Technology meets agriculture: will green knowledge management and green intellectual capital be the game changer for sustainable farming among rural women

Purpose This study investigates how agricultural technology (AgTech) innovation, from the perspective of rural women farmers, enhances green knowledge management (GKM) dimensions – namely acquisition, sharing and application – to drive sustainable farming. Additionally, it explores the moderating role of green intellectual capital (GIC) in strengthening the relationship between GKM dimensions and sustainable farming. Design/methodology/approach Guided by a post-positivist philosophical stance, the study employed a quantitative methodological approach and survey strategy. Data were collected from 450 purposively sampled rural women farmers engaged in livestock farming, mixed farming and crop production across three agriculturally vibrant regions in Ghana. Structural equation modeling (SEM) was used to test the proposed hypotheses. Findings The results reveal that AgTech significantly influences sustainable farming and the GKM dimensions of acquisition, sharing and application. Moreover, GKM dimensions partially mediate the relationship between AgTech and sustainable farming, while GIC moderates the relationship between GKM dimensions and sustainable farming, further strengthening their impact. Originality/value To the best of the authors’ knowledge, this study is the first to integrate the natural resource-based view (NRBV), knowledge-based view (KBV) and resource-based view (RBV) to explore the interrelationships among AgTech, green knowledge management (GKM) and green intellectual capital (GIC) in promoting sustainable agriculture. It offers a novel theoretical contribution by combining these frameworks to explain sustainability outcomes in an underexplored context. Empirically, the study addresses a significant gap by focusing on rural women farmers in a developing economy, an often-overlooked group, in agri-digital research. Methodologically, it demonstrates the robustness of structural equation modeling (SEM) in capturing complex relationships among knowledge, resources and sustainability in agricultural systems.