Abstract
ContextDecentralized autonomous organizations are a new form of smart contract-based governance. Decentralized autonomous organization platforms, which support the creation of such organizations, are becoming increasingly popular, such as Aragon and Colony. Selecting the best fitting platform is challenging for organizations, as a significant number of decision criteria, such as popularity, developer availability, governance issues, and consistent documentation of such platforms, should be considered. Additionally, decision-makers at the organizations are not experts in every domain, so they must continuously acquire volatile knowledge regarding such platforms. ObjectiveSupporting decision-makers in selecting the right decentralized autonomous organization platforms by designing an effective decision model is the main objective of this study. We aim to provide more insight into their selection process and reduce time and effort significantly by designing a decision model. MethodThis study presents a decision model for the decentralized autonomous organization platform selection problem. The decision model captures knowledge regarding such platforms and concepts systematically. This model is based on an existing theoretical framework that assists software engineers with a set of multi-criteria decision-making problems in software production. ResultsWe conducted three industry case studies in the context of three decentralized autonomous organizations to evaluate the effectiveness and efficiency of the decision model in assisting decision-makers. The case study participants declared that the decision model provides significantly more insight into their selection process and reduces time and effort. ConclusionWe observe in the empirical evidence from the case studies that decision-makers can make more rational, efficient, and effective decisions with the decision model. Furthermore, the reusable form of the captured knowledge regarding decentralized autonomous organization platforms can be employed by other researchers in their future investigations.
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