Customer retention in the telecommunications industry poses a critical challenge in data-driven business operations, while high predictive accuracy does not necessarily translate into superior commercial outcomes under asymmetric misclassification costs. This study investigated a profit-oriented decision analytics framework for customer churn management by integrating predictive performance with business value optimization. A cost-sensitive Cost-Sensitive Improved Sparrow Search Strategy Algorithm Stacking (CS-ISSA-Stacking) framework was developed by incorporating customer lifetime value (CLV) and marketing intervention costs into an Expected Total Profit (ETP) objective function. An Improved Sparrow Search Algorithm (ISSA) was constructed using Tent chaotic mapping and Cauchy mutation mechanisms to enhance global optimization capability. EXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) were employed as heterogeneous base learners, and ISSA was used to optimize ensemble fusion weights and decision thresholds for profit-driven prediction. The proposed framework could effectively improve ETP while maintaining competitive predictive performance. Compared with conventional prediction models optimized solely for classification accuracy, the proposed approach achieved a substantial improvement in customer retention profitability. The findings demonstrated that the optimized decision threshold significantly reduced the false negative rate in high-value customer identification. Furthermore, SHapley Additive exPlanations (SHAP)-based interpretation revealed that monthly charges, contract type, and tenure were the most influential factors affecting customer churn behavior. Profit-sensitive decision mechanisms provided a more effective strategy than traditional accuracy-oriented prediction approaches in customer churn management. The proposed framework provides a practical decision-support tool for intelligent customer retention and offers new insights into integrating business analytics with strategically operational decision making in the competitive telecommunications environments.

The increasing integration of environmental, social, and governance (ESG) considerations into financial markets has raised fundamental questions regarding their roles in investment decision making. In particular, it remains unclear whether ESG-oriented investment strategies mirror substantive changes in portfolio construction or primarily follow prevailing market trends. This study examined the decision relevance of ESG signals by analyzing the behavior and performance of ESG-oriented mutual funds. Using a sample of 41 funds, a data-driven analytical framework was employed to evaluate portfolio composition, risk–return performance, and the determinants of ESG ratings. The analysis first considered whether ESG funds systematically allocated capital toward firms with stronger ESG profiles. Although a modest tilt toward higher-rated companies was observed, the differences relative to conventional funds remained limited and, in most cases, statistically insignificant, thus indicating that ESG considerations were not the dominant driver of portfolio selection. The second part evaluated fund performance within a risk–return framework using benchmark-adjusted measures and information ratios (IR) . While all funds generated positive returns over the sample period, the majority failed to outperform their benchmarks. Only a small subset exhibited consistently favorable risk-adjusted performance. These findings suggested that ESG-oriented strategies did not provide a reliable basis for achieving superior financial outcomes and might involve trade-offs in portfolio allocation. Finally, cross-sectional regression analysis demonstrated that ESG ratings were strongly associated with firm-specific characteristics, especially size and profitability. This result indicated that ESG scores might reflect underlying financial capacity rather than deliberate sustainability-oriented decisions. Taken together, this study implied that ESG signals offered limited standalone values for guiding decisions of investment. Effective portfolio design therefore requires a broader analytical approach that integrates ESG metrics with conventional financial indicators.

With the rapid expansion of e-commerce, last-mile delivery in express logistics faces significant challenges, including low efficiency and high operational costs. Taking the Xiqing District of Tianjin as a case study, this research proposes a three-stage framework integrating complex network theory and machine learning. First, the Louvain algorithm is employed to achieve intelligent partitioning of delivery areas, resulting in a modularity increase to 0.789. Second, an eXtreme Gradient Boosting (XGBoost) model is utilized to predict terminal service modes, achieving an accuracy of 87.8%. Finally, a route planning model is constructed using Particle Swarm Optimization (PSO). To validate these methods, a three-day logistics system simulation was conducted via AnyLogic to evaluate the effectiveness of different delivery policies. The results demonstrate that, compared to traditional independent delivery, the joint delivery approach reduces total costs by 25.32%. Furthermore, by introducing a carbon emission accounting model, leading to an estimated 25% reduction in daily carbon emissions, achieving a win-win situation for both economic and environmental benefits.

The vehicle routing problem with time windows (VRPTW) remains a central challenge in urban logistics due to the interplay between spatial dispersion, operational constraints, and service reliability requirements. To address the scalability limitations and premature convergence issues commonly observed in conventional metaheuristics, a two-stage optimization framework is developed that integrates clustering-based spatial decomposition with an adaptive evolutionary search mechanism. In the first stage, $k$-means clustering is employed to partition large-scale customer nodes into geographically coherent subregions, with the number of clusters determined using the silhouette coefficient to ensure structural consistency. This decomposition reduces problem dimensionality and enables localized route optimization. In the second stage, an adaptive genetic algorithm is designed in which crossover and mutation probabilities are dynamically adjusted according to population fitness distribution, thereby improving global exploration in early iterations and enhancing solution refinement in later stages. A mathematical model is formulated to minimize total operational cost, incorporating vehicle activation, transportation distance, and time window penalties under capacity and service constraints. The proposed framework is evaluated using a real-world logistics dataset involving 60 customer nodes. To assess operational robustness, the optimized routing schemes are further validated within an agent-based simulation environment. Comparative results show that the proposed method achieves consistent improvements over baseline strategies, with cost reductions of 28.12% and 20.62% across two service regions, while significantly increasing vehicle utilization and reducing fleet size. The findings indicate that the integration of spatial decomposition and adaptive evolutionary control provides a practical and scalable solution for complex VRPTW instances in dynamic urban logistics settings.

As geopolitical competition intensifies and risks of global technological decoupling rise, the Digital Silk Road (DSR) is undergoing a strategic transition from the hard connectivity of physical infrastructure toward the soft connectivity of software ecological collaboration. Utilizing quarterly high-frequency indicators from the GitHub Innovation Graph (2020Q1–2025Q3), this study empirically examines the evolution of software collaboration networks between China and Belt and Road Initiative participating countries. We introduce the concept of digital gravity shift—a structural reorientation of innovation based on collaboration density and network resilience—to extend traditional innovation gravity theories. The findings reveal that: First, a significant digital gravity shift has occurred; unlike the stagnating Group of Seven (G7)-centric pathways, internal collaboration within the DSR exhibits a unique U-shaped resilience, where geopolitical shocks have paradoxically catalyzed the reorganization of innovation paths. Second, the collaboration model is transforming from unidirectional technology spillover toward bidirectional reciprocal symbiosis, signaling the maturation of digital social capital and mutual dependency within the Global South. Third, the substance of collaboration has achieved a qualitative leap from surface-level tasks to core system engineering, uncovering a leapfrog catch-up mechanism driven by the lower entry barriers of open-source modularity. This research provides granular empirical evidence for an emerging multipolar innovation landscape and offers strategic insights for mitigating technological fragmentation and enhancing national innovation resilience in the post-decoupling era.