Capability conversion in green manufacturing: Green HRM, ethical AI, and dual sustainability performance under the NRBV

Research background: Manufacturing firms in emerging economies face simultaneous pressures to decarbonise and digitalise, yet how green human resource systems, responsible AI governance, and green innovation jointly translate into sustainability outcomes remains insufficiently understood. Purpose of the article: Grounded in the Natural Resource-Based View, this study tests a capability-conversion model linking green human resource management practices (GHRMPs), ethical AI (EA), green innovation performance (GIP), environmental performance (ENP), and operational economic performance (ECP) in Thai manufacturing. Methods: Cross-sectional online survey data were obtained from 422 key informants, with one supervisory, managerial, or executive respondent representing each medium-sized or large Thai manufacturing firm. The model was analysed using two-stage partial least squares structural equation modelling, a reflective–formative higher-order specification of GHRMPs, and 10,000-subsample bootstrapping. Findings & value added: GHRMPs were positively associated with EA (β = .846), EA with GIP (β = .733), and GIP with ENP (β = .810) and ECP (β = .765), whereas the direct GHRMP–ENP and GHRMP–ECP relationships were nonsignificant. The serial indirect associations through EA and GIP were significant for ENP (β = .502) and ECP (β = .475). These findings support an indirect capability-conversion pattern in which green HR architecture functions as a distal enabling capability, EA as an intermediate governance capability, and GIP as the proximal mechanism associated with environmental improvement and operational resource cost efficiency. The environmental coefficients were descriptively larger than the economic coefficients, but these differences were not formally tested and therefore represent tentative environmental–economic variation rather than confirmed asymmetry. The study contributes by integrating human, AI-governance, and innovation capabilities and by operationalising EA as an organisational governance construct in an emerging-economy manufacturing context.