Do large language models enlarge demand? Optimal channel structures and multi-agency mode in online retailing

Large language models (LLMs) show strong text analysis and prediction abilities, enhancing the operational efficiency and demand forecasting in operations and supply chain management (OSCM). To improve the operational efficiency of cross-border e-commerce, suppliers can select appropriate multi-agency modes according to their specific circumstances. However, the influence of LLMs on online sales and the determination of viable operational management recommendations remain unexplored. To address this issue, this paper develops a Stackelberg game model for a cross-border supply chain comprising a manufacturer, a supplier, and an e-retailer. We derive and analyze the optimal order quantities and equilibrium solutions for supplier across different multi-agency modes and LLMs application scenarios in hybrid channels. Additionally, we compare different models and analyze how LLMs application levels and customer channel preferences impact supplier decisions. Our model analysis and numerical study yield the following key findings. First, in the multi-agency mode, supplier can attain higher profits through the semi-managed mode. Furthermore, the direct channel generates more profit than the agency channel. Second, LLMs can significantly boost order volumes in both channels when operating in fully-managed mode. When customers show a preference for purchasing via the agency channel, the application of LLMs in either fully-managed or semi-managed modes can enhance order volumes in both channels. Third, adopting a suitable LLMs application level can markedly boost supplier profits in the fully-managed mode, particularly when customers favor the agency channel, leading to maximum profit. This research offers valuable suggestions for LLMs application and supplier multi-agency mode choices in cross-border e-commerce.