Pathway to achieve sustainable value through task-AI fit: the critical roles of green knowledge management capabilities, competitive advantage and financial resilience

Abstract

This study aims to investigate how artificial intelligence (AI)-based technologies are associated with sustainable value intentions in green-oriented organisations operating under circular economy principles in China. Drawing on an adapted view of task-technology fit and the resource-based view (RBV), this study examines how technology and task characteristics are associated with Task-AI Fit, and how this fit is associated with green knowledge management (KM) capabilities, competitive advantage and financial resilience, which serve as key enablers of sustainable value intentions. A survey was conducted with 373 representatives from Chinese organisations committed to circular-economy goals. Using covariance-based SEM and statistical tools, including SPSS Statistics and SPSS AMOS, the study tested the hypothesised paths, the structural model and mediation and moderation effects. It also examined the association between perceived task-AI Fit and internal capabilities, and their subsequent relationship with sustainable value intentions, while assessing the moderating role of environmental uncertainty. The findings show that perceived task-AI Fit is significantly associated with Green KM capabilities, competitive advantage and financial resilience. Specifically, the Green KM capabilities show the strongest association with intentions to realise sustainable value. Environmental uncertainty significantly moderates only the relationship between Green KM capabilities and sustainable value intentions. By proposing a Task-AI Fit-driven RBV, the study highlights that the perceived Task-AI Fit is associated with internal capabilities that can channel sustainable intentions in the face of environmental uncertainty. Organisations must prioritise, adapt and practice AI characteristics and features to strengthen AI-task alignment and strategically develop sustainability-oriented knowledge routines and value realisation. Future work can examine cross-cultural aspects across regulatory and cultural contexts, adopt a longitudinal design and include objective sustainability performance indicators to identify which potential attributes fail to materialise and to track the development of AI-driven capabilities. This research extends the understanding of AI-enabled sustainability transitions by integrating task-technology alignment with resource-based capabilities in the context of environmental uncertainty. It highlights the central role of green KM practices in explaining sustainable value intentions in circular economy-oriented firms.