• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.353-376
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• 2023

The global pandemic and the development of virtual and augmented reality technologies have led a metaverse boom that enables a lot of interactions in virtual worlds, and is being utilized in various fields such as business, government, and education etc. Despite the growing interest in the metaverse, its scope and definition are still unclear and the concept is still evolving, making it challenging to establish its governance. Governmental entities are also investing intensively in public metaverses to make public value and promote social welfare, but they are underutilized due to lack of systematic governance. Therefore, in this study, we propose a public metaverse governance framework and identify the relative importance of the factors. Furthermore, since a public metaverse should be accessible to anyone who wants to use, we explore the factors of shadow work and examine the ways to minimize it. Based on the socio-technical system theory, we derived public metaverse governance factors from previous literature and topic modeling and then generate a framework with 23 factors through expert interviews. We then tested relative priority of the factors using the analytic hierarchical process (AHP) from the experts. As a result, the top five overall rankings are: 'roles and responsibilities', 'standardization/modularization', 'collaboration and communication', 'law and policies', and 'availability/accessibility'. The academic implications of this study are that it provides a comprehensive framework for public metaverse governance, and then the practical implications include suggesting prioritized considerations for metaverse operations in the public sector.

• Information Systems Review
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• v.26 no.1
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• pp.151-172
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• 2024

Metaverse, which supports social and economic activities in the virtual world, is being cited as the core of future Web 3.0 businesses. However, most of the major metaverse platforms currently adhere to the Web 2.0 system and are operated in a centralized manner. Accordingly, this study investigated the form of metaverse operation by examining the case of Decentraland, a metaverse platform operated as DAO, a decentralized autonomous organization in the form of Web 3.0. From the case analysis, we found that Decentraland had positive characteristics such as a horizontal operating structure, fair profit distribution, and transparency, but there were challenges including the possibility of returning to centralization in the operating process, the possibility of abuse of an autonomous system, and inefficiency in decision-making. Therefore, in this study, the timing of transition to DAO governance was discussed, and DAO's efforts for business continuity and the need for adjustment for autonomous operation were suggested as implications. The implications presented in this study are expected to contribute to materializing not only practical but also theoretical aspects of platform operation aimed at web 3.0 as well as the metaverse.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.133-145
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• 2022

As a result of the study, the poorest keywords were information, big data, management, policy, government, law, and smart. In addition, as a result of network analysis, related research was being conducted on topics such as data industry policy, data governance performance, defense, governance, and data public. The four topics derived through topic modeling were "DG policy," "DG platform," "DG in laws," and "DG implementation," of which research related to "DG platform" showed an increasing trend, and "DG implementation" tended to shrink. This study comprehensively summarized data governance-related studies. Data governance needs to expand research areas from various perspectives and related fields such as data management and data integration policies at the organizational level, and related technologies. In the future, we can expand the analysis targets for overseas data governance and expect follow-up studies on research directions and policy directions in industries that require data-based future industries such as Industry 4.0, artificial intelligence, and Metaverse.