VOLUME 05 ISSUE 08 AUGUST 2026
1Oke Joseph Adewale, 2Obatimehin Folakemi Esther, 3Jimoh Akeem Akande, 4Abdukareem Kehinde
1,2Procurement and Supply Chain Management Department Adeseun Ogundoyin Polytechnic, Eruwa
3General Studies Department Adeseun Ogundoyin Polytechnic, Eruwa
4Business administration and Management Department Adeseun Ogundoyin Polytechnic, Eruwa
DOI : https://doi.org/10.58806/ijsshmr.2026.v5i8n05Google Scholar Download Pdf
ABSTRACT
Artificial intelligence (AI) is now deeply embedded in global supply-chain decision-making, automating tasks ranging from demand forecasting to supplier qualification and logistics planning. Alongside this technological shift, geopolitical fragmentation, expressed through sanctions, data localization mandates, new regional trade blocs, and friend-shoring has reshaped the institutional environment in which firms operate. Existing research examines AI delegation as an efficiency-enhancing mechanism and geopolitical fragmentation as a structural risk, but the two forces have rarely been theorized together. This conceptual paper develops an integrative theoretical framework, that is, Algorithmic Decision Authority Theory (GADAT), to explain how geopolitical fragmentation fundamentally shifts AI delegation from an efficiency-centric architecture toward a legitimacy-centric architecture. Drawing on Resource Dependence Theory, Institutional Theory, Information Processing Theory, and Dynamic Capabilities, the paper introduces several new constructs: Political–Algorithmic Misalignment (PAM), Geo-Institutional Opacity (GIO), Legitimacy-Centric Delegation (LCD), Algorithmic Legitimacy Buffering (ALB), and Delegation Adjustment Capability (DAC). The theory identifies three core mechanisms—coercive legitimacy pressures, political-triggered information constraints, and institutional heterogeneity—through which geopolitical fragmentation reshapes the locus, granularity, and accountability of decision authority in AI-enabled supply chains. The paper then articulates a model predicting four structural outcomes: recentralized authority, federated hybrid authority, trust-partitioned authority, and insulated algorithmic authority. Eight propositions link these configurations to supply-chain resilience, performance, and multinational adaptability. A dedicated section examines alternative theoretical explanations, transaction cost economics, agency theory, classic information processing models, and traditional supply-chain governance perspectives—to demonstrate the added value of GADAT. The paper concludes with a research agenda for empirical testing and proposes managerial implications for configuring AI decision rights in politically heterogeneous environments.
KEYWORDS:Artificial intelligence; geopolitical fragmentation; decision authority; institutional legitimacy; resource dependence; information processing; dynamic capabilities; supply chain resilience; global operations.
REFERENCES1) Ahmed, A. A., Abdullahi, A. U., Gital, A. Y., & Dutse, A. Y. (2024). Application of artificial intelligence in supply chain management: A review on strengths and weaknesses of predictive modeling techniques. Scientific Journal of Engineering and Technology, 1(2), 1–18.
2) Benbya, H., Davenport, T., & Reinmoeller, P. (2021). Algorithmic management and legitimacy in digital firms: The governance challenge. Journal of Management Studies, 58(6), 1601–1627. https://doi.org/10.1111/joms.12689
3) Culot, G., Podrecca, M., & Nassimbeni, G. (2024). Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions. Computers in Industry, 162, 104132. https://doi.org/10.1016/j.compind.2024.104132
4) Donaldson, L. (2001). The contingency theory of organizations. Sage.
5) Dhar, S. (2025). The collaboration paradox: Why generative AI requires both strategic intelligence and operational stability in supply chain management. arXiv preprint arXiv:2508.13942.
6) Farrell, H., & Newman, A. L. (2019). Weaponized interdependence: How global economic networks shape state coercion. International Security, 44(1), 42–79. https://doi.org/10.1162/isec_a_00351
7) Genetti, S., Longobardi, A., & Iacca, G. (2025). Evolutionary reinforcement learning for interpretable decision-making in supply chain management. arXiv preprint arXiv:2504.12023.
8) Ivanov, D., & Dolgui, A. (2022). Digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Transportation Research Part E: Logistics and Transportation Review, 154, 102583. https://doi.org/10.1016/j.tre.2021.102583
9) Lawrence, P. R., & Lorsch, J. W. (1967). Organization and environment: Managing differentiation and integration. Harvard University Press.
10) Lundan, S., & Van Assche, A. (2021). Global supply chains and geopolitical fragmentation: Implications for multinational enterprises. Global Strategy Journal, 11(2), 227–249. https://doi.org/10.1002/gsj.1462
11) Logožar, K. (2025). The role of artificial intelligence in supply chain management: A systematic literature review. ENTRENOVA – ENTerprise REsearch InNOVAtion Journal, 10(1), 328–337.
12) Martin, K., Borah, A., & Palmatier, R. (2019). Data privacy: Effects on customer and firm performance. Journal of Marketing, 83(1), 34–52. https://doi.org/10.1177/0022242918801280
13) Pasmore, W., Francis, C., Haldeman, J., & Shani, A. B. (1982). Sociotechnical systems: A North American reflection on empirical studies of the seventies. Human Relations, 35(12), 1179–1204. https://doi.org/10.1177/001872678203501202
14) Pfeffer, J., & Salancik, G. R. (1978). The external control of organizations: A resource dependence perspective. Harper & Row.
15) Queiroz, M. M., & Wamba, S. F. (2021). AI adoption in supply chains: The role of dynamic capabilities in driving firm performance. International Journal of Production Economics, 235, 108079. https://doi.org/10.1016/j.ijpe.2021.108079
16) Ransbotham, S., Candelon, F., Kiron, D., LaFountain, B., & Khodabandeh, S. (2020). Expanding AI’s impact with organizational learning. MIT Sloan Management Review, 61(4), 1–14.
17) Samuels, A. (2025). Examining the integration of artificial intelligence in supply chain management from Industry 4.0 to 6.0: A systematic literature review. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2024.1477044
18) Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting. Human Relations, 4(1), 3–38. https://doi.org/10.1177/001872675100400101
19) Politeknik Siber Cerdika Internasional. (2025). Global supply chain restructuring: Business strategy amid geopolitical fragmentation. JETBIS. https://doi.org/10.57185/jqn9y759
20) Williamson, O. E. (1979). Transaction-cost economics: The governance of contractual relations. Journal of Law and Economics, 22(2), 233–261. https://doi.org/10.1086/466942
21) Williamson, O. E. (1985). The economic institutions of capitalism: Firms, markets, relational contracting. Free Press.
Cite this Article: Adewale, O. J., Esther, O. F., Akande, J. A., & Kehinde, A. (2026). Strategic Reconfiguration of Decision Authority in AI-Driven Global Supply Chains under Geopolitical Fragmentation: Developing Geo-Algorithmic Decision Authority Theory (GADAT). INTERNATIONAL JOURNAL OF SOCIAL SCIENCE HUMANITY & MANAGEMENT RESEARCHS, 5(8), 2129-2142. https://doi.org/10.58806/ijsshmr.2026.v5i8n05