This study examines whether generative AI can serve as an effective knowledge translation tool, bridging the long-standing theory-practice gap in supply chain management (SCM). While prior SCM scholarship has focused on AI's operational capabilities, we investigate its potential to enhance practitioner engagement with academic research.
Drawing primarily on cognitive load theory, with further support from construal-level theory, we conduct a 2 × 2 between-subjects behavioral experiment with supply chain decision-makers. Participants are exposed to either an excerpt from an academic article or its generative AI-created translation. The vignette is further framed with either near-term or far-term temporal distance. Measures include ICL, practitioner engagement, knowledge retention, and attitudes toward AI.
Results show that Generative AI-created translations significantly reduce ICL compared to original academic articles and increase practitioner engagement. Additionally, we find no loss in knowledge retention. The indirect effect of knowledge source on engagement via ICL is significant, indicating that reduced cognitive effort is associated with higher engagement. Psychological distance shows a partial effect in planned contrasts but does not significantly moderate the mediated pathway.
This work is among the first in SCM to empirically test the role of generative AI in translating scholarly knowledge into practice. We extend cognitive load theory into the SCM knowledge transfer context and position generative AI as a dual-purpose technology. This technology can support both operational efficiency and academic–practitioner alignment, offering a scalable approach to a persistent challenge in the field.
