Decoding Decentralized Autonomous Organizations: A Content Analysis Approach to Understanding Scoring Platforms

Christian Ziegler () and Syeda Rabab Zehra ()
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Christian Ziegler: TUM School of Management, Technical University of Munich, 80333 Munich, Germany
Syeda Rabab Zehra: TUM School of Management, Technical University of Munich, 80333 Munich, Germany

JRFM, 2023, vol. 16, issue 7, 1-12

Abstract: This paper evaluates the scoring platforms for decentralized autonomous organization (DAO), examining their methodologies and highlighting their strengths and limitations. Using content analysis, we scrutinize the scoring methodologies of the Prime Rating, DAO Meter, and DeFi Safety platforms, evaluating code, documentation, security, team composition, governance, and regulatory compliance. We analyze the underlying assumptions and data sources relied upon by these platforms, using a content analysis approach. Our investigation furnishes valuable information for stakeholders, aiming to evaluate or enhance DAO scoring methodologies used by scholars and practitioners in the finance and blockchain fields. By contributing to a more rigorous understanding of DAO performance assessment, this paper supports informed decision making and promotes the development of a dependable and efficient scoring system for the decentralized financial ecosystem.

Keywords: decentralized autonomous organizations; scoring; blockchain (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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