Artificial Intelligence-Driven Environmental Management and Sustainable Performance: The Mediating Role of Green Business Model Innovation

Abstract

The integration of artificial intelligence (AI) into environmental management practices has emerged as a transformative strategy for firms operating in emerging economies. This study develops and empirically examines a conceptual model linking AI capability, environmental process optimization (EPO), green business model innovation (GBMI), environmental performance (EP), and sustainable firm performance (SFP) in the Vietnamese manufacturing context. Drawing on dynamic capability theory, the natural resource-based view, and corporate sustainability theory, we hypothesize a sequential mediation pathway wherein AI capability shapes sustainable outcomes through EPO and GBMI. Using partial least squares structural equation modeling (PLS-SEM) and survey data collected from 312 senior managers and environmental officers across Vietnamese manufacturing enterprises, the results confirm all six hypotheses. AI capability significantly and positively influences EPO (β = 0.483, p < 0.001), which in turn drives GBMI (β = 0.512, p < 0.001). GBMI subsequently enhances EP (β = 0.467, p < 0.001), which meaningfully advances SFP (β = 0.531, p < 0.001). Mediation analyses confirm that EPO fully mediates the AIC–GBMI relationship (β = 0.247, p < 0.001), and GBMI fully mediates the EPO–EP relationship (β = 0.239, p < 0.001). These findings contribute novel theoretical insights to the AI-sustainability nexus in developing economy settings and offer practical guidance for policymakers and enterprise managers seeking to leverage AI for green transformation