With the development of 4G and 5G networks, more and more industries in China are borrowing O2O coupons to drive offline consumption; however, many merchants randomly issue coupons leading to immeasurable marketing costs for brands, and at the same time, there is a certain interference with consumers' choice of coupons for underwriting, and there is no more scientific method to improve the redemption rate. Therefore, this paper adopts the actual data provided in the Aliyun Tianchi competition, preprocesses and feature extraction and selection of the dataset, and constructs three prediction single models based on GBDT, XGBoost and LightGBM, respectively. Through experiments, it is concluded that XGBoost has the best prediction effect and the best stability among the single models, thus XGBoost is selected as the prediction model for judging coupon usage, thus helping merchants to predict the probability of consumers using coupons, and thus realizing personalized coupon placement and improving merchants' marketing ability.

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In the context of accelerating global economic integration and digital transformation, it is crucial to strike a dynamic balance between globalization and localization in corporate supply chains for effective strategic management. From a systems integration perspective, this paper discusses core mechanisms through which digital economy platforms collaborate with human resource management (HRM) to achieve supply chain balance. By synthesizing research on global versus local supply chains, digital-economy platforms, and HRM, the author proposes a “technology—organization” collaboration framework that unveils how digital-platform empowerment and HRM-driven organizational empowerment are intrinsically linked and mutually reinforcing. Research indicates that digital economy platforms provide technological support, while HRM offers organizational capability guarantees; their collaboration optimizes the balance of the supply chain. However, existing studies are often limited by either "technological determinism" or "organizational determinism" perspectives, which lack systematic exploration of their collaborative mechanisms. Based on this gap, the paper proposes a "Platform-HRM Collaboration" theoretical framework, which elucidates the dialectical relationship between technological embedding and organizational adaptation. It reveals how the two factors form complementary and mutually reinforcing relationships at different levels of the supply chain to achieve a dynamic balance among efficiency, resilience, and innovation. This framework not only enriches interdisciplinary research in digital economy and supply chain management but also provides systematic theoretical guidance for companies to achieve coordinated globalization and localization development in complex environments.

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This study focuses on the phenomenon of hidden editions in blind boxes, constructs a framework of consumers' psychological decision-making, and explores the optimization path of corporate marketing strategies. Through research, it is found that scarcity, cognition and interest, and regret psychology are the main psychological driving factors affecting blind box consumption. Furthermore, based on the psychological level of consumers, this paper puts forward targeted marketing strategy suggestions from the aspects of product, price, channel and promotion. The research in this paper provides a theoretical basis for the development of the blind box market.

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The rapid development of artificial intelligence (AI) is reshaping corporate strategic management. In today’s dynamic global environment, organizations face increasing challenges in decision-making, resource allocation, and operational efficiency. Against this background, AI offers significant potential to enhance the speed, accuracy, and effectiveness of strategic management practices. This study examines the applications of AI in corporate strategy, with a focus on how technologies such as machine learning, natural language processing, and automation contribute to improving strategic decision-making. The research employs a non-empirical design, combining literature review and conceptual analysis. The study objects include enterprises across manufacturing, high-tech industries, and SMEs, as represented in existing scholarly works and industry reports. Analytical tools are theoretical models of AI integration, and data sources consist mainly of secondary materials such as peer-reviewed articles, case documentation, and industry analyses. The findings indicate that AI improves strategic accuracy and efficiency, but challenges remain regarding data quality, organizational resistance, and ethical concerns. The study concludes that successful adoption requires aligning technological advances with organizational adaptation. Furthermore, the integration of AI with big data, cloud computing, and blockchain is expected to play a central role in shaping the future of strategic management.

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