World Journal of Management Science (WJMS) is an international peer-reviewed, open access journal.
FLEX-TALENT: AN EXPLAINABLE AND FAIR MACHINE LEARNING FRAMEWORK FOR HIGH-STAKES LEADERSHIP-PIPELINE EVALUATION
Machine learning can prioritize candidates for leadership pipelines, but historical promotion records encode organizational policy rather than objective executive potential. This distinction is critical in high-stakes talent evaluation, where a technically accurate model may reproduce label bias, obscure unequal error burdens, or invite automation bias. This paper presents FLEX-Talent, a construct-aware, budget-constrained framework that combines feature governance, training-only fairness reweighting, predeclared validation gates, group calibration audits, intersectional testing, and explanation-stability checks. The framework is evaluated on a real public HR promotion dataset containing 54,808 employees and 4,668 historical promotions. Gender is never used at inference; it is retained only for auditing and reweighting. Five candidate models are compared under a fixed 10% shortlist budget. The validation rule selected a reweighted CatBoost model, which achieved 0.9134 ROC-AUC and 0.6116 average precision on an untouched test set. However, its absolute gender equal-opportunity difference increased from 0.0138 on validation to 0.0578 on test, slightly exceeding the predeclared 0.05 gate; a 95% bootstrap interval of [0.0060, 0.1497] reveals substantial fairness uncertainty. Rather than treating this as a hidden failure, FLEX-Talent makes fairness generalization a first-class release decision. Global and local SHAP evidence is accompanied by bootstrap stability analysis and explicit non-causal warnings. The resulting contribution is not an automated promotion system, but a reproducible governance architecture for human review, appeal, and post-deployment monitoring.
Copyright (c) 2024 ZiMeng Wang; ZiJian Shen https://creativecommons.org/licenses/by/4.0
AN EXPLAINABLE HYBRID MACHINE LEARNING FRAMEWORK FOR CASH-FLOW-CONDITIONED MULTI-HORIZON LIQUIDITY RISK EARLY WARNING: AN SME-ORIENTED BENCHMARK STUDY
Small and medium-sized enterprises (SMEs) are particularly exposed to liquidity shocks, yet public, transaction-level SME cash-flow panels suitable for reproducible forecasting remain scarce. This paper therefore studies a narrower but operationally related task: cash-flow-conditioned, multi-horizon liquidity-risk early warning. A real public benchmark containing 43,405 financial-statement observations, 64 ratios, missing values, and bankruptcy labels one to five years ahead is used as an evidence base. The proposed framework combines three views: an L2-regularized logistic model, a LightGBM model using all ratios, and a dedicated LightGBM expert using 23 cash-flow and liquidity ratios. Non-negative horizon-specific weights are selected on a calibration split in log-odds space, followed by isotonic probability calibration, a recall-constrained warning threshold, and SHAP-based feature and semantic-group explanations. Across five held-out horizons, the calibrated hybrid achieved mean ROC-AUC 0.914, PR-AUC 0.617, and Brier score 0.0260. The uncalibrated ensemble had higher ranking performance (PR-AUC 0.663) but materially worse probability error (Brier 0.0424), exposing a genuine calibration-discrimination trade-off. The cash-flow expert received positive weight at the two- and five-year horizons, while the full-feature tree dominated the remaining horizons; the PR-AUC gain over LightGBM-all was not statistically significant (one-sided Wilcoxon p=0.50). The study supplies executable code, data hashes, result tables, figures, and verified references. Because the benchmark does not identify firms by a legal SME definition and is not a cash-flow time series, the findings support an SME-oriented methodology rather than a claim of direct SME field validation.
Copyright (c) 2024 XinRan Yue; RuFeng Zhu https://creativecommons.org/licenses/by/4.0
MULTI-PERIOD STAFFING SCHEDULING OPTIMIZATION BASED ON COLUMN GENERATION AND NETWORK FLOW ALGORITHMS
This study develops an algorithmic staffing-scheduling framework for multi-day, multi-group, and multi-period service systems. The model minimizes temporary workforce size while satisfying hourly demand, eight-hour work-pattern rules, and individual assignment restrictions. A fixed-group mode is first modeled through column generation, where a restricted master problem is iteratively expanded by work patterns with negative reduced cost. A flexible mode is then formulated for single-group-per-day service with cross-day reassignment. It introduces employment variables, group assignment variables, pattern variables, and a pricing subproblem represented as a minimum-cost maximum-flow network with node-capacity constraints. MATLAB implementation and branch-and-price obtain a 406-worker solution for the flexible mode, reducing the benchmark workforce by 18 workers and improving allocation efficiency by 4.25%. Visual results verify demand coverage, feasible work calendars, mode-frequency alignment with demand peaks, and balanced workforce distribution across groups and days.
Copyright (c) 2026 JingYi Zhao https://creativecommons.org/licenses/by/4.0
ACHIEVING EFFECTIVE CROSS-BOUNDARY INNOVATION IN TECHNOLOGY-ORIENTED ENTERPRISES: A COMPARATIVE CASE STUDY
Digital transformation and intensified industrial competition have made cross-boundary innovation an important route for technology-oriented enterprises seeking growth and value upgrading. This study applies complex adaptive system theory to compare Sixth Mirror, a little-giant small and medium-sized enterprise, with Meiya Optoelectronic, a listed single-champion enterprise. The analysis identifies three innovation dimensions: factor innovation, value-factor dual innovation, and value innovation. Cross-boundary innovation follows a stimulus-individual-response process in which an enterprise senses internal or external drivers, managers select an innovation direction according to organizational resources and the external environment, and the enterprise produces an innovation outcome through its response mechanism and effectors. Sixth Mirror's two attempts show how strong market sensitivity can be undermined by resource imbalance and managerial short-termism, while university-industry-research cooperation can improve the second attempt. Meiya Optoelectronic demonstrates how technological accumulation, endogenous value orientation, and mature decision-making support high-dimensional innovation. The comparison indicates that SMEs should consolidate technological advantages, strengthen value orientation, improve resource allocation, and introduce higher-quality decision support before entering new industries.
Copyright (c) 2026 YuQing Weng https://creativecommons.org/licenses/by/4.0
THE IMPACT OF FINANCIAL AGGLOMERATION ON REGIONAL FINANCIAL RESILIENCE IN CHINA: AN EMPIRICAL TEST BASED ON PROVINCIAL PANEL DATA
Based on panel data of 30 Chinese provinces from 2015 to 2024, this study measures financial agglomeration using the location quotient method and constructs a composite index of regional financial resilience via the entropy method across three dimensions—resistance and recovery, adaptation and adjustment, and innovation and transformation. A two-way fixed effects model is employed to empirically examine the impact of financial agglomeration on regional financial resilience and its regional heterogeneity. The results show that: (1) Financial agglomeration exerts a significantly positive effect on regional financial resilience; a one-unit increase in the agglomeration level is associated with a 0.021-unit increase in resilience. (2) This effect exhibits significant regional heterogeneity: the eastern region experiences the strongest promotional effect (coefficient = 0.051), whereas the central and western regions show negative and insignificant effects. The siphon effect is identified as the core mechanism explaining why the central and western regions fail to benefit from financial agglomeration. (3) The core conclusions remain robust after excluding exceptional years, adding control variables, and employing instrumental variable approaches. Accordingly, policy recommendations are proposed for optimizing the spatial layout of finance, implementing differentiated regional financial policies, and deepening the integration of industry and finance.
Copyright (c) 2026 BaoGui Wu; Shuang Wang; YiQi Zhu https://creativecommons.org/licenses/by/4.0
STRATEGIC RESEARCH ON PREVENTING FOOD SAFETY INCIDENTS IN HIGH-END HOTELS
Food safety is a core competitive advantage for luxury hotels. This case study analyzes the food safety management system of Hotel A through in-depth interviews and literature review, focusing on its governance structure, operational practices, and existing challenges. The results show that Hotel A has built a three-tier "strategy-operation-implementation" responsibility framework, integrated the HACCP system with digital management tools, and established lean supply chain management as a key competitive strength. However, it still faces bottlenecks such as cost pressures, talent shortages, and multi-format standardization difficulties. Based on these findings, targeted strategies are proposed, including optimizing the dual-inspection process, enhancing scenario-based training, building resilient supply chains, converting safety practices into brand value, and adopting intelligent technologies for cost reduction and efficiency improvement. This study provides both theoretical insights and actionable guidance for food safety governance in the high-end hospitality sector.
Copyright (c) 2026 JiaSui Wang; RunXia Chen; TianTian Feng; HuanYu Lin; DingHua Nan https://creativecommons.org/licenses/by/4.0
A COMPARATIVE CASE STUDY OF ESG INTEGRATION STRATEGIES IN HONG KONG REGION USING THE CEPAR METHODOLOGY
This study adopts the five-stage CEPAR (Challenge, Evaluation, Planning, Action, Review) methodology to conduct a comparative case analysis of ESG integration strategies across five key sectors (Real Estate, Financial Services, Technology, Logistics, and Retail) in Hong Kong Region, based on simulated data of 15 enterprises. It explores sectoral differences in ESG implementation maturity, operational practices, developmental barriers, and alignment with the United Nations Sustainable Development Goals. The results reveal significant cross-sector maturity disparities: Financial Services ranks the highest (84.4/100) with robust evaluation and review mechanisms, followed by Technology (82.0), Retail (77.4), and Real Estate (73.2), while Logistics scores the lowest (67.0) due to inadequate green infrastructure and insufficient evaluation rigor. Key empirical findings demonstrate that sufficient budget input in the Challenge phase substantially improves Action-phase implementation success. Besides, conservative ESG evaluation correlates with more stable target delivery, and outcome-oriented ESG key performance indicators drive stronger market value growth. Additionally, frequent quarterly review cycles enable 40% faster adaptation to regulatory updates than annual reviews. This research validates CEPAR as a structured, flexible framework for Hong Kong Region enterprises to embed ESG into core business strategies and comply with international sustainability standards including GRI and TCFD. It provides practical best practices and policy implications for regional sustainable development amid evolving ESG regulatory regimes.
Copyright (c) 2026 XiaoYing Lu; XiaoFang Liu; Alpha Chi Him Tsang; Juan Zhang; YuXi Chen https://creativecommons.org/licenses/by/4.0
PLATFORM ORGANIZATION CONSTRUCTION AND GOVERNANCE MECHANISMS FOR INDUSTRY-EDUCATION INTEGRATION BASED ON KNOWLEDGE CO-CREATION
The deep integration of industry and education has become a critical driver for innovation-driven development in the global knowledge economy. However, traditional bilateral cooperation models between universities and enterprises suffer from structural fragmentation, knowledge transfer inefficiencies, and inadequate governance mechanisms. This study proposes a novel theoretical framework for constructing a "platform organization" for industry-education integration grounded in knowledge co-creation theory. Drawing upon service-dominant logic, ecosystem orchestration theory, and multi-actor governance perspectives, we develop a tri-dimensional platform architecture comprising knowledge co-creation spaces, multi-stakeholder interaction interfaces, and value co-creation mechanisms. Using a mixed-methods approach combining survey data from 486 industry-education partnership participants across 38 Chinese innovation platforms and qualitative case analyses of four exemplary platforms, we empirically examine how platform organizational characteristics influence knowledge co-creation outcomes and value generation. Our findings reveal that platform organizations significantly enhance knowledge co-creation through three mediating mechanisms: boundary-spanning knowledge brokerage, modular resource recombination, and algorithmic trust calibration. Furthermore, we identify four distinct governance configurations—contractual, relational, incentive-based, and algorithmic governance—that interact synergistically to sustain platform effectiveness. The results demonstrate that platform organizations adopting a balanced hybrid governance model achieve 37.2% higher knowledge co-creation performance compared to those relying on only governance mechanisms. This research contributes to knowledge management and innovation ecosystem literature by theorizing the platform organization as a novel institutional form for industry-education integration and providing actionable governance frameworks for policymakers and practitioners.
Copyright (c) 2026 ChunYu Liu; HaiBo Zhang; YongHui Li; YiLing Yang; FaFu Xiong; ChenXi He; XingCun Zhu https://creativecommons.org/licenses/by/4.0
IMPACTS OF THE BELT AND ROAD INITIATIVE ON PEER EFFECTS IN CHINESE FIRMS’ OFDI
Based on the perspective of peer effects, this paper takes China's A-share listed companies as research samples to explore the impact of the Belt and Road Initiative (BRI) on peer effects in corporate outward investment. The empirical results show that: (1) The BRI can significantly narrow the gap in outward investment between individual firms and their industry peers as well as provincial peers, indicating that both industry peer effects and geographic peer effects prevail in the outward investment activities of Chinese enterprises. (2) For state-owned enterprises and firms located in the 18 provinces along the BRI routes, the Initiative substantially reduces their outward investment gaps relative to peer enterprises, while such effect is not statistically significant for non-state-owned enterprises and firms outside these provinces. (3) Government subsidies strengthen the BRI’s influence on industry peer effects of corporate outward investment in China. Similarly, the degree of marketization across provinces enhances the BRI’s impact on peer effects in outward investment among intra-provincial enterprises.
Copyright (c) 2026 ChangQing Li; HongHong Li https://creativecommons.org/licenses/by/4.0
INFERENCE OF LATENT FAN VOTE DISTRIBUTION IN COMPETITIVE REALITY SHOWS BASED ON MONTE CARLO REJECTION SAMPLING
In competitive reality shows such as Dancing with the Stars, complete fan vote data are usually not disclosed, resulting in typical information asymmetry and difficulty in verifying the fairness of scoring and elimination mechanisms. Aiming at the inverse problem of hidden voting data, this paper constructs a stochastic inference model based on Monte Carlo rejection sampling. Under the constraints of historical elimination results, the model reconstructs feasible fan vote distributions and quantifies the uncertainty of estimation by using posterior mean and standard deviation. Meanwhile, a minimum variance regularization method is adopted to obtain the most conservative and unbiased vote allocation. First, this approach effectively overcomes the ill posed characteristic of the inverse voting problem by imposing strict elimination consistency constraints. Second, the Dirichlet prior distribution is employed to guarantee the non negativity and normalization of inferred vote shares, which conforms to real voting rules. Third, the uncertainty measurement based on sample standard deviation can clearly distinguish safe contestants and at risk contestants in each week. Fourth, numerical experiments on multi season data demonstrate that the model has strong stability and generalization ability. Fifth, the identified 62% threshold can be used as a key quantitative indicator for early warning of popularity domination in competition design. The empirical results show that the critical safety threshold of fan vote share is about 62%, beyond which professional judge scores lose their decisive influence on elimination.
Copyright (c) 2026 HaoYu Tian https://creativecommons.org/licenses/by/4.0
LATENT VOTE INVERSION AND VOTING RULE COMPARISON UNDER RULE CONSTRAINTS
Using 34 seasons of panel data from Dancing with the Stars, this study develops a rule-constrained latent vote inversion model with entropy regularization to reconstruct unobservable weekly audience vote shares from observed elimination outcomes. Model validation yields a high consistency rate of 99.62%, and an entropy-based metric further reveals that audience preferences are generally diffuse and uncertain. Through counterfactual simulations, we systematically compare rank-based and percentage-based aggregation rules. The results indicate that switching from rank-based to percentage-based rules leads to a 10.34% outcome reversal rate, while the reverse switch only causes a 1.21% reversal, confirming that the percentage-based system significantly amplifies audience influence and favors high-popularity contestants. Mechanistic analysis demonstrates that the percentage-based rule preserves vote magnitude differences, whereas the rank-based rule compresses extreme popularity advantages into ordinal rankings. These findings quantify the systemic biases of different voting rules and offer empirical evidence for designing fairer and more balanced competition mechanisms.
Copyright (c) 2026 XinQiao Wu https://creativecommons.org/licenses/by/4.0
E-COMMERCE DEMAND FEATURE IDENTIFICATION AND HIGH-PRECISION FORECASTING BASED ON ENSEMBLE CLUSTERING AND REGULARIZED REGRESSION
To address the issues of uneven distribution of e-commerce warehouse allocation demand and the difficulty in forecasting short-term fluctuations, this study proposes a comprehensive analytical framework that integrates feature extraction, preference identification, and time-series forecasting. Using hierarchical clustering algorithms to construct a clustering tree, combined with Ward’s method, the study divides the national distribution centers into four clusters with similar consumption characteristics. By utilizing the entropy weighting method to quantify the contribution of key indicators to consumption preferences, the study identifies transaction value as the core factor driving the distribution of distribution center demand. Based on this, a 62-dimensional high-dimensional feature space encompassing product attributes, user behavior, and trend characteristics was constructed, and a LASSO regression model with L1 regularization was introduced. By applying an absolute value penalty to the coefficients, this model automatically screened 32 core features. Experimental results demonstrate that this approach delivers superior performance: the R-squared value for the training set reaches 0.9999, and the residuals follow a normal distribution with a mean of 0.03 and exhibit white noise characteristics. In stability tests, the model exhibits exceptional robustness, with a coefficient of variation in R-squared of only 0.09%. This study not only reveals the regional patterns of demand for branch warehouses but also provides a decision-making basis for precise replenishment at the central warehouse over the next 14 days.
Copyright (c) 2026 ZiYang Wang; MeiXuan Li https://creativecommons.org/licenses/by/4.0
THE PREDICAMENT OF PUBLICNESS IN DIGITAL PUBLIC GOVERNANCE AND ITS NORMATIVE RESPONSE
The relentless integration of digital technologies into public governance has yielded substantial gains in efficiency, responsiveness, and administrative reach. Yet beneath these achievements lies a deepening normative concern: is digital public governance becoming more efficient at the cost of becoming less public? This article offers a normative analysis of the predicament of publicness in the age of digital governance, arguing that the core values constitutive of public administration — fairness, accountability, procedural integrity, and democratic responsiveness — are being systematically eroded by three interrelated dynamics: the ascendancy of technological logic that subordinates public values to efficiency metrics; the concentration of data power that creates unprecedented asymmetries between state and citizen; and the proliferation of algorithmic decision-making that fragments responsibility and diminishes administrative judgment. Drawing on the intellectual resources of public administration theory, critical theory, and philosophy of technology, the article traces these predicaments to three deeper structural causes: the overexpansion of instrumental rationality, the institutional lag accompanying technological acceleration, and the dehumanizing tendency inherent in digitally mediated administration. The article then advances four normative pathways for reclaiming publicness: value reorientation through a multi-dimensional evaluative framework, institutional reconstruction that constrains algorithmic power with procedural justice, the cultivation of ethical judgment among public administrators, and the use of digital platforms to expand rather than replace democratic participation. The article concludes that the central challenge of digital public governance is not technical but normative: to ensure that the digital transformation of the state serves, rather than subverts, the public interest.
Copyright (c) 2026 HuiDan Lin; ZhiRuo Chen; Han Wang; DaoWen Fu https://creativecommons.org/licenses/by/4.0
REDUCTION OF POLICY IMPLEMENTATION GAPS: A STUDY ON AIGC INTEGRATION PRACTICES IN LIAONING'S GOVERNMENT SERVICE SANDBOX
In the context of digital transformation, the development of digital government has become a pivotal driver for advancing modern national governance. However, the policy implementation gap between top-level design and grassroots practice remains the primary bottleneck hindering the improvement of local government service quality and efficiency. As a key hub of the Northeast Revitalization Strategy, Liaoning Province—a multi-ethnic province with distinctive regional cultural traits—struggles with a traditional governance model plagued by institutional inflexibility that fails to meet diverse public needs, resulting in particularly pronounced policy implementation deviations. From a public administration perspective, this study introduces the regulatory sandbox mechanism into government services, establishing a "three-tier temperature control" theoretical framework to systematically analyze the formation logic of policy implementation gaps. It ultimately proposes localized optimization recommendations for reducing implementation deviations via the government service sandbox. The research demonstrates that the government service sandbox effectively addresses core challenges such as governance structure ossification and insufficient cultural adaptation through flexible supervision, dynamic adjustment, and collaborative governance mechanisms. This study validates the core enabling value of AIGC dynamic adaptation technology in the government service sandbox system, and provides both theoretical references and practical paradigms for digital government construction in Liaoning and similar regions nationwide.
Copyright (c) 2026 YongHe Wang; YiHan Liu; SiTong Yan; YiXuan Wang; Yalaqiqige Su; Xiao Li https://creativecommons.org/licenses/by/4.0
EVALUATION OF THE EQUITY OF SCHOOL SPORTS RESOURCE ALLOCATION AND OPTIMIZATION SIMULATIO
This study focuses on the equity of school sports resource allocation and develops an analytical framework integrating evaluation, problem identification, and optimization simulation to examine the current conditions, major problems, and possible improvement pathways of school sports resource allocation. First, based on the core components of school sports resources, an equity evaluation system is constructed from multiple dimensions, including physical education teachers, sports facilities, equipment, financial support, and opportunities for sports participation, in order to assess the overall level and disparity characteristics of school sports resource allocation. Second, drawing on the evaluation results, the study identifies the major problems embedded in current allocation practices, showing that the existing system is characterized not only by inter-school and inter-regional disparities, but also by structural imbalance and the accumulation of shortages across multiple dimensions. Finally, by establishing different allocation scenarios, the study conducts a comparative simulation of optimization effects under alternative resource allocation strategies. The results indicate that although school sports resource allocation has reached a certain developmental baseline, there remains substantial room for improvement in balanced development. Simply maintaining the current allocation logic is insufficient to significantly reduce disparities. Compensatory allocation can effectively improve resource shortages in disadvantaged schools, whereas a coordinated optimization strategy that integrates equity and efficiency performs better in narrowing allocation gaps, improving overall performance, and promoting structural coordination. This study provides both theoretical reference and practical implications for the scientific allocation of school sports resources and for informed decision-making in educational governance.
Copyright (c) 2026 QingXu Guo; DingRui Dai; MingYang Chen https://creativecommons.org/licenses/by/4.0
DYNAMIC DECISION-MAKING IN PROFESSIONAL SPORTS MANAGEMENT BASED ON MULTIDIMENSIONAL VALUE ASSESSMENT AND FINANCIAL CONSTRAINTS: A CASE STUDY OF THE LOS ANGELES LAKERS
This study addresses the imbalance between athletic performance and economic returns in modern professional basketball management by constructing a dual-objective dynamic decision-making system. First, by decomposing the core objectives of the Los Angeles Lakers, the study establishes a strategic orientation that balances short-term athletic breakthroughs with long-term financial stability. At the player evaluation level, a multidimensional player valuation model was developed, deeply integrating traditional performance metrics with commercial brand value and age-driven injury risk functions, thereby correcting valuation biases toward older or high-commercial-value players inherent in single-metric approaches. Subsequently, a dynamic team financial model was constructed to accurately simulate the progressive luxury tax, repeat luxury tax, and hard salary cap constraints under the 2023 NBA Collective Bargaining Agreement. The results indicate that the Lakers’ current marginal cost of winning has risen significantly due to luxury tax pressures. Based on this, the study employed a multi-objective weighted maximization algorithm to compare and analyze various roster reinforcement strategies, ultimately proposing a functionalist minimalist strategy. By signing rim-protecting centers at the minimum salary and clearing roster space through lightweight contracts, this strategy aims to maximize roster depth and competitive potential without crossing the high-tax threshold. This study provides a scientific, quantitative basis for asset allocation by professional sports organizations operating within a stringent regulatory environment.
Copyright (c) 2026 PengShuo Zhao https://creativecommons.org/licenses/by/4.0
THE MECHANISM AND EFFECTIVENESS OF AI-DRIVEN ESG RATINGS FOR MANUFACTURING ENTERPRISES
In the continuous evolution and development of artificial intelligence, enhancing the ESG of manufacturing enterprises is of great significance for promoting the high-quality and sustainable development of manufacturing enterprises. This paper uses Chinese manufacturing enterprises from 2009 to 2022 as sample data to empirically examine the impact and mechanism of artificial intelligence on the ESG of manufacturing enterprises. Artificial intelligence can effectively promote the development of ESG in manufacturing enterprises, and this result remains valid after a series of robustness tests. The heterogeneity test results show that AI can effectively promote ESG in state-owned manufacturing enterprises that have not yet applied for patents and have a variable equity structure. Further mechanism studies have shown that AI can effectively enhance the ESG of manufacturing enterprises through three channels: reducing financing constraints and resource misallocation rates, and raising awareness of enterprise risk management. The findings provide empirical evidence and policy implications for improving ESG in manufacturing enterprises from the perspective of artificial intelligence.
Copyright (c) 2026 Ying Wang; Cheng Kuang; YuZhi Li https://creativecommons.org/licenses/by/4.0
INFLUENCE OF TRANSMEDIA STORYTELLING ON BRAND IDENTITY AND CONSUMER BEHAVIOUR
Transmedia storytelling can be defined as a particular method of storytelling where the narrator tells the same story in different mediums. Business organisations use the same method in order to market their products to their target consumers. This kind of marketing method has a deep impact on consumer behaviour and brand identity. This particular research work Asus positive research design primary data collection method in order to understand the impact of transmedia storytelling on the consumer behaviour and the brand identity. On the other hand, descriptive research design and deductive research, approach has been used by the researcher in order to analyse the gathered data. IBM SPSS as a research instrument has been implemented by the researcher in order to analyse and evaluate the data collected from the sample population. The research will focus on identifying the factors of transmedia storytelling that influence consumer behaviour and brand identity. The importance of that transmedia storytelling will be revealed through this particular study.
Copyright (c) 2026 YanHan Xiao https://creativecommons.org/licenses/by/4.0
EVALUATING REGIONAL AGRICULTURAL PRODUCT BRANDS: EVIDENCE FROM MAOMING LITCHI REGIONAL BRAND
Regional agricultural brands are a special type of brand. Due to their unique geographical attributes,stringent quality standards, and high market recognition, they can generate greater brand premiums for the corresponding agricultural products compared to non-branded and drive local economic development. As a result, regional agricultural brand strategies have become an important tool for many countries (and regions) to promote rural development. This study analyzes the connotation of regional agricultural brand value, examines the components and influencing factors of brand value, constructs a model for evaluating agricultural product brand value, and applies the model using the Maoming litchi regional brand as a case study, aiming to advance thedevelopment of regional agricultural brands through evaluation-driven improvement.
Copyright (c) 2026 XiaoPing Tan; HuaWan Zhong; LiXia Zhang; YingYing Shen https://creativecommons.org/licenses/by/4.0
THE PREDICTION OF METEOROLOGICAL AND PHENOLOGICAL TRENDS AND ROUTE DECISION-MAKING DURING THE QINGMING FESTIVAL BASED ON ARIMA AND RANDOM FOREST REGRESSION
Addressing the significant challenges in cultural tourism decision-making caused by unpredictable weather and uncertain flowering periods during the Qingming Festival, this study establishes a robust quantitative framework integrating meteorological forecasting, phenological prediction, and route optimization. Specifically, an ARIMA model is constructed to define “continuous drizzle” quantitatively and forecast precipitation probabilities for seven target cities. Concurrently, a Random Forest Regression model is introduced to accurately predict the onset and duration of flowering for cherry blossoms, peonies, and rapeseed flowers. Feature importance analysis reveals temperature as the dominant driver for cherry blossoms (weight 62.3%), with the model achieving a high goodness-of-fit of 0.91. Furthermore, a multi-objective planning model is developed using integer linear programming, generating an optimal closed-loop tour route connecting Wuhan, Xi’an, Luoyang, and Hangzhou. This route effectively balances flower-viewing experiences with rain-avoidance preferences while minimizing travel costs. The research innovates by establishing quantitative meteorological criteria and successfully transforming natural law predictions into personalized tourism decision-making strategies, providing valuable theoretical support and practical guidelines for smart cultural tourism in complex environments.
Copyright (c) 2026 YiHan Ma https://creativecommons.org/licenses/by/4.0
THE MEASUREMENT OF TRANSPORTATION NETWORK EFFICIENCY AND DIAGNOSIS OF STRUCTURAL DEFICIENCIES IN JILIN PROVINCE’S WINTER TOURISM
Addressing the current situation where the high-quality development of Jilin Province’s winter tourism industry is constrained by infrastructure bottlenecks, this study conducts a systematic assessment of network efficiency by constructing a transportation network model comprising 86 nodes and 327 routes. The findings reveal that the density of Jilin Province’s winter tourism transportation network is only 0.087, indicating weak overall connectivity that is significantly lower than that of neighboring strong provinces such as Heilongjiang. Node centrality analysis reveals severe polarization: core hubs such as Changchun Longjia International Airport and Jilin Station dominate in both degree centrality and betweenness centrality, while remote scenic areas such as Dunhua Laobai Mountain Snow Village have extremely low accessibility. Cluster analysis identified four highly isolated sub-regions, confirming the existence of pronounced regional fragmentation within the province. Further analysis revealed that insufficient network connectivity results in high transfer costs for remote scenic areas, with one-way travel times reaching 5 to 6 hours. Additionally, the overreliance on a single-mode transportation structure (roads and railways) exacerbates operational risks during extreme winter weather. The Matthew effect in node development causes high-quality resources to concentrate in urban centers, trapping remote scenic areas in a vicious cycle of poor transportation access, sparse visitor numbers, and delayed development. This study provides quantitative evidence and decision-making references for resolving the transportation challenges in Jilin Province’s winter tourism sector and promoting regional coordinated development.
Copyright (c) 2026 XiaoYi Shang https://creativecommons.org/licenses/by/4.0
SCIENCE AND TECHNOLOGY MANAGEMENT STRATEGIES TO PROMOTE HIGH-QUALITY DEVELOPMENT OF AGRICULTURAL RESEARCH INSTITUTES
This article analyzes the current policies, technologies and backgrounds of the science and technology management in agricultural research institutes. It points out the existing problems and proposes specific strategies in terms of improving the management system, optimizing resource allocation, enhancing incentive mechanisms, and strengthening strategic development research, for reference.
Copyright (c) 2026 WenTao Chen; Hua Yan https://creativecommons.org/licenses/by/4.0
SPATIAL DISTRIBUTION CHARACTERISTICS AND DIFFERENCE ANALYSIS OF URBAN SERVICE FACILITIES IN NORTH CHINA
This study conducts a comparative analysis of urban service facilities in Changchun and Hohhot by constructing a service density index, applying the coefficient of variation method, and utilizing the Spatial Lag Model (SLM). Results indicate that Changchun achieves a higher overall service level (CV=347.64) compared to Hohhot (CV=307.55), with significant disparities across seven domains, most notably transportation infrastructure. SLM regression reveals that Changchun exhibits stronger spatial integration and spillover effects while Hohhot demonstrates localized competition and fragmented service allocation patterns. Partial differential decomposition further confirms these divergent spatial dependency structures. This research establishes a replicable quantitative framework for cross-city comparative analysis, offering valuable theoretical support and evidence-based insights for optimizing urban resource allocation, informing differentiated planning strategies, and enhancing urban resilience in northern China.
Copyright (c) 2026 Qin Gong; YanQiang Yang; AnNa Li https://creativecommons.org/licenses/by/4.0
THE GAME MECHANISM OF UNIVERSITY BUDGET RESOURCE ALLOCATION FROM THE PERSPECTIVE OF INTERNAL CONTROL
Budget resource allocation is the core link of university financial management, which is directly related to the high-quality development of education and the efficiency of resource utilization in universities. Against the background of an increasingly improved internal control system, the budget resource allocation in universities involves multiple stakeholders such as university-level decision-makers, functional departments, and secondary colleges. The game behaviors of these stakeholders based on their own interest demands profoundly affect the fairness, scientificity and compliance of budget allocation. Based on the five elements of internal control (control environment, risk assessment, control activities, information and communication, internal supervision), this paper systematically analyzes the interest demands and behavioral characteristics of various game players in university budget resource allocation, constructs a multi-player game model (complete information static game, incomplete information dynamic game), clarifies the game equilibrium conditions through formula derivation, reveals the constraint and guiding role of internal control on game behaviors, identifies the internal control shortcomings existing in the current budget allocation game, and puts forward targeted optimization paths. It provides theoretical support and practical reference for universities to standardize budget resource allocation, resolve game conflicts and improve resource allocation efficiency.
Copyright (c) 2026 Jing Hu; WeiBin Zhao; Han Lin; JinHan Cai https://creativecommons.org/licenses/by/4.0
THE IMPACT OF CORPORATE ESG PERFORMANCE ON URBAN CARBON EMISSIONS
With the rapid advancement of urbanization in our country, cities, as the main places for human activities, have always been the focus of the government and all sectors of society for their sustainable and green development. ESG, as a framework for sustainable investment and enterprise management, aligns with China's new development concept of coordinating ecological civilization with economic growth. It helps enterprises enhance their green technological innovation capabilities, thereby promoting the carbon reduction and green transformation of cities. Based on the long-term panel data of A-share listed companies in China from 2011 to 2023, this article deeply explores how the ESG performance of enterprises contributes to urban carbon reduction and the underlying mechanism. The empirical research results show that the improvement of enterprises' ESG performance has a significant positive impact on urban carbon emissions, and this conclusion still holds after the robustness test, further verifying its reliability. The results of the mediating effect indicate that enterprises can significantly enhance the level of green technological innovation by improving ESG performance, thereby reducing urban carbon emissions. The research conclusion of this paper can not only provide an important theoretical basis for the sustainable development of enterprises, but also offer policy inspirations for promoting and building low-carbon and green cities nationwide.
Copyright (c) 2026 Jia Wei; QiChang Guo; ZhiYuan Li; Min Liu https://creativecommons.org/licenses/by/4.0
BEYOND HAIR LOSS: EXPLORING THE EVOLUTION OFANDROGENETIC ALOPECIA RESEARCH BASED ON TEXTMINING AND BIBLIOMETRICS
In this study, word dynamics, co-occurring phrases, and keyword frequency in the area of androgenetic alopecia were thoroughly analysed during a ten-year period. The study tracks changes in word usage, frequency, and context and identifies variations in the distribution and usage patterns of keywords, subjects, and co-occurring phrases using natural language processing techniques and graph theory-based approaches. The research reveals hidden linkages and patterns between diverse ideas, shedding light on the co-occurrence patterns of numerous phrases in the literature on androgenetic alopecia. The study emphasises the value of clearly visualising and disseminating findings to a large audience. In order to communicate the findings of the 10-year trend analysis to patrons, legislatures, and other pertinent audiences, data visualisation tools, infographics, and reports are used. This makes sure that the results are useful, effective, and easily accessible so that they can guide the creation of policies and decisions pertaining to androgenetic alopecia. The results of this study may have substantial ramifications for academics, medical professionals, policymakers, and other industry participants. The research can lead future research paths, prioritise research areas, and suggest areas that require additional examination by highlighting key research challenges and pointing out gaps in the study of androgenetic alopecia. Identifying emergent study issues, analysing the changing patterns in the area, and establishing research strategies can all benefit from an analysis of word dynamics and correlations among terms. The results reveal hidden relationships and patterns, advance knowledge of the research environment in this area, and influence androgenetic alopecia research objectives and policies.
Copyright (c) 2024 Sayan Roy; Hriddhiman Basu https://creativecommons.org/licenses/by/4.0
POTENTIAL INFLUENCE OF TALENT MANAGEMENT ANDTEACHER LEADERSHIP ON TALENT FLOW IN A SELECTEDHEI IN CHINA
The The study determined the potential influence of talent management and teacher leadership on talent flow in a university in China. In the process, the study assessed the three variables through a survey which involved 260 teachers. The result showed that there is a positive talent flow in the selected school in the past two years. It also showed that talent management in terms of attraction, development, and retention is evident. Moreover, the study revealed that teacher leadership in terms of perception, influencing, cooperation, knowledge and skills is also evident. Through a multiple regression analysis, the predictive power of talent management and teacher leadership on talent flow was determined. The statistical result showed that only attraction, a subscale of talent management predicted talent flow. Teacher leadership or any subscale of it did not predict talent flow. Further analysis between the indicators of attraction and talent flow showed that good school culture, and well-designed initiatives predict positive talent flow.
Copyright (c) 2023 LuXi Jiang; Rodil F. Fadri https://creativecommons.org/licenses/by/4.0
INTELLIGENT PERCEPTION AND VALUE GUIDANCE IN STUDENT IDEOLOGICAL DYNAMICS: DESIGNING AN AI-BASED CLOSED-LOOP INTERVENTION FRAMEWORK
With the rapid development of information technology, the application of Artificial Intelligence (AI) in education has become increasingly widespread, demonstrating immense potential particularly in the management of student ideological dynamics. Focused on the theme "From 'Data Monitoring' to 'Value Guidance': Intelligent Perception and Closed-Loop Intervention Pathways for Student Ideological Dynamics in the AI Era," this study explores how AI technology empowers the management of student ideological education. Research indicates that traditional ideological education models suffer from issues such as lag, passivity, and data fragmentation, leading to a core contradiction between educational goals and operational reality. This paper proposes that AI technology can effectively address these challenges and facilitate a paradigm shift in educational models. Through an in-depth analysis of the current status of student management and AI applications in education, this study constructs an intelligent perception system and designs a closed-loop intervention pathway. This framework includes a multimodal data collection mechanism, AI-driven data analysis models, a tiered early warning mechanism, and intelligent recommendation strategies for intervention. The research finds that sentiment tendency recognition based on Natural Language Processing (NLP) and group feature analysis using clustering algorithms significantly enhance the accuracy of sentiment identification and the effectiveness of group profiling. Empirical analysis demonstrates that the designed closed-loop intervention pathway offers significant advantages in early warning response efficiency and the effectiveness of ideological guidance. Furthermore, the paper discusses ethical norms regarding data collection, as well as algorithmic transparency and supervision mechanisms, to ensure the safety and fairness of technological application. The results show that AI technology not only realizes the paradigm shift from "monitoring" to "leading" but also provides an effective path for the reform of ideological and political education in colleges and universities. Overall, this paper expands educational technology theory and offers specific practical recommendations for reform, while also providing an outlook for future research directions. Despite certain limitations, this study serves as an important reference for the in-depth application of AI technology in the field of education.
Copyright (c) 2025 ZeYu Wang; JiaNan Wang; Ting Ye; Jun Ruan https://creativecommons.org/licenses/by/4.0
THE CONSTRUCTION PATH OF ENTERPRISE INTELLECTUAL PROPERTY COMPLIANCE MANAGEMENT SYSTEM
With the intensification of global economic integration and technological competition, intellectual property has become a key element of the core competitiveness of enterprises. At present, Chinese enterprises are in a critical period of transition from ' manufacturing power ' to ' innovation power '. Intellectual property is no longer just an asset, but also a ' passport ' and ' moat ' for enterprises to participate in domestic and foreign market competition. However, companies face increasingly complex intellectual property risks and compliance challenges. The purpose of this paper is to explore the macro background and reality of intellectual property compliance management of Chinese enterprises, systematically sort out the core problems faced by enterprises in the construction of compliance system, and put forward specific and feasible construction paths and coping strategies for these problems, so as to help enterprises realize the systematization, normalization and efficiency of intellectual property compliance management in a complex environment.
Copyright (c) 2025 Peng Du https://creativecommons.org/licenses/by/4.0
EMPOWERING SMALL AND MICRO ENTERPRISES IN LIAONING THROUGH FINTECH
This paper examines the current state and challenges of innovation and entrepreneurship among small and medium-sized enterprises (SMEs) in Liaoning Province, exploring pathways for high-quality development through fintech-enabled mechanisms. Research indicates that while Liaoning's SMEs have improved innovation metrics, their national rankings have generally declined, revealing three structural challenges: a disconnect between innovation and capital, diminished investment appeal, and market entities exhibiting “false prosperity”. To address these issues, the study proposes leveraging fintech solutions such as establishing enterprise digital profiling platforms, promoting blockchain technology applications, and developing equity crowdfunding and big data-driven credit services. These measures aim to alleviate financing constraints, enhance information transparency, and facilitate resource integration, thereby providing actionable strategies for fostering innovation among SMEs and revitalizing the regional economy.
Copyright (c) 2025 YunWei Shen https://creativecommons.org/licenses/by/4.0