Suchergebnisse

PurposeOnline grocery stores are facing challenges. The intense competition in the online grocery market has driven companies to seek technological innovation. Moreover, the operations of online grocery stores on both the supply and demand sides are not sufficiently meeting the requirements of consumers and managers. The powerful capabilities of the Generative Pre-Trained Transformer (GPT) technology align with the needs of online grocery stores for innovation and upgrading. This study uniquely leverages GPT's advanced natural language processing, adaptive learning and generative capabilities to analyze and optimize the online grocery supply chain competition in ways not possible with traditional analytical tools.Design/methodology/approachThis paper constructs a Stackelberg game model, comprising a secondary supply chain consisting of a supplier who provides products and a retailer who sells them. This study explores the impact of GPT technology on online grocery store operations from the demand side and supply side, specifically including the value of service information, demand information and information-sharing behavior.FindingsThe findings reveal several vital conclusions: (1) On the demand side, the service information plays a crucial role in enhancing service levels and increasing consumer demand; (2) On the supply side, demand information provides positive incentives for retailers and suppliers and (3) Information-sharing behaviors can lead to cooperative relationships between upstream and downstream supply chain members, significantly increasing their respective service levels. This study not only explores the impact of GPT on the online grocery supply chain but also presents a rigorous framework for validating GPT-generated insights, ensuring the reliability of our findings.Originality/valueThis study provides valuable insights into a promising field. It employs game theory to analyze the impact of GPT technology on the overall operation of the online grocery supply chain and the market strategy of online grocery stores.

Using the data of 40 host countries during 2010 to 2017, we investigate whether the Belt and Road Initiative (BRI) has brought new opportunities to countries along the routes to participate in global value chains (GVCs). We employ a difference-in-differences (DID) method with propensity score matching (PSM) to solve the self-selection bias problem. The results indicate that BRI has a significant positive effect on promoting the countries along the routes to participate in GVCs, and the effect is lagging and fluctuant. Specifically, we find that the promotion effects are particularly significant in the second year of the BRI's implementation. The heterogeneity analysis of Belt and Road (BR) countries shows that there remains a significant positive effect on developing BR countries as before, while the opposite is true for developed BR countries.

With the awakening of environmental consciousness, more and more firms desire to go "green" by shifting their focus of corporate social responsibility (CSR) from charitable contributions to environmental actions called corporate environmental responsibility (CER). We develop a monopoly differential game to depict optimal corporate strategies of product price, quality, and CER. Using the Hamilton–Jacobi–Bellman (HJB) equation, we analyze optimal feedback equilibrium strategies for pricing and investing in both quality and CER with/without government subsidies. Numerical simulations show that government subsidy can improve CER and profit.