GCC banks with stronger digital and AI capabilities gain market share, a panel study shows; the effect persists after accounting for profitability, bank size and macroeconomic conditions, suggesting technological adoption is a competitive advantage in the region.

Main Finding

Both bank-level digitalization and AI adoption significantly and positively increase banks’ market share in the GCC. These effects persist after controlling for profitability, market valuation, liquidity, bank size, and macroeconomic conditions, and are robust to dynamic panel estimation (System GMM) that addresses endogeneity and serial correlation.

Key Points

• The paper frames technology adoption through the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT): digital and AI capabilities are strategic, hard-to-replicate resources that, when integrated dynamically, expand competitive advantage and market share.
• Novel composite indices were constructed:
  • Digitalization Adoption Index (DAI): counts/weights items such as number of digital services (mobile app, internet banking), digital transaction volumes, and IT spending as % of operating costs.
  • AI Adoption Index (AIAI): captures AI-enabled features (chatbots, robo-advisors, automated credit scoring, fraud detection), investments in AI infrastructure and related keywords in annual reports.
• Main hypotheses:
  • H1: Digitalization positively affects bank market share.
  • H2: AI adoption positively affects bank market share.
• Results are consistent across multiple specifications, supporting the strategic importance of investing in digital and AI capabilities for market expansion in the GCC.

Data & Methods

• Sample: 400 bank-year observations from 40 banks in five GCC countries (Saudi Arabia, UAE, Kuwait, Qatar, Oman), 2015–2024. Bahrain excluded due to data gaps. Sample includes both conventional and Islamic banks.
• Dependent variable:
  • Market Share (MS) = bank total assets / total assets of all banks in the same country.
• Key independent variables:
  • Digitalization Adoption Index (DAI) — constructed from annual reports and activity measures (digital services, transaction volume, IT spending).
  • AI Adoption Index (AIAI) — constructed from annual reports and presence of AI applications/features.
• Controls: Return on Assets (ROA), Market-to-Book ratio (MB), Loan-to-Deposit ratio (LDR), Bank Size (ln total assets), country GDP growth, and inflation.
• Empirical strategy:
  • Dynamic panel models with lagged dependent variable.
  • Estimation via System GMM (Arellano–Bover / Blundell–Bond) to address endogeneity from the lagged dependent variable, possible simultaneity, unobserved heterogeneity, and serial correlation.
  • Robustness checks: Hansen J-test for instrument validity and Arellano–Bond tests for autocorrelation; alternative instrument sets and model variants reported.
• Limitations noted by authors:
  • Bahrain omitted due to data; composite indices built from annual-report keyword-based measures may introduce measurement noise; some indices “are unscalable” (authors flag concerns about indexing approach and generalizability).

Implications for AI Economics

• For theory:
  • Empirical support for treating digitalization and AI as strategic firm-level resources (RBV) that require dynamic reconfiguration (DCT) to convert into market power. Reinforces models linking technology investments to market structure outcomes, not only to efficiency or profitability.
• For managers and banks:
  • Investment in digital platforms and AI capabilities can expand market share beyond short-term cost impacts. Executives should view AI/digitalization as long-term strategic assets—prioritize integration, talent, and processes to capture customer acquisition/retention gains.
  • Use composite measurement of adoption (beyond simple binary proxies) to better guide strategy and benchmark competitors.
• For policymakers and regulators:
  • Encouraging digital and AI adoption (through infrastructure, skills, and regulatory clarity) can strengthen competitiveness of domestic banks. However, monitoring competition and concentration effects is important: technology-led scale advantages can increase market concentration.
  • Support for data infrastructure, cross-border digital payments, and AI governance frameworks will influence diffusion and competitive impacts across the banking sector.
• For researchers in AI economics:
  • This study suggests profitable avenues: quantify the causal channels (customer acquisition vs. retention vs. product mix), heterogeneity across bank types (Islamic vs. conventional), and cross-country institutional moderators (regulation, digital infrastructure).
  • Future work should refine measurement of AI/digital adoption (e.g., machine-read indicators, transaction-level usage data), estimate effect magnitudes on market concentration and welfare, and explore labor, pricing, and risk implications of AI-driven market share shifts.