiency of investment decision-making. This review brings together the research published by Web of Science, Google Scholar and CNKI from 2020 to 2026. It is found that GenAI greatly increases the skill fantasy of retail investors, which makes their overconfidence stronger. The efficiency of investment decision-making is a bit like a bridge in this relationship, and the difference of risk preference is an important variable, which changes the intensity of this effect among different investor groups. This paper summarises the researchers' thoughts today, shows the differences and unknown contents, and puts forward ideas for future research. The purpose of this review is to provide theoretical reference for the interdisciplinary study of GenAI and investor behaviour. It also provides some practical suggestions, which could help individual investors avoid misjudgement and help financial institutions improve their services based on artificial intelligence.
illusion of competence, overconfidence, investment decision efficiency, heterogeneity of risk preferences, behavioural finance
Copyright (c) 2026 Hankun Zhu (Author) https://creativecommons.org/licenses/by/4.0
The Attacks and Defenses Mechanisms of Algorithmic Trading Systems driven by Deep Learning
Deep learning (DL) is now widely applied in quantitative finance, especially in algorithmic trading systems (ATS). However, its defense against adversarial attacks remains vulnerable due to inherent characteristics that pose substantial risks to financial markets. This paper provides a review of the security risks ATS faces, the attacks against it, and the defenses against them. It first presented the theoretical background by decomposing the ATS framework, briefly introducing relevant knowledge of adversarial attacks in DL, and highlighting the specific constraints of financial time series and regulatory oversight. Then, it integrated and analyzed various adversarial attack methods targeting ATS, proposed innovative shifts in the perspectives and criteria for evaluating the impact of adversarial attacks, compared existing defense mechanisms, and discussed their effectiveness and limitations. Finally, it provided theoretical support and practical guidance for building safer and more robust algorithmic trading systems in the future.
cyber security, adversarial attacks, DL (deep learning), ATS (algorithmic trading system)
Copyright (c) 2026 Pengcong Wu (Author) https://creativecommons.org/licenses/by/4.0
A Quantum Approximate Optimization Algorithm (QAOA) Approach to Portfolio Optimization
Portfolio optimization, a core combinatorial optimization problem in quantitative finance, requires balancing return maximization and risk minimization within discrete asset sets. Classical algorithms often struggle with scalability and local optima traps when handling large-scale asset pools, limiting their practicality in dynamic markets. This study proposes a complete Quantum Approximate Optimization Algorithm (QAOA) workflow for portfolio optimization, from problem formalization to experimental validation. We first transform the portfolio optimization problem into a Quadratic Unconstrained Binary Optimization (QUBO) framework, then design a parameterized QAOA circuit with adjustable layers to balance expressiveness and noise resilience. We propose a methodology using Qiskit for quantum simulation and classical optimizers for parameter tuning, we propose implementing the algorithm on real-world S&P 500 stock data. We could also analyze the impact of quantum hardware noise and propose mitigation strategies, providing actionable insights for Noisy intermediate-scale quantum computing (NISQ)-era implementations. This work bridges quantum algorithm design with real-world financial applications, offering a theoretical framework for quantum-driven portfolio optimization.
quantum approximate optimization algorithm, portfolio optimization, quantum computing in finance
Copyright (c) 2026 Yifei Huang (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Robo-Advising on Individual Investors' Behavioral Biases: A Behavioral Finance Review
With the penetration of financial technology, artificial intelligence and big data into wealth management, robo-advising has been one of the useful instruments for individual investors. This review discusses whether robo-advisors can mitigate behavioral biases of individual investors from a behavioral finance perspective. Through a review of relevant literature on robo-advising, modern portfolio theory, prospect theory, disposition effect, overtrading and limited attention, this paper finds that robo-advisors may enhance investor behavior via risk profiling, diversified asset allocation, automatic rebalancing and rule-based execution. These mechanisms could reduce overconfident trading, chasing returns, concentration of holdings and emotional decisions. Nevertheless, robo-advising is unable to completely eradicate irrational behavior. Its efficacy is subject to the validity of risk questionnaires, algorithmic transparency, platform incentives, investor financial literacy and regulatory scrutiny. Inspired by The Man Who Solved the Market and A Man for All Markets, this review also contends that the value of models does not reside in delivering returns, but in fostering probability thinking, discipline and risk control. Finally, this paper proposes enhancements in transparency, risk disclosure, investor education and conflict-of-interest regulation.
robo-advising, behavioral finance, individual investors, fintech, asset allocation
Copyright (c) 2026 Wangbang Shangguan (Author) https://creativecommons.org/licenses/by/4.0
A Review on the Impact of Fintech Development on the Profitability of Commercial Banks in China
This paper takes the development of fintech and the profitability of commercial banks in China as the research objects, adopts the literature review method to systematically sort out the definition, types and related theoretical basis of fintech, and clarifies the core evaluation indicators of bank profitability. On this basis, this paper reviews the action mechanism, transmission paths and empirical evidence of fintech development affecting the profitability of commercial banks, and summarizes the driving factors and restrictive conditions in the impact process. The study finds that fintech influences commercial banks' profitability through technological empowerment, market competition, business innovation and risk adjustment, with both promoting and inhibitory effects. Finally, this paper comments on the existing research deficiencies and puts forward the future research directions, aiming to provide theoretical support for commercial banks' digital transformation and regulatory policy optimization, and verifies a significant U-shaped nonlinear relationship between fintech development and bank profitability based on DuPont decomposition.
fintech, commercial banks, profitability, digital transformation, DuPont analysis
Copyright (c) 2026 Lina Yi (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Information Barriers on Financial Asset Price Discovery: A Theoretical Analysis Based on Information Asymmetry and Behavioural Finance
With the development of digital finance and high-frequency trading in recent years, the information asymmetry between institutions and individual investors has gradually grown, and the problems caused by this phenomenon have become more serious. Based on information asymmetry theory, the efficient market hypothesis and behavioural finance theory, this paper studies how information barriers affect the discovery of financial asset prices and build a theoretical system for this purpose. A concept and analysis were employed in the study to investigate the path of "information barriers - information diffusion - investor behaviour - price formation". The results indicate that information barriers reduce both the speed and quality of information diffusion, thus leading to a delay in the integration of information into asset prices. Investors are not all the same and are also boundedly rational; therefore, the market will fluctuate more readily. Generally speaking, institutional investors have better information and are more likely to trade early, while retail investors are more prone to herd behaviour and overreaction. As a result, the combined effect has reduced the efficiency of price discovery, increased short-term fluctuations in prices, and deviated from the value of assets. This study is mainly in a theoretical form, has not been empirically verified, and has failed to consider other factors that affect the results, such as liquidity conditions and different regulations. In the future, based on the limitations of the above, empirical studies can be conducted using high-frequency or cross-market data and more market microstructure variables added to expand the analytical system.
information barriers, information asymmetry, price discovery, behavioural finance, efficient market hypothesis
Copyright (c) 2026 Shuai Zhang (Author) https://creativecommons.org/licenses/by/4.0
The Latency-Privacy Paradox: A Review of Cryptographic Data Mining in Financial Intrusion Detection
Mandatory end-to-end encryption across contemporary banking infrastructures essentially blinds legacy intrusion detection mechanisms. This review unpacks the ontological collision between applied cryptography and network data mining—a theoretical intersection currently paralyzed by an unresolved latency-privacy bottleneck. By systematically synthesizing recent literature on hybrid Intrusion Detection Systems (IDS), we map a highly fragmented academic landscape. Solutions that enforce absolute mathematical privacy via Fully Homomorphic Encryption (FHE) collapse under their own computational burden—often introducing latency overheads of up to six orders of magnitude—during high-frequency trading (HFT) simulations. Alternatively, heuristic metadata mining bypasses decryption entirely but is highly fragile to adversarial spoofing and generative traffic manipulation. Evaluating these disparate trajectories reveals a critical gap: existing frameworks rarely account for the strict microsecond tolerances required by institutional trading floors. Ultimately, this review argues that feature-level Order-Preserving Encryption (OPE) combined with gradient-boosted classifiers provides a highly operationally viable compromise. This paper concludes by identifying promising future research directions in targeted obfuscation and urges a pivot away from mathematically flawless encryption toward hardware-accelerated, latency-aware detection topologies that secure proprietary trading signals without sacrificing line-speed threat mitigation.
encrypted traffic analysis, homomorphic bottleneck, financial IDS, feature obfuscation, microsecond latency
Copyright (c) 2026 Yingying Wang (Author) https://creativecommons.org/licenses/by/4.0
From “Aged Care” to “Aging Well”: An Integrative Framework for High-Quality Development in the Silver Economy—An Economic Analysis of Demand, Supply, and Institutional Interactions
The accelerating process of population aging in China and the rise of the “new elderly” group (those born after the 1960s and 1970s) are driving a profound transformation of the silver economy from “basic elderly care” to “high-quality enjoyment of old age”. However, existing studies often adopt a single perspective, lacking a systematic analysis of the interactive mechanisms among the demand side, supply side, and institutional side. This paper constructs an integrated analytical framework encompassing demand, supply, and institutional aspects, revealing the interactive mechanisms among these three dimensions and their impact pathways on the high-quality development of the silver economy. The research finds that demand drives supply via scale, structure, and price effects; supply feeds back to demand by creating scenarios, reducing costs, and enhancing experience; and institutions regulate supply-demand matching by correcting market failures, providing incentives, and standardizing order. Furthermore, this paper applies the proposed framework from a dynamic evolutionary perspective to outline the developmental pathways of the silver economy. This research provides an integrated theoretical tool for understanding the intrinsic logic of the silver economy and offers an analytical framework for precise policy formulation and strategic corporate planning.
silver economy, new elderly, demand side, high-quality development, institutional side
Copyright (c) 2026 Jialu Li (Author) https://creativecommons.org/licenses/by/4.0
FoodShare Participation and Household Economic Vulnerability in Wisconsin Counties, 2019–2023
This study examines annual FoodShare recipient rates in 71 Wisconsin counties in 2019, 2021, 2022, and 2023, with particular attention to households below the poverty line and households classified as ALICE (Asset Limited, Income Constrained, Employed). FoodShare participation is used as a county-level proxy for food-assistance use and food-insecurity risk rather than as a direct measure of food insecurity. Combining data from the Wisconsin Department of Health Services, the U.S. Census Bureau/American Community Survey, and the United for ALICE Research Center, the analysis shows that the mean county FoodShare recipient rate was higher in 2021, the first observed pandemic-era year in the dataset, than in 2019, and it remained above the 2019 baseline in 2023. Regression results indicate that both poverty-household rates and ALICE-household rates are positively associated with FoodShare participation in cross-county models. Robustness checks show that these relationships remain present in population-weighted models and when Milwaukee County is excluded, while county fixed-effects models suggest that much of the relationship reflects persistent differences across counties rather than short-run within-county changes. The findings support a broader interpretation of household economic vulnerability: poverty measures identify severe hardship, while ALICE measures capture additional households above the federal poverty line but below the basic cost of living in their county.
FoodShare, food insecurity, ALICE households, poverty, Wisconsin, COVID-19, county-level analysis
Copyright (c) 2026 Sizhe Cheng (Author) https://creativecommons.org/licenses/by/4.0
Application of Regression Analysis in Pricing of Fast-Moving Consumer Products
Against the backdrop of fiercer competition and diversified demands in the fast-moving consumer goods (FMCG) market, scientific new-product pricing is critical for enterprises to capture market share and maximize profits, yet most still rely on experience-based methods. Based on five years of relevant literature, this paper explores regression analysis’s application in FMCG pricing: it clarifies the variable selection logic of pricing regression models, compares the fitting effects and applicable scenarios of mainstream and specialized regression methods, identifies practical implementation obstacles, and discusses the potential of integrating machine learning with regression analysis to capture product substitution effects and optimize profit-maximizing price combinations, providing targeted guidance for FMCG pricing research and practice.
regression analysis, fast-moving consumer products, new-product pricing
Copyright (c) 2026 Wenxi Wang (Author) https://creativecommons.org/licenses/by/4.0
Determinants of Household Risky-Asset Participation and Portfolio Quality: A Critical Review
Household participation in risky-asset markets matters for long-term wealth accumulation. However, many households still avoid these markets. Some households enter the market, but then make weak investment choices. This review examines the main determinants of household risky-asset participation and portfolio quality. The review compares studies on entry costs, wealth constraints, financial capability, behavioural bias, trust, financial advice, and digital investment platforms. It finds that risk aversion alone does not explain non-participation. Non-participation is also linked to participation costs, perceived complexity, low wealth, liquidity constraints, and limited risk capacity. After households enter the market, portfolio quality depends on financial literacy, behavioural bias, diversification, fees, and trading behaviour. Digital finance has mixed effects. Robo-advisors may help households access diversified and low-cost portfolios. However, app-based trading platforms may encourage speculative and attention-driven trading. The review concludes that better market access does not automatically lead to better investment outcomes. Policy and future research should therefore focus on portfolio quality and household financial welfare, rather than participation rates alone.
household finance, risky-asset participation, portfolio quality, financial literacy, digital finance
Copyright (c) 2026 Yichen Hu (Author) https://creativecommons.org/licenses/by/4.0
The Role of Organizational Culture in Business Performance
Organizational culture is important in determining the performance of business, how the employees behave, innovation and how well the organization functions. The research presents a literature review on the organizational culture and business performance. This research will aim at examining theoretical voices and empirical evidence on organizational culture and business performance. Companies that have strong and adaptive cultures enjoy better performance, innovativeness is enhanced, and employees are more satisfied. The organization culture is also relevant to the digital transformation and technological change whereby the organizations need the ability to be flexible, and cultures that are innovative so as to be competitive. The literature, however, also demonstrates that according to the literature, an organizational culture is not a sole determinant of the business performance since the leadership style, organizational structure, and external environmental factors also influence the performance of the organization. The research emphasizes that organizational culture is a significant strategic tool, which the organization can utilize in enhancing performance, innovation, and competitiveness.
corporate culture, businesses performance, innovation, leadership, corporate performance.
Copyright (c) 2026 Yan Cui (Author) https://creativecommons.org/licenses/by/4.0
Procedural Contestability in Algorithmic Platform Enforcement: A Risk‑Tiered Framework for Commercial Automated Decision‑Making
Artificial intelligence-driven automated decision-making (ADM) has become a constitutive feature of contemporary commercial markets. In platform governance, credit assessment, dynamic pricing, targeted advertising, and platform rule enforcement, automated systems increasingly determine who may participate in markets, on what terms, and with what degree of visibility. This development raises a distinctive legal challenge: the problem is not only that algorithms may produce inaccurate outcomes, but that they function as a form of private market governance that is opaque, scalable, and difficult to contest. Using algorithmic platform enforcement against business users as a central case, this paper argues that existing legal regimes, including data protection law, consumer and e-commerce law, contract law, competition law, and platform regulation, only partially address this problem. Each regime captures part of the issue, but none fully integrates algorithmic enforcement with procedural fairness and commercial market access. Methodologically, the paper combines doctrinal analysis, literature synthesis, and comparative legal analysis. Its central contribution is the development of procedural contestability as a regulatory principle for commercial ADM. Rather than relying on transparency as disclosure alone, it proposes a risk-tiered framework that links ex-ante preventive duties with ex-post procedural safeguards, including meaningful notice, reasoned explanation, accessible appeal, human review, and record preservation. The aim is to ensure that automated decisions with significant commercial consequences remain subject to legal standards of justification, review, and correction.
automated decision‑making, platform regulation, procedural contestability, commercial law
Copyright (c) 2026 Jiaqi Cao (Author) https://creativecommons.org/licenses/by/4.0
A Literature Review on Theories and Empirical Evidence Under Asymmetric Information
This article presents a comprehensive literature review investigating the theories and empirical evidence of information asymmetry and its impact on market outcomes. The article integrates classic theoretical frameworks: adverse selection, moral hazard, signaling and screening, and explores applications in four key areas: the lemon market, the insurance market, corporate governance and the labor market. This review points out that information asymmetry is the main cause of market failure, which can lead to inefficient phenomena such as adverse selection, principal-agent problems, and labor market discrimination. Empirical studies in different contexts have confirmed the importance of these theories in reality and demonstrated impact on pricing, resource allocation, and market structure. In addition, this article explores and illustrates various mechanisms to alleviate information asymmetry, including signaling systems, information disclosure, third-party certification, risk adjustment, and compensation systems. These solutions help enhance market efficiency by reducing uncertainty and aligning the interests of economic agents.
information asymmetry, adverse selection, signaling effect, principal-agent problem, market failure
Copyright (c) 2026 Mutian Dai (Author) https://creativecommons.org/licenses/by/4.0
The Impact of ESG Screening on Risk-Adjusted Returns: Evidence from U.S. Equity ETFs
This study investigates the impact of ESG ratings on the risk-adjusted performance of U.S. exchange-traded funds (ETFs). Specifically, it compares the iShares ESG Aware MSCI USA ETF (ESGU) and the iShares Core S&P 500 ETF (IVV) to examine whether an ESG-focused investment strategy can achieve superior financial outcomes relative to traditional market investing. Using daily data from 2017 to 2024, the study employs descriptive statistical analysis, comparative performance metrics, correlation analysis, and paired sample t-tests to evaluate differences in performance, volatility, and risk-adjusted returns between the two ETFs. Empirical results indicate that ESGU exhibits a slightly higher average return than IVV; however, this difference is economically small and statistically insignificant. From a risk perspective, ESGU shows marginally higher volatility, suggesting that ESG screening does not necessarily reduce overall market risk exposure. ESGU’s Sharpe ratio is slightly higher, indicating a modest improvement in return per unit of risk, although this advantage is also not statistically significant. Overall, the findings suggest that ESG screening does not meaningfully enhance or reduce investment performance in the U.S. equity ETF market. This study contributes to the literature by providing updated empirical evidence on ESG screening effectiveness in passive investment strategies and assessing whether ESG-focused ETFs can deliver competitive risk-adjusted returns compared to traditional benchmarks. The results offer practical implications for investors seeking to balance financial performance with sustainability objectives.
environmental, social, and corporate governance screening (ESG), risk-adjusted returns, exchange-traded funds, sustainable investing
Copyright (c) 2026 Yanzhu Tao (Author) https://creativecommons.org/licenses/by/4.0
Gen Z Consumer Psychology and Co-branding in New Media: A Review with Luckin Coffee
Co-branding has become an essential strategy for brands seeking to engage young consumers in today's competitive marketplace. Generation Z, as true digital natives who have grown up with smartphones and social media, demonstrate distinct psychological patterns and consumption behaviors that fundamentally determine the success or failure of brand partnerships. This narrative review employs Luckin Coffee as an illuminating case study to synthesize existing research on co-branding strategies, Gen Z consumer psychology, and new media communication effects. While current academic literature has established foundational understanding of co-branding mechanisms, in-depth analysis specifically targeting Generation Z remains surprisingly limited. Luckin Coffee's innovative business model and digital marketing approaches have attracted considerable scholarly attention, yet the extensive co-branding practice—particularly high-profile collaborations with Moutai and various gaming IPs—sits largely unexamined in academic discourse. Additionally, the differential dynamics of how distinct social media platforms shape co-branding effectiveness require much clearer theoretical articulation. By integrating available knowledge across these domains, this paper illuminates the psychological mechanisms underlying Gen Z engagement with co-branded products in new media environments and offers practical, actionable guidance for brands developing their partnership strategies.
Gen Z, new media, Luckin Coffee, consumer psychology, co-branded marketing
Copyright (c) 2026 Youwen Zhang (Author) https://creativecommons.org/licenses/by/4.0
Post-Earnings Announcement Drift in China's A-Share Market: An Integrated Analysis
This paper examines the Post-Earnings Announcement Drift (PEAD) in the Chinese A-share market. As an overview of the researches carried out from 2011 to 2023 for evaluating why this significant anomaly is still economically and theoretically relevant in emerging countries. As a prevalent market anomaly, PEAD refers to the persistent directional deviation of stock prices following corporate earnings releases. Classical models cannot explain PEAD well. Even after continuous improvements in market reform and development of trade infrastructure, the delay in reflected reaction has yet to fully reconcile with established theories. To explain such persistent inefficiency, this paper constructs a framework combining psychology and structure. Investor inattention explains the under-reaction to earnings. Structural constraints, such as short-sale restrictions, prevent the arbitrageurs from correcting mispricing. Furthermore, based on the above analysis results, different kinds of environmental conditions have been identified to cause changes by means of drifts. Empirical evidence indicates that, recent studies have found that institutional investors may not eliminate this anomaly but rather strengthen or amplify it due to momentum herd behavior in a high-transparent situation. Finally, in terms of understanding behavioural finance at multiple angles through this model; thus possessing considerable value for both theory and practice.
post-earnings announcement drift, limited investor attention, arbitrage constraints, institutional herding, China’s A-share market
Copyright (c) 2026 Runxian Liu (Author) https://creativecommons.org/licenses/by/4.0
Major Shareholder Tunneling and Corporate Earnings Management: A Narrative Review of China’s Non-Financial Listed Firms
Major shareholder tunneling and corporate earnings management are two pervasive issues in modern capital markets, particularly in emerging economies like China. Tunneling refers to the expropriation of firm resources by controlling shareholders for their private benefits, while earnings management involves the manipulation of financial reports to mislead stakeholders or influence contractual outcomes. Although both phenomena have been extensively studied separately, their interrelationship has received less systematic attention. This review synthesizes existing literature on how tunneling affects earnings management, how this relationship varies across governance structures, and what economic consequences arise from their joint occurrence. The findings reveal three main conclusions. First, tunneling and earnings management are positively correlated; tunneling creates incentives for concealment via earnings management, and earnings management-induced information opacity further facilitates tunneling. Second, governance mechanisms significantly moderate this relationship—the positive association is weaker in firms with stronger internal governance and better external monitoring. Third, the joint occurrence of tunneling and earnings management produces severe negative consequences, including reduced firm value, lower information quality, higher stock price crash risk, and dual damage to minority shareholders. This review contributes to theory by integrating tunneling and earnings management literature into a coherent framework and to practice by offering references for regulatory policy design, corporate governance improvement, and investor risk identification.
major shareholder tunneling, earnings management, corporate governance, China’s non-financial listed firms, information asymmetry
Copyright (c) 2026 Xinyao Wang (Author) https://creativecommons.org/licenses/by/4.0
Digital Economy, Industrial Structure Upgrading and Export Technology Complexity: An Empirical Study Based on China's Provincial Panel Data
Based on the panel data of 30 provinces in China from 2015 to 2022, this paper measures the development level of the regional digital economy, the technical complexity of regional exports, and the upgrading coefficient of industrial structure, and empirically tests the impact of digital economy development on export technology complexity and the mechanism of industrial structure upgrading. The results show that the development of the digital economy has significantly promoted the improvement of regional export technology complexity, which is still significant after the robustness test. The upgrading of the industrial structure played a partly intermediary role in the future. Further heterogeneity test shows that the digital economy will increase the technical complexity of exports to a greater extent in areas with low human capital, western China, and low economic level. The results provide theoretical evidence for the effect of the development of digital economy on the technical complexity of regional exports, and put forward targeted policy suggestions.
digital economy, export technical complexity, upgrading of industrial structure
Copyright (c) 2026 Chuting Gao (Author) https://creativecommons.org/licenses/by/4.0
AI-Driven Detection, Prediction and Reduction of Investor Behavioural Biases in Financial Markets
Investor behavioural biases play an important role in financial markets, but their identification and prediction are difficult. While traditional methods including surveys, experiments and econometric proxies provide valuable insights, they are limited in terms of timeliness, scalability and predictive power. This paper reviews recent research on the use of artificial intelligence (AI) methods (machine learning, deep learning and reinforcement learning) in identifying investor behavioural biases. It examines how AI approaches are applied to identify and forecast overconfidence, loss aversion, anchoring, herding, the disposition effect, momentum and overreaction or underreaction in markets. The review concludes that AI methods are a better foundation for real-time and predictive behavioural analyses than conventional methods because they can detect nonlinear signals from trading records, financial text, social media and portfolio information. Random Forests, LSTMs, CNNs, graph-based learning and reinforcement learning are particularly helpful when investor behaviour is dynamic, diverse and hard to measure. At the same time, the review shows that AI does not solve behavioural-finance issues by itself: explainability, generalisation, privacy and the abuse of predicting investor biases are major challenges. So future research should integrate multiple data sources with explainable AI and additional ethical protections to ensure that AI-based tools help investors avoid their biases rather than predictably exploit them.
artificial intelligence, behavioural finance, investor bias, machine learning, explainable AI
Copyright (c) 2026 Peishan Zhao (Author) https://creativecommons.org/licenses/by/4.0