Exploring AI-Driven Digital Banking Platforms: Implications for Business Model Innovation and Sustainability in the Financial Sector

We examine how external pressures and technological adoption impact environmental sustainability performance (ESP) in Chinese banks. Using the Stimulus-Organism-Response (SOR) framework and Institutional Theory, we explore how industry and stakeholder pressures drive the adoption of artificial intelligence (AI) and digital banking services, and how these technologies influence sustainability outcomes. Data were collected from Chinese bank employees via the WJX online platform. The analysis employs partial least squares-structural equation modeling (PLS-SEM) to examine direct and indirect effects, along with necessary condition analysis (NCA) to assess necessary conditions for achieving ESP. Our findings confirm that both industry and stakeholder pressures significantly promote AI adoption and digital banking services. Notably, AI adoption exerts a stronger influence on ESP compared to digital banking services. Our results confirm that AI adoption mediates the link between external pressures and sustainability outcomes, while digital banking services only mediate the relationship between industry pressure and ESP. The NCA findings further identify industry pressure and AI adoption as necessary conditions for improving ESP. This study contributes to the engineering management literature by integrating external pressures, AI adoption, and sustainability in the banking sector and provides practical insights for policymakers and bank managers to enhance their environmental sustainability strategies.