Linking generative artificial intelligence deployment to supply chain resilience: does relationship transparency matter?

Purpose As business process management (BPM) evolves toward greater flexibility and agility, supply chain resilience (SCR) becomes a key objective of process redesign. Achieving SCR increasingly depends on technology that can reconfigure business processes in real time, rendering generative artificial intelligence (GAI) especially pertinent to BPM. Nevertheless, the influence of GAI deployment on SCR remains unclear. The model examines how the extent (in breadth and depth) of GAI deployment affects SCR across readiness, response and recovery. Furthermore, BPM's evolution extends beyond technology deployment and requires complementary organizational arrangements. Accordingly, the moderating role of relationship transparency is analyzed. Design/methodology/approach Building on organizational information processing theory (OIPT), this study uses multiple linear regression to analyze survey data from 287 Chinese manufacturing firms. Findings The results reveal that GAI deployment positively affects SCR, and that both the breadth and depth of GAI deployment contribute to higher SCR. A notable finding is that the breadth of GAI deployment does not show a significant main effect on recovery, but enhances recovery only when relationship transparency is high. In addition, relationship transparency strengthens the positive relationship between GAI deployment and SCR. Originality/value This study jointly examines three dimensions of SCR, which prior research has often treated separately. In addition, we explain that the breadth and depth of GAI deployment may have different effects on SCR. Finally, we examine the moderating role of relationship transparency and highlight information quality as a complement to OIPT.