This study addresses the challenges of the expanding global online retail market by developing an innovative inventory management strategy that promotes sustainable development. We propose an integrated planning model that utilizes a newly enhanced Parallel Chicken Swarm Optimization (PCSO) algorithm alongside the established Economic Order Quantity (EOQ) model and the (s, S) inventory policy. Our model demonstrates its effectiveness through simulation experiments, showcasing its ability to dynamically adjust inventory thresholds based on daily forecasted demand, which significantly optimizes replenishment strategies. Notably, our model achieves a total cost reduction while maintaining a high service level of 0.761467 and reduces computational time to 82.32 s, highlighting its efficiency and adaptability. The innovation of this research lies in the application of the PCSO algorithm to dynamically manage inventory in real-time, aligning with sustainable inventory management practices. This study significantly contributes to the field by enhancing the economic and environmental sustainability of supply chain operations.

As artificial intelligence becomes increasingly integrated into education, intelligent tutoring systems (ITSs) are emerging as important tools for personalized language learning. However, high learner dropout rates indicate that sustaining continuous intention remains a key challenge. This study examines the mechanisms and differential effects shaping IELTS learners’ continuous intention to use ITSs in listening and reading contexts, using an AI test-preparation assistant and 494 valid responses from 522 Credamo surveys. PLS-SEM results show that perceived usefulness, perceived ease of use, perceived enjoyment, and self-efficacy significantly improve learning attitude, while learning attitude and self-efficacy further strengthen continuous intention. Multi-group analysis reveals contextual differences: perceived enjoyment has a stronger effect in listening (β = 0.340, p < 0.001) than in reading (β = 0.178, p < 0.01), whereas perceived usefulness has a stronger effect in reading (β = 0.424, p < 0.001) than in listening (β = 0.226, p < 0.001). ANN results confirm that enjoyment is the strongest predictor of learning attitude in listening (normalized importance = 100%), while usefulness is the key predictor in reading (normalized importance = 100%). The study extends the TAM by incorporating self-efficacy and perceived enjoyment and highlights the importance of context-specific ITS design for AI-supported language learning.

The digital wave sweeping the globe is irreversibly reshaping humanity’s connection with its own history, and the field of cultural heritage is at the heart of this profound transformation. From pr...