Linking AI-Enabled Logistics Optimizations and Sustainable Supply Chain Performance via Logistics Process Efficiency and the Moderating Role of Environmental Uncertainty

Grounded in dynamic capability theory, this research examines the impact of AI-enabled logistics optimization (ALO) on logistics process efficiency (LPE) and sustainable supply chain performance (SSCP). It further explores the mediation of LPE and the moderation of environmental uncertainty (EU). A structured online questionnaire was distributed to 600 participants via stratified random sampling from June to December 2025, resulting in 380 valid responses, and was analyzed using structural equation modeling. The results include a significant influence of ALO on LPE and SSCP. In addition, LPE significantly affects SSCP and partially mediates the ALO–SSCP relationship. Additionally, the EU significantly moderates the ALO–SSCP relationship, identifying that ALO becomes more performance-related under a volatile and uncertain operational environment. The research is based on cross-sectional survey data with self-reported outcomes. Future research is recommended to employ longitudinal or multi-source research methods. It also suggested examining other mechanisms for dynamic capabilities (e.g., agility, resilience) across several sectors. The results of this research extend dynamic capability by elucidating when (under the EU) and how (via LPE) ALO transforms into SSCP.