How generative AI drives sustainable business process transformation in SMTEs: the moderating role of extreme weather

Purpose This study investigates how generative artificial intelligence (GAI) catalyzes green process and product innovation and, in turn, drives environment-friendly business model transformation in small and medium-sized tourism enterprises (SMTEs), especially when operations are disrupted by increasingly frequent extreme weather (EW). Design/methodology/approach Drawing on creating shared value theory, we combine a survey of 459 SMTEs in China and Europe with partial least squares structural equation modeling and ten executive interviews. The multi-method design allows us to test direct, mediating and moderating effects and to triangulate quantitative patterns with rich qualitative insights. Findings GAI adoption significantly enhances green process and product innovation, which together account for nearly half of the variance in environment-friendly business model innovation. EW strengthens the impact of these innovations on business model change. Contrary to expectations, EW does not significantly moderate the relationship between GAI adoption and green innovations, suggesting that resource constraints may limit crisis-driven technology deployment. Practical implications Managers of tourism SMTEs can exploit GAI-based analytics and content generation to streamline resource-intensive processes, design low-carbon offerings and build resilient revenue architectures that withstand climate shocks. Policymakers should target incentives and capability-building programs that close the technical and financial gaps hampering GAI-enabled sustainability initiatives. Originality/value This study embeds digital sustainability within a business-process perspective. By integrating climatic turbulence as a boundary condition, it offers novel empirical evidence on the stage-specific mechanisms through which AI-enabled green innovations convert into enterprise-level value in resource-constrained SMTEs.