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ARTICLES

Taxonomy of digital community currency platformsFootnote*

, &
Pages 69-91 | Published online: 14 Jun 2018
 

ABSTRACT

Community currencies are used to pay for products or services within specific groups defined by geographical boundaries or specific common interests. Financial crises, social emergence in developing countries, and increased access to digital devices have stimulated a growing number of communities worldwide to develop digital currency projects. These projects use technologies ranging from traditional plastic cards to mobile phones and blockchain technologies. Following the design science research approach, this paper analyzes digital community currencies (DCCs) by developing a taxonomy based on platform architecture, governance, transactionality and virtuality. By investigating 22 DCC platforms around the world, 4 groups were distinguished: local, proprietary, commons and cyber. The identification of these four different groups of digital community currencies allows us to better discuss the potentials and limitations of each one of them. The presented taxonomy can be useful to researchers and practitioners both to explain and to design DCC platforms. Discussing each of the emerging categories from the proposed taxonomy helps us to provide insights into DCCs, offering a new theoretical frame for investigating the particular case of digital payment platforms.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Eduardo H. Diniz is professor and head of the Department of Technology and Data Science at Escola de Administração de Empresas in São Paulo at Fundação Getulio Vargas (FGV-EAESP).

Erica S. Siqueira is Ph.D Student (FGV/EAESP) and Professor at Universidade Estácio de Sá.

Eric van Heck is Professor of Information Management and Markets and Chairman of the Department of Technology & Operations Management, Rotterdam School of Management, Erasmus University.

Notes

* Kweku-Muata Osei-Bryson is the accepting Associate Editor for this paper.

Additional information

Funding

This work was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo: [Grant Number 2016/07261-2].

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