Designing Community Governance – Learnings from DAOs
Year: 2025
Type: article
Abstract: This work examines how decentralised autonomous organisations (DAOs) can improve transparency, fairness, and inclusivity in community-based decision-making, using the case of research grant allocation within a university community in Switzerland. The current voting mechanism has been criticised for fostering partiality and favouring personal networks over merit, highlighting the need to improve fairness and objectivity without increasing administrative effort. Drawing on insights from DAO governance, this study aims to propose methods for improving decision-making processes through innovative mechanisms. Adopting an action design research (ADR) methodology, the study integrates theoretical and practical perspectives to design and evaluate innovative methods that address real-world challenges. Schematic analysis of decision-making processes in DAOs provides the foundation for the development of methods applicable to the university’s community context. Proposed solutions are iteratively refined through workshops with community stakeholders to ensure relevance and feasibility. The results present a generalisable model for community-based decision-making processes inspired by DAOs, alongside tailored recommendations for the university’s community-based voting and decision-making process. The findings demonstrate that innovative mechanisms built for large online communities to reach consensus, such as DAO voting systems, can promote impartiality and increase inclusivity, ultimately fostering trust and encouraging broader participation in community governance. This study contributes to the discourse on community governance by bridging theoretical DAO insights with practical applications in a university community setting. Limitations and directions for future research, including implementation and evaluation of the proposed methods in practice, are discussed to pave the way for further exploration of DAO-informed community-based governance models.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $897
OpenAlex ID: W4408680416