Generative AI and Dynamic Capabilities for Sustainable Supply Chain Performance: Systematic Literature Review - Lama, Ganesh (My University, 2026)
Generative Artificial Intelligence (GenAI) is being adopted across global supply chains. Yet, the literature remains fragmented on how GenAI relates to dynamic capabilities development, how those capabilities translate into Triple Bottom Line (TBL) sustainability outcomes, and how this pathway operates across institutionally heterogeneous international business environments. This systematic literature review (SLR) examines how GenAI supports the development of sensing, seizing, and reconfiguring capabilities and how these capabilities relate to sustainability outcomes in international supply chains. Peer-reviewed empirical and conceptual articles in English, published between 2020 and 2026 and addressing GenAI in supply chain, dynamic capability, or sustainability contexts, were eligible. Scopus and Web of Science were searched in March 2026 using a structured Boolean string. After PRISMA-guided screening and a six-criterion quality assessment (clear objective, theoretical grounding, methodological rigor, validity of findings, theoretical contribution, and International Business relevance), 35 studies were retained for thematic synthesis and mapped onto the Dynamic Capabilities View (DCV) and the Knowledge-Based View (KBV), organized around six application clusters, the three capability dimensions, the TBL, and a multi-level moderating context. The review was not pre-registered and received no external funding. The review shows that the GenAI–sustainability link is indirect and capability-mediated. GenAI strengthens sensing most immediately, supports seizing where governance maturity and task-technology alignment are sufficient, and enables reconfiguring only when firms institutionalize learning across organizational boundaries. Sustainability outcomes are reached through capability-enabled practices such as green supply chain collaboration, circular economy implementation, and stakeholder co-creation, and remain unevenly developed across the TBL. Every link is conditioned by organizational, institutional, and contextual moderators, with International Business factors such as institutional heterogeneity, digital divides, and MNE coordination operating as cross-cutting boundary conditions. The thesis offers three theoretical contributions: it extends DCV by repositioning GenAI as a generative meta-capability; integrates it with the KBV to expose the knowledge routines through which GenAI inputs become organizationally meaningful; and advances international supply chain literature by treating GenAI as a cross-border capability infrastructure conditioned by IB-specific moderators. The contributions are synthesized in an integrative conceptual framework with implications for managers, MNE executives, and policymakers.
Keywords: supply chains; sustainable development; green economy; logistics; enterprises; business; dynamic capabilities; artificial intelligence; machine learning; generative artificial intelligence; fi=Kansainvälinen liiketoiminta|en=International Business|; Master's Degree Programme in International Business