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RESEARCH PAPER
Tarzan and chain: exploring the ICO jungle and evaluating
design archetypes
Nina M. Bachmann
1
&Benedict Drasch
2,3
&Gilbert Fridgen
2,3,4
&Michael Miksch
1
&Ferdinand Regner
1
&
André Schweizer
2,3
&Nils Urbach
2,3,5
Received: 16 July 2019 /Accepted: 1 February 2021
#The Author(s) 2021
Abstract
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.
Keywords Blockchain .ICO .Taxonomy .Archetypes .Success analysis
JEL classification G15 .G23 .O33
Introduction
Blockchain is a distributed ledger technology and enables
decentralized and transactional data-sharing across a network
of untrusted participants (Beck et al. 2016). The technology
emerged with the development of Bitcoin in 2008 (Fanning
and Centers 2016; Nakamoto 2008). Over the past few years,
blockchain has evolved into a multipurpose technology that
Responsible Editor: Roger Bons
During a large part of the research activities associated with this paper,
Gilbert Fridgen was Professor at the University of Bayreuth and Deputy
Director of both, the FIM Research Center and the Project Group
Business and Information Systems Engineering of the Fraunhofer FIT.
*Benedict Drasch
benedict.drasch@fim-rc.de; benedict.drasch@fit.fraunhofer.de
Nina M. Bachmann
nina.bachmann@tum.de
Gilbert Fridgen
gilbert.fridgen@fit.fraunhofer.de; gilbert.fridgen@fim-rc.de;
gilbert.fridgen@uni.lu
Michael Miksch
michael.miksch@tum.de
Ferdinand Regner
ferdinand.regner@tum.de
André Schweizer
andre.schweizer@fit.fraunhofer.de; andre.schweizer@fim-rc.de
Nils Urbach
nils.urbach@fit.fraunhofer.de; nils.urbach@fim-rc.de;
nils.urbach@fb3.fra-uas.de
1
FIM Research Center, University of Augsburg, Universitaetsstr. 12,
86159 Augsburg, Germany
2
Project Group Business & Information Systems Engineering of
Fraunhofer FIT, Wittelsbacherring 10, 95444 Bayreuth, Germany
3
FIM Research Center, University of Bayreuth, Wittelsbacherring 10,
95444 Bayreuth, Germany
4
SnT - Interdisciplinary Centre for Security, Reliability and Trust,
University of Luxembourg, 29 Avenue John F. Kennedy,
1855 Luxembourg, Luxembourg
5
Frankfurt University of Applied Sciences , Nibelungenplatz 1,
60318 Frankfurt am Main, Germany
Electronic Markets
https://doi.org/10.1007/s12525-021-00463-6
has attracted interest of both practitioners and academics in a
large number of use cases (Catalini and Gans 2018;Glaser
2017). Particularly, sales of blockchain-based digital tokens
(initial coin offerings / ICOs) are attracting much attention –as
a novel funding mechanism (Boreiko and Sahdev 2018;
Chanson et al. 2018; Drasch et al. 2020; Schweizer et al.
2017). Despite regulatory uncertainty (Amsden and
Schweizer 2018;LiandMann2018; Zetzsche et al. 2017),
ICO fundraising has grown exponentially throughout 2016
(29 ICOs worth $90 m), 2017 (875 ICOs worth $6227 m),
and 2018 (1253 ICOs worth $7812 m) (ICODATA.IO 2019;
Vigna et al. 2018). Although the value dropped significantly
in 2019, the ICO phenomenon’s novelty still raises a number
of questions that remain open (Chanson et al. 2018).
In particular, a systematic understanding of what exactly
constitutes an ICO is missing yet necessary to establish a
shared knowledge base. Given that the inherent idea of ICOs
is to provide open, global, and decentralized access to
funding, regulation of ICOs presents a previously unknown
challenge (Amsden and Schweizer 2018). Regulators and
many governmental institutions have just begun to act in the
mostly unregulated ICO market Bachmann et al. (2019). A
major problem is that although there were first approaches
of standardization, ICOs are still very heterogeneous (EFSA
2017). Further, market observations have shown that ICOs’
likelihoods of long-term success (token market performance)
differ significantly and may depend onICO design parameters
(Adhami et al. 2018; Amsden and Schweizer 2018;Boreiko
and Sahdev 2018; Fisch 2019). In this study, we understand
design parameters as the ICO issuer’s choices when designing
the ICO as a funding mechanism. These choices are compa-
rable to the IPO issuers (i.e., share pricing mechanism, share
allocation, date).
Similar to investments in cryptocurrencies (e.g., Bitcoin
and Ethereum), it remains unclear how beneficial ICOs are
in the short and long terms for both issuers and investors.
Thus, an in-depth analysis of ICO design variations is neces-
sary to better understand this phenomenon and to react appro-
priately from the economic, societal, and regulatory perspec-
tives. Research needs to provide a systematic knowledge base
(Beck et al. 2017), to identify relevant ICO design dimen-
sions, to derive predominant archetypes, and to thoroughly
analyze them. Yet, there have been very few scientific
studies in the young research stream on ICOs. Boreiko and
Sahdev (2018) provided an overview over the evolution of
ICOs, and Chanson et al. (2018) compared ICOs to traditional
crowdfunding mechanisms. Amsden and Schweizer (2018)as
well as Fisch (2019) have begun to analyze potential factors
that influence ICOs’likelihoods of success. Lee et al. (2018)
used data during token sales and analyzed the information
cascade within the investor crowd. However, although these
studies represent first important steps, we lack a comprehen-
sive and in-depth analysis of ICO archetypes and their
likelihoods of success. To address this research gap, we define
the following research questions:
1. What are the design parameters of ICOs as a novel
funding approach?
2. Which ICO archetypes exist and what design parameters
characterize them?
3. What performance characteristics differentiate the iden-
tified ICO archetypes?
We seek to answer these research questions in a multi-
method approach, which contains three phases. We build on
a manually compiled data sample of 131 ICOs collected from
a wide array of sources, including in-depth information on
ICO design parameter characteristics and publicly available
crypto-market performance data. To answer research question
1, we develop a taxonomy of empirically validated ICO de-
sign parameters. Taxonomies are well suited to structure the
groundwork for emerging research fields and serve as the first
step into systematization (Williams et al. 2008). We follow the
established and well-recognized taxonomy development
method proposed by Nickerson et al. (2013). To answer re-
search question 2, we build on our taxonomy and perform a
two-stage cluster analysis to inductively classify the 131 ICO
cases (Aldenderfer and Blashfield 1984;Hairetal.2013;
Ketchen and Shook 1996), utilizing the taxonomy’s 23 di-
mensions as clustering variables. As a result, we propose
and evaluate five ICOs archetypes and their prevailing dimen-
sions and characteristics. We answer research question 3 by
analyzing the identified ICO archetypes’secondary market
performance, following Smith + Crown’s(2017) research ap-
proach. To provide an outlook on token market performance,
we analyze, illustrate, and discuss the individuals’as well as
the archetypes’average short-term and long-term
developments.
Given that our results are based on a sample of 131 ICOs,
we would like to highlight that our findings will need further
research to evaluate their external validity. Research on the
ICO market is a moving target, and further ICOs and new
developments of included ICOs may reveal new information.
However, on this limited basis, we are still confident that we
can provide some interesting theoretical contributions and
practical implications. We develop a taxonomy that contrib-
utes to the descriptive knowledge of the young research do-
main, laying the foundation for further research and higher
theory in the area (Gregor 2006). Further, we propose empir-
ically derived archetypes obtained from a sound clustering
method. Owing to the given sample limitations, these arche-
types can only provide a first understanding of the ICO phe-
nomenon. However, we believe to propose potentially useful
insights for individuals and economic or regulatory organiza-
tions. Also, the analysis of the ICO archetypes’long-term
performance allows to understand some factors that may
N. M. Bachmann et al.
constitute a potentially successful ICO. We enable practi-
tioners and researchers to get to a systematic understanding
of this emerging phenomenon. Further, we allow practitioners
to conclude on concrete design suggestions for potential fu-
ture ICOs concerning existing archetypes.
Foundations
Blockchain and smart contracts
Blockchain isa computer protocol for decentralized and trans-
actional data-sharing across a large network of untrusted par-
ticipants (Xu et al. 2017). Public interest in the first generation
of blockchain was sparked when its role as the basis for
cryptocurrencies was discovered with the publication of the
Bitcoin whitepaper by Satoshi Nakamoto in 2008 (Nakamoto
2008; Zohar 2015). A second generation of blockchains, such
as Ethereum, came with a built-in Turing-complete program-
ming language that also provided a general-purpose program-
mable infrastructure that enables the use of smart contracts
(Buterin 2014). Smart contracts, a concept first introduced
by Nick Szabo in 1994, refer to programs that are executed
on a blockchain; these allow parties to securely transact with
one another without trust, as the correct execution of these
programs is enforced by a consensus protocol (Beck et al.
2016;Glaser2017; Sillaber and Waltl 2017;Szabo1997).
Another key characteristic is the creation and use of tokens
(Buterin 2014). Tokens are defined as digital units of account
that are transferable on the blockchain; they can serve several
purposes, such as currency functions or grant access to a ser-
vice (Glaser and Bezzenberger 2015; Schweizer et al. 2017).
Using blockchain as a decentralized IT infrastructure, smart
contracts to implement program logic, and tokens as transfer-
able digital assets, a wide range of use cases have emerged
(Conley 2017; Nærland et al. 2017), such as managing digital
assets, implementing trust-free asset trade, issuing tokens and
subcurrencies, and providing tokens in exchange for an invest-
ment (Buterin 2017; Nærland et al. 2017).
Initial coin offering: combining blockchain and
crowdfunding
Blockchain combined with crowdfunding enables a new phe-
nomenon: ICOs. The phenomenon was first called the Bitcoin
model for crowdfunding in 2014 and described as a new busi-
ness model for open-source software, where any participant in
a blockchain protocol can participate anonymously in the
funding, development, and revenue collection using tokens
(Ravikant 2014). Recently, ICOs have become a popular al-
ternative financing method for organizations (Arnold et al.
2019; Boreiko and Sahdev 2018; ICObench 2018; Li and
Mann 2018; Schweizer et al. 2017). Instead of giving
investors shares in a company, the general idea of is to dis-
tribute tokens as rewards for investments. Tokens’function-
ality depends on the implementation and differ between appli-
cations (e.g., granting access to a service or platform, voting
rights on strategic decisions). As the distribution of tokens
gives users partial ownership in a network and the possibility
to trade the tokens on secondary markets, it incentivizes both
joining the network early and benefiting from a potential ap-
preciation of the token price (Ehrsam 2016). This new and
completely decentralized approach relies solely on peer-to-
peer mechanisms and strongly contrasts to traditional
crowdfunding, where the matchmaking process between cam-
paign creators and potential investors is often established by
crowdfunding platforms or banks serving as the intermediary
(Danmayr 2014;Ehrsam2016;Haasetal.2015;Schweizer
et al. 2017). Currently, ICOs are used to fund the development
of blockchain-related projects, such as new protocols or apps.
Smart contracts enable the funding in advance, even before
thedefactostartofaproject(Ehrsam2016; Kuo Chuen
2017). According to the venture capitalist Ehrsam, the ICO
modeltofundprojectsinadvancecanalsohelptoovercome
the classic chicken and egg problem for networks (Ehrsam
2016).
The status quo of initial coin offering success analysis
Although ICOs are a very recent phenomenon and are associ-
ated with high uncertainty concerning their development, mar-
ket acceptance, and validity, various organizations (particular-
ly startups) prefer ICOs over traditional funding mechanisms
(Adhami et al. 2018). Research has only begun to address this
uncertainty by analyzing and evaluating ICOs’success.
Owing to the various ICO design options, it is hard to define
ICO success and associated measures, which differ signifi-
cantly between approaches.
Adhami et al. (2018) analyzed the determinants of a suc-
cessful ICO campaign and found that success is more likely if
the source code is available and when a token pre-sale is
organized. Further, the authors argue that ICOs ‘fail’for var-
ious reasons, such as failing the minimum funding goal, fail-
ing owing to a security flaw, low funding that results from a de
facto or a perceived scam, or the project promoters halting the
crowdsale (Adhami et al. 2018). Amsden and Schweizer
(2018) argued that the strongest measure of ICO success is
whether the token is subsequently listed and traded on an
exchange platform. Their approach builds on the assumption
that exchange platforms have sufficient mechanisms (e.g.,due
diligences) in place, and only list legit and promising tokens in
order to maintain their own reputation. Boreiko and Sahdev
(2018) proposed a diverging understanding of and approach to
measuring ICO success. The authors follow a two-step ap-
proach: First, they exclude fraudulent, postponed, dubious,
or cancelled ICO campaigns. Second, they classify ICOs into
Tarzan and chain: exploring the ICO jungle and evaluating design archetypes
top, failed, or average ICOs. In their approach, top ICOs reach
their funding limit before the end of the campaign (hard caps),
or raise more than the third quartile of all capped ICOs (no
hard cap), while failed ICOs are those that raise less than the
self-imposed minimum, and average ICOs summarize all
ICOs that don’t fit in the first or the second cluster.
Also, empirical research into ICOs is growing: Howell
et al. (2018) documented different yet key features of the
ICO structure in practice. Benedetti and Kostovetsky (2018)
reported evidence of significant ICO underpricing, and
resulting high returns, which are also consistent with high
compensation for high investment risk (Benedetti and
Kostovetsky 2018;Momtaz2018). Further, Momtaz (2018)
found that the management quality and the ICO profile are
positively correlated with the funding amount and returns,
whereas highly visionary projects had a negative effect. Lee
et al. (2018) used novel data during token sales and found that
the wisdom of the crowd overcomes the information
asymmetryassociatedwithanICO.Thisproceedsviaa
number of informed investors who verify the quality of the
underlying startup, and the crowd who then harnesses the
wisdom during the fundraising period. Hu et al. (2018)ana-
lyzed secondary market returns and correlations, providing
investment characteristics of 64 ICOs. Fisch (2019)drewon
signaling theory, applying a frequently used approach to indi-
cate investment success in traditional entrepreneurial finance
(Ahlers et al. 2015;Mollick2014) and business angel invest-
ments (Clercq and Dimov 2008; Cumming et al. 2005).
According to Fisch’s(2019) results, signals such as technical
whitepapers and high-quality source codes increase both the
amount raised and ICOs’likelihood of success.
These approaches are first valid steps toward a better un-
derstanding of evaluations of ICO success. However, while
previous studies such as Fisch (2019; 423 ICOs from 2016 to
2018) and Howell et al. (2018;1520ICOsfrom2017to2019)
relied on larger data samples, the observed ICO characteristics
were limited in their number and their coverage of ICO design
parameters. Further, previous studies considered the crypto
market as a whole, and none of the studies analyzed ICOs
characteristics as well as the performance of ICOs based on
an extensive differentiation of their design parameters. By
building our performance analysis on an empirically obtained
taxonomy with 66 characteristics, we aim to enrich existing
literature by exploring determinants of ICOs which have not
yet been included in previous studies. Simultaneously, by
grouping the ICOs into archetypes, we aim to provide a holis-
tic view on ICOs in order to complement previous perspec-
tives which studied the impact of single variables on ICO
performance. In summary, we seek to complement existing
knowledge with our study, relying on a multitude of in-
depth ICO design characteristics and a simultaneous consid-
eration of the crypto-market’s development. The blockchain
research organization Smith + Crown proposes a first
procedure that included this perspective via comparing the
performance of a specific token to Bitcoin and Ethereum
(Smith + Crown 2017). Comparing the token to the two big-
gest cryptocurrencies seems to be a valid approach, since
Bitcoin and Ethereum have proven to be indicators of the
overall market performance.
Research approach: three phases toward an
understanding of ICOs
We will now provide an overview over our multi-method
research approach which consists of three phases to reflect
our three research questions. In Table 1,weoutlinethethree
research phases. We conduct these iteratively and in a partly
overlapping way to cater for this research field’sdynamics.
Particularly, whenever we identified novel developments or
emerging ICO design parameters, we updated our previously
developed research artifacts. This approach is in line with
recent work by various researchers in the IS domain (Beck
et al. 2016; Nickerson et al. 2013; Schweizer et al. 2017).
In phase 1, we developed a taxonomy as a first step toward
structuring the emerging research domain of ICOs and
established a foundation for the subsequent research activities.
In phase 2, we performed a two-stage cluster analysis in line
with the IS literature and the exploratory research setting to
identify meaningful ICO archetypes (Haas et al. 2014;
Malhotra et al. 2005; Püschel et al. 2016). In phase 3, we
utilized the findings from phases 1 and 2 and applied Smith
+Crown’s(2017) market performance analysis to visualize
the ICO archetypes’market development. In the next section,
we will provide details on the data collection process, and then
go on by describing each phase’s methods and results in more
detail.
Data
Our 3-phase research approach requires extensive information
for each ICO. However, a reliable, objective, and universal
database does not yet exist (Fisch 2018). Thus, we first ex-
tracted a list of ICOs from Coindesk’sICOTracker
(Coindesk.com 2018) and retrieved 815 ICOs from various
industries and geographical regions between January 2013
and December 2018. Coindesk is a comparatively reputable
and comprehensive data source (Adhami et al. 2018;Chanson
et al. 2018) for ICOs. We conducted an iterative procedure to
compile our own database by gradually including ICO cases.
For each of the iterations, we randomly assembled ICOs from
the list of 815, and gathered information on the ICOs’design
parameters. Given the exploratory and iterative nature of the
taxonomy development, we kept only cases with exhaustive
information available.This means, we iteratively had to drop a
case as soon as we could not find one information that we had
N. M. Bachmann et al.
considered relevant in previous cases. In iteration 1, we went
through 315 randomly selected cases. After the first iteration,
our database contained 84 ICOs for which sufficiently exhaus-
tive information was available. In the second iteration, we
randomly selected 200 ICOs from the remaining 500 cases.
In this iteration, we added another 47 complete cases. In sum,
we analyzed 515 cases to manually gather our dataset of 131
ICOs. During the two iterations, two researchers collected and
classified each ICO case independently, using insights from
whitepapers, other documents (e.g., legal term sheets, media
releases, and specific websites to inform about an ICO), and
smart contract code where available. In case of disagreements
between the two researchers, they discussed the issue until
they achieved convergence. We acknowledge that this sample
presents a limitation in this study: the sample shapes our tax-
onomy (first research phase) and thereby impacts subsequent
research phases. However, owing to the high cost of collecting
the information, we stopped our data collection after these 131
complete cases because, at the end of the sampling, no new
information was obtained from adding ICOs. This concept is
described by Morse (1995) as saturation, or data adequacy.
Thus, in our view, the sampleserves as a sound foundation for
our purpose. Further, for data triangulation, and since ICOs
are a wholly novel phenomenon, we collected additional pri-
mary data in the form of interviews with experts (cf. Table 2)
(Perks et al. 2005). We chose a semi-structured interview ap-
proach to assure comparability and to preserve
explorativeness (Yin 2017). The interviews lasted between
30 and 45 min. We recorded and then analyzed them accord-
ing to scientific standards (Dexter 2006).
Finally, to be able to answer our third research question, we
additionally collected publicly available performance data for
our data sample of 131 ICOs. Of the original 131 ICOs, 19
were never publicly listed, resulting in 112 remaining cases
for the subsequent analysis in our study. To account for dif-
fering patterns over time (i.e., short-term rallies and long-term
development), we collected data on the short-term (1-month),
medium-term (6-month), and long-term (12-month) intervals
wherever available.
Methods and results
Research phase 1: ICO taxonomy development
A taxonomy is the result of a design science research approach
and can therefore be viewed as an artifact that consists of
dimensions that contain characteristics that are mutually ex-
clusive and collectively exhaustive (Nickerson et al. 2010). A
taxonomy seeks to lay the foundation for further research by
systematically classifying characteristics of objects of interest,
fostering understanding of a phenomenon (Glass and Vessey
1995). The classification process’focus allows for a system-
atic examination of the general principles and issues that un-
derlie a classification scheme. Equally important, taxonomies
can help to predict future development areas, similar to the
periodic table, which predicted the existence of elements de-
cades before they could be isolated (Glass and Vessey 1995).
Multitudes of scientific studies have successfully relied on the
creation or use of taxonomies to lay the groundwork for
emerging research fields. Recent examples include explor-
ative studies on cooperation between banks and FinTechs
(Drasch et al. 2018), cloud networks (Keller and König
2014), decentralized consensus systems (Glaser and
Bezzenberger 2015), smart things (Püschel et al. 2016), agile
IT setups (Jöhnk et al. 2017), and blockchain-based systems
Table 1 Three-phase research approach
Research phase 1 Research phase 2 Research phase 3
Research
question
▪What are the design parameters of ICOs? ▪Which ICO archetypes exist and what design
parameters characterize them?
▪What performance characteristics
differentiate the identified ICO archetypes?
Method ▪Taxonomy development following
Nickerson et al. (2013): Iterative taxon-
omy development with both
empirical-to-conceptual and
conceptual-to-empirical iterations
▪Two-stage cluster analysis based on
Balijepally et al. (2011): Hierarchical clus-
tering using Ward’s methods and nonhier-
archical clustering using the k-modes al-
gorithm
▪Validation: Pearson’sχ2 tests and Cramer’s
V index, pairwise post hoc tests
▪Success indication analysis based on Smith +
Crown (2017): Analysis of the secondary
market performance of individual ICOs and
of the archetypes for the 1-month, 6-month,
and 12-month intervals
Data ▪Qualitative data on 131 ICOs gathered
from whitepapers and additional
sources, including legal term sheets,
media releases, and websites
▪Interviews with six experts
Building on the data from phase 1:
▪Clustering objects: 131 ICOs
▪Clustering variables: the taxonomy’s
dimensions (n=23) with the respective
characteristics as values
Building on the data from phases 1 and 2:
▪Token sale market performance data for 112
real-world ICOs
▪Market performance data for Bitcoin and
Ethereum
Result ▪Literature-based, empirically shaped,
and evaluated taxonomy for ICO design
parameters with 23 dimensions and 66
characteristics
▪Five statistically verified ICO archetypes
based on their design parameters
▪Visualization and discussion of the
archetype’s performance
Tarzan and chain: exploring the ICO jungle and evaluating design archetypes
(Xu et al. 2017). In line with these role models, we follow the
iterative design-oriented taxonomy development method pro-
posed by Nickerson et al. (2013), which goes beyond the
traditional approach proposed by Bailey (1984). It integrates
conceptual and empirical perspectives into one comprehen-
sive method, fostering the iterative use of both paradigms
(Nickerson et al. 2013). The taxonomy development method
has seven steps: (1) determination of a meta-characteristic, (2)
determination of ending conditions, (3) choice of approach
(i.e., empirical-to-conceptual or conceptual-to-empirical), (4)
conceptualization of characteristics and dimensions, (5) exam-
ination of objects, (6) design (i.e., creation or revision of the
taxonomy), and (7) testing of the ending conditions. While the
researcherchooses the meta-characteristic and the ending con-
ditions at the start of the development process, several itera-
tions of taxonomy design and improvement follow (steps 3 to
6).
We defined our meta-characteristic as follows (step 1):
Design parameters and characteristics of ICOs as a novel
crowdfunding type. Further (step 2), as our ending conditions,
we define the eight objective and five subjective ending con-
ditions
1
as proposed by Nickerson et al. (2013). We then car-
ried out reciprocal empirical-to-conceptual and conceptual-to-
empirical iterations to develop the taxonomy (steps 3 to 7). In
empirical-to-conceptual iterations, we used a subset of our
ICO cases, examined them in detail to identify characteristics,
and, subsequently, grouped the characteristics into distinct
dimensions. The grouping step involves the creation of labels
for sets of related characteristics (Bailey 1994). In conceptual-
to-empirical iterations, we deducted characteristics and di-
mensions based on literature, for example on auction theory,
IPO processes, and crowdfunding, since this literature prom-
ises the identification of dimensions and characteristics rele-
vant to our meta-characteristic. We then examined our ICO
cases to verify these characteristics and dimensions’applica-
bility. Non-appropriate dimensions are eliminated. After each
iteration, we evaluated whether the current state of the taxon-
omy meets the ending conditions. During later iterations, we
conduct this evaluation with the help of expert interviews (see
Data section, Table 2), discussing the current state of the tax-
onomy. During the interviews, we thoroughly went through
each dimension of the current taxonomy and discussed the
integrity concerning both objective and subjective ending con-
ditions (Nickerson et al. 2013). This allowed us to evaluate the
proposed taxonomy based on real-world experience (empiri-
cal-to-conceptual) (Schultze and Avital 2011). After 14 itera-
tions, our taxonomy met the determined ending conditions.
Our research artifact is shown in Table 3. Overall, our
taxonomy consists of 23 relevant dimensions, encompassing
66 characteristics that we defined according to the specified
meta-characteristic. A detailed definition of the dimensions
and characteristics can be found in Fridgen et al. (2018)and
Bachmann et al. (2019).
Research phase 2: identifying ICO archetypes
To identify prevailing ICO archetypes, we performed a cluster
analysis, in line with the IS literature and the exploratory re-
search setting (Haas et al. 2014; Malhotra et al. 2005; Püschel
et al. 2016). A cluster analysis is a statistical technique that
seeks to group similar entities into various clusters. It mini-
mizes the within-group variance while maximizing the
between-group variance (Aldenderfer and Blashfield 1984;
Hair et al. 2013). Generally, cluster analysis is applicable to
describe generic archetypes of entities (Everitt et al. 2011;
Hair et al. 2013). An analysis of 55 IS articles indicated that
this method is often chosen to classify observations of specific
objects of interest (Balijepally et al. 2011). Our cluster analy-
sis consists of three steps: (i) Selection of the clustering vari-
ables; (ii) determination of an appropriate cluster algorithm;
and (iii) confirmation of the results’reliability and validity
through the application of statistical methods.
The selection of clustering variables is a fundamental step
in cluster analysis, because it strongly affects the outcome
(Punj and Stewart 1983). Following a deductive approach
(Ketchen et al. 1993), the chosen variables must be closely
linked to extant theory (Ketchen and Shook 1996), which is
why selected our taxonomy’s both empirically and conceptu-
ally developed dimensions as clustering variables. Note that
this choice may potentially lead to an overweighting of under-
lying constructs among the dimensions if clustering variables
are correlated (which we expect when searching for arche-
types) (Ketchen and Shook 1996). We therefore conducted a
multiple correspondence analysis (MCA). We obtained low
eigenvalues for the resulting factors, which is why we kept all
23 taxonomy dimensions as clustering variables.
After the selection of the cluster variables, we selected an
appropriate clusteringalgorithm. We applied a two-stage clus-
tering algorithm, combining advantages of both
1
Among others, the fundamental objective ending conditions include that all
dimensions are mutually exclusive and collectively exhaustive, and that all
objects in the sample have been examined (Nickerson et al. 2013).
Subjective ending conditions are conciseness, robustness, comprehensiveness,
extendibility and explanatority. For reasons of brevity, we refer for further
details to Nickerson et al. (2013) and Fridgen et al (2018).
Table 2 Expert interviews
Current position and experience
1 Board member of cryptocurrency community –ICO investor
2 Academic researcher –ICO advisor, ICO researcher
3 Consultant –ICO advisor
4 Attorney –ICO advisor
5 Academic researcher –ICO researcher
6 Academic researcher –ICO researcher
N. M. Bachmann et al.
nonhierarchical and hierarchical procedures, to improve the
clustering performance and to get more accurate and
reproduceable results (Aldenderfer and Blashfield 1984;
Ketchen and Shook 1996; Milligan and Sokol 1980; Punj
and Stewart 1983). This approach is also supported by various
IS research experts (Balijepally et al. 2011). In this the two-
stage clustering process, the clustering algorithm starts with a
hierarchical clustering. We applied Ward’s method, which is
the most commonly applied algorithm among the hierarchical
methods (Balijepally et al. 2011) owing to the production of
reliable cluster results (Haas et al. 2014; Malhotra et al. 2005;
vandeVrandeetal.2009). For the distance measure between
categorical data points, the literature recommends using the
Jaccard, the simple matching, and the Dice distance measures
(Berkhin 2006; Finch 2005). We tested different measures and
found that they all produce very similar results (Haas et al.
2014). We then inspected the dendrograms and the scree-plots
that result from the hierarchical clustering with the Jaccard,
the simple matching, and the Dice distance measures, to de-
termine the appropriate number of clusters (Aldenderfer and
Blashfield 1984). This step revealed that five clusters repre-
sent the favorable number of clusters, since any additional
cluster would not significantly lower the total within-cluster
sum of squares. The clustering dendrogram with the Jaccard
Distance Measure is presented in Fig. 1.Further,wecomputed
the average silhouette width and the gap statistic (Tibshirani
et al. 2001) which both confirmed the five-cluster solution.
As a second stage in the two-stage clustering algorithm, we
then conducted nonhierarchical clustering. IS researchers
widely use the k-means approach with Euclidean distance
measure (Balijepally et al. 2011). However, research indicates
that k-means is not the optimal approach to process categori-
cal data, since Euclidean distances are not meaningful on a
discrete sample space (Chaturvedi et al. 2001). Thus, Huang
(1998) proposed a nonhierarchical clustering algorithm called
k-modes, using a simple dissimilarity measure and substitut-
ing the means of the clusters with modes (Chaturvedi et al.
2001; Xu et al. 2017). K-modes, similar to k-means, requires
the pre-specification of the number of clusters, Herein, we use
the results from the first stage as input, and therefore set the
number of clusters to five. The application of the k-modes
algorithm to the dataset resulted in our five final clusters
which we then define as our five archetypes.
Finally, for validation purposes, Hair et al. (2013) sug-
gested finding significant differences between the clusters
among their respective characteristics of the clustering vari-
ables (i.e., dimensions). We therefore conducted a cross-
tabulation analysis to identify which variables significantly
Table 3 Taxonomy of ICOs
Dimension Characteristics
Token implementation level on-chain native sidechain
Token purpose/type usage work funding staking equity security non-equity security
Token supply growth fixed adaptive inflation fixed inflation
Token supply cap capped uncapped
Token burning yes no
Token distribution deferral yes no
Token holder voting rights yes no
Issuing legal structure foundation limited
Team company token share minority majority half
Team lockup period no single period multiple periods
Pre-sale before ICO no private public both
Pre-sale discount yes no
Planned occurrence multiple rounds single round unspecified
Registration needed yes no
Eligibility restrictions none geographic accreditation multiple
Purchase amount limit none minimum maximum both
Auction mechanism yes no
Sales price fixed floating
Price fixing currency crypto fiat
Funding currency crypto both none
Funding cap none hard cap soft cap multiple
Time horizon block time fixed date open end
Time-based discount none single rate multiple rates
Tarzan and chain: exploring the ICO jungle and evaluating design archetypes
contribute to the differentiation of clusters (i.e., archetypes)
(Everitt et al. 2011). We applied Pearson’sχ
2
and Cramer’s
V, which measure for a relationship’s strength, to analyze
global differences across all clusters in the categorical data
points (Haas et al. 2014; Malhotra et al. 2005). We then ran
post hoc tests for a pairwise comparison of single clusters to
one another, using Pearson’sχ
2
with correction for alpha in-
flation (Bonferroni style).
The main results of the cluster analysisare summarized and
displayed in Table 4. First, based on the cluster analysis, we
propose five prevailing ICO archetypes. Second, the results
also indicate the validity of the taxonomy as the basis of our
analysis, since the values indicate significant contributions of
the characteristics. The χ
2
reported significant values for most
cluster variables, and the Cramer’s V reported medium to
strong association. The exceptions reflected some sales terms
variables, i.e., the funding currency and the fixing of the price,
closely related to the auction mechanism, as well as two time-
related sales terms. Little information was gained from these
variables, and there was low variation among clusters. We
also conducted the clustering without these variables and re-
ceived nearly identical results. Thus, we kept the variables in
the taxonomyso as to avoid information loss (Soh and Markus
2002), since we perceive them as important dimensions in the
characterization.
Research phase 3: the ICO archetype performance
analysis
Research phase 3 investigated the development of the arche-
types to provide an outlook on both single cases and on the
overall archetype development. In line with existing studies,
this research phase drew on the token value performance in
the secondary market (Amsden and Schweizer 2018; Smith +
Crown 2017). Smith + Crown’s(2017) approach considers
the volatility and the idiosyncrasies of the crypto market and
combines and extends previous research attempts. It extends
Amsden and Schweizer’s(2018) approach, since it not only
evaluates if a token is listed on an exchange platform, but also
analyzes the performance over time. Further, it integrates
Boreiko and Sahdev’s(2018) idea by excluding dubious
ICOs prior to the analysis.
Since the cryptocurrency ‘gold rush’heavily influenced
the entire token market development between 2016 and 2018
(Amsden and Schweizer 2018), there is also the necessity to
account for these market specificities. Thus, we compared
the ICOs’market performances to those of cryptocurrency
with identical time intervals. Bitcoin and Ethereum served as
the main representatives. Combining these perspectives and
following our research objectives, the following analysis
consists of four steps and is structured as follows. In step 1,
as a basis for the subsequent steps, we visualize the devel-
opment of single ICOs with regard to their archetype. In step
2, we aggregate the return rate for all ICOs within one arche-
type to compute the mean return rate for the overall arche-
type. This allows for the indicative analysis of the short-term,
medium-term, and long-term development of each arche-
type. In step 3, we compare the mean return rate for each
archetype to the Bitcoin and Ethereum return rate during the
same period. Thus, the development is more realistic and
accounts for market specificities. Finally, in step 4, we cal-
culate the relative and absolute number of ICO cases in each
archetype that performed better/worse than the mean of our
entire sample, and that performed better/worse than Bitcoin
and Ethereum in the identical period. As a first step, an out-
look on the development of single ICOs with regard to their
archetype is provided. Figures 2,3, and 4illustrate the 1-
month, 6-month, and 12-month development of all 112
ICOs. The archetype assignment is visualized by color and
the ICOs are chronically plotted according to their issuing
date.
In step 2, we calculated the mean return rate for each ar-
chetype (Table 5). We aggregated these three return rates for
each archetype and each interval as follows. We calculated
each ICO’s token value development for the aforementioned
periods. We then aggregated the 1-month, 6-month, and 12-
month intervals and calculated a mean return rate for the ar-
chetype per interval. Figure 5provides an exemplary calcula-
tion, where we aggregated an exemplary archetype which
consists of three ICOs.
The results indicated a positive and increasing secondary
market performance in the 1-month, 6-month, and 12-month
intervals for Archetypes 1 and 2. Archetype 4 indicated an
increasingly negative development throughout all three inter-
vals. Overall, the sample’s return rate increased from the 1-
month to the 6-month to the 12-month interval. However (see
Table 5), since the subsample sizes for Archetypes 3 and 5
were very small (<20), we focused on Archetypes 1, 2, and 4
Fig. 1 Dendrogram after hierarchical clustering with the Jaccard distance
measure
N. M. Bachmann et al.
Table 4 The results of the cluster analysis
Dimension
(i.e., clustering variable)
Archetype Significance tests
1(n= 42) 2 (n=42) 3 (n=16) 4 (n=21) 5 (n=10) X
2a
Cramer
V
b
Pairwise post hoc tests
c
Token implementation
level
on-chain (83%) on-chain(86%) on-chain (63%) on-chain (90%) on-chain (80%) 9.34 0.189 3–4*
Token purpose type usage
token(67%)
usage token
(74%)
usage token
(63%)
usage token (76%) usage token (80%) 13.77 0.162
Token supply growth fixed (83%) fixed (90%) fixed (69%) fixed (86%) fixed (80%) 5.88 0.15
Token supply cap capped (86%) capped (98%) capped (69%) capped (100%) capped (80%) 14.12** 0.328** 2–3**; 3–4*
Token burning no (88%) no (60%) no (94%) yes (81%) no (70%) 36.67*** 0.529*** 1–2**; 1–4***; 2–3*; 2–4*; 3–4***; 4–5*
Token distribution
deferral
yes (71%) no (88%) no (50%) yes (81%) no (80%) 43.49*** 0.576*** 1–2***; 1–5**; 2–3***; 2–4***; 4–5**
Token holder voting
rights
no (83%) no (74%) no (63%) no (86%) no (100%) 7.1 0.233*
Issuing legal structure limited (86%) limited (95%) foundation
(50%)
limited (76%) limited (90%) 18.24** 0.373** 1–3*; 2–3***
Team company token
share
minority (93%) minority (98%) minority (88%) minority (95%) minority (100%) 9.16 0.187
Team lockup period single period
(45%)
no (52%) no (81%) multiple periods
(48%)
multiple periods
(50%)
28.31** 0.268* 1–2*
Pre-sale before ICO private (64%) no (45%) no (88%) private (43%) public (70%) 66.28*** 0.411** 1–2***; 1–3***; 1–5**; 2–3*; 2–4*; 3–4***;
3–5***; 4–5*
Pre-sale discount yes (76%) no (62%) no (88%) yes (81%) yes (80%) 32.48*** 0.498** 1–2***; 1–3***; 2–4**; 2–5*; 3–4***; 3–5**
Registration needed yes (76%) yes (86%) no (100%) yes (95%) no (60%) 53.93*** 0.642*** 1–3***; 2–3***; 2–5**; 3–4***; 3–5*; 4–5**
Eligibility restriction none (45%) geographic
(57%)
none (100%) geographic (67%) none (80%) 38.28*** 0.312** 1–3**; 2–3***; 2–5*
Planned occurrence single round
(83%)
single round
(93%)
single round
(56%)
single round (95%) single round (70%) 24.61** 0.307** 1–3*; 2–3**; 3–4*
Purchase amount limit none (74%) none (67%) none (81%) minimum (67%) minimum (60%) 38.93*** 0.315** 1–4***; 1–5**; 2–4***; 2–5*; 3–5*
Sales price fixed (86%) fixed (95%) fixed (81%) fixed (95%) fixed (70%) 7.35 0.237*
Price fixing currency crypto (67%) fiat (64%) crypto (81%) crypto (62%) fiat (80%) 18.34** 0.374** 1–2**; 1–5*; 2–3**; 3–5**
Funding currency crypto (83%) crypto (81%) crypto (94%) crypto (81%) both (70%) 17.42** 0.365** 1–5**; 2–5**; 3–5**; 4–5*
Funding cap hard cap (64%) multiple (83%) none (56%) multiple (81%) hard cap (70%) 87.97*** 0.473** 1–2***; 1–3***; 1–4***; 2–3***; 3–4***; 3–5**
Time horizon fixed date (81%) fixed date (98%) fixed date (63%) fixed date (100%) fixed date (80%) 18.68* 0.267* 1–2*; 2–3**; 3–4**
Auction mechanism no (90%) no (100%) no (94%) no (100%) no (100%) 6.88 0.229*
Time-based discount no (67%) multiple rates
(69%)
multiple rates
(50%)
multiple rates
(67%)
multiple rates
(70%)
31.62*** 0.347** 1–2***; 1–4***; 1–5**; 2–4*
*p≤0.05; ** p≤0.01; *** p≤0.001
a
Percentages in one cluster that show a given characteristic
b
Threshold *** v > = 0.5; ** v > = 0.3; * v > = 0.2
c
Post hoc significance between single clusters are tested
using Pearson’sχ2
Tarzan and chain: exploring the ICO jungle and evaluating design archetypes
for the further analysis and do not report any quantitative
analyses for Archetypes 3 and 5 (Tables 6and 7).
Step 3: To account for the market volatility in the
cryptocurrency market during the past months, we analyzed
the mean performance of the ICO archetype along with the
two most important cryptocurrency values in the same period
(Table 6).
The results revealed that, in the short term (1-month return
rate), Archetype 1 and 2 ICOs had better average returns than
Bitcoin and Ethereum in the same periods. Archetype 4 ICOs
revealed negative short-term average return rates, while both
Bitcoin and Ethereum had slightly better return rates. In the 6-
month return rate comparison, Archetype 1 and 2 ICOs had
more positive average return rates than the market
representatives. The return rates for Archetype 4 and
Ethereum remained negative, whereas Bitcoin turned from
almost negative (1-month interval) to positive (6-month
interval) in the same period. In the long term (12-month inter-
val), the return rates for Archetype 1 and 2 ICOs continued to
increase and outperformed Bitcoin and Ethereum. The aver-
age return rate for Archetype 4 ICOs remained negative and
decreased even further. In the same period, Bitcoin increased
its return, while Ethereum remained at a similar negative level.
A key factor to consider is the all-time high of Ethereum in
January 2018 (January 13, 2018: $1385.02) compared to one
year before (January 13, 2017: $9.78).
Step 4: Building on the results from step 3, we compared
the number of ICOs for every archetype. Thus, we counted the
-100%
-50%
0%
50%
100%
150%
200%
250%
300%
350%
400%
Jan-15 Jul-15 Jan-16 Jul-16 Jan-17 Jul-17 Jan-18 Jul-18 Dec-18 Jun-19 Dec-19
Archetype 1 Archetype 2 Archetype 3 Archety pe 4 Archetype 5
Fig. 2 Visualization of the
singular ICO short-term absolute
performance (1 month; no aggre-
gation, ICOs assigned to their
initial issuing date)
-100%
0%
100%
200%
300%
400%
Jan-15 Jul-15 Jan-16 Jul-16 Jan-17 Jul-17 Jan-18 Jul-18 Dec-18 Jun-19 Dec-19
Archetype 1 Archetype 2 Archety pe 3 Archetype 4 Archety pe 5
Fig. 3 Visualization of the
singular ICO medium-term abso-
lute performance (6 months; no
aggregation, ICOs assigned to
their initial issuing date)
N. M. Bachmann et al.
-100%
0%
100%
200%
300%
400%
Jan-15 Jul-15 Jan-16 Jul-16 Jan-17 Jul-17 Jan-18 Jul-18 Dec-18 Jun-19 Dec-19
Archetype 1 Archetype 2 Archetype 3 Archetype 4 Archetype 5
Fig. 4 Visualization of the
singular ICO long-term absolute
performance (12 months; no ag-
gregation, ICOs assigned to their
initial issuing date)
Table 5 The archetype token value return rates and the number of cases (1, 6, and 12 months after the ICO)
Total cases
(excluded)
1-month return rate in %
(number of cases)
6-month return rate in %
(number of cases)
12-month return rate in %
(number of cases)
Archetype 1 42 (3) 43.8% (39) 204.2% (37) 417.6% (33)
Archetype 2 42 (8) 19.8% (34) 102.5% (30) 135.4% (24)
Archetype 3 16 (1) −22.5% (15) 412.9% (15) 223.0% (15)
Archetype 4 21 (2) −17.8% (19) −65.2% (18) −71.6% (16)
Archetype 5 10 (5) 20.6% (5) 66.2% (5) 52.8% (4)
Sample 131 (19) 16.1% (112) 152.2% (105) 211.3% (92)
ICO#1
1month;
+20%
6months;
+100%
12 months;
+100%
ICO#2
1month;
+6%
6months;
-25%
12 months;
-6%
ICO#3
1month;
+50%
6months;
+100%
12 months;
+150%
tnme
pol
e
ve
d
e
u
l
a
Vn
e
koT
Time
Fig. 5 Calculations of the tokens’
and the archetype’smeanreturn
rates after the ICO (1 month,
6 months, and 12 months)
Tarzan and chain: exploring the ICO jungle and evaluating design archetypes
number of ICOs that indicated better development than
Bitcoin or Ethereum. Further, we compared each ICO to our
sample’s mean return rate, and again counted the number of
ICOs in each archetype that had higher or lower return rates
than our sample mean. Table 7summarizes the percentage of
each archetype’s ICOs that performed better than Bitcoin,
Ethereum, and the entire sample.
The results revealed that between 12% and 34% of the
Archetype 1 ICOs and between 25% and 39% of the
Archetype 2 ICOs obtained better average return rates than
Bitcoin, Ethereum, or the entire sample, depending on the
interval length, and with decreasing proportions over time.
Overall, throughout all intervals and all comparison measures,
Archetype 2 indicated higher proportions of overperforming
ICOs than Archetype 1. Similar to archetypes 1 and 2, the
proportion of overperforming ICOs of Archetype 4 decreased
over time. For this archetype, all ICOs’return rates in the 6-
month and 12-month intervals were below the sample’s
average.
Table 6 Return rates per archetype and the corresponding return rates for bitcoin and ethereum
1-month return
rate (in %)
6-month return
rate (in %)
12-month return
rate (in %)
Archetype 1
Token return rate 43.9% 204.2% 417.6%
Bitcoin return rate 21.7% 132.3% 143.7%
Ethereum return rate 10.6% 123.2% 287.5%
Archetype 2
Token return rate 19.8% 102.5% 135.4%
Bitcoin return rate 7.4% 40.1% 64.3%
Ethereum return rate 2.7% 13.8% 85.1%
Archetype 4
Token return rate -17.8% -65.2% -71.6%
Bitcoin return rate -3.6% 5.6% 42.1%
Ethereum return rate -16.1% -36.2% -35.7%
-100%
0%
100%
200%
300%
400%
500%
1 Month 6 Months 12 Months
Development [%]
Archetype 1
Bitcoin
Ethereum
-100%
0%
100%
200%
300%
400%
500%
1 Month 6 Months 12 Months
Development [%]
Archetype 2
Bitcoin
Ethereum
-100%
0%
100%
200%
300%
400%
500%
1 Month 6 Months 12 Months
Development [%]
Archetype 4
Bitcoin
Ethereum
N. M. Bachmann et al.
Discussion
We will now first discuss the results for each archetype sepa-
rately; second, we will provide anecdotal evidence to visualize
each archetype; third, we will combine the insights and will
discuss the implications in a broader context.
Archetype 1: the visionary ICO
Archetype 1 (see Table 8) is one of the two large (42 cases)
clusters in the sample. It may offer multifaceted value prop-
ositions for investors who are truly interested in the issuer’s
business development and who are willing to engage in the
initiative. The archetype’s implementation level is mostly
on-chain (83%), with only few cases with native (14%) or
sidechain solutions (2%). Two-thirds of the ICOs had pro-
posed usage tokens (67%), followed by staking tokens com-
mon (19%). In nearly all cases (93%), the development team
received a minority of the tokens, and only for 76% of the
cases was there a single or multiple lockup period, securing
the long-term pursuit of objectives. Further, the tokens were
not immediately distributed to the buyers after the ICO.
Generally, distribution deferrals and lockups prevent the re-
sale of tokens directly after the closing of the ICO, which
stabilizes the post-ICO token price (Lee et al. 2018). Token
supply growth was predominantly fixed (83%) and the sup-
ply was capped (86%). Usually, registration is needed
(76%), and the time horizon for the sale was set to a fixed
date (81%). A private or public pre-sale (83%) allowed the
issuer to raise funding prior to the regular sale. The team can
then focus on developing the product early, whereas the early
investors benefit from a discount. Thus, we conclude that
this ICO archetype goes beyond being just a funding mech-
anism, but targets investors that truly believe in the business
model and in its long-term success.
The overall average return rate of the visionary ICO arche-
type was the best of all the archetypes –it ranged from 43.8%,
to 204.2%, to 417.6% for the 1-month, 6-month, and 12-
month periods. Further insights on the influences of charac-
teristics regarding the ICO campaign, the venture, or technol-
ogy capabilities on the raised amount of funding are discussed
by Fisch (2019).
Anecdotal evidence: SALT lending (SALT)
The SALT lending platform allows users of blockchain assets to
lever their holdings as collateral for cash loans. It is the first asset-
backed lending platform to give blockchain asset holders access
to liquidity without them having to sell their tokens. Thus, it
bridges the gap between crypto-assets and conventional assets.
The SALT ICO distributed a usage token, with fixed token sup-
ply growth and a capped token supply. Token holders have no
voting rights. The token share for the team is minor and the team
lockup period is set to a single period. There was a pre-sale with
discount before the ICO.
Archetype 2: the average ICO
Archetype 2 (see Table 9) represents the other large cluster (42
cases) in our sample. Based on its characteristics, which re-
semble the patterns of a traditional crowdfunding campaign,
we perceive this archetype as the most typical (average) one
when considering ICOs as a novel funding approach. Based
on an existing blockchain, the issuer raises a capped amount
of funding (98%) for mostly on-chain (86%) usage tokens
(74%), staking tokens (12%), or funding tokens (7%).
Tokens are immediately distributed after the ICO. Further,
the announcement of a clear funding target range (83%) con-
veys the message that the issuer intends to raise an amount
aligned with the expected network development costs
(Buterin 2017). In many cases, the whitepaper specifies that
all funds are returned to investors if the ICO fails to reach the
soft cap (Amsden and Schweizer 2018). This design charac-
teristics reduces the investor’s risks and indicates that the team
Table 7 The proportion of ICOs per archetype that performed better than bitcoin, ethereum, and the overall sample
1-month relative value (in %) and
the absolute number
6-month relative value (in %) and
the absolute number
12-month relative value (in %)
and the absolute number
Archetype 1 compared to…Bitcoin 30.8% 12/39 24.3% 9/37 12.1% 4/33
Ethereum 34.2% 13/38 22.2% 8/36 21.9% 7/32
a
Entire sample 25.6% 10/39 18.9% 7/37 12.1% 4/33
Archetype 2 compared to…Bitcoin 35.3% 12/34 26.7% 8/30 20.8% 5/24
Ethereum 38.2% 13/34 30.0% 9/30 25.0% 6/24
Entire sample 35.3% 12/34 20.0% 6/30 16.7% 4/24
Archetype 4 compared to…Bitcoin 21.1% 4/19 11.1% 2/18 6.3% 1/16
Ethereum 36.8% 7/19 27.8% 5/18 18.8% 3/16
Entire sample 10.5% 2/19 0.0% 0/18 0.0% 0/18
a
For listings in 2013 or 2014, a comparison to the Ethereum return rate was not possible (Ethereum available starting on 30.07.2015)
Tarzan and chain: exploring the ICO jungle and evaluating design archetypes
closely links its funding to the development costs. Since this
archetype does not transfer voting rights or company shares to
the token holders (74%), it tends to target investors who are
interestedin the de facto use case (i.e., the access to a provided
service or platform) rather than for instance investment returns
or decision rights.
Archetype 2’s average return rate was positive for all three
intervals and increased from the 1-month to the 6-month to the
12-month interval. As noted, Archetype 2 focuses on a col-
laborative setup and fairness (lockup period, return of funds in
case of failure). Thus, for both the issuer and investor, the ICO
is directed toward long-term success rather than very high
short-term return rates. Especially in cases where issuers and
investors want to closely work together, this archetype seems
agoodfit.
Anecdotal evidence: BLOCKv (VEE)
The BLOCKv platform enables the creation and emission of
crypto-objects on a blockchain. The VEE token serves as a
utility token to fuel any transaction on the platform. The to-
kens are implemented as ERC20 tokens, and the contract caps
the total supply of VEE tokens. The tokens first get sold in a
pre-sale, followed by a main sale, without a specific upper or
lower limit of the purchase amount. Both sales are capped and
have a fixed time horizon. The tokens allocated to the team are
locked for multiple periods.
Archetype 3: the liberal ICO
Differences regarding the technical token terms predominant-
ly characterize Archetype 3 (16 cases) (see Table 10), since it
covers on-chain (63%) and native (38%) tokens. While many
tokens use the ERC20 token standard from the Ethereum
blockchain, native ICOs distribute tokens that are native to
their own blockchain. These tokens are often referred to as
protocol tokens. They can be used as simple currency and in
other use cases, such as a stake to participate in a network
(19%). The developers often seek to create novel use cases
based on these tokens. These innovative features appear to
aim at overcoming challenges of existing blockchain solu-
tions, such as scalability (Porru et al. 2017). The ICOs show
comparably less governance from issuers regarding sales
terms and issuer terms. Archetype 3 seeks to maximize the
target group of prospective buyers, since it does not require
prior registration (100%), has no eligibility restrictions
Table 8 The distribution of characteristics within each dimension for Archetype 1 (n=42)
Dimension Characteristics
Token implementation level on-chain (83%) native (14%) sidechain (2%)
Token purpose/type usage (67%) work (7%) funding (7%) staking (19%) equity (0%) non-equity (0%)
Token supply growth fixed (83% adaptive inflation (10%) fixed inflation (7%)
Token supply cap capped (86%) uncapped (14%)
Token burning yes (12%) no (88%)
Token distribution deferral yes (71%) no (29%)
Token holder voting rights yes (17%) no (83%)
Issuing legal structure foundation (14%) limited (86%)
Team company token share minority (93%) majority (2%) half (5%)
Team lockup period no (24%) single period (45%) multiple (31%)
Pre-sale before ICO no (17%) private (64%) public (17%) multiple (2%)
Pre-sale discount yes (76%) no (24%)
Planned occurrence multiple rounds (17%) single round (83%) Unspecified (0%)
Registration needed yes (76%) no (24%)
Eligibility restrictions none (45%) geographic (36%) accreditation (5%) multiple (14%)
Purchase amount limit none (74%) minimum (12%) maximum (5%) both (10%)
Auction mechanism yes (0%) no (100%)
Sales price fixed (86%) floating (14%)
Price fixing currency crypto (67%) fiat (33%)
Funding currency crypto (83%) both (17%)
Funding cap none (10%) hard cap (64%) soft cap (2%) multiple (24%)
Time horizon block time (10%) fixed date (81%) open end (10%)
Time-based discount none (67%) single rate (14%) multiple rates (19%)
N. M. Bachmann et al.
Table 9 The distribution of characteristics within each dimension for archetype 2 (n= 29)
Dimension Characteristics
Token implementation level on-chain (86%) native (12%) sidechain (2%)
Token purpose/type usage token (74%) work token (2%) funding token (7%) staking token (12%) equity token (2%) non-equity token (2%)
Token supply growth fixed (90%) adaptive inflation (7%) fixed inflation (2%)
Token supply cap capped (98%) uncapped (2%)
Token burning yes (40%) no (60%)
Token distribution deferral yes (12%) no (88%)
Token holder voting rights yes (26%) no (74%)
Issuing legal structure foundation (5%) limited (95%)
Team company token share minority (98%) majority (0%) half (2%)
Team lockup period no (54%) single period (22%) multiple periods (24%)
Pre-sale before ICO no (45%) private (14%) public (29%) multiple (12%)
Pre-sale discount yes (38%) no (62%)
Planned occurrence multiple rounds (7%) single round (93%) unknown (0%)
Registration needed yes (86%) no (14%)
Eligibility restrictions none (31%) geographic (57%) accreditation (7%) multiple (5%)
Purchase amount limit none (67%) minimum (21%) maximum (0%) both (7%)
Auction mechanism yes (0%) no (100%)
Sales price fixed (95%) floating (5%)
Price fixing currency crypto (36%) fiat (64%)
Funding currency crypto (81%) both (19%) none (0%)
Funding cap none (0%) hard cap (17%) soft cap (0%) multiple (83%)
Time horizon block time (2%) fixed date (98%) open end (0%)
Time-based discount no (29%) single rate (2%) multiple rates (69%)
Tarzan and chain: exploring the ICO jungle and evaluating design archetypes
(100%), and does not come with a purchase amount limit
(81%). This indicates that the tokens are sold liberally on a
first-come, first-served basis without favoring wealthy or in-
stitutional investors. The ICO issuer grants voting rights to its
investors in some cases (37%); thus, these investors can par-
ticipate and influence the ongoing development of the initia-
tive. Another peculiarity of this archetype is the structuring of
the issuing legal entity as the foundation for half the cases.
Also, this archetype partially includes ICOs planning multiple
funding rounds (44%) instead of just a single round, thereby
allowing for additional financing rounds when needed. This is
similar to venture capital practices, where funding traditional-
ly takes place in multiple rounds: after the seed round, the
founders can prove their concept before raising additional
funds for company growth (Davila et al. 2003). A conse-
quence is that the issuing team remains incentivized to achieve
ongoing success, since it directly affects subsequent funding
rounds. This is why blockchain experts also believe that an
iterative funding approach could be the future of ICOs
(Petrovcic 2017).
Archetype 3’s characteristics are very liberal, since the ICO
gets along with close to no restrictions(no registration; no pre-
sale; no purchase amount limit; first-come, first-served). This
is particularly interesting, since our empirical analysis indicat-
ed that liberal and less regulated ICOs reveal better develop-
ment on average, and thus the higher indication for success,
which stands in contrast to the current public opinion that calls
for strongerregulation. The results suggest that the liberal idea
behind ICOs to provide open, global, and decentralized access
to funding is successful. Even at this early stage, liberal ICOs
are able to retain the inherent value proposition and offer an
alternative to conventional funding mechanisms.
Anecdotal evidence: Golem (GNT)
Golem is a decentralized supercomputer that can be accessed
by anyone. The system consists of the combined power of
users’machines, from personal PCs to entire datacenters.
Golem uses an Ethereum-based transaction system to clear
payments. It is the first truly decentralized supercomputer
and creates a market for computing power by connecting com-
puters in a peer-to-peer network. Golem’sICOwasliberal,
with no token burning, no distribution deferral, no pre-sale,
and no eligibility restriction.
Table 10 The distribution of characteristics within each dimension for archetype 3 (n=16)
Dimension Characteristics
Token implementation
level
on-chain (63%) native (38%) sidechain (0%)
Token purpose/type usage token
(63%)
work token
(13%)
funding token
(6%)
staking token
(19%)
equity token
(0%)
non-equity token
(0%)
Token supply growth fixed (69%) adaptive inflation (13%) fixed inflation (19%)
Token supply cap capped (69%) uncapped (31%)
Token burning yes (6%) no (94%)
Token distribution deferral yes (50%) no (50%)
Token holder voting rights yes (38%) no (63%)
Issuing legal structure foundation (50%) limited (50%)
Team company token
share
minority (88%) majority (13%) half (0%)
Team lockup period no (81%) single period (6%) multiple periods (13%)
Pre-sale before ICO no (88%) private (13%) public (0%) multiple (0%)
Pre-sale discount yes (13%) no (88%)
Planned occurrence multiple rounds (31%) single round (56%) Unknown (13%)
Registration needed yes (0%) no (100%)
Eligibility restrictions none (100%) geographic (0%) accreditation (0%) multiple (0%)
Purchase amount limit none (81%) minimum (19%) maximum (0%) both (0%)
Auction mechanism yes (6%) no (94%)
Sales price fixed (81%) floating (19%)
Price fixing currency crypto (81%) fiat (19%)
Funding currency crypto (94%) both (6%) none (0%)
Funding cap none (56%) hard cap (45%) soft cap (5%) multiple (30%)
Time horizon block time (25%) fixed date (63%) open end 13%)
Time-based discount no (44%) single rate (6%) multiple rates (50%)
N. M. Bachmann et al.
Archetype 4: the compliant ICO
The prevailing pattern in Archetype 4 (21 cases) (see
Table 11) represents the regulatory orientation of the ICO
design. By burning the unsold on-chain tokens (100%)
post-ICO (81%), the issuer keeps the token allocation per-
centages between the issuer and the investors stable.
Usually, the token burning benefits the token holders,
since it decreases the total number of available tokens,
and thus may increase the value of each individual token
(Ferrara 2017). In 100% of the ICOs, the token supply is
capped. Regarding the sales terms, the issuer has more
information and more control over the investors, since they
need to register before they can purchase tokens (95%).
Additionally, pre-defined purchase limits restrict the token
sale (minimum 67%, minimum and maximum 19%).
Defining a minimum purchase amount can prevent a frag-
mentation of the token ownership, while limiting the max-
imum purchase amount can enhance a wider distribution of
the tokens, preventing a token concentration.
The average return rate of Archetype 4 ICOs was negative
for the short-term, medium-term, and long-term intervals.
Whereas in the short-term interval, more than 20% of the
Archetype 4 ICOs still had higher return rates than Bitcoin
or Ethereum, this proportion strongly decreased throughout
the medium and long terms. Further, none of the Archetype
4 ICOs developed better than the sample’s average return rate
after 6 months. Compared to the Archetypes 1 and 2, that
focused on a positive and specific issuer and investor collab-
oration, or Archetype 3, that gets along with a very liberal
setup, the design of a very restrictive ICO seems to have
drawbacks. Thus, we conclude that the design of ICO
Archetype 3, more than others, considers the current regula-
tory uncertainty and seeks to comply with potential upcoming
ICO regulations. This is also in line with several who warn of
the downsides of the strict regulating of ICOs (Amsden and
Schweizer 2018;LiandMann2018).
Anecdotal evidence: 0xcert (ZXC)
0xcert is a framework with a set of on-chain and off-chain
rules for managing Xcerts and other standard nonfungible
tokens. Xcerts represent opinionated nonfungible tokens,
which also hold an imprint of an asset. With the 0xcert
Table 11 The distribution of characteristics within each dimension for archetype 4 (n=21)
Dimension Characteristics
Token implementation
level
on-chain (90%) native (0%) sidechain (5%)
Token purpose/type usage token
(76%)
work token
(14%)
funding token
(10%)
staking token
(0%)
equity token
(0%)
non-equity token
(0%)
Token supply growth fixed (86%) adaptive inflation (10%) fixed inflation (5%)
Token supply cap capped (100%) uncapped (0%)
Token burning yes (81%) no (19%)
Token distribution deferral yes (81%) no (19%)
Token holder voting rights yes (14%) no (86%)
Issuing legal structure foundation (24%) limited (76%)
Team company token
share
minority (95%) majority (0%) half (5%)
Team lockup period no (24%) single period (29%) multiple periods (48%)
Pre-sale before ICO no (10%) private (43%) public (29%) multiple (19%)
Pre-sale discount yes (81%) no (19%)
Planned occurrence multiple rounds (5%) single round (95%) Unknown (0%)
Registration needed yes (95%) no (5%)
Eligibility restrictions none (29%) geographic (67%) accreditation
(0%)
multiple (5%)
Purchase amount limit none (14%) minimum (67%) maximum (0%) both (19%)
Auction mechanism yes (0%) no (100%)
Sales price fixed (95%) floating (5%)
Price fixing currency crypto (62%) fiat (38%)
Funding currency crypto (81%) both (19%) none (0%)
Funding cap none (0%) hard cap (14%) soft cap (5%) multiple (81%)
Time horizon block time (0%) fixed date (100%) open end (0%)
Time-based discount no (14%) single rate (19%) multiple rates (67%)
Tarzan and chain: exploring the ICO jungle and evaluating design archetypes
protocol, one can validate proof of existence, authenticity,
and ownership of these digital assets without third-party
involvement. 0xcert offers the ZXC usage token. These
are fungible tokens that comply with Ethereum’sERC-20
standard. 0xcert is an open-source project that strives to be
community-driven, and a decentralized governance model
can also be introduced. The 0xcert ICO was restrictive in
its characteristics. Token supply growth was fixed, and
token supply was capped. The remaining tokens were
burnt, and registration was needed prior to the sale.
Archetype 5: the fundraising ICO
The number of ICOs in Archetype 5 (see Table 12)was
fairly low. Differences regarding the sales terms predomi-
nantly characterize Archetype 5 (10 cases). The issuing
legal organization is limited in 90%, and the team receives
minority token shares (100%). The token price is fixed in
fiat currency, which can be expected, owing to less fluctu-
ation than cryptocurrencies. While a minimum contribu-
tion was set in 60% of the ICOs, no eligibility criteria
restricted the participation in 80% of the cases. At the same
time, the issuer accepts both fiat currency and
cryptocurrencies, whereby they may ease the participation in
the ICO to crypto-novices. This may further be sponsored via
a discounted public pre-sale (70%). Further, for most
Archetype 5 ICOs, the issuer offers time-based discounts (sin-
gle rate 10%, multiple rates 70%). There is a hard funding cap
(70%), and the time horizon is fixed (80%). Nonetheless, ow-
ing to our very small cluster size, we opted not to go into
further performance analysis for this archetype, leaving it as
an interesting subject to future research.
Anecdotal evidence: Tradelize (TDZ)
Tradelize provides an ecosystem and platform for the trading
of crypto-assets. Users can spend their tokens as an internal
means of payment to access the platform’s services. During
the ICO, the token is priced at 1$, with multiple discount rates
during the pre-sale as well as during the main sale. The issuers
installed a hard cap and asked for a minimum contribution by
investors. While the distribution of the tokens to the investors
was deferred for two weeks, no lockup periods applied to the
team.
Table 12 Distribution of characteristics within each dimension for archetype 5 (n=10)
Dimension Characteristics
Token implementation
level
on-chain (80%) native (20%) sidechain (0%)
Token purpose/type usage token
(80%)
work token
(0%)
funding token
(10%)
staking token
(10%)
equity token
(0%)
non-equity token
(0%)
Token supply growth fixed (80%) adaptive inflation (10%) fixed inflation (10%)
Token supply cap capped (80%) uncapped (20%)
Token burning yes (30%) no (70%)
Token distribution deferral yes (20%) no (80%)
Token holder voting rights yes (0%) no (100%)
Issuing legal structure foundation (10%) limited (90%)
Team company token
share
minority (100%) majority (0%) half (0%)
Team lockup period no (30%) single period (20%) multiple periods (50%)
Pre-sale before ICO no (20%) private (0%) public (70%) multiple (10%)
Pre-sale discount yes (80%) no (20%)
Planned occurrence multiple rounds (30%) single round (70%) Unknown (0%)
Registration needed yes (40%) no (60
Eligibility restrictions none (80%) geographic (10%) accreditation (0%) multiple (10%)
Purchase amount limit none (20%) minimum (60%) maximum (10%) both (10%)
Auction mechanism yes (0%) no (100%)
Sales price fixed (70%) floating (30%)
Price fixing currency crypto (20%) fiat (80%)
Funding currency crypto (70%) both (30%) none (0%)
Funding cap none (0%) hard cap (70%) soft cap (0%) multiple (30%)
Time horizon block time (10%) fixed date (80%) open end (10%)
Time-based discount no (20%) single rate (10%) multiple rates (70%)
N. M. Bachmann et al.
Key findings
Our three research phases, i.e., taxonomy development, clus-
ter analysis, and performance analysis of the ICO archetypes,
allow us to derive three key findings:
1) A taxonomy provides a structure for ICOs
To answer research question 1, we focused on the
identification and evaluation of ICO design parameters.
To achieve this, we followed the taxonomy development
method of Nickerson et al. (2013) and proposed a taxono-
my for ICOs that has 23 dimensions and 66 characteristics
and therefore integrates the relevant proportion of the nec-
essary ICO design parameters. For both primary and sec-
ondary data, no further dimensions or characteristics were
necessary. The taxonomy describes ICOs in detail, and can
be expanded further if necessary (e.g., owing to changes in
the ICO market). Further, the taxonomy depicts an explan-
atory artifact that helps one to understand the details in the
ICO market.
2) Archetypes cluster similar kinds of ICOs
Building on the findings of research questions 1 and 2, we
applied a clustering approach to identify five ICO archetypes.
The five archetypes differ concerning value propositions, tar-
get groups, and existing challenges. We clustered ICOs that
follow a visionary idea, feature the average crowdfunding
idea, reveal liberal characteristics, show especially compliant
setups, or a fundraising enclosed scope. Further, we examined
these clusters and presented a qualitative interpretation for
each archetype. We were able to classify existing real-world
cases to one of our five archetypes and identified within-
cluster similarities. For investors and founders who are inter-
ested in proposing an ICO, understanding the archetypes can
be a great starting point for future endeavors.
3) Indicative performance analysis provides an outlook on
ICO development
Based on our understanding of the ICO market, we com-
bined our results from research phases 1 and 2 with secondary
data and provided an outlook on ICO and archetype develop-
ment. We also accounted for market specificities. Further, we
differentiate between archetypes and compare the average
ICO performance in the short, medium, and long terms.
Overall, our findings incorporate important aspects in the
discussion about ICOs generally, their potential to become a
commodity funding alternative, and the regulation of ICOs.
Besides the need for regulation to protect investors, the issuer
needs a certain ability and freedom to determine the condi-
tions of an ICO. This might enable the issuer to conceptualize
an ICO that fits both the issuer and the investors.
Consequently, this freedom could create the opportunity to
incorporate collaboration between issuer and investor that
goes beyond the financial aspect and contributes to long-
term success of a blockchain business model. Based on our
findings, we expect that the design and the regulation of ICOs
might require the breaking of new ground and may include
some uncertainty. However, according to our findings, it also
leads to more successful ICOs and ultimately to better funding
for novel and innovative ideas, which then support the econ-
omy and society.
Conclusion and outlook
An ICO as a novel funding mechanism represents a very
promising example of a blockchain use case that has recently
drawn much attention in both research and practice. Although
first research projects analyzed specific aspects of the emerg-
ing phenomenon, we still have a poor understanding of the
implications of ICOs. In this research paper, we bridged this
gap and investigated ICOs concerning their design parame-
ters, predominant archetypes, and their short-term and long-
term token value developments.
Before outlining our contributions to both research and
practice, we will acknowledge limitations and will highlight
promising starting points for future research. First, we limited
our sampling procedure to ICOs with exhaustive data avail-
able so as to allow for comprehensive structuring according to
the taxonomy’s dimensions and characteristics. Owing to the
high effort of the data collection, the small sample size limits
the generalizability of our results. This affects the taxonomy,
and, consequently, the clusters as well as the results in re-
search phase 3. Thus, future research should focus on ap-
proaches that allow for an exhaustive inclusion of ICO cases,
and should even seek to focus on subsets where all cases are
included in the analysis. A valid approach could also be to
condense the research question, so that more ICOs can be
included. Note, however, that although we excluded some
ICOs, our definition of success and the study perspective were
dedicated to including all the available data. Nonetheless, our
manual data gathering approach enabled this research to draw
first conclusions form real-world data. Second, we only ad-
dressed ICO design parameters, rather than other ICO aspects
which have been examined in previous crowdfunding litera-
ture, such as the business model, industry, or the quality of
marketing. However, since our focus was on deriving arche-
types, our results constitute a valid and enlightening first ap-
proach toward the goal of understanding ICO patterns.
Nonetheless, these aspects could be subject to further research
that may help us to better understand the ICO phenomenon.
Third, the ICO market is very dynamic and most ICO issuers
are startups. Token sale models are constantly evolving,
Tarzan and chain: exploring the ICO jungle and evaluating design archetypes
leading to dynamic emergences of novel ICO design patterns.
In this context, we further acknowledge that the identification
of the five archetypes was limited by the selected sample, and
the addition of new ICOs to the sample and to the clustering
could result in slightly modified archetypes. However, since
the ICO market is constantly changing, our research reflects
current developments in the ICO market. Fourth, clustering
methodology has certain natural limitations (Hair et al. 2013)
since it produces a non-inferential solution which heavily de-
pends on the selection of the clustering variables, similarity
measures and algorithms (Balijepally et al. 2011). However,
we are confident that our chosen methodology reduces the
danger of producing unstable results, since we apply the rec-
ommended two-stage clustering (e.g., Balijepally et al. 2011),
and since our clustering variables “emanate from past research
[…and are] consistent with the objectives of the study”
(Balijepally et al. 2011,p.377).
Our theoretical contributions addressed the research gap in
four ways: First, we have provided a systematic overview over
predominant ICO designs. Thus, we suggested five ICO ar-
chetypes with different value propositions, target groups, and
challenges. These archetypes abstract from single peculiarities
of specific ICOs, enabling generalizable propositions. Second,
the archetypes extend existing classifications of ICOs by var-
ious aspects and allow for generalizable findings, instead of
considering single characteristics. Third, we have laid the
foundation for further research in the area of ICOs. Since the
archetypes were theoretically grounded on an existing taxon-
omy and were empirically verified, they provide a more sys-
tematic and in-depth perspective on the phenomenon. This
will help to synthesize research into ICOs and opens future
promising research avenues. Further, we have built on the
existing knowledge and have combined different approaches
in order to provide an outlook on the ICO and archetype de-
velopment on the secondary market. Future research can build
on these insights and can propose additional research projects.
Fourth, our findings of ICO archetypes are crucial for the
research into ICO and blockchain governance issues, since
they allow one to derive the impacts of different governance
configurations.
Moreover, our research provides practitioners with various
backgrounds and perspectives on the ICO phenomenon. First,
with our proposed taxonomy, we provide a classification
scheme that allows one to comprehensibly structure this com-
plex domain. Second, the classification into predominant ar-
chetypes may provide structured guidance for ventures that
plan to conduct an ICO. Thus, our taxonomy and archetypes
allow one to reduce complexity in the heterogeneous ICO
market. Third, from an investor perspective, the archetypes
can lead to more informed and grounded investment deci-
sions. Additionally, for traditional financial intermediaries,
including early-stage venture capitalists or crowdfunding plat-
forms, the taxonomy and archetypes may help to characterize
potential competitors. Our analysis of the ICO archetypes may
help regulators and government institutions to perform regu-
latory tasks more effectively.
Funding Open Access funding enabled and organized by Projekt
DEAL. Supported by PayPal and the Luxembourg National Research
Fund FNR, Luxembourg (P17/IS/13342933/PayPal-FNR/Chair in DFS/
Gilbert Fridgen).
Appendix
Ticker Cluster Issue date
1ST 1 28-Sep-16
3 DC 2 24-Sep-19
ABL 2 14-Aug-18
ABYSS 2 08-Jun-18
ADB 2 04-Feb-18
ADM 2 18-Jan-19
AEN 4 06-Apr-19
AISI 5 no trading
ANS 1 09-Sep-16
ANT 2 18-May-17
ARR 2 18-Jun-19
AST 1 17-Oct-17
ASTRO 2 17-Nov-17
BAT 3 01-Jun-17
BCK 1 no trading
BITX 4 01-Aug-18
BNT 4 22-Jun-17
BPL 1 10-Nov-17
BST 2 05-Jun-19
CAN 2 08-Jan-18
CFI 1 19-Jun-17
CHI 4 08-Nov-18
CRBT 4 17-Sep-18
CRON 2 02-Sep-19
CRV8 2 no trading
CSM 1 27-Jul-18
CVC 1 17-Jul-17
DACC 2 27-Jul-18
DANK 5 no trading
DATA 1 03-Nov-17
DCT 3 02-Jul-17
DENT 3 13-Aug-17
DFN 3 no trading
DGCT 2 no trading
DGD 3 18-Apr-16
DOOH 2 no trading
N. M. Bachmann et al.
DOT 1 15-Feb-15
DREAM 4 24-May-19
DTX 1 11-Jul-18
DTx2 2 11-Jul-18
ELY 4 19-Jul-18
eMTV 2 no trading
EQUI 1 24-May-17
ESS 1 07-Jul-18
ETH 3 07-Aug-15
ETKN 4 27-Jun-18
FIL 1 13-Dec-17
FTM 1 30-Oct-18
FXP 4 28-Dec-18
GBT 4 no trading
GNO 1 01-May-17
GNT 3 18-Nov-16
GoC (former ELI) 4 04-Aug-18
GOT 1 05-Jul-18
GRFT 2 09-Mar-18
GXC 1 25-Jun-17
HGT 1 12-Oct-17
ICN 2 30-Sep-16
IMT 2 31-Aug-18
INCX 1 02-Aug-18
IOTA 3 13-Jun-17
KNC 1 24-Sep-17
Komodo KMD 3 06-Feb-17
LCS 2 25-Jul-18
LDX (LeadRex) 4 13-Mar-18
LENDO 2 no trading
LKK 1 14-Nov-16
LSK 1 06-Apr-16
Lunes 5 15-Mar-19
MAID 3 28-Apr-14
MANA 5 17-Sep-17
MAS 4 27-Sep-18
MASP 3 27-Sep-18
MCO 2 03-Jul-17
MET 3 26-Jun-18
MGX 1 09-Nov-19
Minter 1 no trading
MKR 3 20-Dec-17
MLN 5 22-Feb-17
MOD 2 23-Oct-17
NEU 4 29-Dec-17
OLT 2 12-Jul-18
OMG 1 14-Jul-17
OMNI 3 25-Dec-13
OOT 4 12-May-18
ORBS 1 03-Apr-19
PAY 1 08-Jul-17
PIX 1 25-Sep-17
PXLT 2 11-Jul-19
QBX 1 19-Jul-19
QNT 2 11-Aug-18
RDN 1 08-Nov-17
REP 3 27-Oct-15
RSK 1 05-Dec-18
S 2 11-Oct-18
SALT 1 29-Sep-17
SAN 1 12-Jul-17
Scorum 2 02-Sep-19
SHA 1 03-Apr-19
SHR 1 29-Nov-19
SILK 5 16-Oct-18
SNT 2 28-Jun-17
SQR 2 10-Jun-19
STM 5 05-Oct-18
STORJ 1 02-Jul-17
STORM 1 20-Dec-17
T2T 1 no trading
TDZ 5 no trading
TERN 1 27-Jul-18
TERN2 5 no trading
TEZ 3 02-Oct-17
TGAME 4 18-Jul-18
TKLN 2 04-Sep-19
TNG 2 no trading
TNT 1 27-Aug-17
UBT 2 21-May-18
UBX 4 14-Aug-18
UMT 5 no trading
UP 2 21-Mar-18
VEE 2 28-Nov-17
VID 4 28-Aug-19
VIDT 2 06-Apr-19
VIN 4 07-Aug-18
VIRT 2 no trading
VITO 2 no trading
VLR 4 no trading
WGP 1 09-May-19
WTL 2 19-Oct-18
XBASE 2 26-Mar-19
ZRX 2 16-Aug-17
ZXC 4 12-Jul-18
Tarzan and chain: exploring the ICO jungle and evaluating design archetypes
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