Artificial intelligence enabled transformation of green capabilities into sustainable performance: a process-based view

Purpose This study aims to examine how green sensing capability is transformed into sustainable performance through a sequential process of green organizational learning and green reconfiguring capability, and to assess whether AI capability strengthens the contribution of reconfiguring capability to sustainable performance. Design/methodology/approach This study adopts a quantitative, time-lagged survey design conducted in three waves over six months to minimize common method bias. Data were collected from 320 managers and executives in Vietnam across multiple industries. Grounded in natural resource-based view (NRBV) and dynamic capabilities, the model tests a sequential mechanism where green sensing triggers organizational learning, enabling resource reconfiguration. Partial least squares structural equation modeling was used for analysis, with artificial intelligence (AI) integrated as a moderating boundary condition affecting sustainable performance outcomes. Findings Empirical results support all five hypotheses. Green sensing positively impacts organizational learning, which subsequently drives green reconfiguration, leading to enhanced sustainable performance. The sequential mediation analysis confirms that sensing influences performance indirectly through these intermediate capability stages rather than via direct effects. Furthermore, AI significantly moderates the relationship between reconfiguration and sustainable performance. High AI capability allows firms to translate strategic changes into tangible results more effectively, optimizing resource allocation and execution efficiency in uncertain environments. Originality/value This research advances the NRBV by integrating it with dynamic capabilities to reveal the sequential microprocesses of green value creation. Its primary originality lies in shifting the focus from direct effects to a sequential mediation mechanism, demonstrating how green sensing must be internalized through learning and reconfiguration to achieve sustainability. In addition, to the best of the authors’ knowledge, this study is among the first to empirically position AI as a digital catalyst that moderates the transformation of green capabilities. These insights provide a novel framework for leveraging digital technology to enhance environmental and organizational outcomes.