Artificial Intelligence and Big Data Analytics for Sustainable Value Creation: A Systematic Review of Digital Capabilities across Supply Chains and Industrial Ecosystems

Ismail Rasulong (Universitas Muhammadiyah Makassar)
Abdul Khaliq (Universitas Muhammadiyah Makassar)
Asrijal Bintang (Universitas Muhammadiyah Makassar)
Kahar Kahar (Universitas Syekh Yusuf Al Makassari Gowa)

Jurnal Studi Manajemen Organisasi
Vol 23, No 1 (2026): 2026

Artificial intelligence (AI) and big data analytics (BDA) are increasingly recognized as strategic enablers of sustainable value creation across supply chains and industrial ecosystems. Despite the rapid growth of research on AI- and data-driven sustainability initiatives, existing studies remain fragmented across sectors, theoretical perspectives, and levels of analysis. This systematic literature review (SLR) synthesizes peer-reviewed research to elucidate how AI- and BDA-enabled digital capabilities contribute to economic, environmental, and social sustainability outcomes. Guided by the PRISMA framework, the review examines 26 journal articles indexed in Scopus. Drawing on dynamic capability theory, the resource-based view, and complementary institutional and ecosystem perspectives, the findings are organized into four thematic domains: (1) AI and BDA as digital capabilities for sustainable value creation, (2) mediating mechanisms linking digital capabilities to sustainability outcomes, (3) ecosystem- and institution-level contingencies shaping impact realization, and (4) emerging research frontiers, including Industry 5.0, circular economy models, and responsible AI. The review reveals that AI and BDA seldom generate sustainability outcomes directly; instead, their impacts are largely mediated by organizational capabilities, supply chain coordination, and governance structures. Moreover, contextual factors such as regulatory environments, market turbulence, and ecosystem maturity significantly influence the direction and magnitude of sustainability outcomes. Persistent research gaps are identified, including limited longitudinal evidence, inconsistent sustainability metrics, the underrepresentation of social sustainability, and insufficient integration of ethical and governance considerations. Overall, this SLR advances a capability-based understanding of AI-enabled sustainability and provides actionable insights for scholars, managers, and policymakers seeking to support responsible and ecosystem-oriented digital transformation.