PurposeDrawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.Design/methodology/approachThe authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.FindingsLocal social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.Originality/valueThe paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.

Natural disasters can have devastating economic and financial consequences for those affected. This research note explores the potential of artificial intelligence (AI) in disaster relief through lending services. By collaborating with a credit-scoring company, we investigate how AI-empowered lenders can effectively reduce delinquency rates for borrowers in the aftermath of disasters. Our findings reveal that borrowers applying to lenders that utilize AI in their loan assessment process experience improved outcomes in terms of delinquency reduction, particularly for borrowers with lower credit scores. This research underscores the positive impact of AI in the lending context, benefiting both lenders and borrowers. Furthermore, we highlight that AI indirectly supports disaster relief efforts through financing, providing a compelling use case for AI fairness in lending. Our findings have significant implications for leveraging AI as a valuable tool in mitigating the financial impact of disasters and promoting fairness in lending practices.

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