The rapid integration of Artificial Intelligence (AI) in India’s banking sector offers operational benefits but also raises sustainability challenges. This study focuses on “Green AI,” defined as AI technologies optimized for energy efficiency and carbon conscious practices, by extending the Technology–Organization–Environment (TOE) and Technology Acceptance Model (TAM) frameworks with sustainability-linked factors. Data were collected from 412 mid- to senior-level professionals across six leading public and private banks, and Structural Equation Modeling (SEM) was employed to test the proposed hypotheses. Findings reveal that Banking Infrastructure ( β = 0.419), Financial Investment ( β = 0.401), and Competitive Pressure ( β = 0.329) are the strongest predictors of Green AI adoption, while Regulatory Influence ( β = 0.147), Perceived Usefulness ( β = 0.129), and Perceived Ease of Use ( β = 0.098) exert weaker but significant effects. Adoption of Green AI demonstrates a positive link to sustainability outcomes ( β = 0.446), indicating its potential to convert structural readiness into measurable environmental gains. Although direct energy-consumption data were unavailable, perceptual measures provided valid proxies aligned with emerging-market studies. The results suggest that resource and market drivers outweigh attitudinal factors, offering actionable insights for infrastructure investment, regulatory refinement, and ESG integration, with implications for other emerging economies.
Stored only when both papers are in the corpus — 10 citations exist upstream.
Imported from OpenAlex (CC0) — nothing here is generated.