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Tarzan and Chain : Exploring the ICO Jungle and Evaluating Design Archetypes

Title data

Bachmann, Nina M. ; Drasch, Benedict ; Fridgen, Gilbert ; Miksch, Michael ; Regner, Ferdinand ; Schweizer, André ; Urbach, Nils:
Tarzan and Chain : Exploring the ICO Jungle and Evaluating Design Archetypes.
In: Electronic Markets. Vol. 32 (2022) . - pp. 1725-1748.
ISSN 1422-8890
DOI: https://doi.org/10.1007/s12525-021-00463-6

Official URL: Volltext

Abstract in another language

The phenomenon of a blockchain use case called initial coin offering (ICO) is drawing increasing attention as a novel funding mechanism. ICO is a crowdfunding type that utilizes blockchain tokens to allow for truly peer-to-peer investments. Although more than $7bn has been raised globally via ICOs as at 2018, the concept and its implications are not yet entirely understood. The research lags behind in providing in-depth analyses of ICO designs and their long-term success. We address this research gap by developing an ICO taxonomy, applying a cluster analysis to identify prevailing ICO archetypes, and providing an outlook on the token value market performance for individual archetypes. We identify five ICO design archetypes and display their secondary market development from both a short-term and a long-term perspective. We contribute to an in-depth understanding of ICOs and their implications. Further, we offer practitioners tangible design and success indications for future ICOs

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Blockchain; ICO; Taxonomy; Archetypes; Success analysis
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT
Research Institutions > Affiliated Institutes > FIM Research Center for Information Management
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 03 Dec 2024 08:51
Last Modified: 03 Dec 2024 08:51
URI: https://eref.uni-bayreuth.de/id/eprint/91310