Sustainable Growth and Token Economy Design: The Case of Steemit

Kim, Moon Soo; Chung, Jee Yong

doi:10.3390/su11010167

Cited by 54 publications

(23 citation statements)

References 25 publications

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“…To do this, there will be the need to include an understanding of the influence of culture on behavior. Finally, another limitation of this research has been the non-inclusion of digital currencies based on blockchain technology and token economy, such as Bitcoin [87] or Steemit [88], especially taking into account the influence that they can have on sustainability and development.…”

Section: Discussionmentioning

confidence: 99%

An Analysis of the Process of Adopting Local Digital Currencies in Support of Sustainable Development

Giménez

Tamajón

2019

Sustainability

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Local digital currencies (LDCs) can be defined as technological platforms in which transactions occur between users without the use of any form of official tender. Although there is empirical evidence with regard to the sustainable development-related benefits of this type of currency, its use is still highly marginal and incipient, with relatively short lifecycles. To understand why LDCs have not gained a firmer footing or implementation in society and subsequently gain the ability to define a series of actions making this possible, an integrated model has been developed. Based on a review of the available literature, this model permits an analysis of the perceptions, beliefs, and attitudinal factors impacting upon the process of adopting this kind of currency. Based on a sample of 1559 subjects, the results obtained from the structural equation modelling performed provide important conclusions in terms of both the theory and the management of LDC-based communities, whether this is for the creation of new communities or the attraction of new users, or to consolidate existing communities or the participation of users once they have registered with them. Perceptions (attitude, subjective regulation, and perceived control), beliefs (compatibility, perceived utility, interpersonal influences, self-efficacy, and enabling conditions) and previous experience have a significant influence on the intention to be part of a community based on a local digital currency, with all that this implies for the defining of strategies to promote the use of LDCs.

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Section: Discussionmentioning

confidence: 99%

An Analysis of the Process of Adopting Local Digital Currencies in Support of Sustainable Development

Giménez

Tamajón

2019

Sustainability

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“…Yet, analog to the question of how to design token economies capable of effectively democratizing access to input training data, the design of effective token economies for democratizing access to AI models requires further research. Furthermore, the modeling of assets as tokens is a topic of ongoing research (Kim and Chung 2018;Laskowski et al 2019) and, thus far, we lack knowledge on what and how AI-related assets (e.g., training data, model data, algorithms) can be modeled and represented adequately as tokens.…”

Section: Avenuementioning

confidence: 99%

Trustworthy artificial intelligence

2020

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Artificial intelligence (AI) brings forth many opportunities to contribute to the wellbeing of individuals and the advancement of economies and societies, but also a variety of novel ethical, legal, social, and technological challenges. Trustworthy AI (TAI) bases on the idea that trust builds the foundation of societies, economies, and sustainable development, and that individuals, organizations, and societies will therefore only ever be able to realize the full potential of AI, if trust can be established in its development, deployment, and use. With this article we aim to introduce the concept of TAI and its five foundational principles (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. We further draw on these five principles to develop a data-driven research framework for TAI and demonstrate its utility by delineating fruitful avenues for future research, particularly with regard to the distributed ledger technology-based realization of TAI.

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“…Recent scientific findings support the fact that rewards affect data acquisition [41], [73]. In our method, we provide financial future rewards in the form of exchangeable cryptocurrency to achieve sustainable data collection [74], [75].…”

Section: B Systematic Data Storagementioning

confidence: 81%

Reliable Data Collection in Participatory Trials to Assess Digital Healthcare Applications

Park

Kim

et al. 2020

IEEE Access

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The number of digital healthcare mobile applications in the market is exponentially increasing owing to the development of mobile networks and widespread usage of smartphones. However, only few of these applications have been adequately validated. Like many mobile applications, in general, the use of healthcare applications is considered safe; thus, developers and end users can easily exchange them in the marketplace. However, existing platforms are unsuitable for collecting reliable data for evaluating the effectiveness of the applications. Moreover, these platforms reflect only the perspectives of developers and experts, and not of end users. For instance, typical clinical trial data collection methods are not appropriate for participant-driven assessment of healthcare applications because of their complexity and high cost. Thus, we identified the need for a participant-driven data collection platform for end users that is interpretable, systematic, and sustainable, as a first step to validate the effectiveness of the applications. To collect reliable data in the participatory trial format, we defined distinct stages for data preparation, storage, and sharing. The interpretable data preparation consists of a protocol database system and semantic feature retrieval method that allow a person without professional knowledge to create a protocol. The systematic data storage stage includes calculation of the collected data reliability weight. For sustainable data collection, we integrated a weight method and a future reward distribution function. We validated the methods through statistical tests involving 718 human participants. The results of a validation experiment demonstrate that the compared methods differ significantly and prove that the choice of an appropriate method is essential for reliable data collection, to facilitate effectiveness validation of digital healthcare applications. Furthermore, we created a Web-based system for our pilot platform to collect reliable data in an integrated pipeline. We compared the platform features using existing clinical and pragmatic trial data collection platforms. INDEX TERMS Digital health, digital healthcare app data collection platform, crowdsourcing, participatory trial, biomedical informatics.

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Sustainable Growth and Token Economy Design: The Case of Steemit

Cited by 54 publications

References 25 publications

An Analysis of the Process of Adopting Local Digital Currencies in Support of Sustainable Development

An Analysis of the Process of Adopting Local Digital Currencies in Support of Sustainable Development

Trustworthy artificial intelligence

Reliable Data Collection in Participatory Trials to Assess Digital Healthcare Applications

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