Drivers and Barriers to Artificial Intelligence Adoption in the Banking Sector: Evidence from Cambodia

Driven by the Fourth and Fifth Industrial Revolutions, global financial institutions are aggressively deploying artificial intelligence (AI) technologies to optimize operations, risk management, and customer engagement. However, empirical investigation into AI adoption dynamics within emerging economies remains critically sparse, as established technology acceptance frameworks predominantly reflect advanced or industrialized environments. This study investigates the primary determinants—encompassing both technological, organizational, and environmental drivers and barriers—influencing AI adoption across the banking sector in Cambodia, evaluating 25 hypotheses to establish an integrated conceptual model tailored to emerging financial settings. A mixed-methods design with concurrent triangulation was executed. Quantitative data were gathered via a survey of 83 banking institutions and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Qualitative insights were extracted via content analysis on 17 semi-structured interviews. As a result, PLS-SEM path modeling confirmed 17 of 25 hypotheses as statistically significant drivers. Primary drivers include top management’s innovativeness, business scope, top management’s support, familiarity with innovation, and relative advantage. Surprisingly, traditional economic and structural inhibitors—technical complexity, cost, centralization, formalization, organization size, slack, government policies, and geographical settings—were statistically unsupported as firm-level barriers in the quantitative model. Nonetheless, qualitative content analysis supported 24 of 25 factors, exposing a profound perception-reality gap where operational friction is overridden by top-down executive mandates and competitive survival pressures. AI adoption in emerging banking markets operates under a Strategic Survival Mandate, wherein banking institutions bypass cost and complexity constraints to maintain market relevance. Policy interventions are critical to promoting sound adoption.