Volume 27 , Issue 2 , PP: 444-465, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Aya Merhi 1 * , Chadi Baalbaki 2
Doi: https://doi.org/10.54216/IJNS.270236
The Gulf Cooperation Council (GCC) banking sector has experienced rapid digital transformation, regulatory shifts, and disruptions in recent years, especially during periods of crisis and recovery. Despite extensive studies on banking efficiency, there remains uncertainty and inconsistency regarding which bank-specific factors most influence performance. Traditional models often assume deterministic relationships, overlooking the indeterminate and ambiguous nature of real-world decision environments. Guided by Neutrosophic theory, this study reinterprets efficiency as a state influenced simultaneously by degrees of truth, falsity, and indeterminacy, acknowledging that the impact of Artificial Intelligence (AI) and Data-Driven Decision Making (DDDM) on efficiency may vary across contexts and times. The study analyzes 43 banks from six GCC countries between 2010 and 2024. In the first stage, efficiency is estimated using Data Envelopment Analysis (DEA). In the second stage, panel regression models are applied to examine the influence of bank-specific factors, including AI adoption, capital adequacy (CAR), asset quality (NPL), returns (ROA, ROI), branch footprint, and bank age. Within a Neutrosophic theoretical lens, these relationships are interpreted not as fixed or absolute but as having degrees of certainty and uncertainty that coexist within the decision environment. Findings reveal significant variation in efficiency across countries and banks. AI adoption, CAR, and ROA show strong positive associations with efficiency (high truth-values), while NPLs exhibit negative effects (high falsity values). ROI and branch footprint demonstrate mixed or indeterminate influences, suggesting that their roles depend on contextual and temporal factors. This perspective highlights how efficiency drivers in the GCC banking sector cannot be fully captured by binary or crisp evaluations. By applying Neutrosophic theory, this study provides a novel conceptual understanding of banking efficiency under uncertainty. It recognizes managerial and policy decisions are often made in environments where information is incomplete, contradictory, or evolving. The Neutrosophic interpretation enhances the explanatory depth of traditional efficiency analyses and offers a more flexible lens for understanding how digital transformation and AI adoption contribute to organizational performance amid indeterminacy.
Neutrosophic Theory , Data Envelopment Analysis (DEA) , Artificial Intelligence (AI) , Data-Driven Decision Making (DDDM) , Efficiency , GCC Banking Sector , Uncertainty , Indeterminacy
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