Unlocking Satisfaction Dynamics in the AI-Driven Banking Era: Insights from a Hybrid SEM-fsQCA Analysis

The development of smart teller machines (STMs) which incorporate artificial intelligence (AI) raises new research questions around customer satisfaction. This study provides insights into satisfaction factors amidst the AI-driven transformation of banking services. We reveal complex dynamics between factors that are vital for developing customer-centric strategies. A conceptual framework is established by using variables from the American customer satisfaction index (ACSI) model with the variable of trust. A hybrid SEM-fsQCA approach is employed to examine data collected from 252 samples. The findings substantiate positive and significant impacts of perceived quality, customer expectations, perceived value, and trust on overall satisfaction, and reveal that perceived quality and customer expectations exhibit indirect effects through perceive value. Meanwhile, the fsQCA analysis explores intricate non-linear dynamics and reveals that no isolated factor is necessary for high satisfaction, but combinations of condition variables can play a pivotal role. The sufficiency analysis emphasizes the requirement of at least two condition variables for achieving high satisfaction. Overall, we highlight the necessity for comprehensive strategies in shaping the banking ecosystem undergoing rapid adoption of AI. JEL classification codes: C19, C83, O14, O33.