This paper examines how artificial intelligence integration influences the resource efficiency of circular economy systems. Although recent empirical research has found support for the importance of digital technologies in promoting sustainable production, specific mechanisms between AI capabilities and their resource optimization outcomes, there remains a relative lack of research regarding how specific AI-driven analytics functions of firms are related to circular economy performance outcomes. Th paper aims to explain the effect of AI adoption on circular economy performance. It also aims to identify the determinants of AI capability, organizational motivation, and circular design practices that lead to an increase in a firm’s resource efficiency. Through a quantitative research approach, comprising of a structured questionnaire and multivariate analyses of survey data in the manufacturing sector, it is demonstrated how AI analytics capability, circular process innovation and digital collaboration, in interaction with organizational readiness, affect the level of resource efficiency. The study’s findings show AI capability positively affects circular process innovation and resource efficiency; here, using SMART-PLS bootstrapping analysis. The analysis reveals previously unknown pathways of the investigated constructs leading to improvements in resource efficiency, with varying roles depending on firm size and industry characteristics. This study’s findings contribute to the circular economy and digital transformation literature by explaining the role of different dimensions of AI capability in determining a firm’s resource efficiency (including the reduction and reuse practices of the organization).
Xasan et al. (Wed,) studied this question.
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