Mapping stakeholder dynamics: Understanding knowledge, values, and engagement in the Murray-Darling Basin

Poor implementation of stakeholder engagement (SE) in water governance has reduced decision-making legitimacy and damaged stakeholder relations. While many theoretical studies agree that the effectiveness of SE depends not only on the structure of the network but also on the composition of its stakeholders, these studies often remain largely conceptual and non-operational. We develop an explicit and quantitative approach to measuring and assessing stakeholders’ opinions and interests, knowledge and expertise, and engagement networks using content analysis, text mining, topic modeling, and social network analysis. The data were sourced from public comments (submissions) on water resources policy initiatives under the Basin Plan in the Murray–Darling Basin. We find that the agricultural sector, characterized by dominant participation, coherent knowledge, and highly negative opinions, exerted the greatest impact among all stakeholder groups. The federal government showed strong centralization but lacked efficiency and brokerage capacity. Academia and environmental advocacy groups demonstrated high knowledge diversity but scored low on coherence and participation. Indigenous individuals and groups consistently scored below average across all metrics except for knowledge diversity. Our approach provides a diagnostic lens to explicitly identify critical fragilities in SE configurations and offers practical insights to improve stakeholder engagement and governance outcomes. • A quantitative approach to measuring the stakeholders' opinions, knowledge, and engagement network. • The agricultural sector accounted for the largest negative impact on the SE network in the Murray-Darling Basin. • The Federal government was strong in centralization but fell short in efficiency and brokerage. • Academia and environmental advocacy groups were strong in knowledge diversity but lacked coherence. • Indigenous groups consistently scored lower than average across all metrics except knowledge diversity. • Our approach provides a diagnostic lens to explicitly identify where critical fragilities exist in SE configurations.