Understanding the impact of boundary-spanning employee displacement by Artificial Intelligence (AI) on supply chain innovation - Ekezie, Uchenna; Gligor, David; Gölgeci, Ismail; Hong, Seock-Jin; Dhillon, Gurpreet (Taylor & Francis, 2026)
While AI use in supply chain (SC) management is widely acknowledged to render some SC jobs redundant, little is known about the influence of this dynamic on boundary-spanning employees (BSEs). With this in mind, we specifically evaluate the effects of AI-induced displacement of boundary-spanning roles, as links for critical tacit knowledge, on SC innovation. We adopt a three-stage mixed-method approach. First, we interviewed SC professionals. Second, we collect SC practitioner survey data and analyse it using structural equation modelling. Then, probe further via follow-up interviews in phase 3. We find that, in addition to influencing SC innovation, BSEs are instrumental in exchanging tacit knowledge with SC partners and ensuring trust in SC partners, revealing the complex relationship between AI-induced BSE displacement and interfirm trust. The study extends social network theory by showing that qualitative attributes of business ties are more important than quantitative attributes when sourcing external innovation. Our findings reinforce actor-network theory (ANT) by highlighting the role BSEs leveraging non-human ‘actors’ –like technology– play in interorganizational collaboration. The ensuing contributions emphasize the role of BSEs as part of a human-AI hybrid, providing insight into the need for an optimal human-AI balance in the SC to ensure continuing innovation.
Keywords: Boundary-spanning employees; Actor-network theory; Social network theory; AI; Innovation; fi=Kansainvälinen liiketoiminta|en=International Business|