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PACIS 2021 Proceedings Paci8c Asia Conference on Information
Systems (PACIS)
7-12-2021
The Tokenization of Everything: Towards a Framework for The Tokenization of Everything: Towards a Framework for
Understanding the Potentials of Tokenized Assets Understanding the Potentials of Tokenized Assets
Roger Heines
University of St. Gallen
, roger.heines@student.unisg.ch
Christian Dick
University of St. Gallen
, christian.dick@alumni.unisg.ch
Christian Pohle
University of St. Gallen
, christian.pohle@hsgalumni.ch
Reinhard Jung
University of St. Gallen
, reinhard.jung@unisg.ch
Follow this and additional works at: https://aisel.aisnet.org/pacis2021
Recommended Citation Recommended Citation
Heines, Roger; Dick, Christian; Pohle, Christian; and Jung, Reinhard, "The Tokenization of Everything:
Towards a Framework for Understanding the Potentials of Tokenized Assets" (2021).
PACIS 2021
Proceedings
. 40.
https://aisel.aisnet.org/pacis2021/40
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Towards a Framework for Understanding Asset Tokenization
Twenty-fifth Pacific Asia Conference on Information Systems, Dubai, UAE, 2021
1
The Tokenization of Everything: Towards a
Framework for Understanding the Potentials of
Tokenized Assets
Completed Research Paper
Roger Heines
University of St.Gallen,
St.Gallen, Switzerland
roger.heines@bei-sg.ch
Christian Dick
University of St.Gallen,
St.Gallen, Switzerland
christian.dick@alumni.unisg.ch
Christian Pohle
University of St.Gallen,
St.Gallen, Switzerland
christian.pohle@hsgalumni.ch
Reinhard Jung
University of St.Gallen,
St.Gallen, Switzerland
reinhard.jung@unisg.ch
Abstract
By enabling a new way to digitize transactions, distributed ledger technology allows to
fundamentally change how value is digitally issued, transferred, and stored. Accordingly,
«tokenization» refers to the concept of creating a singular identifier on a distributed ledger
in terms of a token that may represent anything from financial assets, goods, to other
valuable resources. Where tokenization may disrupt our economic system leading to more
efficiency or democracy, it is required to gain insights and facilitate the development of use
cases associated with this concept. To illustrate how firms can apply tokenization to
innovate their businesses, we propose a framework of different token properties, drivers,
and barriers for adoption based on literature and expert interviews and present eight
archetypical cases derived from an analysis of 129 ventures. This work provides strategic
guidance in a token economy and a starting point for future research of viable applications.
Keywords: Tokenization, Token Economy, Distributed Ledger Technology
Introduction
Without relying on mediation by trusted third parties, distributed ledger technology (DLT) provides a
new technological paradigm in the operation of highly available, tamper-resistant distributed databases
for transactions (Beck et al. 2017). Besides widely discussed implications for innovative information
systems, the concept of «tokenization» has emerged in recent years by following DLT’s basic abilities
to enable a system for the management of asset ownership of unique digital representations (Hrga et al.
2020). In this context, tokenization refers to the process of creating a singular identifier on a distributed
ledger in form of a token. A unique and persistent reference can be established to digitally represent
anything that ranges from financial assets and goods to other valuable resources (Harwood-Jones 2019).
It is assumed that the issuance, transfer, and storage of token on decentralized platforms reduce the
drawbacks of intermediaries (e.g., single points of failures, lagging processing times). Especially the
digital representation of bankable assets (e.g., stocks, bonds) has become a promising use case, where
the Financial Times estimates that DLT-driven market infrastructures may save asset managers up to
$2.7 billion per year just in the process of buying and selling funds (Mooney 2018).
Towards a Framework for Understanding Asset Tokenization
Twenty-fifth Pacific Asia Conference on Information Systems, Dubai, UAE, 2021
2
Where many projects focus on the issuance of tokenized financial assets (Sazandrishvili 2020), it is also
assumed that tokenization may serve as an enabler for new services and frictionless collaboration in a
novel type of economy, a so-called token economy (Sunyaev et al. 2021). Accordingly, tokenization
can be extended and utilized for many other purposes within a system and is not primarily limited to
the transfer of ownership of tradeable objects. Unique tokens might be used as a means of tracking and
tracing for a more transparent product lifecycle (e.g., food, pharmaceuticals) (Madhwal 2020). The
access to services can be tokenized, letting the holder use a car sharing platform once a specific token
is acquired. Organizations can issue a limited amount of tokens for licensing digital content or other
digitized resources (Zhou et al. 2019). In each of these cases the role and reason for using tokenized
assets can differ (e.g., improved transparency, increased liquidity) and extends the perspective on
individual token design (e.g., native token, non-fungible token) (Oliveira et al. 2018). Although the
technical feasibility of the concept has been broadly tested at prototype stage (e.g., transfer of bonds),
expected adoption in established industries has not kept pace with the huge amounts of investments
(Grilo and Zutshi 2020). While everything from art, real estate even oceans or stars may be tokenized,
risks and legal aspects of use cases have to be considered (Sazandrishvili 2020). Decision makers are
required to align their activities by identifying not only potentials but also weaknesses of a DLT-project
(Naqvi and Hussain 2020). To the best of our knowledge, existing decision-aid tools (e.g., token
standards) have fallen short to take these factors into account and lack strategic guidance for successful
use case design and token-based business model development (Harwood-Jones 2019).
Especially for practitioners, the possibilities to create novel products and services seem endless and add
complexity in assessing new business opportunities. For this concept to work, knowledge is required
that defines what a token offers to represent, what tokenization enables, and which decentralized
platform requirements have to be considered (Zachariadis et al. 2019). Previous research associated
with tokenization focus mainly on two separated streams: Approaches to describe a token and general
applicability of tokenization. The first topic deals with a range of different classifications to distinguish
cryptoassets from one another. Some publications establish a common knowledge base for
categorization and design (Freni et al. 2020; Oliveira et al. 2018) but neglect the study of broader
implications. With regards to the second topic, there are only limited contributions that investigate value
drivers or barriers. Mostly, they refer to industry-specific applications in banking (Sazandrishvili 2020)
or supply chain management (Babich and Hilary 2020). It remains unclear how to address applicability
between specific tokenized objects (e.g., financial assets, physical goods) in a particular scenario (e.g.,
increased liquidity, reduced costs). To cover the wide range of use cases offered by a token economy
and identify the role of a token as part of an operating and business model, it is required to unify both
research streams. By extending the understanding beyond descriptive token design, we address criteria
for an assessment framework to support organizations in this decision-making and selection process
through the following research question: What are criteria for assessing asset tokenization use cases
and which archetypical asset tokenization use cases exist?
We applied a three-step research approach to develop a framework and to derive archetypes of this
application area. At first, we carried out a literature review and analysis (Braun and Clarke 2006;
Webster and Watson 2002) to establish an understanding about token-based solutions. Secondly, we
conducted semi-structured interviews with 22 participants (e.g., C-level executives, product managers
from Banking and FinTechs) following the recommendations by Ayres (2008) to refine the findings for
our final framework. Lastly, we sampled a database of 129 firms in this field and derived eight
empirically founded archetypes on basis of the identified criteria.
With our study, we contribute to practice as the framework allows a first assessment of use cases and,
thus, facilitates decision making for the selection of viable applications. We contribute to research by
synthesizing existing approaches and generating new means through empirical data. Therefore, our
research serves as a foundation for new solutions and adoption towards a token economy. The remainder
of this work is organized as follows. First, we introduce the domain background and current state of
research. Second, we explain our research design applied in the development of the framework and
derivation of archetypical use cases. Third, we present the identified criteria for assessment and
generated archetypes. Finally, we discuss our findings, implications for both practice and research, as
well as limitations, and present an outlook for future research.
Towards a Framework for Understanding Asset Tokenization
Twenty-fifth Pacific Asia Conference on Information Systems, Dubai, UAE, 2021
3
Background
The Tokenization Concept
Many transactions involve trusted third parties as a necessity to authenticate a transfer of ownership
(e.g., international bank transfers) between unknown participants. These operations are often associated
with increased costs, time-consuming processes, and the exposure to a single point of failure. To
overcome such challenges, DLT can be utilized to ensure the integrity of transactions in a decentral
manner. Basically, it enables an append-only distributed database characterized by high tamper
resistance in untrustworthy environments, known as byzantine fault tolerance (Kannengießer et al.
2020). Cryptocurrencies represent a known application, where its features allow a peer-to peer transfer
of ownership without challenging the legitimacy of the transaction through a third party (Nakamoto
2008). According to new DLT frameworks (e.g., Ethereum), advanced features enabled a new form of
crowdfunding in the past. So-called, initial coin offerings (ICO), introduced “tokens” to be sold in
exchange for cryptocurrencies to investors seeking public investments for their company in return (Roth
et al. 2019). As a known concept in non-DLT systems, tokens are utilized within closed environments
such as casino chips, laundry credits or for IT access (Oliveira et al. 2018). Thus, DLT-based token
further developed into an instrument to digitize anything that may represent an asset, utility or
ownership due to their usefulness in their respective field. Pilkington (2015) highlights the purpose of
tokens as ideal value containers for divisibility, ease-of-use, and facilitated trade. In this context, the
concept of asset tokenization has gained growing attention. Research and practice defines the term and
its surrounding environment in various, sometimes conflicting ways. Where Babich and Hilary (2020)
mention tokenization to create ownership rights for trade facilitation across the supply chain, Zhou et
al. (2019) addresses the concept to convert data for a secure handling in an IT-system. In fact, the terms
‘digital assets’ and ‘cryptoassets’ are also synonymously used to describe token that are mostly
associated with an investment purpose (OECD 2020). Where an asset is primary defined as anything
that has value to a certain stakeholder (Greer 1997), it is important to gain a deeper understanding of
asset tokenization that goes beyond the mere representation of financial and monetary value.
According to the various meanings, tokenization can be initially described as the process of creating a
token on a shared ledger in terms of a singular identifier that enables a unique and persistent reference.
Dependent on the features of the underlying DLT, the token relies on a specific data structure and
implemented logic to achieve a desired functionality (Roth et al. 2019). As everything may be
represented by creating such reference, we define a token as a cryptographically secured digital
representation of value or contractual rights (Distefano et al. 2020). A token may be then distinguished
by serving as a bearer instrument (e.g., liability between issuer and owner), once a legal relationship is
established and a specific bearer is assigned to that right (e.g., ownership rights embedded into a smart
contract). By adding individual properties of an asset into a token, it can be designed to be unique,
tradeable, scarce, and much more. From a practical perspective, a difference must be further drawn
between tokenized assets that exist “off-chain” and “on-chain”. The tokenization of a “physical asset”
that exists “off-chain” relies on an underlying object (e.g., digital twin of a car). Such systems differ
from native record keeping in which a “native” token is built “on-chain”. It is only existent within the
system and derives its value in and of themselves (e.g., Bitcoin) (OECD 2020).
Applicability of Tokenization
There is a substantial body of knowledge that mainly refers to grey literature, but only few scientific
contributions investigate the distinct driver and barrier for adoption. Narayan and Tidström (2020)
explore the usage of tokens in building a circular economy to facilitate coopetition. In this context,
tokenization creates an effective incentive mechanism to generate ideas and scale up innovation
between disconnected product platforms. Lotti (2019) investigates the features of tokenization for the
art market and its potential for disintermediation. By rethinking the social relations and interactions in
art production, art tokens may create new opportunities for digital design incentivized by so-called
crypto economics. Beside these benefits in governance, the adoption of tokenization is primarily
motivated by economic reasons. By overcoming the drawbacks related to costly intermediation, the
concept improves the security of business processes and increases the usability in transaction handling.
Towards a Framework for Understanding Asset Tokenization
Twenty-fifth Pacific Asia Conference on Information Systems, Dubai, UAE, 2021
4
Zhou et al. (2019) converts medical data into on-chain tokens to establish a safe and efficient data
exchange. Babich and Hilary (2020) apply digital claim tokens in supply chain management for
production, inventory, and financial controlling that enables a facilitated sharing, trading, and exchange
among multiple stakeholders. Especially a reduced need for third parties is considered highly disruptive
for the financial sector and other industries (e.g., intellectual property, collectibles) (Sazandrishvili
2020). Indicated by the surge of projects and initiatives, most references focus on digital representations
and management of asset ownership using tokens for existing bankable assets. As a corollary of DLTs
basic capabilities, the possibilities for automation (e.g., smart contracts), transparent record keeping,
and trusted reconciliation between parties are widely discussed in the context of financial market
infrastructures (Hartung et al. 2019). Shtybel (2019) explores the benefits of tokenised private securities
for improved issuance, trading, and settlement. Also, the impact of tokenized assets on liquidity for
otherwise untradable asset classes is highlighted (Harwood-Jones 2019). Although the opportunities for
DLT and asset tokenization seem promising, often new obstacles before mass adoption may occur.
While some publications state that technical immaturity leads to use cases stuck at prototyping phase
(Hughes et al. 2019), other contributions assume that challenges in terms of governance, regulation, and
operations need to be overcome to reach commercial adoption (Harwood-Jones 2019). Especially legal
aspects and transition risks relating to the issuance of digital securities must be appropriately evaluated
against nationally binding law (Savelyev 2018). Existing research on the applicability of tokenization
mainly focuses on general potentials and challenges and is limited to a few selected applications with a
strong emphasis on the financial sector. While various industries may benefit from this concept, it still
remains unclear how to address these multifaceted use cases. Here, it would be appreciated to establish
a common understanding and to provide a basis for strategic guidance in this highly innovative area.
Token Properties
Although the potentials of cryptocurrencies have been extensively discussed in literature, there is still
a lack of consensus on what a token might represent (OECD 2020). To close this gap, a review has been
conducted to highlight relevant token properties. Oliveira et al. (2018) proposes a token classification
with four dimensions: purpose, governance, functional, technical groupings based on previous research
and empirical data. Another categorization is presented by Euler (2018), where token are allocated along
five groupings: purpose, utility, legal status, underlying value, and technical layer. According to these
classifications, archetypes were further identified by mapping tokens against those parameters (Oliveira
et al. 2018). A comprehensive approach of specific attributes and features is presented by Freni et al.
(2020). With regards to a morphological framework, the shift from economics towards a token economy
and the token’s key role within these ecosystems is analyzed. A stronger industry focus is presented by
integrating token characteristics into traditional finance (Ankenbrand et al. 2020). The taxonomy
consists of selected attributes to classify tokens (e.g., claim structure, total supply, redemption).
Interestingly, it can be stated that the perspective on token slowly shifts from cryptocurrency driven use
cases (e.g., ICO) towards more comprehensive token standards to set the basis for enterprises with a
more diverse and wide view of tokens for existing economic models (e.g., tokens for private DLTs).
Beside academia, there are a couple of industry-oriented classifications that add regulatory and utility-
driven aspects for a practical implementation of tokenization solutions. Mueller et al. (2018)
distinguishes three major classes in terms of native utility, counterparty, or ownership token. The
International Token Standardization Association (ITSA) establishes a common understanding on basis
of a classification along the four dimensions purpose, industry, technological setup and legal claim
(Ketz and Sandner 2019). Another initiative represents the InterWorkAlliance that drives standards for
interoperability. By defining the most industry relevant token properties in predefined application areas
(e.g., financial services, healthcare), a technical foundation for cross platforms is established to bridge
the gap between different stakeholders (e.g., developers, business executives) based on similar technical
features and token behaviours. Most of the frameworks reduce complexity for redundant dimensions
and associated attributes. Strongly dependant on a business-, regulatory- or technically oriented
perspective, a harmonization of the terminology and standardization of artifacts may help to overcome
potential confusion and expand over time. However, previous research has fallen short to provide a
framework that defines the unique business value of use cases in the context of respective token
characteristics and to empirically derive wider implications of tokenization.
Towards a Framework for Understanding Asset Tokenization
Twenty-fifth Pacific Asia Conference on Information Systems, Dubai, UAE, 2021
5
Research Method
Literature Review
To generate an initial set of criteria for tokenized objects and to define the applicability of tokenization
as part of an operating and business model, we started the development of our framework by means of
a structured literature review following Webster and Watson (2002). We used the scientific databases
EBSCOhost, IEEEXplore, AISeL, Science Direct, ProQuest, and ACM Digital Library in September
2020 and defined the following search string (“Blockchain*” OR “Distributed Ledger*”) AND
(“Token*). To increase topicality, we included a title and abstract search with the terms (“Tokenization”
OR “Tokenisation”). The search resulted in 214 documents. We identified the publications relevant to
answer our research question and excluded irrelevant papers as well as duplicates. After the full-text
screening, the remaining 23 documents represented the basis for a subsequent forward and backward
search to enhance theoretical contributions with a practical perspective. At the end we selected 34
publications. To identify themes for a distinct assessment of use cases, we applied thematic analysis
(Braun and Clarke 2006). According to the six phases (familiarization, initial code generation, search
for themes, review themes, define and name themes, and report generation), we selected relevant (1)
token attributes, functions, and properties; (2) drivers, potentials, and value propositions of tokenization
as well as (3) challenges, limitations, and barriers for adoption. Where the themes represent preliminary
dimensions of the framework, we refined the results in discussion rounds with researchers. We omitted
highly technical features (i.e., burnability, expirability, issuance), adapted criteria for applicability (e.g.,
real-time processing, process automation) and grouped similar aspects for evaluation (e.g., facilitated
access, democratization). Within this iteration, we derived 21 dimensions with a set of 76 sub-criteria.
Semi-structured Interviews
Based on the initial framework, we conducted two semi-structured interview rounds to complement our
findings and to validate our results (cf. Table 1). All interviewees were either subject matter experts or
C-level managers familiar with the concept of tokenization or participating in the development of such
DLT-driven solutions. Despite a focus on the financial sector, we brought together interview partners
from interdisciplinary backgrounds to widely represent the current state of the tokenization ecosystem.
Table 1. Interview Overview
At first, we aimed for a clear understanding towards the problem domain. The questions addressed the
overall phenomenon designed to identify additional potentials and challenges of tokenization illustrated
as a basic theme in the framework. We noticed that the experts highlighted similar aspects from our
initial review (e.g., fractional ownership, legal issues). We adapted the structure of the interview guide
and initiated a second interview round with the goal to directly validate the proposed criteria according
Interview Round Goal of the Interviews ID Function of the Interviewee Affiliation Company Size
B1 Head New Markets Bank Medium
D1 Co-Head Clients Digital Asset Bank Medium
D2 Head Token P latform Digital Asset Bank Medium
L1 Lawyer & Founder Legal & Regulatory Services Medium
L2 Swiss Senior Legal & Regulatory Expert Legal & Regulatory Services Large
F1 Co-Founder und Chief Investment Officer FinTech Small
P1 Head of Fintech IT-Provider Large
P2 Manager Technology IT-Provider Large
P3 Manager Tokenization Services IT-Provider Large
D3 CEO / Head of Crypto Payments Digital Asset Bank Medium
E1 Product Manager Exchange Large
2nd Interview Round S1 Business Development Start Up Small
D4 Chief Client Officer Digital Asset Bank Medium
F2 Chief Executive Officer & Founder FinTech Small
F3 Chief Executive Officer & Founder FinTech Small
B2 Project Manger Bank Large
R1 Researcher Research Medium
F4 Chief Sales Officer FinTech Medium
F5 Chief Executive Officer & Founder FinTech Small
D5 Head of Business Unit Digital Asset Bank Medium
F6 Chief Marketing Officer FinTech Medium
D6 Board of Directors Digital Asset Bank Medium
Reevaluation of prior
results, refinement of the
framework and identification
of tokenization use cases
Understanding of the
problem domain,
identification of value
potentials and barriers of
tokenization
1st Interview Round
Towards a Framework for Understanding Asset Tokenization
Twenty-fifth Pacific Asia Conference on Information Systems, Dubai, UAE, 2021
6
to a solution artifact. The experts actively reflected the categories and discussed use cases from their
domains. In total, we conducted 22 interviews and started the analysis of the results following Gioia et
al. (2013). We established first and second level codes to identify confirmatory aspects towards our
framework. Accordingly, we added new dimensions on token properties (e.g., token supply), aggregated
sub-criteria on token representations (i.e., digital, physical, contract) and merged redundant aspects
(e.g., technical complexity associated with oracle problems). This iteration resulted in the final
framework illustrated in Table 2, where we conclude 12 dimensions with a set of 44 sub-criteria.
Deriving Archetypes
For the third phase, we applied the framework as an analytical tool (e.g., morphological box) to derive
archetypical use cases on basis of a company analysis. This problem-solving technique is used for multi-
dimensional questions, where an instantiation is assigned to its specific parameters in a grid box
(Zwicky 1969). For the data collection, we conducted a screening in December 2020 for companies on
crunchbase.org, chaineurope.org, and e-foresight.ch. The focus of our selection was on DLT and
Blockchain based firms in German-speaking Europe (i.e., Germany, Austria, Switzerland,
Liechtenstein) representing a viable ecosystem around tokenization. The initial database contained 183
company profiles as we considered additional sources (i.e., company website, whitepaper, press reports)
to ensure data quality for the subsequent analysis. We included only firms that utilize tokenization as
an integral part of their operating and business model (e.g., token issuance services, asset tokenizer).
We excluded wallet providers, cryptocurrency exchanges, miners, or broader DLT-infrastructures (e.g.,
Ethereum) because their usage of tokens are not primarily driven by a tokenized representation
associated with a specific product or service. Next, we assigned coding schemes to assess the companies
according to the predefined criteria of the framework and omitted cases that resulted in insufficient
information. We validated the instantiations in various iterations, where disputes were resolved in group
discussions (Strauss 1987). The direct instantiations highlighted recurring combinations of criteria
implying a tendency of companies with similar tokenization use cases. As a result, we identified eight
archetypes (see Appendix) and demonstrated utility of the framework covering a final set of 129 firms.
Assessment Framework for the Tokenization of Assets
Given the identified token properties, value drivers, and barriers for tokenization, we present a
comprehensive framework to be applied in the description and assessment of use cases associated with
asset tokenization. Described below, the resulting framework consists of 12 dimension and 44 sub-
criteria along the three overarching themes allowing for a distinct analysis, comparison, and discussion.
Table 2. Framework for the Assessment of Tokenization Use Cases
Industry
Focus
Financial &
Insurance
Activities
Prof., Sc. &
Technical
Activities
Blockchain-
Specific
Application
Transport &
Storage
Arts, Enter-
tainment &
Recreation
Wholesale &
Retail Trade
Information
& Commun.
Public Adm.
& Defence
Tokenized
Representation
e.g., Art
e.g., Gold
e.g., Votes
e.g., Shares
e.g., Membership
Underlying
Representation
Digital
Physical
Contract
Function
Access to a
Service
On-Chain
Reward
Potential
Off-Chain
Cash Flow &
Dividend
Store of Value
Collectibles
Means of
Exchange
Voting Right
Purpose
Payment Tokens
Utility Tokens
Asset Tokens
Unit
Fractional
Whole
Singleton
Tradability
Transferable
Non-Transferable
Fungibility
Fungible
Non-Fungible
Supply
Fixed
Unfixed
Technical Setup
Ledger Native
Ledger Non-Native
Driver of
Tokenization
Democratization &
Facilitated Access
Increased
Liquidity
Disintermediation
Increased
Transparency
Process
Optimization
Digital Scarcity
Barriers of
Tokenization
Legacy Structures &
Transition Risk
Data Privacy
Regulatory & Legal
Governance Issues
Oracle Problem
Towards a Framework for Understanding Asset Tokenization
Twenty-fifth Pacific Asia Conference on Information Systems, Dubai, UAE, 2021
7
Token Properties
• Industry Classification: a token can be described based on the industry of the use case. UK
Standard Industry Classification (SIC) is applied (e.g., Financial and Insurance Activities,
Professional, Scientific and Technical Activities) (Marshall et al. 2018).
• Tokenized Representation: specifies the reference, value or proxy of the tokenized
representation in particular such as art, gold, credit loans, baseball bards, etc.
• Underlying Representation: indicates the superordinate category with regards to the
underlying collateral or generic nature of the token-based asset: digital (e.g., bankable assets,
cryptoassets), physical (e.g., real estate) or contract (e.g., usage right) (Oliveira et al. 2018).
• Function: the reason for holding tokenized assets is based on the function or target use of a
token: access to a service, on-chain reward potential (e.g., staking, airdrops), off-chain cash
flow (e.g., dividends), store of value (e.g., stablecoins, gold), collectibles with intrinsic value
(e.g., CryptoKitties), means of exchange (e.g., currency), voting right (Marshall et al. 2018).
• Purpose: classifies the underlying economic purpose of a token into payment token (e.g.,
Bitcoin), utility token (e.g., Ether), and asset token (e.g., Crowdlitoken) (Mueller et al. 2018).
• Unit: indicates whether a token is sub-divisible into smaller fraction (fractional or partial,
whole with no subdivision, singleton with a quantity of one) (InterWorkAlliance IWA 2020).
• Transferability: relates to the transferability of ownership to another party (e.g., sale of a
registered security) (Oliveira et al. 2018).
• Fungibility: indicates whether a token can be interchanged. While a fungible token has
interchangeable value with one another, a non-fungible token is unique and cannot be
interchanged due to different values (Oliveira et al. 2018).
• Total Supply: describes to which limit a number of assets can be generated: fixed (e.g.,
capped), unfixed (e.g., based on predefined conditions, schedule-based supply or managed by
authorized parties) (Ankenbrand et al. 2020).
• Technical Setup: describes on which layer (e.g., protocol-level) of the distributed ledger a
token is applied: native (e.g., Bitcoin), non-native (e.g., ERC20) (Ketz and Sandner 2019).
Driver of Tokenization
We extend the perspective beyond token properties and consider applicability in terms of six distinct
drivers of tokenization to identify the role of a token as part of an operating and business model. Each
driver is to be selected, if one or more of the following aspects explicitly applies:
• Democratization & Facilitated Access refers to the degree of financial inclusion. While
tokenization of real estate enables retail clients to participate in large scale real estate
development projects and therefore is highly correlated to this factor, a token to trace the
provenance of items (e.g., diamonds) is not.
• Increased Liquidity: through the release of untradeable or private assets (e.g., venture capital,
real estate in certain market, collectibles such as wine, old-timers etc.) and 24/7 market access,
tokenization helps to create liquidity and facilitates the trading and settlement (Harwood-Jones
2019; Shtybel 2019)
• Disintermediation: tokenization has the potential to reduce the need for trusted intermediaries.
Peer-to-peer trading and atomic settlement are examples of disintermediation in financial
markets (Shtybel 2019).
• Increased Transparency: tokenization increases transparency and traceability of token
ownership (Shtybel 2019). Single-source-of-truth can improve efficiency, correctness and
coordination requirements significantly.
• Process Optimization: typical examples of process optimization through tokenization are
corporate action (e.g., automated dividend payments through smart contracts) (Shtybel 2019).
• Digital Scarcity: tokenization introduces the concept of scarcity or predictable supply to the
digital domain, which contradicts the characteristics of digital medium such as mutability and
copyability (Chen 2020; Lotti 2019; Macedo 2019)
Towards a Framework for Understanding Asset Tokenization
Twenty-fifth Pacific Asia Conference on Information Systems, Dubai, UAE, 2021
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Barrier of Tokenization
We present further five main barriers of tokenization describing identified challenges in adoption and
implementation of token-based solutions. One or more of the following barriers may explicitly apply:
• Legacy Structures & Transition Risk: existing structures and legacy systems may represent
a barrier when implementing use cases (e.g., core banking system, existing infrastructure).
• Data Privacy: depending on the business case the sensitivity to data privacy may vary and can
even be in conflict with existing traditional security law (Shtybel 2019).
• Regulatory & Legal: uncertainties originating from legal, regulatory and compliance. Limited
enforceability, lack of global standards and slow adaption of regulation and law represent
examples of potential legal barriers for tokenization (Savelyev 2018).
• Governance: key aspects in token-enabled business models are new governance mechanisms.
The extent of barriers might depend on the complexity of the underlying network, the number
of involved partners in the ecosystem and applied incentive mechanism.
• Oracle Problem: gates between the digital and physical world pose challenges in terms of
security, authenticity and trust. The more a business case relies on off-chain data, the higher its
correlation to this factor. Barriers may vary the number of different sources, existence and
design of technical interface, audit, and quality requirements.
Dependencies between Token Properties, Drivers, and Barriers of Tokenization
The analysis of 129 firms highlighted recurring combinations of criteria and allowed us to draw
conclusions on various dependencies. For example, we found that bankable assets are often combined
with fractionality to enable increased liquidity. To derive archetypes, we structured these dependencies
according to their level of abstraction and referred to the tokenized representation at first to examine
the interrelationships between properties, drivers, and barriers in the respective case. Our findings
revealed that the tokenization of physical assets (e.g., real estate, artwork) is manly driven by the
financial sector, logistics, and arts industry. While tokenized commodities were utilized for track and
tracing, tokenized paintings were further applied for provenance and authenticity. However, high value
assets (e.g., gold, watches) are mainly tokenized for specific investment purposes. This pattern is also
highlighted by the majority of other products that already exist in a digitized form (e.g., bankable
assets). Regarding shares and bonds, a fractional, tradable, and fungible token design is required. They
fulfill the function to exchange and store value and enable off-chain or on chain reward potentials.
Tokenized contracts (e.g., voting, usage rights) show strong dependency towards service and platform
access for various industries. The ownership of such utility token does not only grant general access but
also provides discounts and participation in decision-making processes. They are characterized by
fungibility, flexible supply, and transferability between holders. Other utility token exist that provide
individual asset ownership (e.g., digital twins, identities). Assigned to a unique bearer, they follow a
capped token supply and a non-fungible design. Also for collectibles (e.g., luxury cars) a unique
reference is established through non-fungibility, as transferability and fractionality is not desirable.
Tokenized virtual items on basis of a capped supply (e.g., digital gaming cards) enable further
ownership and possession for a limited series of objects. Where fungible and tradable token are mainly
represented through non-native DLT-frameworks (e.g., ERC-20), non-fungible token may require
extended functionality and application access and refer more often to proprietary DLT-systems.
Democratization and facilitated access is highly associated with the tokenization of physical assets,
whereas the correlation with tokenized contracts is somewhat less pronounced. Some interrelations
highlight the potentials to minimize investment amounts of non-bankable assets typically considered
illiquid (e.g., watches, wine). But also tokenized equity and loans increase financial inclusion for a
broader investor base through fractionality (e.g., peer-to-peer lending). Beside these benefits in product
innovation, it is assumed that the financial sector is further driven by process optimization and increased
transparency. This dependency refers specifically to automation of administrative tasks and a tamper
resistant asset ownership of transactions. Interestingly, the disintermediation of existing structures is
strongly pronounced for native digital currencies as a means of exchange (e.g., stablecoins). However,
these aspects can be neglected for tokenized contracts. Where accessibility to services is a prerequisite,
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the use of tokens is driven by process innovation. This combination is also identified for life cycle
tracking of physical objects. To provide provenance (e.g., artwork, diamonds), increased transparency
is highly appreciated. To prevent replication of tokenized assets, digital scarcity is further introduced
on basis of a fixed token supply. Accordingly, a non-fungible and singleton token design with less
pronunciation on fractional ownership and increased liquidity is required.
By taking a closer look at the barriers, we need to distinguish between transformational and enabling
capabilities of tokenization. Especially for existing bankable assets, a seamless integration into legacy
system is often required and poses a transition risk for implementation. On the contrary, physical
tradable assets show only low to no correlation towards compliant legacy systems. It is assumed that a
functional market infrastructure is not established yet and that such use cases offer high potential for
new products and services. Use cases for the transfer of asset ownership and tokenization of financial
asset classes (e.g., shares, currencies) require further a regulatory basis. While common law often refers
to known legal structures, the legislation for a dematerialization of a fully digitized asset ownership is
often not established yet (e.g., legal assertion of smart contracts). Where token solely provide access to
a service platform (e.g., tokenized goods in logistics) or serve as a basis for non-fungible collectibles
(e.g., trading cards) a legal framework is not necessarily required. Some use cases show increased
requirements for data privacy. Tokenized health data, for instance, may rely on restricted permission
for monitoring sensible transactions. Additional complexity is added when new actors have to be
coordinated or incentivized to join a decentral platform. A minimal viable ecosystem is often necessary
that consist of additional stakeholders (e.g., asset tokenizer, custodian). With regards to physical
representations (e.g., watches, container), a consistent data connection between the physical and digital
world is appreciated. Especially for unique non-fungible token, this oracle problem has to be considered.
Archetypical Use Cases for Asset Tokenization
We present eight archetypical use cases covering the different manifestations for tokenization among
the analyzed sample of firms (cf. Figure 1). They represent similar configurations of parameters to be
understood as recurring applications in existing operating and business models (see Appendix).
Figure 1. Asset Tokenization Archetypes
Primarily driven by the financial industry, programmable money represents applications associated
with tokenized currency (e.g., stablecoins, central bank digital currencies) for an efficient means of
exchange and store of value. By overcoming drawback of existing applications, payment tokens highly
optimize processes and make it possible to trigger events and automate a multitude of services on
automated decentralized platforms (e.g., micropayments). However, governance between stakeholders
(e.g., central and commercial banks) and the adaptation of a legal framework, increase transition risks
as centralized solutions already exists. Smart bankable assets are driven by similar aspects in terms of
process optimization and investment rewards potentials. By implementing a financial contract logic,
recurring cash flows and voting rights can be automated. Also market accessibility as well as the usage
of tradable and fungible asset token increases transparency and liquidity. The integration into existing
legacy systems and complexity of such market structures pose a risk, where legal and regulatory
foundations are slowly established. Beside transformational aspects, the opening of illiquid assets
focuses on the creation of token as a proxy for physical value. Due to the increased efficiency delivered
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by the digitalization process, representations of real estate or classic cars add additional accessibility by
design. The fractionalized investment denominations of token may decrease market friction. Arranged
at low amounts, access is provided to a much broader investors base to increase liquidity. However, the
complex network of actors and required external data through oracles needs to be considered. Another,
more specialized, form of democratization is represented by crowd funding. Emerged in form of ICOs,
such applications are characterized by reduced transaction costs through elimination of external service
providers. Where many associated business models were not economically viable before, tokens allow
efficient, fractionized, real-time crowd investments ranging from private projects to participation in
venture capital. A focus lies on fast and flexible tradability as basis for increased liquidity. However,
obstacles remain for establishing such regulated market infrastructures and to incentivize the usage
among stakeholders. Defined as service access, many use cases utilize tokens as an instrument for
acquiring rights to a specific service. Implemented by various industries, tokenized licenses or
memberships allow a facilitated and effective access to decentralized platforms. They are categorized
according to the functions a token grants to the holder (i.e., general-, exclusive-, DLT-infrastructure
access). Both fungibility and non-fungibility may be implemented to assign a unique private or public
functionality. Unlike other cases, the tokens are not regulated at all. The services offered are solely
depending on the provider and pose challenges for governance and implementation of external data
sources. Platform governance represents a more abstract field of use cases for tokenizing incentive
mechanisms as a means of coordination and cooperation. Embedded into a DLT protocol, tokens are
issued as a reward to users for completing specific tasks or meeting a certain behavior (e.g., mining
reward for transaction validation). Beside DLT-specific means of consensus, a token may also be
demanded and valued on its own to further incentivize community building or voting. They are not
primarily based on future monetarization but have social value by governing voting power and creating
community sense. Democratization, process optimization, disintermediation, data privacy (e.g.,
pseudonymity, anonymity) and increased complexity are characteristic of such use cases. While DLT
enables verifiable digital scarcity, we introduce digital sovereignty for applications that require unique
representations and decentralized data access control for token holder in their own right (e.g., digital
identities). Once implemented as a non-fungible token, the assets provide proof of authenticity and
cannot be replicated by one governing entity. This democratization of interaction on decentralized
platforms allows users to control in-game assets, for instance, independently from a single platform
owner (e.g., CryptoKitties). Referring to the tokenization of unique physical assets or sensible data, data
privacy and a secure link between the physical and the digital object have to be considered. At last,
track & tracing is used to establish a tamper-proof-record of ownership among various stakeholders.
Such use cases are often associated with logistical processes in many industries and allow organizations
to utilize token for increased transparency along the lifecycle of tangible or intangible assets. Where
tokenized consumer goods may take on fungible properties, non-fungible token are appreciated for high
value items. Especially for provenance across supply chains, external data authenticity and integration
into legacy systems must be considered. Several combinations between archetypes have been further
identified. For example, use cases in supply chain finance that rely on both, track and tracing as wells
as the opening of illiquid assets. As one archetype exhibits a main purpose, there might be additional
purposes which are combinable and may extend token utility in a decentralized system.
Discussion
Given the increasing interest in asset tokenization, this work proposes a first qualitative framework that
provides an understanding about the practicability of this application area by identifying relevant
criteria for assessment, comparison, and documentation of use cases. To identify the role of tokenized
representations as part of a broader business model, we extended the perspective beyond token
properties and integrated selected aspects of applicability on basis of a literature review and expert
interviews (i.e., drivers and barriers of tokenization). Our empirical data confirmed our initial
assumptions that the same tokenized object can exhibit a multipurpose ability for different use cases.
We further applied the framework in a structured comparison process based on a sample of companies
associated with tokenization. Following our morphological approach, the different instantiations
allowed us to further identify dependencies between the different dimensions and criteria to highlight
similarities. Given the dataset, we propose eight archetypical use cases. Moreover, our study revealed
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that assessing the applicability of asset tokenization requires an interdisciplinary and multifaceted
approach. The involved criteria uncover a range of various possibilities, strengths, and weaknesses on
an abstract basis and represent a first set of formal descriptions for the design and development of uses
cases and token-based business models. In assisting businesses to align their activities and assess
potential trade-offs, applicability between the drives and barriers of a specific asset tokenization use
case has to be illustrated in the first place. These two dimensions of the framework help to judge
feasibility and to discern the pros and cons. Subsequently, the token property dimensions further specify
the unique business value of a use case in the context of respective token characteristics and provide
additional aspects from a business standpoint to clarify scenarios at the backdrop of token design. We
regard the framework also as an initial approach for the operationalization of the tokenization concept.
Although the development of a quantitative decision model is beyond the scope of this study, the
identified dimensions may be weighted individually to improve an assessment. It could be shown that
an additional perspective on applicability in terms of drivers and barriers is required to evaluate the role
of tokens in a particular scenario. Designed to remain as simple as possible, we were further able to
capture significant meaning by representing the current state of the tokenization ecosystem in terms of
archetypical use cases. Finally, our results suggest that there is a huge potential offered by a token
economy that is not limited to the tokenization of assets in the financial sector.
Implications
The presented findings contribute to practice in two ways. Firstly, the framework allows strategists and
managers to support a comparison of use cases associated with asset tokenization helping to assess the
potential in the context of different token designs, drivers, and barriers for adoption. By defining
applicability according to the predefined criteria, the framework may be utilized as a decision-aid tool
to develop and compare viable solutions for different tokenized objects and improve managerial
practices for entering this highly innovative area. Secondly, the identified dependencies and archetypes
highlight the usability of the framework and reduce complexity by providing an overview of major
differences in existing token-based solutions. They provide strategic guidance in the design of tangible
applications and illustrate how firms can apply this concept to innovate their businesses towards a token
economy. Even though various classifications exist and are applied in practice, we observed that the
tokenization domain is still heavily influenced by grey literature and that most frameworks are primarily
geared towards cryptoassets (e.g., ITSA) without really examining new roles of tokens as part of an
operating and business model. So far, there has been no overview about tokenization use cases that
involves relevant factors for analysis (e.g., increased liquidity, governance issues). Accordingly, this
study contributes to this gap through the synthesis of the research foci token description and
applicability of tokenization. Our contributions to research are three-fold. First, we extend the existing
knowledge base beyond token classifications by generating new empirical insights about existing use
cases. Secondly, the archetypes serve as a cornerstone for the development of feasible solutions and
may foster research on interdisciplinary applications in other industries. Thirdly, we support the multi-
perspective discussion on the opportunities of a token economy. By combining the requirements of
established industries with the disruptive innovations of its fast-moving open-source community, we
may further establish a common understanding from a technological and economic perspective.
Limitation and future research
As the framework is initially based on scientific literature, we cannot generalize the presented findings
without limitation. Some of the identified drivers and barriers stem from a very new phenomenon that
highlight the need to enhance data collection using grey literature and more practical sources. Therefore,
it cannot be claimed that the proposed framework is complete nor stops the need for further research at
this intersection. Exhaustive and mutually exclusive principles associated with a structured approach to
building a taxonomy were therefore neglected. Also limitations of the interview process have to be
considered. The chosen experts may have found it difficult to verbalize feedback on the proposed
framework. Especially interviewees with profound knowledge in banking and finance showed a
stronger bias towards aspects associated with the tokenization of bankable assets in comparison to other
industries. However, we considered this subjective interpretation by constantly reflecting our interim
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findings by the introduction of two broader discussion rounds. Due to the fast dynamics in this field,
we cannot claim general transferability of the archetypes but rather indicate the status quo of a
prevailing tokenization ecosystem. By design, there is a notable overlap of groupings. Validation is
therefore pivotal to improve ambiguity and inconsistencies during the coding process. Nevertheless, the
findings provide a first useful foundation with relevant distinctions and characteristics. In future
research, efforts should be made by generating further insights using longitudinal data or case study
approaches. Potentially, this will improve the understanding of token-based business model associated
with tokenization and might lead to an extension of the framework with additional metrics and
operationalizations (e.g., weighted sum model) . New companies may follow with entirely new services
and products than the ones included in our dataset. With an emergence of new projects and players, it
is not unlikely that the vast majority of assets will be digitized in the near future.
References
Ankenbrand, T., Bieri, D., Cortivo, R., Hoehener, J., and Hardjono, T. 2020. “Proposal for a
Comprehensive (Crypto) Asset Taxonomy,” in 2020 Crypto Valley Conference on Blockchain
Technology (CVCBT), June, pp. 16–26.
Ayres, L. 2008. “Semi-Structured Interview,” in The SAGE Encyclopedia of Qualitative Research
Methods, L. Given (ed.), Thousand Oaks, CA, USA: SAGE Publications, Inc., pp. 810–811.
Babich, V., and Hilary, G. 2020. “OM Forum—Distributed Ledgers and Operations: What Operations
Management Researchers Should Know About Blockchain Technology,” Manufacturing & Service
Operations Management (22:2), pp. 223–240.
Beck, R., Avital, M., Rossi, M., and Thatcher, J. B. 2017. “Blockchain Technology in Business and
Information Systems Research,” Business & Information Systems Engineering (59:6), pp. 381–384.
Braun, V., and Clarke, V. 2006. “Using Thematic Analysis in Psychology,” Qualitative Research in
Psychology (3:2), Routledge, pp. 77–101.
Chen, Y. 2020. “Information Integration in Libraries,” Library Hi Tech (38:1), pp. 210–219.
Distefano, B., Pocher, N., and Zichichi, M. 2020. “MOATcoin: Exploring Challenges and Legal
Implications of Smart Contracts through a Gamelike DApp Experiment,” in Proceedings of the 3rd
Workshop on Cryptocurrencies and Blockchains for Distributed Systems, CryBlock ’20,
Association for Computing Machinery, September 25, pp. 53–58.
Euler, T. 2018. “The Token Classification Framework: A Multi-Dimensional Tool for Understanding
and Classifying Crypto Tokens.” Untitled INC, January 18. (http://www.untitled-inc.com/the-
token-classification-framework-a-multi-dimensional-tool-for-understanding-and-classifying-
crypto-tokens/, accessed July 27, 2020).
Freni, P., Ferro, E., and Moncada, R. 2020. “Tokenization and Blockchain Tokens Classification: A
Morphological Framework,” in 2020 IEEE Symposium on Computers and Communications, July,
pp. 1–6.
Gioia, D. A., Corley, K., and Hamilton, A. L. 2013. “Seeking Qualitative Rigor in Inductive Research:
Notes on the Gioia Methodology,” Organizational Research Methods (16:1), SAGE Publications
Inc., pp. 15–31.
Greer, R. J. 1997. “What Is an Asset Class, Anyway?” The Journal of Portfolio Management (23:2),
Institutional Investor Journals Umbrella, pp. 86–91.
Grilo, A., and Zutshi, A. 2020. “Preface for Special Issue on Blockchain and Tokenization for Industry
and Services,” Computers & Industrial Engineering (146), N.PAG-N.PAG.
Hartung, G., Rutter, K., and Stroemer, G. 2019. “A Solution for Managing High-Quality Liquid Assets:
How Distributed Ledger Technology Can Benefit the Securities Lending Market,” Journal of
Securities Operations & Custody, p. 11.
Harwood-Jones, M. 2019. “Digital and Crypto-Assets: Tracking Global Adoption Rates and Impacts
on Securities Services,” Journal of Securities Operations & Custody, p. 10.
Hrga, A., Capuder, T., and Žarko, I. P. 2020. “Demystifying Distributed Ledger Technologies: Limits,
Challenges, and Potentials in the Energy Sector,” IEEE Access (8), pp. 126149–126163.
Hughes, L., Dwivedi, Y. K., Misra, S. K., Rana, N. P., Raghavan, V., and Akella, V. 2019. “Blockchain
Research, Practice and Policy: Applications, Benefits, Limitations, Emerging Research Themes and
Research Agenda,” International Journal of Information Management (49), pp. 114–129.
Towards a Framework for Understanding Asset Tokenization
Twenty-fifth Pacific Asia Conference on Information Systems, Dubai, UAE, 2021
13
InterWork Alliance IWA. 2020. “Token Taxonomy Framework Overview TTF,” GitHub, , May.
(https://github.com/InterWorkAlliance/TokenTaxonomyFramework, accessed January 18, 2021).
Kannengießer, N., Lins, S., Dehling, T., and Sunyaev, A. 2020. “Trade-Offs between Distributed
Ledger Technology Characteristics,” ACM Computing Surveys (53:2), pp. 1–37.
Ketz, C., and Sandner, P. 2019. “Market Standards for the Global Token Economy - International Token
Standardization Association,” Medium, , July 16. (https://medium.com/@itsa_global/market-
standards-for-the-global-token-economy-7d9f3d0cde37, accessed January 18, 2021).
Lotti, L. 2019. “The Art of Tokenization: Blockchain Affordances and the Invention of Future Milieus,”
Media Theory (3:1), Rethinking Affordance, Media Theory, pp. 287–320.
Macedo, J. M. 2019. “A Taxonomy of Token Models and Valuation Methodologies,” Medium, , May
28. (https://medium.com/amazix/a-taxonomy-of-token-models-and-valuation-methodologies-
7b6c0a1d02a9, accessed January 18, 2021).
Madhwal, Y. 2020. “Implementation of Tokenised Supply Chain Using Blockchain Technology,” in
2020 IEEE 21st International Symposium on “A World of Wireless, Mobile and Multimedia
Networks” (WoWMoM), August, pp. 66–67.
Marshall, L., Repin, A., Tsavliris, C., and Humberstones, C. 2018. “CryptoCompare Cryptoasset
Taxonomy,” CryptoCompare, October.
Mooney, A. 2018. Blockchain ‘Could Save Asset Managers $2.7bn a Year.’
(https://www.ft.com/content/b6171016-171f-11e8-9e9c-25c814761640).
Mueller, L., Glarner, Linder, and Meyer. 2018. “BCP Framework for Assessment of Crypto Tokens -
MME - Blockchain Crypto Property.”
Nakamoto, S. 2008. “Bitcoin: A Peer-to-Peer Electronic Cash System,” Original Whitepaper, Manubot.
Naqvi, N., and Hussain, M. 2020. “Evidence-Based Blockchain: Findings from a Global Study of
Blockchain Projects and Start-up Companies,” The Journal of The British Blockchain Association
(3), pp. 1–13.
Narayan, R., and Tidström, A. 2020. “Tokenizing Coopetition in a Blockchain for a Transition to
Circular Economy,” Journal of Cleaner Production (263), p. 121437.
OECD. 2020. “The Tokenisation of Assets and Potential Implications for Financial Markets,” OECD
Blockchain Policy Series, OECD.
Oliveira, L., Zavolokina, L., Bauer, I., and Schwabe, G. 2018. “To Token or Not to Token: Tools for
Understanding Blockchain Tokens,” ICIS 2018 Proceedings
Pilkington, M. 2015. Blockchain Technology: Principles and Applications, Rochester, NY: Social
Science Research Network.
Roth, J., Schär, F., and Schöpfer, A. 2019. “The Tokenization of Assets: Using Blockchains for Equity
Crowdfunding,” SSRN Electronic Journal.
Savelyev, A. 2018. “Some Risks of Tokenization and Blockchainizaition of Private Law,” Computer
Law & Security Review (34:4), pp. 863–869.
Sazandrishvili, G. 2020. “Asset Tokenization in Plain English,” Journal of Corporate Accounting &
Finance (31:2), pp. 68–73.
Shtybel, U. 2019. “A New Era of Private Securities: Application of Blockchain in Private Capital
Markets Infrastructure,” Journal of Digital Banking (4:2), pp. 152–160.
Strauss, A. L. 1987. Qualitative Analysis for Social Scientists, Cambridge: Cambridge University Press.
Sunyaev, A., Kannengießer, N., Beck, R., Treiblmaier, H., Lacity, M., Kranz, J., Fridgen, G.,
Spankowski, U., and Luckow, A. 2021. “Token Economy,” Business & Information Systems
Engineering.
Webster, J., and Watson, R. T. 2002. “Analyzing the Past to Prepare for the Future: Writing a Literature
Review,” MIS Quarterly (2:26).
Zachariadis, M., Hileman, G., and Scott, S. V. 2019. “Governance and Control in Distributed Ledgers:
Understanding the Challenges Facing Blockchain Technology in Financial Services,” Information
and Organization (29:2), pp. 105–117.
Zhou, T., Li, X., and Zhao, H. 2019. “Med-PPPHIS: Blockchain-Based Personal Healthcare
Information System for National Physique Monitoring and Scientific Exercise Guiding,” Journal
of Medical Systems (43:9), N.PAG-N.PAG.
Zwicky, F. 1969. Discovery, invention, research through the morphological approach, Macmillan.
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Archetype
Label
Industry Focus
Toke nized
Representation
Underlying
Representation
Function Purpose Unit Tradability Fungibility Supply Te chnical Setup Driver of Toke nization
Barriers for
Tokenization
Financial & Insurance Activities Currency Digital Means of Exchange
Payment
Tokens
Fractional Transferable Fungible Fixed Ledger Native
Democratization &
Faciliated Access
Legacy Structures &
Transition Risk
Money Physical Store of Value Unfixed Ledger Non-Native Increased Liquidity Regulatory & Legal
…Process Optimization Governance Issues
Financial & Insurance Activities Stocks Digital On-Chain Reward Potential Asset Tokens Fractional Transferable Fungible Unfixed Ledger Non-Native
Democratization &
Faciliated Access
Legacy Structures &
Transition Risk
Bonds
Off-Chain Cash Flow & Dividend Increased Liquidity Regulatory & Legal
…
Voting Right Increased Transparency Governance Issues
Store of Value Process Optimization
Financial & Insurance Activities Art Physical Off-Chain Cash Flow & Dividend Asset Tokens Fractional Transferable Fungible Unfixed Ledger Native
Democratization &
Faciliated Access
Regulatory & Legal
Transport & Storage Real Estate Store of Value Ledger Non-Native Increased Liquidity Governance Issues
Arts, Entertainment & Recreation …Disintermediation Oracle Problem
Financial & Insurance Activities Equity Digital Off-Chain Cash Flow & Dividend Asset Tokens Fractional Transferable Fungible Unfixed Ledger Native
Democratization &
Faciliated Access
Legacy Structures &
Transition Risk
Debt Contract Store of Value Ledger Non-Native Increased Liquidity Regulatory & Legal
…
Voting Right Disintermediation Governance Issues
Increased Transparency
Process optimization
Financial & Insurance Activities Membership Digital Access to a Service Utility Tokens Fractional Transferable Fungible Fixed Ledger Native
Democratization &
Faciliated Access
Data Privacy
Prof., Sc. & Technical Activities Discount Contract Whole Non-Fungible Unfixed Ledger Non-Native Increased Transparency Governance Issues
Blockchain-Specific Application …Singleton Process Optimization Oracle Problem
Transport & Storage Digital scarcity
Arts, Entertainment & Recreation
Wholesale & Retail Trade
Information & Commun.
Public Adm. & Defence
Arts, Entertainment & Recreation Governance Digital Access to a Service Utility Tokens Fractional Transferable Fungible Fixed Ledger Native
Democratization &
Faciliated Access
Data Privacy
Blockchain-Specific Application
Incentive
Mechanism
Contract On-Chain Reward Potential Whole Unfixed Ledger Non-Native Disintermediation Governance Issues
Financial & Insurance Activities Reward Voting Right Singleton Process Optimization
Prof., Scientific & Technical Activities …
Arts, Entertainment & Recreation Identity Digital Collectibles Asset Tokens Whole Non-transferable Non-Fungible Fixed Ledger Native
Democratization &
Faciliated Access
Data Privacy
Financial & Insurance Activities Classic Cars Contract On-Chain Reward Potential Utility Tokens Singleton Ledger Non-Native Disintermediation Orac le Problem
Information & Commun. Digital Cards Physical Off-Chain Cash Flow & Dividends Digital scarcity
Transport & Storage …Store of Value
Arts, Entertainment & Recreation Artwork Digital Access to a Service Asset Tokens Whole Non-transferable Fungible Fixed Ledger Native Increased Transparency Data Privacy
Financial & Insurance Activities Diamonds Physical Store of Value Utility Tokens Singleton Transferable Non-Fungible Unfixed Ledger Non-Native Process Optimization
Legacy Structures &
Transition Risk
Blockchain-Specific Application Watches Collectibles Digital scarcity Oracle Problem
Transport & Storage …
Wholesale & Retail Trade
Digital
Sovereignty
Track &
Tracing
Appendix: Tokenization Archetypes
Programmable
Money
Smart Bankable
Assets
Opening of
Illiquid Asse ts
Crowd
Funding
Service
Access
Platform
Governance
