Turning regulation into sustainability performance: the role of AI-enabled absorptive capacity in manufacturing firms

Abstract

This study aims to examine how government regulations are translated into sustainability performance in manufacturing firms by investigating the mediating roles of artificial intelligence (AI) capabilities and absorptive capacity. Drawing on institutional theory and the resource-based view, it explains how regulatory pressure stimulates internal digital capability development and knowledge-processing mechanisms rather than directly improving sustainability outcomes. Data were collected from 297 managers and senior executives in Vietnamese manufacturing firms. The proposed research model was tested using partial least squares-structural equation modeling. The results show that government regulations do not have a significant direct effect on sustainability performance but significantly enhance AI capabilities. AI capabilities, in turn, positively affect sustainability performance and both dimensions of absorptive capacity. Potential absorptive capacity significantly improves sustainability performance, whereas realized absorptive capacity has no significant direct effect. These findings reveal asymmetric effects of absorptive capacity: firms appear better able to acquire and assimilate external knowledge than to convert it into measurable sustainability outcomes. This study contributes to competitiveness and sustainability research by showing that government regulations enhance sustainability performance indirectly through AI-enabled knowledge capabilities. It reconceptualizes AI capabilities as a higher-order strategic resource that strengthens sustainable competitiveness in manufacturing firms, while highlighting possible implementation barriers to converting knowledge into performance.