A QUALITATIVE ANALYSIS BY CASE STUDY APPROACH FOR IDENTIFYING THE IMPACT OF SMART CONTRACTS USING THE BLOCKCHAIN TECHNOLOGY ON CUSTOMER RELATIONS WITHIN BUSINESS MODELS AND SERVICE OFFERING(S)

Aantal woorden/ Word count: < 34.586 >

Cedric Manhaeve Stamnummer/ Student number : 01304414

Promotor/ Supervisor: Prof. Dr. Geert Poels

Masterproef voorgedragen tot het bekomen van de graad van:

Master’s Dissertation submitted to obtain the degree of:

Master of Science in Business Engineering: Finance

Academiejaar/ Academic year: 2018-2019

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PERMISSION

Ondergetekende verklaart dat de inhoud van deze masterproef mag geraadpleegd en/of

gereproduceerd worden, mits bronvermelding.

I declare that the content of this Master’s Dissertation may be consulted and/or reproduced,

provided that the source is referenced.

Naam student/name student: Cedric Manhaeve

Handtekening/signature

IV

Foreword

This master’s dissertation is the final piece before graduating as a Business Engineer at Ghent

University. A few years ago, I attended a workshop on the blockchain technology beyond

cryptocurrencies that was hosted by VEK Recruitment, part of the VEK the student association,

in cooperation with a consultancy firm. Since then the technology has sparked my interest. This

master’s dissertation provided me the chance to investigate the impact of the blockchain

technology on the business world within the scope of my studies. Therefore, I would like to show

my gratitude towards the people listed below.

In the first place, I would like to thank my supervisor, Prof. Dr. Geert Poels, for inspiring and

mentoring me through this master’s dissertation. Furthermore, I would like to show my gratitude

towards the interviewees Koen Vingerhoets, Frederik-Jan Roose and Thomas Vandoorne for their

time and insights offered on this subject. Hereby, I would like to thank my friend, Matthias Beckers,

for introducing me to his colleague Thomas and who also provided valuable feedback related to

the cases investigated. At last, I would also show my gratitude towards my parents and my

girlfriend for their support. Not only did they proofread my master’s dissertation, they also took

time to discuss the contents of the research.

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Table of Contents

Foreword ................................................................................................................................... IV

Table of Contents ....................................................................................................................... V

List of used abbreviations ......................................................................................................... VII

List of figures ........................................................................................................................... VIII

1. Introduction ...................................................................................................................... 1

1.1 Problem statement ........................................................................................................ 1

1.2 Research question and objective ................................................................................... 1

1.3 Background information of the blockchain technology ................................................... 2

1.4 Master’s dissertation structure ....................................................................................... 2

1.5 Research methodology .................................................................................................. 3

1.5.1 Research strategy ................................................................................................... 3

1.5.2 Research design ..................................................................................................... 4

2. Literature review ............................................................................................................... 7

2.1 Conceptual delimitation: Blockchain .............................................................................. 7

2.1.1 General information on the blockchain technology .................................................. 7

2.1.2 Blockchain technology innovations ........................................................................ 12

2.1.3 Possible blockchain applications ........................................................................... 17

2.1.4 Blockchain technology: a game changer? ............................................................. 18

2.1.5 Existing frameworks to describe, explain or predict the impact .............................. 23

2.2 Enterprise modelling & business models ..................................................................... 26

2.2.1 Defining Enterprise modelling................................................................................ 26

2.2.2 Importance of enterprise modelling ....................................................................... 27

2.2.3 Defining business models ..................................................................................... 28

2.2.4 Chosen enterprise modelling techniques ............................................................... 29

3. Cases ............................................................................................................................. 35

3.1 CrowdBC: a blockchain-based crowdsourcing framework ........................................... 35

3.1.1 Traditional triangular crowdsourcing model structure ............................................ 35

3.1.2 CrowdBC explained .............................................................................................. 38

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3.1.3 BMC in the context of crowdsourcing .................................................................... 42

3.1.4 E³value model in the context of crowdsourcing ..................................................... 51

3.1.5 Conclusion of the CrowdBC case .......................................................................... 55

3.2 Case “Know Your Customer” onboarding in Belgium ................................................... 57

3.2.1 The KYC landscape today ..................................................................................... 58

3.2.2 Possible KYC landscape with blockchain .............................................................. 59

3.2.3 BMC in the context of KYC .................................................................................... 64

3.2.4 E³value model in the context of KYC ..................................................................... 71

3.2.5 Conclusion about KYC in Belgium case ................................................................ 75

3.3 Chickens on the blockchain in supply chain ................................................................. 77

3.3.1 Today’s food supply chain ..................................................................................... 77

3.3.2 Chickens on the blockchain with IBM Food Trust framework ................................. 79

3.3.3 Business Model Canvas Carrefour ........................................................................ 83

3.3.4 E³value model of the poultry supply chain ............................................................. 89

3.3.5 Conclusion about chickens on the blockchain case ............................................... 95

4. Combined results of the cases ........................................................................................ 99

5. Conclusion and further research ................................................................................... 103

6. Reference list .................................................................................................................... X

7. Attachments ...................................................................................................................... 1

Attachment 1: A blockchain infographic (Morrison & Sinha, 2016) .......................................... 1

Attachment 2: Datasets of a chicken on the blockchain (Javier, 2018) .................................... 1

VII

List of used abbreviations

• BM: Business model

• BMC Business Model Canvas

• DAO: Decentralised autonomous organisation

• DLT: Distributed ledger technology

• EM: Enterprise modelling

• ERP: Enterprise resource planning

• FI: Financial institution

• IoT: Internet of Things

• KYC: Know Your Customer

• P2P: Peer-to-Peer

• PoC: Proof-of-Concept

• PoS: Proof-of-Stake

• PoW: Proof-of-Work

VIII

List of figures

Figure 2.1: Simplified example of a blockchain transaction (Thomson Reuters, n.d.) ................ 9

Figure 2.2: Simple visualization of asymmetric cryptography (Blockgenic, 2019)...................... 10

Figure 2.3: Types of blockchain ledgers (Beck et al., 2018) ...................................................... 14

Figure 2.4: The potential advantages of smart contracts compared to traditional paper-based

contracts (Morrison, 2016) ........................................................................................................ 15

Figure 2.5: Overview of possible blockchain cases (Pastor, 2015) ........................................... 17

Figure 2.6: Gartner's Hype Cycle for emerging technologies 2018 (Panetta, 2018a) ................ 19

Figure 2.7: Hype Cycle for blockchain applications (Levy, 2018) .............................................. 20

Figure 2.8: Blockchain Innovation Process Framework (Beck & Müller-Bloch, 2017) ............... 24

Figure 2.9: Extended IT Governance Framework (Beck et al., 2018) ........................................ 25

Figure 2.10: an empty Business Model Canvas (Osterwalder & Pigneur, 2010) ....................... 30

Figure 2.11: Value network of an internet radio station (Gordijn et al., 2006) ............................ 32

Figure 3.1: Traditional model of crowdsourcing with a centralised third party (Li et al., 2018) ... 36

Figure 3.2: The system model of CrowdBC (Li et al., 2018) ...................................................... 39

Figure 3.3: Overview of the CrowdBC architecture (Li et al., 2018) .......................................... 41

Figure 3.4: The BMC of a crowdsourcing platform (as-is) ......................................................... 42

Figure 3.5: The BMC of a Worker using the platform (as-is) ..................................................... 43

Figure 3.6: The BMC of a Worker using the Framework (to-be) ................................................ 43

Figure 3.7: Value network in the as-is situation ........................................................................ 51

Figure 3.8: Possible value network in the to-be situation .......................................................... 52

Figure 3.9: Current KYC onboarding and frustrations (Verhaest, 2018) .................................... 58

Figure 3.10: Proof-of-Concept KYC onboarding with blockchain (Verhaest, 2018) ................... 60

Figure 3.11: Estimated financial impact on the KYC ecosystem in Belgium (Verhaest, 2018) .. 61

Figure 3.12: Current BMC of a FI with focus on KYC (as-is) ..................................................... 64

Figure 3.13: Proof-of-Concept BMC of a FI in a blockchain ecosystem (to-be) ......................... 65

Figure 3.14: Current e³value model of a FI performing the KYC process (as-is) ....................... 71

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Figure 3.15: Proof-of-Concept e³value model (to-be) ................................................................ 72

Figure 3.16: The BMC of Carrefour with focus on poultry products (as-is) ................................ 84

Figure 3.17: The BMC of Carrefour with focus on poultry products on the blockchain .............. 84

Figure 3.18: Value network of a poultry supply chain (as-is) ..................................................... 90

Figure 3.19: Value network of a poultry supply chain on the blockchain (to-be) ........................ 92

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1. Introduction

1.1 Problem statement

The blockchain technology is a hot topic and has the potential to create new foundations for

economic systems and business processes. Consequently, interorganizational processes could be

improved using the technology while for other businesses, such as the “trusted” third party, it might

lead to obsolescence. The technology is still in its infancy and the impact on new and established

companies remains unclear and needs further research. It also needs to be investigated if and to

what extend the end-consumer would also reap the potential benefits put forward by the

technology.

1.2 Research question and objective

The objective of this master’s dissertation is to analyse the potential and the impact of the

blockchain technology and smart contracts in the light of business models and service offerings to

customers. Consequently, this master’s dissertation aims to answer the following research

question:

RQ: “How can enterprise modelling techniques serve to analyse the impact of the

blockchain distributed ledger technology on business models and value of service

offering(s) to customers?”

The answer to the main research question is developed through evaluating the following

considerations based upon the literature study:

1) How can the technology make an impact on the business model?

2) What impact(s) can be demonstrated using the chosen enterprise modelling techniques?

3) How are qualitative parameters like “trust”, “security”, “traceability” and “process efficiency”

affected in the context of service offerings to customers?

4) Is generalisation of the cases investigated possible?

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1.3 Background information of the blockchain technology

With Bitcoin, being its first documented application, the blockchain technology came into spotlights

about a decade ago. The goal of Bitcoin is to tackle inequities and corruption of the traditional

financial system by providing an alternative, decentralized and secure payment system for

transacting value between two parties (Nakamoto, 2008).

Fast-forward to present situation, the technology behind the Bitcoin has won a lot of interest and

has become the foundation of other cryptocurrencies beyond Bitcoin as well. Furthermore, the

technology is also regarded as a potential new means for improving processes as it is the base for

new developments such as smart contracts, permissionless and permissioned ledgers (Swanson,

2014; Treleaven, Brown, & Yang, 2017). In times of exploiting digitalization for improving

businesses, organisations are experimenting with the technology, but the outcome is still unknown

(Lokøy & Nyberg, 2018; Panetta, 2018c). However, seeking to eliminate the whole established

“trusted” third party across business processes and beyond financial transactions seems to be

rather utopic at this point in time as there are technical and societal challenges to overcome at first.

For example, a certain degree of centralization for governance is still required (Allen, Lane, &

Poblet, 2019).

1.4 Master’s dissertation structure

The first chapter of this master’s dissertation describes the problem statement, research question

and dissertation objective, provides brief background information on the blockchain technology and

details the research methodology used to solve the research question. The second chapter reports

on a literature review done serving an explanation and thoughts on the blockchain technology. An

important focus of the literature review is on smart contracts and their application. In addition, a

definition of enterprise and business modelling is given in the second part of the literature review.

The third chapter describes three selected application cases, their respective models and case

specific conclusions. The fourth chapter provides cross-case conclusions in the results and

answers the secondary considerations. The fifth and final chapter summarizes the master’s

dissertation, provides conclusions, identifies the research’s limitations and suggests topics for

further research on this subject.

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1.5 Research methodology

1.5.1 Research strategy

The chosen research strategy is a qualitative research by case study approach. Tsang (2014)

argues that a case study approach, used as a qualitative method, has three merits over a

quantitative approach in cross-populations, namely:

1) it supports generalizing to theory,

2) it allows identifying disconfirming cases and

3) it provides contextual information which helps judging whether a case study is

generalisable.

It is important that internal validity, construct validity and reliability are taken into account. Attempts

are made to keep internal validity high by having more than one method for data collection. For this

study, academic literature was revisited as well as a few open interviews were conducted with focus

on the blockchain technology and in support of the cases studied. This approach is also referred

to as triangulation of data. Next, the cases are not focusing on one industry in particular. Instead,

the three different cases are located in different application fields as to keep external validity high.

This master’s dissertation attempts to provide information in the most transparent way, but it must

also be emphasized that the reliability of this research is linked to time, since future developments

may lead to a different outcome (Yin, 2013, 2014).

Yin (2014) also states that the case study approach can be a desired strategy if the following three

conditions are met. The first condition is that the research question must be of the type ‘how’ or

‘why’, thus this condition is met. The second condition is that the observer must not be in control of

the situation. The observer is not exactly in an ex post situation for this research, but situations,

which show how a blockchain technology application will most likely look like, will be considered.

The third requirement is that the focus should be on a contemporary problem. In this case, the

blockchain technology is present in the three cases and the research aims to investigate the impact

on the current situation.

Furthermore, Yin (2009, 2014) notes that other elements are also important for a good execution

of the research. For example, cases should be clearly defined. In this research, the focus is on:

• the deployment of blockchain in the scope of CrowdBC, a decentralized autonomous

organisation (DAO);

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• the Know-your-customer (KYC) application of blockchain in the Belgian banking

environment;

• the Carrefour’s “chickens on the blockchain” as an example of deployment of the

technology in a supply chain environment.

Important is also that the research should develop and test hypotheses as to have a guideline for

the main research question. In this master’s dissertation, the four considerations mentioned are

the hypotheses serving to give guidance to formulate an answer to the main research question.

Finally, the gathered data and hypotheses should verify whether the answers, that are deduced

from the research, are also valid in the real world.

1.5.2 Research design

The research design is according to the guidelines proposed by Yin (2014). A literature study is an

important part before investigating the different cases. Therefore, an in-depth literature study on

the blockchain technology is performed and documented to define the technology and to examine

available knowledge relevant to the scope of this master’s dissertation. Furthermore, a brief report

on the literature study on enterprise modelling techniques is written supporting the selection of the

two techniques used. The literature study also serves as a guideline to define qualitative

parameters which can help to some extent in measuring the impact.

The cases selected are based on three types of situations found in the literature as well as on

interviews conducted whilst also considering the research strategy argued above. At first sight, the

cases meet the requirements of a ‘good’ blockchain case, namely a need for immutability and

multiple actors in a market environment (Gordijn, Wieringa, Ionita, & Kaya, 2019). The following

case types are considered:

A) The situation where the trusted third party becomes obsolete. Thus, there is no central

authority since the blockchain technology promises to replace this.

B) The situation where the trusted third party attempts to use the blockchain technology to

its advantage in becoming more cost efficient instead of becoming obsolete.

C) The situation where the actors in a network work together to create and deliver an end-

product to the consumer, e.g. in a supply chain, were aspects like data security,

customer confidence and traceability are key. In this case the actors have no special

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need for a trusted third party in the existing situation, but collaboration can be

advantageous towards the customers as well as for the business processes.

The order of the cases discussed is based on what is argued to be the ranking of most interesting

features of this emerging technology. Since Nakamoto (2008) proclaimed that the blockchain

technology can lead to a peer-to-peer network without the need of a third party for making

transactions, the case of CrowdBC is analysed first as an example of type A case. This meets the

requirements of Gordijn et al. (2019) as immutability of transactions in combination with the market

environment, where all users are equal, are key elements for a blockchain application. Next, in a

case of type B is analysed as literature and interviews are arguing that the technology can bring

improvements for business processes in financial services amongst other third-party services. For

this kind of situation, collaboration and trust are essential, but a degree of centralization is required

due to a highly regulated industry. It can be argued that the multiple financial institutions create a

‘market’ between them for the KYC process. Finally, Carrefour’s “chickens on the blockchain” is

used as an example of a type C case. This application is discussed last because this kind of

deployment wasn’t the initial goal of blockchain, but research indicated that certain blockchain

features for the blockchain technology may well serve for an environment where trust and

collaboration are desirable (Gordijn et al., 2019; Levy, 2018; Panetta, 2018c).

The data selection is performed using the cases found in the literature study and by conducting a

few explorative interviews. To extract specific data from the interviews, questions were asked in

terms of the enterprise modelling techniques “Business Model Canvas” and “e³value modelling” as

these techniques were used as an investigating baseline. This was especially the case for building

the models for the KYC case. There was also room for open questions and dialogue. After an

interview, a report was discussed with the interviewee to validate the data and models.

The data analysis in this research design shows how the possible impact is analysed. The data

was analysed case per case to make an individual case report. Therefore, the literature of

blockchain technology in general was used as background information. Specific literature and

interviews were used to describe a specific case and to build the models. Per technique an ‘as-is

model’ was built first as to get an idea of the existing case situation. Second, a ‘to-be model’ was

built per technique to get an idea of the most likely case situation with the blockchain technology

applied to it. Depending on certain viewpoints, there can be more than one as-is or to-be model for

a case. Such is the situation in the CrowdBC case. A conclusion based on the literature, interviews

and comparing models is written at the end of every case.

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At the end, cross-case conclusions are drawn within the scope of the technology to increase the

validity of the outcome and to answer the question of generalization (Yin, 2014). Therefore, the

focus also remains on the general impact and on the impact of the specific parameters on business

models and value offerings to customers.

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2. Literature review

2.1 Conceptual delimitation: Blockchain

Since the blockchain technology can be challenging to comprehend, the aim of this literature review

is to explain the most important aspects of the blockchain technology which will be used further on.

This includes a general explanation of the blockchain concept and specific insights on smart

contracts.

2.1.1 General information on the blockchain technology

The blockchain technology gained popularity with Nakamoto (2008) introducing one of the first

blockchain applications in the Bitcoin whitepaper. Since then, many (fin)tech experts and academic

researchers produced short as well as more extensive explanations on this concept. Treleaven et

al. (2017) provide a clear and comprehensible article on how the blockchain technology works. The

same idea is featured in Koeppl and Kronick (2017), but the latter paper gives a more in-depth

description on the terminology. The most considerable aspects of these documents are combined

below to give an extensive explanation on the technology.

The blockchain technology can be seen as an open source technology serving to create a secured

distributed ledger. It should be noted that not all distributed ledgers are blockchains, but that all

blockchains are distributed ledgers. Participants, connected to a ledger by using a computer for

example, form a peer-to-peer (P2P) network and are referred to as nodes. Important to state is that

the nodes have equal privileges. The ledger itself is a digital, decentralized and synchronized

database with the transaction records of its related nodes. An important quality of this technology

is that transactions are irreversible once added to the ledger. One of the main goals is avoiding

sending a copy but sending the digital object itself when making financial transactions. By doing

so, the problem of being sure that the same object hasn’t already been sent to another party can

be tackled (Koeppl & Kronick, 2017; Nakamoto, 2008; Treleaven et al., 2017).

A further explanation on the word ‘blockchain’ gives a view on how the technology works. A ‘block’

represents one or more transactions and is a uniquely identified record. When zooming in on a

block, three aspects are important: the data, the hash of the block and the hash of the previous

block. A block can also contain a smart contract, which is discussed further in this chapter (Koeppl

& Kronick, 2017; Treleaven et al., 2017).

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The data itself depends on the type of the blockchain. For example, if the blockchain is made for a

cryptocurrency, the data will be the details of a transaction between two users of this

cryptocurrency. The data structure that Nakamoto (2008) intended for the transactions was a

Merkle tree. The hash of the block is a string of numbers and letters of a fixed length, created by a

hash algorithm. A hash can be comprehended as the digital identification label of that block and is

also like a fingerprint as it is unique and refers to the data within that block. The hash of the previous

block is used as the pointer of the record of a block which refers to the previous block (Koeppl &

Kronick, 2017; Nakamoto, 2008; Treleaven et al., 2017).

This way, multiple blocks are ‘chained’ together in a chronological sequence by the latter two

aspects and a new block can be added to the last block in the chain after verification by all nodes

through consensus. Communication between the members is established in real time using a digital

peer-to-peer network (the distributed ledger). This method requires an internet connection (or

network connection in case of permissioned ledgers, see chapter 2.1.2) of all members who

participate. All the members get an unalterable record of any changes made to the blockchain, so

it becomes rather impossible for just one member to cheat by making changes to the current

block(s) in the chain (Koeppl & Kronick, 2017; Nakamoto, 2008; Treleaven et al., 2017).

It should be noted that other distributed ledgers other than the “classic” blockchain ledger such as

pegged side chains and non-block DLTs are also being investigated but this is beyond the scope

of this research (Back et al., 2014; IOATA Foundation, 2018).

To give a simplified example of how a blockchain is constructed, imagine if a group of travelers

who want to book together a world tour, consisting out of a sequence of multiple cities. The following

two assumptions are made:

1) All travelers have access to all necessary data such as the different cities, prices of transport

and prices of the accommodations.

2) A booking agency (the third party) is not directly involved in the process of planning the tour.

If this group wants to use a blockchain approach to plan their trip, the travelers must connect in a

P2P network without using a booking agency as third party. Using this approach every friend is

collectively responsible, in contrast to a more traditional approach where one member arranges

everything for the group. In this context, the blockchain contains the world tour and each city trip is

a different block in the chain. Since a block in the chain is just one city, everybody must accept the

city and the prices of transportation and accommodation before the block is added to the chain.

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This process proceeds city by city. Suppose that not everybody agrees on adding a certain city,

the rest of the planning of the world tour will be blocked until further agreement by all on either

changing the city or changing the transportation or accommodation of the specific city trip.

Everybody also gets a copy of the cities accepted before the one that has been blocked. This way,

nobody may be fraudulent, or put earlier made agreements to question.

Figure 2.1: Simplified example of a blockchain transaction (Thomson Reuters, n.d.)

As can be seen in the Figure 2.1, which illustrates the use of blockchain for financial transactions,

the computers are the nodes, each represent one member and they can directly communicate with

each other. A member can join the network by for example installing a digital wallet which is in fact

a software protocol on their computer, phone, tablet… This way, every node gets a copy of the

data, the data is distributed around the network and the network is also open so new members can

join easily. By using asymmetric cryptography, the content of the transactions is made secure while

the users can operate anonymously in a public network. Upon joining the network, every user gets

a public and private key pair for encrypting and decrypting data. The public key can be seen as a

user’s digital address. Consequently, every user can see each other’s public key. The private key

is unique for every user and must not be shown to other users. The public and private key pair

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correspond to each other just like a username and password. Thus, the private key cannot be

derived from the public key. Figure 2.2 shows how cryptography works. The sender creates a hash

value of the original message that must be sent. The hash value is a string with predefined length

(not shown in Figure 2.2). The sender encrypts the hash value with the receiver’s public key (light

green in Figure 2.2) to create the encrypted message which is then sent to the receiver. The

receiver is the other party and can check whether the message comes from the sender. Therefore,

this person needs to perform two actions:

A) Also create a hash value of the intended message.

B) Decrypt the encrypted message by use of the receiver’s private key (dark green in

Figure 2.2).

If the hash value of A is identical to B, the transaction can be approved as it is verified that the

message came from the sender (Blockgenic, 2019; Drescher, 2017; Treleaven et al., 2017). This

way, the public can see that an amount is sent, but the public can’t see who the actual parties are

and what the exact content of the transaction is since the latter is encrypted. As a result of the

distributed ledger, a “trusted” middleman is no longer required between transacting parties

(Nakamoto, 2008; Treleaven et al., 2017). Nakamoto (2008) remarked in the whitepaper that

creating privacy using the key pair isn’t fully fool proof as multiple transactions could link to a

common owner, unless a new key pair would be used per transaction. It must also be remarked

that cryptography is not 100% safe to use as there have been reports of stolen cryptocurrencies

for example. The theft happened due to phishing and other hacking attacks at the users, leading

to private keys probably being stolen (Valinsky, 2019).

Figure 2.2: Simple visualization of asymmetric cryptography (Blockgenic, 2019)

The paragraph above only gives an explanation of the key elements of a blockchain and its security

but gives no answer on how peers can accept a new block, why the copy becomes unalterable and

why it is almost impossible to cheat. A new chunk of data can only be added after acceptance

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(verification) of that block by all members in the network. The underlying mechanism are consensus

algorithms which are in fact key to constructing a successful blockchain in which each member

receives a synchronized copy and that can be trusted by all nodes. This can be seen as a set of

rules which point out what path for updating should be taken. Because new (trans)actions are first

validated in a new block and further managed by these consensus algorithms, all nodes can verify

the block’s authenticity and can accept changes to the ledger. When one member wants to make

changes to a record in the blockchain, then this means that the hash of this record will also change.

The participating members will see that the new blockchain does not correspond to their own copy

and a new consensus among all members will be required, otherwise the change will be undone

(Drescher, 2017; Peck, 2017; Treleaven et al., 2017).

Building on previous paragraph, Koeppl and Kronick (2017) add that to make changes, computation

power is required for making the update, i.e. a new block, and in return a reward is given to this

peer. This process is often referred to as ‘mining’, especially in terms of cryptocurrencies. As

already mentioned before, a new block is linked with the previous block and therefore it becomes

harder and more expensive to change longer transaction histories within the ledger because it

would take more resources for altering a longer chain. The consequence is that there is only little

incentive to cheat because the peers will compete for the right for updating the ledger in order to

get the reward. More recent implementations use concepts like Proof-of-Work (PoW) protocols,

where the competition ‘winner’ is verified by the other nodes as the one who spent a lot of

resources. Another protocol, which is still in development, is the Proof-of-Stake (PoS), where the

probability of being the ‘winner’ to update the ledger depends on the amount of resources -their

stake- a node has used in this particular chain. To conclude this paragraph, by using certain

software protocols and giving a reward based on the work a node has done, it is logical that a

longer blockchain has a positive effect on the trust and security amongst participating members.

The downside of a longer blockchain is that more computational resources are required (Koeppl &

Kronick, 2017).

As a recapitulation of what is stated above, the professional services company PwC uses a self-

explaining chart in one of their technology blogs to describe a blockchain process (Morrison &

Sinha, 2016). This chart can be found in Attachment 1 to this master’s dissertation.

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An important remark must be made: The literature above is mainly focused on the validation of the

transaction and keeping the ledger immutable. However, this doesn’t address the terms of the

transaction. For example, it should be checked if someone has paid €100 or only €10. Whether

smart contracts could be a solution, is discussed in the next subchapter.

Concluding this subchapter, the blockchain technology is a means for parties to transact with each

other in a secure way without the necessity of a third party. This means can have implications on

how we communicate and transact over electronic networks. Many people think that the blockchain

technology is equal to cryptocurrencies like Bitcoin. Blockchain is in fact much broader than this as

will be shown in the next subchapter where the innovative character of blockchain technology is

addressed.

2.1.2 Blockchain technology innovations

This subchapter talks about the latest implementations or expansions. Since the technology behind

blockchain got a lot of attention, in recent years more and more companies started to investigate if

using the blockchain technology could influence how business is done. The most important

novelties worth mentioning are permissioned ledgers and smart contracts. At first, the differences

between public and permissioned ledgers will be discussed. The remainder of this subchapter gives

an in-depth explanation and discussion on smart contracts, along with associated problems and

solutions.

The blockchain technology described in previous subchapter concerns primarily public blockchain

ledgers. In fact, there have been other types of blockchain ledgers developed in the recent years.

The academic researchers Peters and Panayi (2016) distinguish different types based on two

categories: access to the blockchain ledger and whether nodes are authorised to validate and verify

transactions.

Public and private blockchain ledgers are two types of blockchain ledgers that fall into the first

category. The difference between public and private is that reading or submitting transactions is

restricted to members within the organisation or consortium that uses the private ledger. Other

parties can join after being granted access by the organisation or consortium, e.g. by voting. Also,

a user typically has no access to transactions other than these of the individual of the organisation.

Another feature of private blockchains is that one organisation is in control of the write permissions

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while the read permissions can be either public or limited to selected nodes. On the contrary,

everyone can join and participate in a public ledger. Moreover, everyone can read and/or submit

transactions and participate in the consensus process. Consequently, all nodes have access to the

same information while there is no central party. Public ledgers are often used for cryptocurrencies

(Peters & Panayi, 2016).

The second category also includes two types of blockchain ledgers, namely permissionless and

permissioned blockchains. Permissionless means that any member is authorised to validate

transactions and get a monetary reward in return by virtue of an incentive mechanism such as

PoW. Furthermore, all nodes must agree using consensus protocols on adding the new block. This

way, participation is encouraged, which is vital for a permissionless blockchain ledger. Examples

of these kind of ledgers are Bitcoin and Ethereum. On the contrary, only a selection of nodes is

allowed to validate and/or verify transactions in permissioned blockchains. Additional nodes can

also perform validation and/or verification after agreement of current members. Permissioned

blockchain ledgers are argued to be more suitable in use cases like KYC as they have

fundamentally different operations and outcomes (Swanson, 2014). They should be compatible

with current business applications to streamline business processes. Moreover, the actors aren’t

anonymous which leads to being accountable. Often, the goal is to store transactions of off-chain

assets that are referred to by a digital identity in the blockchain. Since there are fewer nodes that

accept or reject changes to the ledger, scalability could be increased. However, only a few nodes

in control of validation or verification could lead to the permissioned ledger being not censorship

resistant in comparison to permissionless blockchain ledgers. An example of a permissionless

ledger is the Hyperledger Fabric (Peters & Panayi, 2016). Brody et al. (2017) add to this that hybrid

consortium blockchain ledgers, which allow the different blockchain ledgers to interact with each

other by using sidechains can also exist. As such transactions between multiple blockchain

networks are enabled and the network profits from the advantages of both ledger types. Thus, they

can be partially decentralized.

Concluding, when overlooking the description of permissioned blockchain ledgers, one could

question whether blockchain will make the trusted third party obsolete.

Beck, Müller-Bloch, and King (2018) illustrate the different types of blockchain ledgers as shown in

Figure 2.3, based on the description in the journal article provided by Peters and Panayi (2016).

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Figure 2.3: Types of blockchain ledgers (Beck et al., 2018)

In practice, permissionless blockchain ledgers are public and permissioned ledgers are mostly

private. From here on, we will distinguish the different types by using the terminology permissioned

and permissionless ledgers to keep clarity (Peters & Panayi, 2016).

Another development of the recent years is the ability to program smart contracts within the

blockchain. Treleaven et al. (2017) perceive smart contracts as a piece of code that runs on the

blockchain. This is as a set of coded rules, i.e. a protocol, that nodes connected to the blockchain

collectively agree on. The smart contracts could automatically execute the rules and move a certain

value from one party to another. This could lead to automation of (digital) processes and thus

resulting in more efficiency. Furthermore, the transactions are kept and encrypted in the

decentralized ledger, meaning that parties with corresponding keys can decrypt the data they are

entitled for. This could lead to more transparency for these parties. In general, smart contracts

could be a solution to validate if the contents of a possible transaction are in line with the agreed

terms.

To give an idea of the possible impact of smart contracts, Morrison (2016), senior research fellow

at PwC’s Center for Technology and Innovation, provides Figure 2.4 showing the differences

between traditional and smart contracts.

In his blogpost, Morrison (2016) nuances that not all potential advantages of smart contracts are

applicable to every case. For example, it could be that smart contracts cost more, depending on

the current IT systems or practices of the businesses.

To get potential out of smart contracts in a blockchain context, several obstacles must be

overcome. First, the regulatory environment is lagging concerning the governance that should

accommodate smart contracts. Second, our society has complex business ecosystems.

Businesses’ processes and procedures should be revisited, and new use cases need to be

developed in practice to convince current companies to adopt the technology. Third, smart

15

contracts have to compete with mature and other growing IT systems, e.g. systems in the cloud.

Forth, even though there is research ongoing, there is no standard for best practices yet (Morrison,

2016).

Figure 2.4: The potential advantages of smart contracts compared to traditional paper-based contracts

(Morrison, 2016)

It should be stated that the term ‘smart contract’ is not new as Nick Zsabo, computer scientist and

cryptographer, already made notion of smart contracts around 1994. Thus, smart contracts can

also be viewed separately from the blockchain technology. He defined a smart contract as follows:

A smart contract is a computerized transaction protocol that executes the terms of a

contract. The general objectives of smart contract design are to satisfy common contractual

conditions (such as payment terms, liens, confidentiality, and even enforcement), minimize

exceptions both malicious and accidental, and minimize the need for trusted intermediaries.

Related economic goals include lowering fraud loss, arbitration and enforcement costs, and

other transaction costs. (Nick Szabo, 1994, para. 1)

Kiviat (2015) tries to link the definition above with possibilities for the blockchain technology in the

light of exchanging value. The author gives the example of digital rights management (DRM) to

show that smart contracts already found applications before the blockchain technology was

developed. In the case of DRM, the smart contract is seen as a centralized network like Apple’s

iTunes Store for example. The goal was to fight copyright infringement as it limits the ability to copy

digital audio files with encryption and as it enforced the iTunes Store’s Terms.

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In the light of the blockchain technology, smart contracts could be stored within the ‘application

layer’ of a blockchain. There is also a layer beneath it, called the ‘fabric layer’ which is in fact the

blockchain protocol (Glaser, 2017). Smart contracts can have the same functionalities as Szabo

(1994) described and can be used for the same purposes as the DRM example. Yet, they are

decentralized and thus do not rely on a centralized server for recordkeeping and enforcement. To

argument this, Kiviat (2015) based himself on Tim Swanson’s book Great Chain of Numbers - a

guide to smart contracts, smart property and trustless asset management. Swanson (2014) sees

smart contracts as a tool to automate human interactions in the context of exchanging value in a

decentralized way. This tool is merely an algorithm, i.e. some code, which can execute, enforce,

verify and constrain contractual terms for exchanging value by itself. Moreover, Swanson (2014)

argues: “They do not have a physical enforcement arm the way legal contracts do. Rather, because

they embody complex contractual relationships in computational material, they move certain

defined asset(s) automatically under certain conditions” (Swanson, 2014, p. 16).

This way, transacting parties can design contractual relationships for automatically transferring

value without additional costs of monitoring and enforcing the terms (Kiviat, 2015). Swanson (2014)

states that smart contracts aren’t silver bullets for solving any dubiety or other misunderstandings

in human interactions. Moreover, there is still discussion going on the governance of smart

contracts, for example see the journal article The Governance of Blockchain Dispute Resolution by

Allen, Lane, and Poblet (2019). Yet, it is argued that these ‘contracts’ are broader than just legal

contracts, e.g. applications for financial instruments. Adding to this, new markets will be developed.

This could be a disintermediated contract market where there should be less to no concern about

counterparty risk thanks to smart contracts (Kiviat, 2015; Swanson, 2014).

To code a smart contract and thus the open protocol of a contractual agreement, an (open source)

blockchain platform could be used. Take for example the Ethereum Foundation that developed the

Ethereum blockchain (The Ethereum Foundation, 2019). Using the Ethereum Virtual Machine

(EVM) and the programming language Solidity, a user can code executable software applications

for smart contracts on the distributed computer network. Solidity is an object-oriented programming

language for smart contracts. As stated before, the smart contracts manage the behaviour of

transacting nodes. In this case, this happens within the Ethereum state. Doing so smart contracts

can be programmed for voting, crowdfunding, and so on (Solidity, 2019).

An important note should be made on coding smart contracts: once the smart contracts are being

implemented, they become difficult to recode. Thus, this is an important challenge since the

business context is complex and can change quickly (Beck et al., 2018).

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2.1.3 Possible blockchain applications

This subchapter focusses on practical applications of the blockchain technology and the use of

smart contracts.

Pastor Luis (2015), Business Consulting and Innovation Partner at Grant Thornton, provides an

overview in his blogpost about the possible impact the technology can have on business models in

various industries. Figure 2.5 illustrates the overview and gives an idea of all sorts of cases that

are being the subject of research.

Figure 2.5: Overview of possible blockchain cases (Pastor, 2015)

According to Figure 2.5, the blockchain technology doesn’t only have possible use cases in the

financial services but in a variety of different industries. For example, it could be used in the health

care industry where one health care agency could share the patient’s encrypted data on the

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common distributed ledger with the patient and the other health care agencies (Pastor, 2015;

Weinberg, 2019).

In line with Figure 2.5, it can also be noticed that some large IT companies are developing possible

solutions that can create a higher trust, security, transparency and efficiency compared to

traditional systems. IBM, for example, has developed their own permissioned blockchain ledger

called the ‘Hyperledger Fabric’. They are working on numerous cases in various industries among

which are solutions in food supply chain, global trade (shipment), identity management and so on.

To develop their solutions, they closely work together with key players of a certain industry such as

Carrefour in the food supply chain or Maersk and key ports when it comes to shipment. It should

also be noted that some of these applications also require other technologies such as Artificial

Intelligence (AI) or Internet of Things (IoT) to reach improved efficiency by automatically linking

physical products with their respective digital identity on the ledger (IBM, 2019a).

The case study analysis in chapter three discusses three (possible) use cases of the blockchain

technology with respect to the research objective of this master’s dissertation. The next subchapter

addresses the feasibility of the applications in current and future business context.

2.1.4 Blockchain technology: a game changer?

Since the previous subchapter argues that there are many fields for applications, the question can

be asked if the blockchain technology will have a significant impact on our operations and business.

The reader should also get an idea when certain applications could become widely adopted.

The globally leading research and advisory company Gartner closely monitors new IT trends such

as blockchain. According to Gartner, blockchain is more than just a technology as it could provide

opportunities for businesses to interact, transact and represent assets in a digitalized manner.

These opportunities result from the technology’s capabilities to lower costs, trace transactions by

increasing transparency and collaborate with untrusted parties without the need for intermediaries.

However, the (pure) blockchain technology is argued to be immature and difficult to scale as of

speaking mid-2018. This is discussed in the next paragraphs (Panetta, 2018a, 2018b).

Gartner is also known for their hype cycles, which give indications for the emerging technologies’

development phases. Figure 2.6 shows the hype cycle for 2018 where blockchain technology in

general is now reaching the ‘Trough of Disillusionment’. The prognosis for reaching maturity and

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adoption is five to ten years from mid-2018. It is stated that the blockchain technology has passed

most of the hype where expectations are too high. At this point, many blockchain concepts face

practical implementation issues because of the unreadiness of current IT and business systems to

get maximal potential out of blockchain technology’s capabilities. Moreover, there are less

businesses exploring and implementing the technology compared to in the ‘Peak of Inflated

Expectations’ (Levy, 2018; Panetta, 2018a). In Belgium, for example, the fintech platform B-hive

saw a decrease from 25 percent in 2018 to 11 percent in 2019 concerning the amount of Belgian

companies developing blockchain applications. It is argued that the decrease happened due to an

attention shift to other (emerging) technologies such as cloud solutions and AI. It should be

remarked that the shift in Belgium happens in order to mainly follow the needs of financial services.

Current needs are mainly applications that help in analysing data. Moreover, AI also has potential

for recognizing abnormal patterns (Suy, 2019). Other researchers add to this that many third parties

only “experiment with the technology to learn their enemy” (Gordijn et al., 2019, p. 2).

Figure 2.6: Gartner's Hype Cycle for emerging technologies 2018 (Panetta, 2018a)

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Levy (2018) states in her blogpost ‘The reality of Blockchain’ that the different possible applications

using the blockchain technology can also be put on a hype cycle. This can be seen in Figure 2.7.

Depending on the sector and type of application, there are differences. For example, it clearly

shows that some applications such as cryptocurrencies (Trough of Disillusionment) are in a further

stage than applications that are being developed for customer service (Innovation Trigger). In order

to avoid the hype, Panetta (2018c) argued in one of the Gartner’s articles that CIOs should check

whether they require the core components of this technology to create a successful blockchain

project. Otherwise, it is better to investigate blockchain-inspired solutions which use certain benefits

and parts of the blockchain technology, but which aren’t the pure blockchain technology with its

premised highly decentralized consensus models. The core components are (i) encryption of data,

(ii) immutability of records, (iii) distribution across all nodes, (iv) decentralization of trust and (v)

tokenization.

Figure 2.7: Hype Cycle for blockchain applications (Levy, 2018)

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Furthermore, the technology faces many challenges according to Gartner that are also in line with

what authors like Kiviat (2015) and Morrison (2016) have argued in the previous subchapter. First,

a blockchain project requires collaboration of all stakeholders. There are strategic challenges linked

to this because competitors may need to collaborate, for example in a consortium. Second, the

technology lacks technical interoperability and scalability between the systems of different complex

businesses. Third, there remain security issues. For example, one person’s key pair could be stolen

and thus hackers could access vulnerable data. Forth, hidden data management issues, such as

the GDPR law, needs further investigation. Fifth, there also challenges because of established

laws. Legal, tax and accounting frameworks should be developed. Sixth, despite many

opportunities, current cost benefit analyses conclude the total cost of ownership of too high

compared to the possible return (Levy, 2018; Panetta, 2018a, 2018c). Peck (2017) adds to these

challenges that there are also issues with blockchain and smart contracts which need to be

revolved. First, current blockchains weren’t designed to store large amounts of data. It could lead

to a so called ‘blockchain bloat’ if there is too much data on the ledger, resulting in slower

processing of the records. IBM tries to resolve this in the Hyperledger Fabric by storing the raw

data off-chain and only storing hashes of that data on the blockchain itself. The raw data can be

privately accessed by the authorised parties using a ‘gossip protocol’ (Hyperledger Fabric, 2019).

Second, even though a contract is put on the blockchain using a smart contract for example, the

contract doesn’t automatically interact with the real world yet. It is stated that everything about the

contracts has to be programmed. Even basic programs can be challenging to program since

everything that could go wrong should also be anticipated in the program. If there’s no answer to

the problem in a certain situation, then the parties should need to go off-chain for that situation

(Peck, 2017).

Even though many challenges exist, the Gartner’s articles still argue that the technology can have

impact on operating and business models when complete blockchain solutions have been

developed. Levy (2018) states that business models will change as it changes the way operations

and businesses are run. The impact is not seen yet due to the fact that most of the current

applications are being built in existing models whereas the actual goal is to disrupt and

disintermediate centralized entities, operations, processes and business models in general.

Business leaders should ask themselves questions such as who needs to be in the blockchain, if

it is possible to use existing technologies, who has control of the service model to (new) customers,

what the impact will be on cash flows, and so on (Levy, 2018; Panetta, 2018c).

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In line with Levy (2018) about applications being built in existing models, Lokøy and Nyberg (2018)

conclude in their thesis that their interviewed businesses find it difficult to analyse the impact on

their business models due to the decentralized aspect of the technology. Consequently, this could

lead to more exploration on permissioned blockchains which do less interfere with the existing

business model. They also question whether these permissioned blockchains bring new value

propositions and business opportunities since the benefits of the blockchain technology compared

to other (cloud) storage technologies come from the decentralized P2P network. The authors

conclude in their thesis that further research is necessary to measure the impact on business

models (Lokøy & Nyberg, 2018). EY, one of the leading companies in advisory, adds that it should

also be kept in mind that the technology raises concerns on which agencies should establish the

standards and protocols for a certain P2P network (Cudahy et al., 2016).

Other literature such as Holotiuk, Pisani, and Moormann (2017) also see possibilities in existing

business models. Their research focuses on the payment industry, which is argued to be one of

the first industries to see complete blockchain solutions (Panetta, 2018a). The authors conducted

a Delphi study on 45 experts in the industry who also have knowledge of the blockchain technology.

The experts came to the consensus that the technology will impact the business models as three

new service areas are proposed: direct transactions in a P2P network, improving international

transactions and new blockchain services. The experts argue that blockchain can provide a solution

to the inefficient payment infrastructure thanks to the technology’s capabilities. First, efficiency can

be increased by a common infrastructure which is also automated with the help of smart contracts.

Second, service offerings can become cheaper when the technology replaces expensive

intermediaries such as currency exchange businesses. Third, transactions become more easily to

trace leading to more possibilities for data analytics. This can result in better services such as fraud

detection and prevention, conversion of traditional to blockchain payments and better personal

management. Furthermore, new BMs can come into existence like the distributed autonomous

organization (DAO) for example. These organizations could have the business rules coded and

executed under certain conditions within the organization. To conclude, there will be impact in both

existing and new BMs. The impact will mainly be on the financial structure of the BMs in the

payment industry. Furthermore, existing services can be improved, and new services can also be

created. Finally, certain BMs will render obsolete as blockchain can replace the trusted aspect of

intermediaries such as exchange value businesses (Holotiuk et al., 2017).

Concerning the public sector, Hileman and Rauchs (2017) state in their case study that there can

be an impact thanks to the argued aspects above including improved operational efficiency,

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traceability by increased transparency within the network and so on. Although the argued benefits,

the case study participants state that there are challenges to be overcome. Immature technology,

scalability, unclear regulatory framework, performance concerns and confidentially issues are

argued to be the most significant. Furthermore, some participants such as central banks stated

they are rather reluctant to overhaul existing systems and infrastructure that already give the

expected (customer) service. Political input could also play a role in the decision of overhauling

business models and the processes.

2.1.5 Existing frameworks to describe, explain or predict the impact

Literature lacks theoretical and practical frameworks according to Beck and Müller-Bloch (2017).

That’s why the researchers developed a framework for existing companies to engage with the

radical innovation blockchain has to offer. They argue blockchain requires new business methods

and materials since the radical innovation also brings a lot of uncertainty with it. The proposed

framework is based on the framework for radical innovations in established firms (O ’connor, 2006).

Moreover, a case study was conducted at a leading investment bank to investigate how companies

can respond and how the companies can build capabilities to successfully engage with the

technology. The framework should be seen as a blueprint that illustrates how the process of

discovery, incubation and acceleration with blockchain can look like. This can be found in Figure

2.8. It is concluded that organisations in the financial sector and engaged with blockchain follow

the three steps of the framework. Furthermore, most of the firms are situated in the incubation

phase. More information about the framework is given in the paragraph below the Figure 2.8 (Beck

& Müller-Bloch, 2017).

In the discovery phase, it is important to develop exploratory and conceptualization skills to

recognize opportunities for the business. This can be done by internal and external research.

Afterwards, the findings should be communicating to the members involved in the business and

processes to motivate them for possible change. To transition to the next phase, financial

resources, a proactive environment and involvement of business functions and external vendors

are important. The next phase, incubation, has experimentation as a core component to able to

transform opportunities into business proposals. Hypotheses for new BMs as well as use cases of

new applications or processes should be created. An ‘innovation laboratory’ can be useful for

collaboration between the different business units and external stakeholders. This way, diverse

knowledge can be merged as well as involving owners of business processes at an early stage to

24

minimize potential resistance to change. The end result at this phase should be a prototype of a

new application, a new business process or even a new business model. To transition to the final

phase, business process owners need to take over the project. This way, the project can be

exploited to become viable on its own in the acceleration phase. Consequently, the borders of

organizations will fade as businesses become more decentralized (Beck & Müller-Bloch, 2017).

Figure 2.8: Blockchain Innovation Process Framework (Beck & Müller-Bloch, 2017)

In the light of DAOs, an ‘Extended IT Governance Framework’ was recently published by Beck,

Müller-Bloch, and King (2018). They argue that a blockchain economy, i.e. a system where agreed-

upon transactions are autonomously executed in a decentralized manner by the defined protocols,

requires rethinking of governance. Figure 2.9 shows this framework, which is based on three

governance dimensions: decision rights, accountability and incentives.

25

Figure 2.9: Extended IT Governance Framework (Beck et al., 2018)

The authors analysed the blockchain economy compared to the current digital economy. They

argue decision rights are decentralized in the context of consensus. By blockchain’s nature, records

are kept decentralised, but the transactions are also decided upon decentralized consensus.

Furthermore, disagreements could be split from the main group by copying the code and building

on that separately in line with their goals. The authors also note that the blockchain economy at

present still has a high degree of centralization. Addressing the dimension accountability,

blockchain promises to be more technically enacted as transactions are being autonomously

enforced by smart contracts. However, errors in code or even worse, when a smart contract can’t

be implemented in a certain situation will lead to disputes that require to be resolved by institutions,

i.e. in more a centralized way. It is argued that institutions will continue to play key roles for

accountability in the upcoming years. Addressing the final dimension incentives, it is stated that

incentives are important for an effective blockchain economy since they are a necessary piece for

achieving consensus. Therefore, incentives should be aligned (Beck et al., 2018).

The authors also state that a blockchain economy is highly dependent on the governance

mechanisms and require further research, especially in the light of DAOs. Moreover, previous

subchapters argued the technology could bring in decrease in transaction costs and other

economic activities, but the costs for governance can possibly be high. Smart contracts will be

costly to develop and will also accompany high risks for coding errors and for changes in the

business environment (Beck et al., 2018).

26

Apart from academic frameworks, software developers offer frameworks for businesses in a multi-

party environment to accelerate enterprise adoption by meeting key requirements on an enterprise

level. Microsoft, for example, developed the Confidential Consortium Framework (CCF) which is

open source and compatible with many blockchain protocols such as Ethereum. The framework

offers a different approach to constructing the ledger without the trade-off of one key enterprise

requirement to another on the cloud platform Azure. It should be stressed that the framework is

specifically designed for confidential consortiums, thus all nodes are declared and controlled.

Furthermore, the framework is not limited to blockchain applications only (Microsoft, 2019).

IBM, for example, offers the Hyperledger which is hosted by The Linux Foundation and which

contains distributed ledger frameworks. One example of such framework is the Hyperledger Fabric.

This is also an open source protocol that the community can use to develop transparent, reliable

and interoperable enterprise blockchains. The goal is to power business transformation by

providing the ability to create frameworks and codebases for diverse industries ranging from

healthcare to supply chain (IBM, 2019a; The Linux Foundation, 2018).

As already argued in previous subchapters, it can be concluded that further research is necessary

to investigate whether the blockchain technology will impact businesses in practice. There are few

frameworks to measure the impact in academic literature. Software developers are building

frameworks for the community to transform businesses but especially frameworks for governance

need to be refined to explain and predict the impact for a cost benefit analysis.

2.2 Enterprise modelling & business models

This chapter covers the concepts of enterprise modelling and business models. The literature study

performed will serve as the basis for choosing two enterprise modelling techniques to be used to

determine the blockchain adoption effects.

2.2.1 Defining Enterprise modelling

A significant amount of academic literature discusses the enterprise modelling (EM) concept. The

literature also discusses various modelling methods that can be used for business-IT alignment

(Sandkuhl et al., 2016).

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Bernus (2001) explains that the term enterprise modelling equals a collective name used to refer

to models used in enterprise engineering and operation. Previously and in line with this, Fox and

Gruninger (1998) defined EM as a computational representation of an enterprise. The goal of an

EM technique is to provide a language which aids in defining the enterprise and the associated

business logic. Consequently, using such language the enterprise’s structure, activities, processes,

resources, people, objectives and limitations become clear in a holistic abstraction. The model

provides information serving to answer business questions on an enterprise design and operations

level. An EM technique can also be used to discover the impact of modifications as it serves

modelling the existing business as well as a how the enterprise should be organised (Fox &

Gruninger, 1998; Wortmann, Hegge, & Goossenaerts, 2001).

Frank (2014), professor of Business Informatics at the university of Duisburg-Essen, argues that

there also can be an interorganizational aspect in EM. Therefore, he defines an enterprise model

as follows:

An enterprise model comprises conceptual models of software systems, e.g., object or

component models, that are integrated with conceptual models of the surrounding action

systems, e.g., business process models or strategy models. Action system and information

system are not limited by the boundaries of a particular organization. Instead, an enterprise

model may represent interorganizational aspects as well. (Frank, 2014, pp. 942–943)

Furthermore, he notes that there are three interrelated purposes of an EM, namely, to foster

communication and collaboration, to control and to change the enterprise. In the light of this

master’s dissertation and based on the blockchain literature, the interorganizational aspect will be

of importance for the cases considered. The purposes are related to the business-IT alignment and

can be refined into high-level requirements. It is important that the abstractions are understandable

for all the stakeholders. Therefore, corresponding modelling tools should be used (Frank, 2014).

2.2.2 Importance of enterprise modelling

EM is important for better understanding of the enterprise’s structure, processes and requirements.

Partly by previous knowledge and artefacts, an abstraction of the company is visualized. The

abstraction is not only communicated to the stakeholders, it also helps in improving the business-

IT alignment thanks to modelling the business logic, business processes and information systems

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in one framework. Moreover it can help to improve collaboration between different departments

and partners (Frank, 2014; Sandkuhl et al., 2016).

Even though EM exists for some time, Sandkuhl et al. (2016) argue that EM still has potential to

grow in importance, since only a small number of people in an organization develop and analyse

the models. Consequently, just a fraction of it is captured at this moment. For example, how people

use and communicate about the models can be enhanced.

2.2.3 Defining business models

The term business model (BM) has been around for over half a century. Since the 90’s it has been

a focal point for academic research, most likely because of the rise of the Internet (Osterwalder,

Pigneur, & Tucci, 2005). According to Zott, Amit, and Massa (2011), there is no commonly accepted

definition, as researchers use definitions which fit their study the most. However, there the following

similarities are observed:

• The prime focus is on the organisation itself but in some cases boundaries beyond the

organisation are considered as well.

• BMs are holistic abstractions that describe how business is done.

• A core in business models is also a focus on the activities of the organisation and its

partners.

• BM’s typically explain where value is created and captured (Zott et al., 2011).

From the blockchain literature review, it is clear that the emerging technology can be seen as a tool

to influence business logic as it allows creating a P2P network for example. Moreover, The

definition by Osterwalder et al. (2005) suits the research question of this master’s dissertation the

most as it also states that BMs explain the value offerings to the customer segment(s):

A business model is a conceptual tool that contains a set of elements and their relationships

and allows expressing the business logic of a specific firm. It is a description of the value a

company offers to one or several segments of customers and of the architecture of the firm

and its network of partners for creating, marketing, and delivering this value and relationship

capital, to generate profitable and sustainable revenue streams. (Osterwalder et al., 2005,

p. 10)

29

They argue that a BM can be seen as a business plan to describe business logic in a simplified

way. A BM should also focus on value creation and on how an enterprise should be organised to

meet the business requirements. Consequently, it should also picture the financial consequences

(Osterwalder et al., 2005).

Veit et al. (2014) argue that firms can use BMs in times of technological changes and increased

competition. Roelens and Poels (2015) state that BMs are crucial in business-IT alignment as they

bridge the gap between design of the enterprise strategy and the enterprise processes. BMs do

not only communicate IT requirements but can also help in identifying business opportunities which

could be exploited using IT. The relationship between strategy and BMs is bidirectional:

1) a BM can be developed to operationalize the strategy and

2) a BM can be used to evaluate if an enterprise operationalizes its current strategy.

Linking to the previous chapter about enterprise modelling, BMs can be conceptualized with the

help of EM as these techniques offer an explicit representation of the company. A large-scale

adoption of this approach is however lacking due to a different focus in current EM languages. This

is the reason why Value Delivery Modelling Language (VDML), a language with a focused BM

viewpoint, was developed which also encompasses other EM techniques such as the Business

Model Canvas and e³value modelling technique amongst other techniques (see chapter 2.2.4)

(Roelens & Poels, 2015).

2.2.4 Chosen enterprise modelling techniques

The objective of this master’s dissertation is to measure the impact using two EM techniques.

These two techniques are explained and motivated in chapter 2.2.4.1 and 2.2.4.2.

2.2.4.1 Business Model Canvas

The Business Model Canvas (BMC) technique was developed by Osterwalder and Pigneur (2010),

serving as a conceptual and agile management tool with the main goal of visualising and explaining

how the analysed company creates and capture value. Therefore, the value proposition and the

way how this proposition is monetized are important. This technique is applicable for existing

companies as well as start-ups. In addition, an existing BMC is adaptable when enterprises evolve

(Osterwalder & Pigneur, 2013). The fact that this EM technique doesn’t include the company’s

strategy isn’t a problem in this master’s dissertation, as, based on the blockchain literature review,

the technology is still in its infancy.

30

Figure 2.10: an empty Business Model Canvas (Osterwalder & Pigneur, 2010)

Previous research by Osterwalder, Pigneur, and Tucci (2005) states that a BM has four dimensions,

namely: Product, Customer Interface, Infrastructure Management and Financial Aspects.

Osterwalder and Pigneur (2010) define nine interrelated building blocks in the BMC which are

based on the four dimensions. Together, they form the overall BM of a company. Figure 2.10 shows

the framework, which can be filled with key words comparable to notes on a post-it. The blocks are

explained in the paragraphs below.

The dimension “Product” is represented by the building block Value Proposition. It is centred in the

middle of the BMC since this is one of the most important blocks. It answers the what-question by

providing an overview of the products and services that are valuable to the customer segment(s)

and thus satisfy their needs. The value proposition shows why customers prefer the analysed

company (Osterwalder & Pigneur, 2010; Voigt, Buliga, & Michl, 2017).

The second dimension, “Customer Interface”, includes the three building blocks on the right:

Customer Segments, Channels and Customer Relationships. Channels are ways to get connected

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with the targeted customer segment(s). These can be communication, distribution and sales

channels. Hereby, Customer Relationships are important as they describe the link between the

company and each segment (Osterwalder & Pigneur, 2010).

“Infrastructure Management” is the third dimension and includes the three blocks on the left: Key

Resources, Key Activities and Key Partnerships. Key Activities are necessary to keep the business

running. Therefore, the input of Key Resources is required. These are assets used in offering and

delivering the Value Proposition. A company can also have Key Partnerships which are outsourced

activities and resources which are obtained from outside of the enterprise (Osterwalder & Pigneur,

2010).

Finally, the dimension “Financial Aspects” is represented by the blocks Cost Structure and Revenue

Streams. To provide the product or service, the BM elements will come at a price. An organisation

creates revenue by offering Value Propositions in a successful way (Osterwalder & Pigneur, 2010).

The reasons why the BMC is chosen as one of the two techniques are the following:

1) It visualizes and explains how business is done by breaking the (often complex) company

into smaller building blocks which are understandable to all stakeholders.

2) It shows how value is created and captured.

3) It can be used for all sorts of companies, ranging from start-ups to large incumbent

organizations.

4) As a BMC can be easily adapted to a changing business, the technique suits this research.

2.2.4.2 E³value modelling

The e³value technique is a conceptual modelling tool which is used in requirements engineering.

The tool was developed by Gordijn and Akkermans (2001) and further refined since then (Gordijn

& Akkermans, 2003, 2018; Gordijn, Yu, & van der Raadt, 2006). The focus is on the ‘value’ concept,

as it describes “how economic value is created and exchanged within a network of actors” (Gordijn

& Akkermans, 2001, p. 1). It models value objects, actors and value exchanges. Thus, it visualises

the business logic in terms of its value network. The tool combines an IT systems analysis in

combination with an economic value perspective. This way, e-business models can be understood

as they require an IT system that works in an interactive and distributed context. In the context of

e-businesses, economic value creation is key to develop an economical sustainable BM. Hereby,

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the value network in its whole can be seen as the economic value proposition. Apart from a

qualitative analysis, the tool can also serve to perform a quantitative analysis which maps profit

streams to investigate economic feasibility and what-if scenarios.

Figure 2.11: Value network of an internet radio station (Gordijn et al., 2006)

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Figure 2.11 shows the example of an Internet radio station and its value network, modelled with an

e³value tool. The syntax of this technique is provided in the legend. The network consists of the

market segments Listener, Internet radio station, Producer and Musician. Furthermore, there is

also a single actor Rights society. There could also be a composite actor encompassing other

actors or market segments in a partnership for example, but this is not the case in this example.

(Composite) actors and market segments perform activities and exchange value objects with each

other by offering an object and getting another object in return. These value objects can be a service

or a good in (in)tangible form. It is obligatory that a value object must have value to at least one

actor. An example of a value object is Fee. To exchange objects, the businesses have value ports

which are combined in a value interface. A value interface is a combination of at least one value

offering and one value request. The value exchange then connects value ports that are placed in

opposite direction (Gordijn et al., 2006).

A scenario can be created by modelling the customer need, dependencies (here: connection

element) and dependency boundaries. The scenario is as follows: A Listener has a need to listen

to music. This can be satisfied by obtaining the object Radio stream from an Internet radio station.

In return, the radio station has an audience. The ability to stream music is dependent on the Rights

society which gives Rights to make music public in return for a Clearance fee. Again, this is also

dependent on both Producer as well as Musician who want a fee in return for giving their

permission. This value network ends here at the two boundary elements, but in reality, this could

be modelled even further. The analyst can determine the boundaries in an arbitrary way (Gordijn

& Akkermans, 2001; Gordijn et al., 2006).

Recently, Derks, Gordijn, and Siegmann (2018) researched the financial sustainability of Bitcoin

miners using the e³value technique. The methodology analysed the different actors in the bitcoin

system and their value flows. The focus is on the miners and doesn’t take into account transactions

where Bitcoin is used as a currency to shop. The researchers state that a blockchain case should

be:

1) economically sustainable,

2) able to scale up transaction volumes and

3) fully decentralized.

Based on this in combination with a quantitative study, the authors Derks et al. (2018) conclude

that Bitcoin miners need higher rewards or must have access to more energy efficient consensus

algorithms instead of the energy-consuming PoW. Thus, the consensus mechanism is a key

34

component. The mechanism should feature scalability and be economically sustainable. In this

context, they argue that a mechanism such as Proof-of-Elapsed-Time (PoET), which is used in the

Hyperledger Fabric, would be more scalable and energy efficient.

The e³value model is chosen for multiple reasons:

1. The technique helps in improving the business-IT alignment in an innovative e-business

context. Furthermore, in this context, the technique can be used as an exploration of value

proposition for innovative e-business models.

2. it models a value network where enterprises and customers interact with each other.

Collaboration is argued to be an important factor for creating a P2P network in blockchain

literature.

3. the e³value technique gives possibilities for quantitative research to get a long-term view on

cashflows and sustainability in case the technique is able to model the three cases

(Akkermans & Gordijn, 2001, 2003).

A final remark comes from Roelens and Poels (2013) as they argue that the focus of this technique

is on external value exchanges with other businesses and customers. Looking intra-organizational,

it only models internal value exchanges between the actor’s activities.

At the moment of writing this dissertation, there are only few blockchain cases where EM is applied.

If both techniques are found to be useful in modelling the impact, further research could investigate

measuring the impact using VDML as it offers more possibilities thanks to the focused BM viewpoint

(Roelens & Poels, 2015).

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3. Cases

This chapter addresses three cases found online doing the literature review and resulting from

interviewing consultants. The three types of cases have already been discussed in chapter 1.5.2

where the key take-aways are the methodology of Yin and the required elements to form a ‘good’

blockchain case (Gordijn et al., 2019; Yin, 2009, 2014).

In this research, the following interviews were taken:

1) An explorative interview with Thomas Vandoorne, a senior consultant at one of the ‘Big

Four’ consultancies firms to get an idea of the opportunities and challenges of the

blockchain technology. This interview debunked some myths and set me on track to

investigate the case studies more in detail.

2) An in-depth interview with Koen Vingerhoets, a blockchain project and digital transformation

coordinator at a leading bank in Belgium. This was an in-depth interview about the existing

as well as the proof-of-concept blockchain Know Your Customer onboarding. Afterwards,

questions were asked in the context of the chosen EM techniques.

3) An interview with Frederik-Jan Roose, a blockchain solutions lead at a prominent IT

consultancy firm. The first part of the interview explored the main features and different

cases in practice. The second part consisted out of open questions followed by in-depth

questions about supply chain in the context of the chosen EM techniques.

3.1 CrowdBC: a blockchain-based crowdsourcing framework

3.1.1 Traditional triangular crowdsourcing model structure

Jeff Howe (2006) is one of the first to use the term ‘crowdsourcing’, which is a contraction of the

words ‘outsourcing’ and ‘crowd’. Crowdsourcing means outsourcing a task by distributing an open

call to a crowd that possess certain talents with the objective to have a solution developed. The

crowd could be one or multiple persons who voluntary -or against payment- provide a solution. The

goal of crowdsourcing is to increase open innovation by using external knowledge and in this way

reducing in-house costs and increasing flexibility and speed (Chesbrough, 2006; Howe, 2009).

36

Since 2006, it has become a concept popular as internet, communities with shared interests and

availability of tools are booming (Hossain & Kauranen, 2015; Howe, 2009).

Popular examples of crowdsourcing platforms are Freelancer and Upwork. The platform Upwork,

for example, follows a distributed problem-solving model where a client (e.g. a company) has a

problem and posts an open request on the centralized Upwork platform. The platform can be

considered as a freelance marketplace where freelancers can compete for a job and receive a

reward in the end. Many firms use crowdsourcing in different categories ranging from addressing

accounting problems over solving technical problems to copywriting (Freelancer, 2019; Upwork,

2019).

Figure 3.1: Traditional model of crowdsourcing with a centralised third party (Li et al., 2018) © 2018 IEEE.

The traditional as-is model is a centralised two-sided platform which involves three actors:

Requesters, Workers and the Crowdsourcing System. Although the actors are in direct relationship

with each other, the prime relationship involves the platform. Figure 3.1 above illustrates this

relationship. A Requester has a certain problem and posts a task via the system on the to find a

counterparty who can provide a solution to the task. Instead of hiring expensive personnel, the

requester outsources its task to one or more experts, for example an expert in accounting. A

requester could be an enterprise or entrepreneur. The Workers are the experts, serving to execute

the task, and who could work alone or as a group (crowd). Interested workers see the open calls,

i.e. the tasks or problems posted by the requester, and could compete to work on the task.

37

Requesters could eventually also select a solution from the ones that multiple workers have put in

the system. Often, this is the first or best solution to the task. The worker who proposed the selected

solution will be rewarded by the requester. A worker in this context is a freelancer. The third actor

is the Crowdsourcing System, which acts as an intermediary to broadcast the open calls to the

workers. In return, a worker who gets paid by the requester for his/her solution also has to pay a

service fee to the platform. The system is often centrally organised with one centralised database

(Freelancer, 2019; Li et al., 2018; Upwork, 2019).

A third party, such as Upwork, in this context has two main functions. It connects businesses with

talented freelancers and resolves disputes between its users by evaluating solutions. The

evaluation happens in a rather subjective way. In return, the platform charges a service fee of 20

percent of the requester’s payment (Freelancer, 2019; Upwork, 2019). Although the current model

has a straightforward architecture, this traditional trust-based system also has some vulnerabilities.

The authors Li et al. (2018) debate a few vulnerability cases related to this approach in their paper.

The main vulnerabilities identified are briefly listed in the paragraphs below.

First, running the business on a centralized database is prone to an issue called the ‘single point

of failure’, e.g. hardware failure. Whenever the database crashes, users are unable to use the

system or could lose their data. This could be solved by developing an IT architecture on distributed

databases. Centralised servers are also prone to distributed-denial-of-service (DDoS) and other

malicious attacks. In the worst case, the system could temporary become unusable (Li et al., 2018).

Second, the system is often run by one entity. This means the customers must trust this third party

and its people who run the platform, own their data and take measures to prevent data leaks (Li et

al., 2018).

Third, privacy disclosure in a trust-based system forms another kind of vulnerability as sensitive

data is saved within the database of the crowdsourcing system. Remote hijackers could steal and

use this data. Encryption of personal data could provide protection (Zhuo, Jia, Guo, Li, & Li, 2017).

Finally, current platforms must cope with false reporting and free riding issues leading to disputes

between requesters and workers. The trust-based system is the only actor which can resolve the

issue. As stated earlier, this often happens in a subjective way. However, Xie, Lui, and Towsley,

(2015) propose reputation-based mechanisms to counter subjectivity.

Concluding, although solutions exists to deal with most of the vulnerabilities identified that can

occur in a traditional trust-based structure, this still faces issues of trust. The authors and designers

38

of ‘CrowdBC’ believe that a blockchain-based decentralized framework could be an all-in-one

solution to the vulnerabilities including trust as well. In the next subchapter, more information on a

possible to-be model is presented, based on the paper of CrowdBC (Li et al., 2018).

3.1.2 CrowdBC explained

In recent years, possible blockchain applications such as the blockchain-based crowdsourcing are

being subject of studies. At present there are only few papers in literature dealing with this subject.

In their conference paper, Jacynycz, Calvo, Hassan, and Sánchez-Ruiz (2016) propose an

application called ‘Betfunding’, which is a blockchain-based crowdfunding platform. Crowdfunding

is a specific type of crowdsourcing. Another example is the article of Zhu and Zhou (2016) which

handles about the possibilities of the blockchain technology to private equity crowdfunding in China.

A generalized crowdsourcing application is ‘CrowdBC’, a blockchain-based decentralized

crowdsourcing framework (Li et al., 2018).

From here on, the blockchain-based crowdsourcing framework CrowdBC is the focal point of this

case and is referred to as ‘Framework’. It is a simple, yet generalised concept of a DAO used in

this research to build the to-be Business Model Canvas and an e³value models. The Framework is

an open source protocol that is being developed by academic researchers Li et al. (2018) and that

gets its functionality by smart contracts. Important to note is that the Framework is still in its

prototype phase and has been tested in the Ethereum public test network “Ropsten” using Solidity,

Java and Javascript. The authors are also testing if the Framework could work on other blockchain

platforms like the Hyperledger Fabric. Smart contracts are programmed to the complex

crowdsourcing logic concept. More info about algorithms deployed can be found within the paper

of CrowdBC (Li et al., 2018).

Figure 3.2 illustrates how requesters and workers interact with each other with the help of the

Framework, which has the same functionality as a traditional platform. A Requester posts open

calls for tasks using the Framework’s client. Then, Workers see the task and opt to provide a

solution and could get a reward in return. The Framework’s client is a software application based

on the open protocol and interacts with the Framework’s blockchain ledger in order to store the

data. Miners are used to validate transactions and to add messages, open calls or validated

transactions in blocks by using their computational resources. In all cases where a Miner validates

a block, this party should be rewarded (e.g. in cryptocurrency) for its effort. This reward is referred

to as a transaction cost paid by the Worker or Requester that want to put something on the

39

blockchain. This way, the security and trust of the blockchain ledger depends on independent

miners. Miners could also be requesters or workers but will be regarded as separate parties to keep

the models clear in this master’s dissertation as also miners see the data in a different format than

what the requesters and workers see in their specific roles. The structure of the Framework enables

every user to run the client locally on his computer, tablet or mobile phone. The client software

applications are based on the open source protocol of CrowdBC whereby everyone should be able

to develop and distribute the applications. This way a P2P network with a permissionless ledger is

established in which every user has the same level of access to the ledger’s (encrypted) data and

in which every user can be in direct contact with each other. Thus, no central party has full

ownership and control over the ledger (Li et al., 2018). It must be remarked that researchers, a

company or blockchain enthusiasts must first code the smart contracts to get a functioning P2P

crowdsourcing Framework. Moreover, users must be willing to update the Framework from time to

time to compete with other frameworks developed.

Figure 3.2: The system model of CrowdBC (Li et al., 2018) © 2018 IEEE

The researchers Li et al. (2018) argue that the goal of the Framework is to give a reliable, fair,

secure and decentralized alternative to the current crowdsourcing platforms. Keeping in mind the

vulnerabilities of the traditional trust-based systems, some potential key features and remarks of a

blockchain-based decentralized crowdsourcing Framework are explained in the remainder of this

chapter.

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A first feature is increased fairness to combat free riding by using smart contracts that represent

the crowdsourcing logic and crowdsourcing processes. To achieve this, requesters must put the

reward and a penalty upfront when posting a certain task. They could also demand for a worker

who has built a certain level of reputation. On the other side, workers must make a money or

reputation-based deposit when accepting a task. The deposits are time-locked by protocols

included in the smart contracts. This way, participation should be encouraged. Furthermore, smart

contracts could also serve evaluation purposes as to determine how much reward each of the

workers should receive when finishing a task in group (Li et al., 2018). Users should be cautious

about blockchain applications being “trustless” according to the first interviewee. He states that

blockchains still have a degree of trust, since trust shifts to the developers and miners (T.

Vandoorne, personal communication, November 26, 2018).

A second feature is the user’s security. Privacy is respected through use of encryption and

anonymous operation as new users aren’t obligated to reveal their true identity when registering

on the Framework. They will be given a public and private key pair in order to encrypt and decrypt

solutions linked to their account. Workers provide (references of) solutions through the Framework

that are encrypted in cyphertext by the requester’s public key and can only be found and decrypted

by the corresponding private key. This also leads to stolen encrypted data becoming unusable

when the hacker doesn’t have the key pairs. However, when the keys are compromised, hackers

have access to the ledger and (part of) its contents. If enough nodes are compromised, transactions

could be reversed (T. Vandoorne, personal communication, November 26, 2018). The Framework

can protect its users against malicious attackers in a few ways. First, Li et al. (2018) have proven

that malicious users cannot tamper results and perform acts of false-reporting under the

assumption that most of the miners are honest. Furthermore, there’s a limited probability to create

a fork in the malicious user’s advantage. Second, launching DDoS attacks could carry an enormous

cost for the malicious user thanks to the deposit-based mechanism described in the paragraph

above. An in-depth security analysis has been explained in the Framework’s paper (Li et al., 2018).

A third feature is coping with the trust-based centralised architecture and giving options for

scalability. However, as literature in the first chapter mentioned, a blockchain ledger isn’t ideal to

store large amounts of data. To solve this, the authors propose to build three layers in the

Framework’s architecture as illustrated in Figure 3.3. The three layers are the application,

blockchain and storage layer. The storage layer saves the actual master data off-chain using

distributed servers. It should be noted that a centralized platform could also use distributed servers

to cope with the problem of single point of failure. In the paper of CrowdBC, the proposed system

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lets multiple (distributed) storage providers coexist to diminish chances for single point of failures.

Examples of providers are Apache Ignite and IPFS (Apache Software Foundation, 2018; Protocol

Labs, 2019). Different from traditional systems is the blockchain layer which interacts with the

application layer, the miners and the storage layer. Only metadata (data size, owner, pointer,

timestamp, data hash value) of the actual data is used in the blockchain. To give a broader

explanation on the paragraph above, whenever a worker posts a solution, this will be encrypted

using the requester’s public key and saved in the storage layer as cypher text. At the same time, a

smart contract will ensure that a pointer and a hash value, referring to the encrypted solution, are

put into a block and added to the blockchain layer with the help of the miners. The data pointer is

a query string and serves the purpose of finding data in the storage layer. The hash value is based

on the data and guarantees no changes are made to the actual data in the storage layer. This

means users should not need to worry about the trust of the data in the storage layer as checks for

authenticity and integrity can be done by comparing the digital signature and data’s hash in the

blockchain layer with the help of the miners (Li et al., 2018).

Figure 3.3: Overview of the CrowdBC architecture (Li et al., 2018) © 2018 IEEE

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3.1.3 BMC in the context of crowdsourcing

3.1.3.1 Discussion of BMC models

For this case, two as-is BMCs are developed to model the current situation. The BMC in Figure 3.4

is the model of a crowdsourcing platform such as Upwork. The BMC in Figure 3.5 models the

viewpoint of the Worker since freelancers are a business on their own to provide solutions in return

for a fee charged (Osterwalder & Pigneur, 2010). The scope for this BMC is on freelancers only

using a crowdsourcing platform.

Also, one to-be BMC (see Figure 3.6) is developed to model the most likely situation where a

Worker would use the Framework as a means to find tasks (Osterwalder & Pigneur, 2010).

Consequently, it is assumed the Framework replaces the traditional third party. This results in the

inability to model a traditional third party to use blockchain in a to-be model.

Figure 3.4: The BMC of a crowdsourcing platform (as-is)

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Figure 3.5: The BMC of a Worker using the platform (as-is)

Figure 3.6: The BMC of a Worker using the Framework (to-be)

44

Customer segments

In the current situation modelled in Figure 3.4, Requesters are (small) enterprises and

entrepreneurs who have a certain problem or task which they can’t solve due to lacking internal

resources, know-how, manpower etc. (Howe, 2006). The crowdsourcing platform in the current

situation acts as an intermediary (a two-sided platform) to connect requesters with workers through

their distribution channels with respect to the value proposition. Requesters can opt for membership

options (e.g. receiving an overview of pre-screened workers) on most platforms. Workers are

freelancers who are talented in a certain work field, ranging from driving the requester safely to

his/her destination to developing a website, product, etc. Workers could also operate together as a

group to finish a task. A percentage of the reward for their job must be paid to the platform.

Furthermore, there exist membership plans for workers, for example serving to get more tasks

assigned in a month for example (Freelancer, 2019; Upwork, 2019). From a viewpoint of the

Worker, the only customer segment are the requesters (see Figure 3.5).

There is a difference worth mentioning when comparing the as-is models with the to-be model in

Figure 3.6 created from the viewpoint of the Worker. In the latter case, the customer segment is

populated by the requesters only since a worker is delivering a service or creating a good for the

requester directly. From this perspective, the aspect of trust and security are important features of

this the blockchain-based crowdsourcing tool (Li et al., 2018).

Value propositions

The value proposition in the as-is model of the traditional platform is to connect talented workers

with requesters, i.e. the businesses. This is organised in a centralized way where the platform takes

ownership of all data which is often stored in one server facility. The solutions offered by the workers

should meet the requirements, be qualitative strong and at a competitive price within a trusted

online platform. A crowdsourcing platform brings social as well as economic value: both customer

segments can build a network through the platform and requesters bring job opportunities and thus

represent possible income for the workers (Freelancer, 2019; Upwork, 2019). Looking at the as-is

model of the Worker, the focal point is using their talent to deliver the required solution at a fair,

competitive price.

The value proposition changes when comparing the as-is models with the to-be model. The

proposition focuses more on the more competitive price since the decentralized P2P network may

lead to lower transaction costs. Moreover, the aspect of trust, cybersecurity and decentralization

45

are highlighted. Requesters as well as workers themselves don’t have to rely anymore on a

centralized third party to connect and share tasks or solutions. This can have effect on the customer

relationships (see below). The underlying mechanisms can provide more fairness which can lead

to more trust (Li et al., 2018).

Channels

Starting with the as-is BMC of the platform, the various channels mainly serve to attract users of

both customer segments in various ways. Furthermore, these channels distribute tasks of the

requesters. The platform, mobile app and website are also important to receive the revenue. In the

case of the Worker, the channels platform, mobile app and website are used to connect with

requesters. Other channels such as e-mail and video conferencing are used to develop and deliver

a solution to the task or problem opposed (Freelancer, 2019; Upwork, 2019).

Concluding from the to-be BMC, there are no significant changes between the channels’ sections

of the as-is BMC of the worker and the to-be BMC other than the functionalities of the

crowdsourcing platform, website and mobile app being replaced by the Framework. The worker

communicates, receives tasks and provides solutions through the Framework (Li et al., 2018).

Customer relationships

In the light of this master’s dissertation, the ‘Customer Relationships’ aspect is considered as one

of the prime aspects of the Business Model Canvas.

The value proposition in the as-is model of the platform helps to create satisfying relationships

between the customer segments actors themselves and between the customer segments and the

platform. The customer relations in the as-is of the Worker are quite similar to the ones of the

platform with an exception of network effects community and centralized trust instead of security.

The most important aspects of the as-is models are briefly explained hereafter:

• Network effects: the value of (using) the platform increases proportional to the user count.

This works in two ways: more requesters lead to more diversified tasks; more workers lead

to more diversified talent to finish the open tasks (Banton, 2019).

• Co-creation: both the platform as well as the customer segments couldn’t exist without each

other. This relationship leads to more active involvement, resulting in an enriched customer

46

experience (Businessdictionary.com, n.d.). In the context of the Worker’s BMC, seeing the

relationship as personal support is more appropriate.

• Self-service: requesters can select the best freelancer or solution for the task on the

platform. Workers can pick tasks which fit them (Freelancer, 2019; Upwork, 2019).

• Platform-binding: Current crowdsourcing platforms often have specified exclusivity clauses

in their Terms of Service. Typically, it is stated that once a relationship is established through

the platform, it cannot be moved away from the platform for a certain period. If both the

Requester and Worker want to have a direct relationship within the specified period, an opt-

out fee must be paid. This relationship also holds in the context of the Worker’s BMC, as

the scope is that freelancers only use the platform (Freelancer, 2019; Upwork, 2019).

• Traceability of (previous) projects: In the as-is model, the intermediary crowdsourcing

platform owns all data (accounts, relationships, tasks, etc.) and provides an overview for its

customer segments. In the as-is of the Worker, both freelancers and requesters have an

overview thanks to the project management functionalities of the platform (Freelancer,

2019; Upwork, 2019).

• Reputation of the online profile: delivering the solution often happens resulting from a direct

contact between the two customer segments. This could lead to unfair situations, for

example, a requester could evaluate the solution to be of low-quality even though it is

qualitative and the worker has put a lot of effort in it. Workers on the other hand could also

provide a low-effort solution just to reap the task reward. Therefore, reputation is important

(Zhang & van der Schaar, 2012).

• Centralized trust and security: as previously stated, a third party who stores all data on a

centralized or even decentralized server and who has full control over the user’s data, faces

risks from attackers either external or internal ones as the platform itself is still run by a

central authority. Furthermore, trust is also an important factor as workers and requesters

have to rely on the centralized system (Freelancer, 2019; Upwork, 2019).

Like the as-is model of the Worker, the to-be model has the same co-creation and personal support

to the customer segment(s). However, a worker using the blockchain-based decentralized

Framework instead of using a traditional platform possess abilities to change relationships (Li et

al., 2018). Below are some of the most important possible changes listed:

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• The platform-binding relationship can change to a relationship with more freedom. The to-

be model allows to address the exclusivity clause and related opt-out fee by providing

means to exclude this kind of clauses and thus giving more freedom to the relationship

between a worker and a requester. However, this freedom could be nullified by the

pseudonymity since the developers of CrowdBC state a party could not be able to see the

identity of the other party. Whether this is an advantage or disadvantage depends on the

preferences of the parties. It could be that one party wants to know with whom they are

actually dealing with.

• Traceability of (previous) projects: Even though the mechanisms to give an overview of

(previous) tasks are different, the end result is quite similar to the as-is BMCs. The feature

now relies on the client which is based on the blockchain protocol.

• Reputation of the online profile: The reputation still depends on review by other party.

However, deposit and evaluation protocols could decide how much reward each worker

should get based on their input when working on a group project for example. This could

result in more fairness and trust. The authors of CrowdBC argue that developing an

automated reputation mechanism is still a though job.

• Decentralized trust (and security): it could lead to no single point of failures anymore.

Furthermore, better protection against attacks and ownership of data which isn’t dependent

on one party that needs to be trusted. Adding to the previous relationship about reputation,

security could also be improved within the smart contracts themselves, since complex

deposit and evaluation protocols could be programmed for the tamperproof ledger. This

way, malicious workers and requesters wouldn’t be able to get an unfair advantage within

the system.

Revenue Streams

The traditional platform charges a service fee, which is a percentage of the payment given by the

requester to the worker in return for a satisfying solution. The percentage often ranges from 5 to

20 percent as platforms handle various ways to define the percentage. Often, current platforms

also offer membership plans in return for a monthly subscription to the actors of the customer

segments. The benefits for the actors could be increased visibility, increased connections per

month, and so on (Freelancer, 2019; Upwork, 2019). It is logical that a Worker receives revenue

for successfully completing a task.

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In the to-be model of the Worker, the received payments could be impacted in a positive way

because of the smaller cut the Framework may charge (see section costs). Thus, net income may

be higher (Li et al., 2018).

Key resources

The as-is BMC of the platform requires e-business resources to create, maintain and update a

crowdsourcing platform. Similar to any other business the platform requires continuous

development and personnel with various expertise ranging from legal advisers over IT staff to

customer support. The key resources required for Workers themselves are:

1) a network connection to interact with the platform and requesters,

2) a computer, tablet or phone to accept tasks and to deliver solutions and

3) a (software) toolkit in case certain tools are required to do the job (Freelancer, 2019;

Upwork, 2019).

From the Worker’s viewpoint in the to-be situation, the client based on the open protocol is also a

resource as it serves as the required tool to connect with requesters and allows to manage

(potential) tasks on the Worker’s computer, tablet or mobile phone (Li et al., 2018).

Key activities

The main activity of current crowdsourcing platforms is to enable tasks and supporting these tasks

and their execution with project and financial management options. To support project

management, most as-is business models provide options for defining milestones. For a requester,

this gives the possibility to monitor the progress of the task. For a worker, this leads to a more

structured approach in finishing the task. Furthermore, time spent on a task can also be traced

better. In the as-is model, automated tracing is part of the network. In the scope of financial

management, tasks can be offered at a fixed payment or on an hourly basis. Some platforms such

as Upwork let the charged service fees depend on the lifetime billings between a requester and

worker. Resolving disputes between the two customer segments is another supporting but

important activity in the as-is model (Freelancer, 2019; Upwork, 2019). The key activities in the as-

is of the Worker are partly different as the core activity is to accept and handle tasks in order to

provide a solution. In general, handling tasks is independent from using a platform.

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Addressing the to-be BMC of the Worker, it can be noticed that this is similar to the as-is BMC of

the Worker. The platform is replaced by the Framework, but activities to provide solutions remain

the same (Li et al., 2018).

Key partnerships

Today’s crowdsourcing platforms have reached a phase of maturity and seek new opportunities to

attract and retain (potential) customers by partnering with other businesses. These could be online

learning platforms which provide web seminars to enhance talent but could also be (cloud) toolkit

providers such as Microsoft Dynamics 365 to help workers. Workers and requesters themselves

are important partners in the as-is BMC of the platform. Without workers, requesters wouldn’t put

their tasks on the platform. Without requesters, there wouldn’t be job opportunities for the workers

(Freelancer, 2019; Upwork, 2019). For a Worker, the crowdsourcing is an important partner who

shows a selection of interesting tasks. Furthermore, workers form a ‘crowd’ in order to solve

complex problems which may require multiple persons. In the to-be model of the Worker, the key

partners remain other workers to solve complex problems (Li et al., 2018).

Cost structure

Developing and maintaining a website or mobile app can be costly for the current platforms.

Furthermore, marketing expenses are necessary to get visibility through various channels as

already multiple crowdsourcing platforms exist. The as-is platform also has operational expenses

and wages as any other e-business. Looking at the as-is model of a Worker, the major costs are

the specified resources required to provide a solution. As an example, this could be a yearly license

fee for a software package to edit photos. Another significant cost is a percentage of the revenue

that must be paid to the platform for their services. Furthermore, a subscription fee can be paid for

a membership plan (Freelancer, 2019; Upwork, 2019).

Apart from the cost for specified resources, the to-be model is different from the respective as-is

model. According to Li et al. (2018), the percentage of the revenue is replaced by a lower

transaction fee since the Framework isn’t owned by a party, but miners and distributed storage

providers are necessary to maintain the integrity of the P2P network. This may increase the tool’s

attractiveness. It could also be that part of the transaction fee must go to independent developers

to update the Framework such that it remains usable on the long term. The to-be model doesn’t

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explain that the customer segments, i.e. the requesters, also have to pay a transaction fee when

posting a message or task on the blockchain.

3.1.3.2 Conclusion BMC

A conclusion from the BMC model application is that only the to-be BMC of the Worker can be

modelled, since the Framework is merely a non-profit tool for requesters and workers to connect

with each other. When comparing this to-be model with the as-is models, the impact is significant

for the financial building blocks, key resources, value proposition, customer relations and channels.

However, the to-be BMC doesn’t state that the customer segment also has to pay transaction costs

to the Framework.

When drafting further conclusions with respect to the objective, it is argued that the overall impact

of using a DAO is positive on value propositions, customer relations and the financial building

blocks. Furthermore, the following points regarding the qualitative parameters are worth

mentioning:

• There is no significant impact on the parameter “traceability”. Users already have an

overview of their (previous) tasks in the traditional model. This feature shifts to the client

based on the protocol.

• Addressing “efficiency”, the project and financial management activities could become more

automated with the use of smart contracts, which could lead to more time for workers to

provide personal support to their clients. The cost of a solution may also decrease to a more

efficient project management activity in combination with lower transaction costs that have

to be paid to the Framework. However, a traditional platform could also provide updates to

automate the project and financial management even further. Thus, only the lower

transaction costs really depend on the blockchain technology in this case.

• The use of a DAO can have impact on “trust’. The workers’ service is delivered in a trusted

P2P environment where reputation mechanics provide fairness. Furthermore, the aspect of

decentralization ensures that nobody has power over all users.

• The “security” in the environment of workers and requesters using the DAO is also

impacted. There could be enhanced cybersecurity of data thanks to two of the blockchain’s

features, namely encryption and decentralization, in combination with the DAO linked to

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distributed storage providers to store raw data off-chain. Also, smart contracts on the

tamperproof ledger may demotivate malicious attackers.

The to-be BMC model in Figure 3.6 provides an overview of the essential elements required and

impacted when using a DAO as a Worker. Further research could investigate the financial streams

to evaluate if using a blockchain tool would lead to lower transaction costs in practice.

3.1.4 E³value model in the context of crowdsourcing

3.1.4.1 As-is e³value model

Figure 3.7: Value network in the as-is situation

In the current value network (see Figure 3.7), three actors can be identified: the market segment

Requesters, the market segment Workers and actor the Crowdsourcing platform. The requesters

have the need to get a solution to their task or problem. Therefore, two value exchanges need to

happen. First, the requester must have access to the platform. In return, this user signs the Terms

of Service which state the user should only have relations with workers through the platform. The

intangible objects Platform access and Platform binding are exchanged. Second, the requester

receives the object Solution in return for Money in the value exchange with the worker. The activity

provide solution depends on a value exchange between four value objects. Just like the requesters,

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workers also need to have access to the platform in return for binding themselves to the platform

first. Furthermore, the objects Task and Money are exchanged as the workers receive tasks via the

platform. In return, they will give a service fee existing out of a percentage of the reward for a

finished task to the platform.

Additional, both requesters as workers could have the need to upgrade their account with a

membership plan. In this case, a value exchange between the objects Money and Membership

plan is established (Freelancer, 2019; Li et al., 2018; Upwork, 2019).

3.1.4.2 To-be e³value model in the context of blockchain

Figure 3.8: Possible value network in the to-be situation

Figure 3.8 shows the possible to-be value network based on the paper of Li et al. (2018). The actor

Crowdsourcing platform changes to the new composite actor Blockchain-based decentralized

crowdsourcing framework. In this abstraction, this composite actor exists out of four actors:

Blockchain technology provider, Miner, Distributed storage provider and Distributed ledger. An

important remark must be made here: the reader can misinterpret Distributed ledger since 1) this

is a decentralized actor which is important for the P2P network and 2) contrary to a normal actor,

it doesn’t pursue profit. Thus, it is only an actor by approximation.

The main need for customers remains the same as in the as-is situation, but the value exchanges

to satisfy this need are different. First, to get access to the Framework, a requester could get access

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by registering and will get a public and private key pair in return for encrypting and decrypting data

on the ledger. Second, the value exchange to provide solution also depends on four objects but

three of them are different. Just like a requester, a worker should also get a Key pair when

registering to the Framework. In return, the Framework gets a new Registered user. Furthermore,

a Worker should only pay Money, existing out of a smaller maintenance fee than the service fee in

the as-is situation. In return, the user could receive a task from the Framework.

Different from the as-is situation, the activity enabling tasks is now part of the Distributed ledger

and depends on three value exchanges with the other actors within the Framework. First, a

Blockchain provider, provides the technology in the form of the object Blockchain framework. Often,

this is an open source, but the provider might ask for a payment in return. If IBM would provide the

Hyperledger Fabric, then they would ask for a (subscription) fee in return. If Ethereum would be the

blockchain provider, then there would be no value exchange. In case of Ethereum, one could

question who would perform necessary updates and at which price. Second, Miners are required

to update the ledger. They could put the Verified data on the ledger in return for Money. Third,

distributed (side-)databases are required since the blockchain ledger itself isn’t ideal to store large

amounts of data. The providers could facilitate Storage space in return for Money.

An important note is that nobody of these actors owns the Framework and all its data. The ledger

only uses metadata referring to the master data which is stored on distributed servers. Another

important note is that there are no additional needs to upgrade for membership plans for both

requester and worker as the development of the P2P network is still in its infancy (Li et al., 2018;

The Ethereum Foundation, 2019; The Linux Foundation, 2018).

3.1.4.3 Comparison e³value models

When comparing the e³value models, the following general remarks must be made. First, it is clear

that the traditional Crowdsourcing platform can be replaced by the Blockchain-based decentralized

crowdsourcing framework such that a requester’s primary need can be satisfied. However, the

reader could misinterpret the Framework since it is only an actor by approximation. The reason is

that the e³value technique doesn’t take the ability to model decentralized ‘actors’ into account yet.

Second, in the as-is model, the value proposition can indirectly be deduced by the value exchanges

as the platform serves to connect the two types of users. This is not the case in the to-be model

where argued features such as decentralization are not clear. Third, the models show the

dependencies and the, mainly external, transfer of value objects, but lack to model the sequences.

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Therefore, it is argued that investigating the case with a Business Process Modelling (BPM) can

be useful. Finally, the value object Service fee is replaced by a (smaller) Transaction fee for

enabling tasks through the P2P network in the to-be model.

The following conclusions about the qualitative parameters can also be drawn in the context of

value offerings to customers:

• There is no significant impact on the parameter “traceability” when using the blockchain

client. The activity “enabling tasks” keeps track of projects in both models. However, the

internal process can be different, but such cannot be deducted from the models.

• The impact argued on “efficiency” can indirectly be deduced from the models as it mainly

would be an internal aspect of automating the activity enabling tasks in both as-is and to-

be model. In the context of blockchain, there is no impact within the scope, i.e. between the

relationship worker and requester (viewpoint Worker). Furthermore, the characteristics of

smart contracts are difficult to model with an e³value technique in this case. The e³value

model can demonstrate the lower transaction fee.

• The impact on “trust” can only be deduced indirectly when making assumptions about the

integrity of the blockchain protocol within the ‘actor’ Distributed ledger. Consequently, the

e³value model can’t measure the impact on this parameter.

• In the as-is model, the parameter “security” depends on the centralized actor

Crowdsourcing platform. Centralized servers would also be able to rely on distributed

storage providers to store the data. However, the centralized party remains the only gate to

the distributed storage. In to-be model, Miners and coexisting Distributed storage providers

support in keeping the P2P network safe for its users. Despite that the fairness mechanisms

and that the blockchain protocol could provide a copy of the ledger to its users, the e³value

modelling technique fails to model this functionality. In reality, the P2P network has multiple

gateways to the raw data on the distributed servers.

Further research could include improving the e³value technique in order to model a decentralized

actor or network. Moreover, research could also include a first attempt at a quantitative analysis on

the long-term sustainability of the value network. The focal point could be on the transaction fees

from a Worker and a Requester which have to be shared between Miners, Storage providers and

eventually also the Blockchain storage provider.

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3.1.5 Conclusion of the CrowdBC case

Keeping in mind the objective to answer the main research question within the predefined scope,

the following paragraphs attempt to answer and conclude the considerations of chapter 1.2 for the

CrowdBC case.

Based on the literature, a decentralized blockchain application such as CrowdBC may impact

existing business models in their respective application field. The DAO has the goal to replace a

traditional third party in crowdsourcing but faces new challenges. The permissionless ledger

doesn’t have a BM on its own in the context of making profit, otherwise a centralized third party

would operate the application. Thus, the DAO could be compared with a tool for requesters and

workers to connect with each other.

The features also have impact on the BMs of the users. First, addressing trust and security, this

depends on the integrity of the decentralized P2P network which isn’t controlled by one centralized

party anymore. Smart contracts can support to keep the network fair. Second, the financial section

of a user’s BM can be impacted by potentially increased efficiency and lower transaction costs, but

this requires further research. DAOs could also lead to a new type of BMs as miners are required

to keep the network honest in return for a financial reward.

Some remarks can also be made when comparing the traditional systems and blockchain-based

Framework. Solutions exist to address the current vulnerabilities with exception of being trust-

based. However, the current platforms put high amounts of effort into this to be able to compete

with other crowdsourcing platforms. Current platforms can also develop more automated services.

The Framework offers potential for an all-in-one solution where no third party has full ownership

and demands high service fees but needs further development to serve more complex

crowdsourcing situations. Moreover, the Framework is an open source protocol but it should be

remarked that independent developers should be willing to often update the Framework. Users of

the DAO should also keep in mind the remarks made by the first interviewee about the blockchain’s

features such as trustless, tamperproof and security.

Addressing the impact demonstrated by the two techniques, the following conclusions can be

drawn:

• Only a to-be BMC of the Worker could be made, but not of the Framework. This has been

argued earlier. Specific in the crowdsourcing context, the impact argued in literature is quite

accurately measured when comparing the as-is and to-be BMCs.

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• The e³value technique fails to give a clear representation of the P2P network in this case

as it cannot correctly model the permissionless ledger which replaces the traditional

platform. Thus, the ledger is only modelled by approximation. Moreover, the impact on the

value proposition when using the blockchain cannot be deduced at first sight. A quantitative

analysis could be based on the value exchanges but a Business Process Modelling (BPM)

technique would likely be more useful to get an overview of the underlying processes and

sequences.

In this case, the impact is also noticed in the context of the qualitative parameters. When comparing

both EM models, the following becomes clear in the context of value offerings to customers:

• Both models do not measure an impact on traceability. In case of the BMC, requesters

and workers already had an overview as shown in the key activities and customer

relationships. In case of the e³value network, this parameter mainly depends on the activity

enabling tasks in both the as-is and to-be model.

• The developers suggest efficiency can be impacted by the automation of current activities

with smart contracts. This impact can happen on the key activities in the BMC.

Consequently, this can influence the competitive cost for a solution and personal support in

the customer relationships. The BMC models the outcome in the value proposition.

However, this can only indirectly be deduced from the e³value model, since efficiency is an

internal aspect of the activities. The competitive cost aspect from the BMC can partly be

measured by the value object Transaction fee.

• Trust is an important feature argued by the developers as smart contracts can support

fairness using reputation mechanisms for example. This is shown in the BMC in the value

proposition and in the customer relationships. For the e³value model, it is assumed that trust

depends on the integrity of the Framework, thus no direct impact is measured at first sight.

• Security is another important feature argued by the developers. In the BMC model, this is

addressed in the value proposition and customer relations. However, it must be noted that

the reader must understand these keywords (e.g. decentralization) in order to derive the

real impact. The e³value network shows only part of the security such as the Miners who

are required to verify data or the key pair to encrypt and decrypt data. The concept of

decentralization in the P2P network and its impact on security can only be derived indirectly

by making assumptions about the actor by approximation.

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The conclusion is that this type A case follows the arguments given by Gordijn et al. (2019). All

users are equal, the ecosystem requires trust and immutability is desirable to keep track of projects,

payments and reputation. This standard crowdsourcing case can be generalized to other cases

within the same problem domain, i.e. when the main features are addressed such as replacing the

trusted third party by a decentralized P2P network.

However, the question whether generalisation for all DAOs is possible, cannot be answered based

on this case alone. There could be applications in many different problem domains. Depending on

those practical uses, features such as governance in smart contracts can be important when

deciding to adopt the technology.

3.2 Case “Know Your Customer” onboarding in Belgium

Literature states the blockchain technology can have an impact on financial services. Nakamoto

(2008) argues that a decentralized currency can have the potential to combat fraud and make (parts

of) the financial sector obsolete. Since then, financial institutions themselves have been exploring

the blockchain technology and investing in it to know the “enemy” and to use it to their advantage

(Gordijn et al., 2019). This is also stated by Zhu and Zhou (2016) who concluded that recent

blockchain applications have found their way to centralized organizations. In this view, there is no

real decentralization, but other peers can obtain access to verified data where the origin of the

documents had already been proven. Guo and Liang (2016) propose that the blockchain

technology could help in solving issues such as lack of mutual trust, high transaction costs, fraud

and so on in their paper Blockchain Application and Outlook in the Banking Industry. Moreover, the

research paper KYC Optimization Using Distributed Ledger Technology investigates the

possibilities of optimizing the KYC onboarding process by implementing DLT in a multi-banking

ecosystem (Parra Moyano & Ross, 2017).

Based upon the online literature, the three interviews, of which the interview with the project

coordinator at a Belgian bank is the most important, and the received slides from the second

interviewee, the Know Your Customer (KYC) case is investigated within the scope of onboarding

in Belgium.

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3.2.1 The KYC landscape today

When opening a new bank account, KYC forms a major part in the onboarding process that is used

to verify the identity of the prospect customer and other data such as transactional behaviour of the

potential customer (PWC, 2015). In Europe, financial institutions (FIs) and other regulated

companies are obligated to do an (extensive) due diligence to identify their potential customers

before doing financial business with them (EU, 2015).

Within the mandatory KYC process, the financial institution must do a compliance check, i.e.

aligning business with external rules and internal controls with the goal to identify risks and to

design and implement controls to protect transacting counterparties from fraud. This is important

for access authorization, document signature, fraud prevention and addressing money laundering,

and so on (PWC, 2015).

Figure 3.9: Current KYC onboarding and frustrations (Verhaest, 2018)

Today, most natural persons and legal entities have bank accounts with different FIs. As shown in

Figure 3.9, the onboarding process happens separately and independently for every bank. This

leads to the difficulties of today according to the second interviewee (K. Vingerhoets, Personal

communication, December 11, 2018):

• Duplication of effort: Every Belgian bank has to do its own onboarding and compliance for

every client. This results in the fact that a customer has to repeat the onboarding process

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for every new account he or she want to open at a different bank. According to the

interviewee, validation of the provided documentation is often very time consuming. Every

bank uses more or less the same resources to validate datasets against each other.

Moreover, the largest part of the requested KYC documents by the different banks from a

prospect customer are the same as they must be conforming to the rules set by the

regulatory authorities. It is no surprise that this brings frustration to clients as well as to the

FIs due to repetitive paperwork as they believe it is inefficient at this moment. This is also

stated by Parra Moyano and Ross (2017) as they argue the business relationship can be

delayed and resulting in opportunity costs. Furthermore, the customer’s documents must

be renewed every one to three years, depending on the risk profile, leading to more

frustrations on the long term.

• Poor transparency in data: Before a KYC check is performed, parts of the data required for

the process is found at various stakeholders. In the context of GDPR and other regulations,

this increases risks for errors. Furthermore, it can lead to a lengthier audit process. Also,

provided data could be out of date.

• Corporate identity: Compared to natural persons who can login on websites of the

government and FIs with their online ID (e.g. itsme®), there is no common identity for

corporates. Their login can be different from bank to bank, which makes it cumbersome for

all parties concerned

3.2.2 Possible KYC landscape with blockchain

The goal of developing a blockchain KYC application is to address the current difficulties. The

application serves the purpose of creating an ecosystem where multiple FIs, regulatory institutions,

bank data providers and the government (e.g. KBO-UBO) are interconnected. Using a blockchain

approach, a reference of a customer’s verified dataset can be added to and retrieved from the

permissioned ledger which is built on the Hyperledger Fabric for example.

Figure 3.10 shows how a reference to the verified dataset is added to the blockchain. To do so,

one FI has manually done the KYC checks to validate and verify the prefilled online KYC form.

Then, the transactions are validated and added to the ledger using consensus mechanisms (e.g.

PoW, PoS, etc.). Afterwards, FIs can use the reference to obtain the verified KYC data to make

and log transactions in an automated way using smart contracts within the blockchain.

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Figure 3.10: Proof-of-Concept KYC onboarding with blockchain (Verhaest, 2018)

An important actor is the Isabel Group which may provide multi-banking services on the blockchain

in Belgium. This signifies that an extra fintech third party is added to the blockchain instead of being

removed. It is an independent and trusted market infrastructure provider for payment transactions

in Belgium. In this context, the Isabel Group is a blockchain application provider that aims at

providing a unique digital company identification to corporates and aims at creating an interbanking

network for the KYC process. Furthermore, the blockchain technology makes the originality of the

documents provable as documents already present can only be read by all parties involved and

not be altered. Thus, everyone gets a copy of the validated blockchain. Designated parties can add

new information by writing to the ledger. The information is still private by the use of cryptography

(Isabel Group, 2018).

All stakeholders in the ecosystem have the application’s client which is based on a permissioned

blockchain protocol. The role of Isabel is to provide and support the application and its

permissioned ledger. When assuming the stakeholders want to collaborate with each other, the

application can have the following benefits over the traditional KYC process according to the

interviewee (K. Vingerhoets, personal communication, December 11, 2018):

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• KYC on the blockchain can remove the duplication of effort by creating a single truth when

mutualizing effort on a corporate KYC blockchain platform. Consequently, the cumbersome

KYC onboarding process only needs to be done manually by just one FI. The reason is that

the financial industry is highly regulated. Afterwards, other FIs and other stakeholders can

use the verified data since most of the required data by for another bank’s KYC process is

the same. They only have to validate the content of the documents when doing their KYC

checks. Thus, they do not have to worry anymore about the authenticity of the data. The

end result is argued to be faster subsequent KYC processes and significant smaller burdens

of paperwork for both customer and financial institution thanks to abbreviated KYC checks

when opening accounts at subsequent banks with the ecosystem.

• Transparency can be improved because references of data are shared within the P2P

network. It can become easier to monitor the data. The customer has control over its own

data using smart contracts and can tell which financial institution(s) he/she belongs. This

also works the other way around for FIs (Parra Moyano & Ross, 2017).

• Addressing the identity issue, the customer has only one online corporate identity within the

P2P network. This results in faster processing of the onboarding and compliance.

Furthermore, it is possible to sign documents for different FIs using the blockchain client’s

interface. It must be noted that there is no clear answer yet on how non-residents without a

Belgian eID can have a corporate identity.

Figure 3.11: Estimated financial impact on the KYC ecosystem in Belgium (Verhaest, 2018)

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According to research done by Norbloc on behalf of the Isabel Group (see Figure 3.11), the

improved efficiency does not only lead to a cost reduction thanks to duplication removal and less

time consuming, but new revenues may also arise. The financial institution that manually does the

first onboarding, could be remunerated by the other FIs within the blockchain ecosystem (K.

Vingerhoets, personal communication, December 11, 2018).

Other business models are also possible. For example, Parra Moyano and Ross (2017) see

possibilities in sharing the common cost of the KYC process.

The interviewee remarks that putting to large data files on the blockchain is a hassle and can lead

to slower processing of documents. It may be necessary to store the authentic data off-chain and

only put the hashes, which reference to this data, on the blockchain. There is still discussion going

on how the off-chain data should be stored (K. Vingerhoets, personal communication, December

11, 2018). A few options are listed, together with some remarks:

• A hybrid solution where data is stored on a shared server, for example a centralized server

of the Isabel Group. In this case, the data should be encrypted to remain private and only

the designated persons should have access to their own data. However, this becomes a

new weakness for security and hardware failure compared to the integrity (e.g.

decentralization) of the DLT. Moreover, using this approach may increase costs for storing

data since Isabel Group also must support the servers. It also questioned then whether

sharing data without a blockchain would be more efficient and would inquire less

development costs for the blockchain architecture.

• The data could also be stored internally on the servers of the FIs but this leads to the same

remarks as with the hybrid solution.

• Another option is to store (encrypted) data on distributed servers which have the aspect of

decentralization. Nodes in the network can store (part of) the data in a redundant way. This

option can be difficult with regulations such the GDPR since it cannot be checked whether

all nodes removed (part of) the data. Moreover, only the designated persons should have

access and be able to decrypt their data (Protocol Labs, 2019).

• A final option are the side databases, for example provided by the Hyperledger Fabric.

Similar to the previous option, the blockchain’s integrity remains. The off-chain data is

stored in a private state database on the peers of authorized participants. Using a ‘gossip

protocol’ the authorized parties can see or exchange data (Hyperledger Fabric, 2019).

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Concerning the first option, it must be stressed that the Isabel Group already provides multi-banking

solutions in a centralized way and thus without the use of DLT. One example is ISABEL6, an online

payment solution that gives overview to maximum 25 of the client’s bank accounts and that gives

the ability to do payments using just one clear interface using one secure connection. Thus, this

solution could also be updated to support in the KYC processes. At the one hand, the current IT

architecture wouldn’t require a large overhaul. At the other hand, the Isabel Group would be the

most vulnerable point in cybersecurity, in privacy concerns and for single point of failure issues.

For the sake of clarity of the onboarding process, the assumption was made that the FIs aren’t

connected to Isabel’s multi-banking platform in the current, as-is, BMC. In reality, it is possible to

connect to the platform without usage of the blockchain technology. In that case, customers get a

snapshot about their accounts and their data. However, banks still have separate onboarding

processes and thus can’t benefit from another FI that has already done an onboarding process for

the same customer (K. Vingerhoets, personal communication, December 11, 2018).

Addressing the blockchain’s integrity, it should also be remarked that authenticity still depends on

the first compliance. Human errors may happen when doing the first KYC check. Mutual trust

between banks in each other services is challenging, as every bank remains responsible for her

own customers. The blockchain can support in overcoming trust issues but therefore governance,

the validators and the application’s operator Isabel Group are important. Furthermore, cryptography

is an important feature for keeping documents private. However, a user may still lose his private

key due to malicious attacks aimed at this user specifically (K. Vingerhoets, personal

communication, December 11, 2018).

The interviewee argued a blockchain solution could be beneficial when scaling to larger

environment with a lower degree of trust than in Belgium but still requires a better governance

framework about financial and data regulations in a DLT context first. The interviewee states:

“Regulatory problems pop up as present security guidelines dictate a separation between internet

and internal databases. By applying blockchain, storing master data will shift to the ledger and thus

the internet will be pushed deeper into the IT infrastructure” (K. Vingerhoets, personal

communication, February 11, 2019).

In addition, he stated that there are also challenges for project coordination. For example,

Partnership between Norbloc and the Isabel Group was terminated mid 2018 due to strategic

differences. The disagreement between the two led to a complete restart of the project, although it

was already beyond the proof-of-concept phase (K. Vingerhoets, personal communication,

December 11, 2018).

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3.2.3 BMC in the context of KYC

3.2.3.1 BMC discussion

The BMC models in this section take the onboarding process as the focal point in this analysis

because the interviewee argued that the blockchain application primarily focuses on this process

(K. Vingerhoets, personal communication, December 11, 2018). Consequently, the models are

based on the description given in previous chapters 3.2.1 and 3.2.2. Figure 3.12 shows the as-is

BMC which is an abstraction of the current onboarding BM of a FI. Figure 3.13 shows the to-be

BMC with the possibly impact in bold (Osterwalder & Pigneur, 2010). An explanation and discussion

are given hereafter.

Figure 3.12: Current BMC of a FI with focus on KYC (as-is)

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Figure 3.13: Proof-of-Concept BMC of a FI in a blockchain ecosystem (to-be)

Customer segments

The current as-is BMC model focuses on two customer segments. One segment are natural

Belgian persons, often referred to as retail customers. The other segment are legal entities, i.e.

enterprises, which require an extensive due diligence (K. Vingerhoets, personal communication,

December 11, 2018).

In the to-be BMC model the same customer segments are targeted. Moreover, the KYC approved

documents can be sold to other FIs in the network (K. Vingerhoets, personal communication,

December 11, 2018).

Value propositions

The focus is on the customer’s experience within a KYC onboarding process when wanting to open

an account. As of today, the onboarding happens separately for every account at a different FI and

is mandatory by law in order to reduce financial risks. FIs must also take care of the customer’s

sensitive data. Yet every FI uses a different digital identity, especially in the case of legal entities.

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Even though FIs put effort in the onboarding process, the customer experience is sometimes

affected by delayed business relationships (K. Vingerhoets, personal communication, December

11, 2018).

In the future, the customer experience could be enhanced when a DLT is used. A common digital

identity makes it possible to create an integrated interface for the customer segments. This can

also give customers more ownership over their data. Furthermore, time and effort could be reduced

significantly whenever one bank of the financial ecosystem has already done the onboarding

process and uploaded its results to the blockchain ledger. Other FIs could use the verified data on

the ledger to shorten the onboarding process. Smart contracts within the ledger could be

programmed to automate the guaranteed compliancy and faster processing of data. The

onboarding process remains important for reducing financial risks (Verhaest, 2018).

Channels:

Today, FIs use an omnichannel approach to communicate to (potential) customers. Hereby, bank

offices as well as online banking tools (e.g. website, app) are not only key channels to reach to

(potential) customers, but also to make the onboarding process as smooth as possible. A different

account and login are required for every account at a different bank (Verhaest, 2018).

In the to-be model, it would be possible to have just one login for all banking accounts through the

dashboard of Isabel. Isabel would use a common API which is connected to the KYC blockchain.

Customers, for instance elderly citizens, can still reach out for support at a bank office (K.

Vingerhoets, personal communication, December 11, 2018).

Customer relationships

In the as-is situation, delivering an expected customer experience happens in two major ways.

First, the onboarding process starts with the potential customer filling in a KYC form. In fact, this is

a lot of paperwork, by which the personnel could assist to make the onboarding process as

comfortably as possible. Second, a bank can provide financial services at the request of the

customer after the onboarding process (K. Vingerhoets, personal communication, December 11,

2018).

In the to-be situation, smart contracts on the blockchain ledger could improve customer

relationships. Opposed to the current situation where paperwork has to be filled in per new account

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at a different FI, a customer should need to fill in the form only once. The bank will then verify the

data and upload it to the ledger. Additionally, other financial services could also be automated

whenever these are programmed in the smart contracts. This could lead to more precise and faster

services. However, users become more dependent on technology. Last, working together with

other banks could create communities between FIs and customers, as the latter also gets more

ownership over their data and could have a say in the data maintenance. Also, there would be

more clarity about which bank a customer belongs to and vice versa (Verhaest, 2018)

Revenue streams

The current onboarding process, whereby the customer experience is closely monitored, provides

(more) new customers. FIs can generate revenue by delivering financial services to their customers

afterwards. Thus, a new account has potential to create revenue for FIs (K. Vingerhoets, personal

communication, December 11, 2018).

With a KYC blockchain, FIs could still be able to generate revenue by delivering services after

onboarding. Furthermore, they could be incentivised for uploading the KYC approved data to the

blockchain ledger. Other (smaller) FIs could then ‘buy’ this data in return for a fee. This way, the

cost centre around onboarding could become a profit centre for the bank that performs the first

onboarding. Although this looks promising, the interviewee states there are still heavy discussions

about pricing the data because there is nothing to compare it to yet. Moreover, a FI will only adapt

its behaviour based on the incentive (K. Vingerhoets, personal communication, December 11,

2018).

Key resources

The traditional KYC onboarding process (as-is) requires many resources. Every FI requires a

server and database for online banking, its own application programming interface (API) and

intellectual resources such as personnel with an expertise in onboarding and compliance.

Furthermore, the onboarding process itself requires lots of data. First, official data should be

provided by the potential customer and compared to the official data provided by the government

(e.g. National registration number, mandates, etc.). Second, the FI must also obtain essential data

of other data providers. This data could be lists of politically exposed persons (PEP-lists) (K.

Vingerhoets, personal communication, December 11, 2018).

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KYC with blockchain requires even more resources. Most of the resources in the current situation

are necessary in combination with the resources required to form a blockchain ecosystem. To start,

more investments for research and development (R&D) about blockchain are useful. This R&D

ranges from in-house research to advise from academics and consultants. Also, investments in the

IT infrastructure between FIs are required as the infrastructure must also be highly secured against

attackers. Next, software developers are required to code and implement the blockchain-based

ecosystem. Moreover, DLT and Fintech experts are also required to implement this ecosystem.

Final, all FIs in the ecosystem should also show the same amount of willingness to cooperate in

order to let the project succeed (K. Vingerhoets, personal communication, December 11, 2018).

Different from the as-is BMC model, the to-be model requires a common API for all FIs within the

ecosystem instead of a separate API per FI. Furthermore, there are multiple options to organize

the database, but it hasn’t been finalized yet (see previous chapter 3.2.2 about storing data off-

chain).

Key activities

Today, the main activity is the KYC process which is a major part of the customer onboarding

process. Three sub activities can be identified: identification of the potential customer, verification

of this person’s data and making sure it complies with regulations, especially when it comes to legal

entities. It needs to be stated that renewal of the KYC documents is necessary at least every three

years. In case of customers with high risk profile, the renewal has to be done every one to two

years (K. Vingerhoets, personal communication, December 11, 2018).

When a FI would cooperate in a blockchain ecosystem (to-be), the main activity itself remains the

same as in the as-is model. However, the time and effort could be reduced significantly when

collaborating in the ecosystem, in particular when the same customer wants to open an account at

other FIs connected to the ledger. It should be stated that own systems should be integrated into

the ecosystem. In the to-be model, there is also a new activity, namely setting new guidelines

together with this ecosystem and regulators since the financial industry is a highly regulated one

(K. Vingerhoets, personal communication, December 11, 2018).

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Key partnerships

In the current situation, the most important partners are official data providers (e.g. facilities run by

the government) and other data providers (e.g Refinitiv providing PEP-lists and other data). Even

though most FIs compete with each other, they can also be partners in economic studies for

example. Furthermore, banks are also investigating whether it is possible to partner with third-party

financial services providers since the PSD2 directive forced banks to give access these parties

access to the customer’s data through open APIs (European Commission, 2015).

In the situation with blockchain, partnering with official and other data providers is still a necessity.

Furthermore, a new partner is necessary to provide the blockchain P2P network, this could be the

fintech Isabel Group. It should be stated that all partners must be willing to work together in order

to get the maximum potential for automating the onboarding (and financial) processes in the

blockchain situation.

Cost structure

According to the study performed by Norbloc over 40 banks, the current cost of KYC in the Belgian

ecosystem is €335 million, of which onboarding and compliance costs over €100 million and

renewal of documents costs €219 million (Verhaest, 2018). The costs are still increasing because

more companies get a “high risk” profile. This leads to shorter renewal intervals of one to two years

(K. Vingerhoets, personal communication, December 11, 2018). IT development and maintenance

are also included within this cost per bank.

With blockchain, the same study performed by Norbloc suggests that costs could significantly be

reduced by €157 million per bank in total. Costs are reduced thanks to time reduction and

duplication removal since separate onboarding becomes redundant. However, when the proof of

concept was discussed, the blockchain ledger provider (e.g. Isabel) suggested free usage at the

start and would determine a usage fee later on. Even if the usage of the ledger would be free,

investment costs would still be required to develop and maintain the IT infrastructure within the

bank and in connection with the ledger (Verhaest, 2018).

3.2.3.2 BMC conclusion

It can be concluded that a to-be BMC can be modelled. The bank’s KYC onboarding process

remains important and is required by law.

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The BMC technique is able to demonstrate the impact argued in literature and by the interviewee.

In this case, all building blocks are impacted when a bank would adopt the DLT. However, the BMC

doesn’t illustrate how collaboration is established between key partners.

The to-be BMC clearly models the positive impact on value offering to the customer segments. The

building blocks “value proposition” and “customer relationships” demonstrate the advantages

argued. This is mainly thanks to the impacted activities, when collaboration is assumed.

Furthermore, the following can be concluded from the BMCs about the qualitative parameters in

the context of value offerings to customers:

• Traceability: It can become easier to track changes in datasets such as mandates for

example. Also, customers are more actively involved in onboarding process as they get an

overview and transparency of data through the blockchain application. Thus, ownership of

data may shift to the customer.

• Efficiency: Assuming there is an inter-banking collaboration within a new framework of

guidelines, then the efficiency of the process can be improved drastically for subsequent

accounts. Customers would only need to fill in the paperwork once when wanting to open

other accounts at different banks. Furthermore, the customers get a better overview in the

client thanks to the common open API. Also, the process becomes more automated using

smart contracts for the FIs and requires less resources. This can result in faster and more

correct services following on the onboarding process compared to the as-is situation.

• Trust: The customers are more in control of their data thanks to smart contracts. This is

also seen in the value proposition and relationships. Only the trust within P2P network has

to be deduced implicitly.

• Security: In this BMC analysis, the focal point is on transparency and efficiency. Security

can be deduced indirectly from the blockchain’s integrity. However, it still mainly depends

on how data is stored off-chain (key resources).

Based on the to-be BMC, in-depth quantitative research can investigate whether the new revenues

in combination with the argued cost reduction can outweigh the development and implementation

costs on the long term.

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3.2.4 E³value model in the context of KYC

Like the approach for modelling the BMCs, the e³value networks modelled are based on chapters

3.2.1 and 3.2.2.

3.2.4.1 As-is e³value model

Figure 3.14: Current e³value model of a FI performing the KYC process (as-is)

The abstraction of current system in Figure 3.14 has four market segments as actors: Potential

customers, Belgian banks (FIs) and two kinds of attributes (data) providers. One kind of attributes

providers are the Government databases such as the National Register, KBO-UBO, etc. Another

kind of attributes providers are independent from the government and are referred to as Other

attributes providers. For instance, ‘Refinitiv’ falls within this category (Refinitiv, n.d.).

A potential customer is in need of an account at a certain Belgian bank. Therefore, an account has

value for this person and the bank gets a new customer in return. The new customer has value for

the bank since it could create revenue from providing financial services for the customer after the

onboarding process (Verhaest, 2018).

This value exchange of the objects New verified customer and Account depends on the onboarding

activity KYC check, which needs the two necessary value exchanges between the bank and the

data providers and which should be done manually by the bank. In the upper value exchange, the

objects Official dataset and Societal importance are exchanged. Societal importance is an

intangible object as banks could alarm the government about money laundering practices for

example. In the lower value exchange, the objects Money and the KYC dataset of this potential

customer are exchanged. Often, FIs have a certain deal with this kind of providers in terms of

money.

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It can be concluded from this as-is model that the onboarding process could made more efficient

within the value network. Every time a potential customer wants to open an account at a different

FI, the FI should perform the same KYC check activity with the same provided data of that

customer. It should also be noted that the bank has full ownership over the customer’s KYC data,

thus security of data is dependent on this centralized party.

3.2.4.2 To-be e³value model in the context of blockchain

Figure 3.15: Proof-of-Concept e³value model (to-be)

This to-be model in Figure 3.15, based on the blockchain KYC proof of concept for Belgian banks

and customers, is quite different from the as-is model. This model demonstrates a different way of

working when a customer wants to open accounts at subsequent banks. The other Belgian FIs are

a new market segment in this model. Furthermore, Isabel’s multi-banking platform is also a new

actor (by approximation) and important for providing the P2P network based on the distributed

blockchain ledger. The Isabel Group itself has not been modelled here since their first idea doesn’t

involve charging any transaction costs in return for providing the DLT.

In this abstraction of a possible to-be KYC onboarding process and network in Belgium, a client

has two needs. First, he or she wants to open an account for the first time at a bank who is part of

the Belgian blockchain ecosystem. The same value objects as in the as-is model are exchanged

between the customer and the bank of first onboarding. The remaining of the to-be model proceeds

differently from the as-is model. At this point, it should already be stated that this e³value model is

unable to model the different steps with no real value exchange prior to final step where value

objects are exchanged. This becomes clear when comparing the to-be model with the process

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overview chart (see Figure 3.10) provided by Isabel. When it comes to gathering data, smart

contracts of Isabel’s multi-banking platform would now take this role and would also send this data

to the bank of first onboarding. This is not modelled as there is no value exchange yet. The KYC

verification should still be done manually by this bank identified by the activity KYC check.

Whenever there’s a successful onboarding, the objects Verified dataset and Money are exchanged.

Yet, this value exchange depends on the activity consensus within the blockchain ledger. This

activity requires value exchanges of the official and other, independent data providers (analogue

to the as-is), but also requires the value exchange of another activity performed by the blockchain

ledger. This activity is called ledger referencing and exchanges the value objects Verified dataset,

verified by the bank of first onboarding, and Money.

Smart contracts are also related to this activity serving to update the ledger with verified data (and

to release money in return) or retrieve a Verified dataset from the ledger (and ask for Money in

return). The latter happens when the same (potential) customer wants to open an account at

another Belgian FI (secondary FI). This secondary FI must be part of the blockchain ecosystem

and would like to perform its own KYC onboarding process with the same -but now already verified-

customer’s dataset.

Thus, the bank of first onboarding (primary FI) could be rewarded for doing her KYC check when

giving an account to the potential customer. The reward comes then from the secondary FIs who

wants to use the verified data from the primary FI’s KYC check to save time and effort. It should be

noted that the KYC checks themselves aren’t automated by the smart contracts that only serve to

update the ledger with manually verified data in a tamperproof manner. For secondary FIs, time

and effort could be saved as they don’t need to worry anymore about the origin of the data, since

this data is already verified by the primary FI and data has been made tamperproof thanks to the

distributed ledger (K. Vingerhoets, personal communication, December 11, 2018).

The interviewee notes that the determining the amount of reward is still a difficult exercise since

there aren’t other cases to base on. Thus, further quantitative cost analysis about the value

exchanges could investigate whether this ecosystem is economically sustainable on the long term.

The emphasize should be on the value exchanges between the bank of first onboarding, the

blockchain platform and the other Belgian FIs. The cost savings in Figure 3.11 can also be linked

to the activities in this to-be model. The activities affected would mainly be the two KYC checks.

For example, the bank of first onboarding may produce a verified dataset by doing its KYC checks.

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This dataset could be used by for example five subsequent FIs. These five banks could then ‘buy’

the dataset to have a shorter KYC check themselves (K. Vingerhoets, personal communication,

December 11, 2018).

To conclude the analysis of the to-be model: it suggests a third party, i.e. a FI, is necessary to do

the first KYC check activity because this is a complex and highly regulated process. Afterwards,

the data can be added to the immutable ledger to be used by other FIs connected to the ledger.

This doesn’t prevent human errors when doing the first KYC onboarding. However, it can be easier

to trace back the mistakes or fraudulent behaviour.

3.2.4.3 Comparing e³value models

When comparing the as-is and to-be e³value networks, it is clear that there is a different way of

working concerning the onboarding at subsequent banks. The to-be model introduces a new actor

(by approximation). When looking at the (external) value exchanges of the to-be model, the new

revenue argued for the bank of first onboarding also becomes clear. The notion of collaboration

becomes very important to let this model succeed. Despite the overall picture of the value network,

a BPM technique would be useful to get insights into the processes prior or underneath the value

exchanges. Furthermore, an explanation is required to communicate where and how smart

contracts can be used in this value network.

Addressing the qualitative parameters in the context of the research, the following is worth

mentioning:

• The e³value modelling technique is partly able to model the impact on traceability because

it shows the value exchanges of verified datasets to the ledger and by which FI. Eventually,

the model also keeps track of which other FIs have used a certain dataset.

• The “efficiency” mainly depends on activities ledger referencing and the KYC checks at the

other FIs market segment. Whenever another account should be opened at a different FI

within the blockchain network, this FI doesn’t need to ask questions anymore about the

content of documents as this content is already verified. The time required for the KYC

check activity by this FI could be reduced significantly. Indirectly deduced, these activities

should be supported by the smart contracts on the blockchain, for example to automate the

transactions of the value objects.

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• The parameter “trust” depends on integrity of the blockchain and how well smart contracts

are coded. Thus, it cannot directly be seen in the model.

• It could be stated that the parameter “security” shifts to the blockchain ledger and the off-

chain storage. Thus, it is indirectly deducible from the blockchain platform’s integrity when

making assumptions. Furthermore, it should be important that the IT infrastructure is also

able to safeguard the value exchanges.

A quantitative analysis with focus on the most significant activities and value exchanges within the

onboarding process can be part of further research and be compared with the study of Norbloc.

3.2.5 Conclusion about KYC in Belgium case

Keeping in mind the objective to answer the main research question within the predefined scope,

the following paragraphs attempts to answer and conclude the considerations of chapter 1.2 for

this case in particular.

Based upon the third interview and literature used as secondary source, the DLT can have an

impact on the BMs by overcoming (part of) current inefficiencies. Although the argument that

blockchain could make third parties obsolete within the financial services, this is not the case within

the field of KYC activities. Financial institutions remain important for the first compliance as this is

also a highly regulated industry. Important to mention is that the impact relies on assumptions: 1)

the FIs must be willing to collaborate, 2) a governance framework is developed with all parties

involved and 3) smart contracts must be coded appropriately.

Based upon the assumptions, the EM techniques demonstrate the following impact:

• The BMC technique is able to demonstrate the impact argued. Hereby, it shows the impact

on the different building blocks with regard to the KYC onboarding. The value proposition

and customer relationships are clear in the to-be model. Although this technique models the

different key partners, it doesn’t clearly show the impact on how the partners interact with

each other.

• Compared to the BMC technique, the e³value technique is only able to demonstrate the

(external) value exchanges within the collaborating value network in the to-be situation. The

sum of these exchanges partly compares to the value proposition in the to-be BMC. The

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new third-party actor is only an actor by approximation, since the platform is decentralized

similar to previous case. A BPM technique is required to understand the processes

underlying the value activities and exchanges. Smart contracts are implicit within this

network and the actor’s activities.

In the context of the value offerings to customers, the following impact on the qualitative parameters

is concluded:

• The comparison between as-is and to-be BMC shows the impact on the parameter

traceability by the improved transparency and overview for customers. Differently, the

e³value modelling technique shows the impact by the different value exchanges.

• Addressing efficiency, the BMC gives a first idea of the most likely new way of working and

where smart contracts can be used for. In the e³ value network, the value object Verified

dataset is important. Other improvements can only implicitly be deduced by having

background information on the activities.

• The parameter trust appears in the value proposition (and implicitly the customer

relationships) of the to-be BMC. In the e³value network, this is only implicitly deducible from

the blockchain actor by approximation.

• The BMC doesn’t give a clear answer to the impact on security, as this depends on the

final decision of the key resources concerned the off-chain data storage. Furthermore, the

reader can only indirectly deduce it by having background information about a DLT’s

features. The impact on this parameter in the e³value network is similar to the arguments

concerning “trust”. Furthermore, it also depends on how data will be stored off-chain, but

this hasn’t been modelled in this to-be model.

Keeping the arguments of Gordijn et al. (2019) in mind, it is not entirely convincing that this type B

case is an ideal blockchain case. Indeed, there are multiple entities active in this network but there

should be a level of trust since this is a highly regulated industry in the context of western society.

Moreover, immutability of data can be important to keep history records and cannot suddenly be

changed by parties involved, but sometimes it can be desirable to adapt data. The impact of this

KYC case can be generalized to other, international environments where trust is a more important

factor.

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The question remains whether a centralized approach with a common API would be able to give

the customers the same ownership and would be able to be more cost efficient on this (small) scale.

At the moment, the actors involved take risks in developing the platform using an emerging

technology without governance framework while centralized systems are more mature. The

required intermediaries, i.e. the FIs for first onboarding, should be trusted, otherwise competitors

would take away their clients in this environment. However, this could be a different case in the

perspective of scaling the network the Europe or even larger instead of just one country.

3.3 Chickens on the blockchain in supply chain

3.3.1 Today’s food supply chain

The supply chains in the food industry are important in today’s globalized society. They provide us

with food commodities starting from the farms to the end consumers. The supply chain consists out

of the following key parts: 1) production, 2) processing and packaging, 3) distribution and 4)

retailing. This can be a sequential flow of goods or a more complex web-based interaction of

companies. Along with improvement of living standards eating habits and shopping requirements

have changed. This results in consumers having more attention to food quality and safety in general

(Ericksen, 2008).

In recent years, environmental sustainability also got more attention. Consumers want to know

more about the origin of a product before consuming it, i.e. they want to see a more transparent

product supply chain. It is important for them that the product is safe and that it has been produced

with respect for animal and environment. Trust is an important factor that has been highlighted by

to recent scandals in the food supply chain (Hegnsholt, Unnikrishnan, Pollmann-Larsen,

Askelsdottir, & Gerard, 2018; VRT NWS, 2018)

Hegnsholt et al. (2018) state that next to other interrelated challenges to be resolved, there are also

opportunities to cut costs in for the current food value chains. The main aspects are:

• Suboptimal collaboration between partners. Most of the time, the collaboration happens

through complex contracts. It is argued that increased complexity lead to more risks for poor

contract management. For example, it happens that the technical department doesn’t get

all information on the terms that the sales department of the same organization has

negotiated. This leads to disputes that require to be resolved. Dispute resolution involves

tracing back to the root cause which takes time and is error prone. Moreover, trust and

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stakeholder management are also required to comply with regulations when processing a

manufacturer’s good (Chang, Iakovou, & Shi, 2019). It is estimated that improved

collaboration, especially between producers and processors, can save approximately $60

billion annually on a global scale thanks to less waste and reduction of transaction costs

(Hegnsholt et al., 2018).

• Poor traceability. This is caused due to lack of transparency within the supply chain resulting

from manual paper-based processes for tracking goods along the supply chain which take

a lot of effort. Often, a player has only overview of the transactions between the organization

just before and right after him in the supply chain. According to the blockchain solutions

lead interviewee, this is strongly tied to the challenge above about collaboration as improved

collaboration across the complete supply chain can lead to more transparency. Improving

traceability can also lead to better estimation of the product orders. Consequently, the

interviewee argues that increased transparency can tackle the bullwhip effect, i.e.

fluctuations in demand (in combination with lack of transparency) make it difficult to estimate

the correct amount, thus leading to stock shortages or high safety stocks (F.-J. Roose,

personal communication, December 21, 2018). Traceability can be in an important driver to

overhaul current systems as customers are also demanding more detailed information

about the authenticity of a product. According to Hegnsholt et al. (2018) the low

transparency leads to an estimated global food waste of $260 billion annually.

• Supply chain governance. This is also closely linked to collaboration as the regulatory

requirements must be monitored along the whole supply chain. Breaching the requirements

can lead to a damaged reputation and significant fines. To address the issue, it should be

questioned how the parties coordinate and communicate the requirements to other

members of the supply chain (Chang et al., 2019).

• Integrity and security. In general, the parties store and manage data in their own IT silos,

i.e. the company’s database. These centralized servers are prone to cyberattacks and

single point of failures. Moreover, documents hold in their own IT silos can be lost, stolen

or tampered with when transferring ownership of the goods. This could result in shipping

counterfeits or other scandals (Chang et al., 2019).

• Slow adoption of digital tools. The final and important challenge is based on the fact that

manual paper-based processes are still widely used. Consequently, data duplications,

inconsistencies, etc. may occur. This also leads to transparency problems. Digitalizing

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companies in combination with setting KPIs, which focus on food waste or loss, is estimated

to save $120 billion annually on a global scale (Hegnsholt et al., 2018).

Before investigating the possible impact of blockchain, it must be noted that part of the challenges

can already be overcome by voluntary digitalizing the organization with rather mature enterprise

resource planning (ERP) systems. This requires a centralized third party which stores and partly

shares information between the organizations. Some also offer track-and-trace options, for

example in e-commerce. However, this still requires trust and also the aspect of cybersecurity shifts

to the centralized party (Nakasumi, 2017).

3.3.2 Chickens on the blockchain with IBM Food Trust framework

This chapter explains the situation where a complete food supply chain collaborates and adopts

the blockchain technology to face the challenges of chapter 3.3.1. An example of such technology

provider is IBM, which developed the IBM Food Trust framework. Ideally and according to the

blockchain solutions lead interviewee, this adoption may happen in combination with Internet-of-

Things (IoT) sensors and applications to monitor and automate processes even more besides the

use of smart contracts. The goal of blockchain adoption in this case is different than previous cases.

Adoption will not lead to making a third party obsolete in this case, but the technology serves to

enhance traceability, food safety, cybersecurity and reduce tampering (F.-J. Roose, personal

communication, December 21, 2018). The question remains if new challenges brought with

blockchain adoption such as collaboration and governance can also be overcome.

The IBM Food Trust framework is a cloud-based Solution-as-a-Service which is built on the

Hyperledger Fabric. This permissioned ledger can provide a blockchain ecosystem with equal

means as in the KYC case. The technology is built on top of the enterprise technology with a

standardized governance structure and where all partners get added value for their investment.

The goal is to share a copy of the ledger to all stakeholders involved (IBM, 2019b).

The discussion on the topics following assumes that every partner in the supply chain is part of the

blockchain ecosystem. If this is not the case, then there would be data missing to fully optimize the

supply chain. The data used to discuss the challenges was mainly obtained by the third interview

and the available information on Carrefour’s and IBM’s website (F.-J. Roose, personal

communication, December 21, 2018) (Carrefour, 2018a, 2018b, 2018c; IBM, 2019b):

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• Concerning collaboration, the assumption above was made that every stakeholder is

involved and migrates their IT to a common infrastructure. This way, ‘tamperproof’ data of

the good can be shared with all stakeholders involved. Trust in other stakeholders is

required to a smaller degree as this is built in the framework (T. Vandoorne, personal

communication, November 26, 2018). Since this is a permissioned ledger, the identity of

stakeholders is revealed. In a first approach information related to the flow of goods can be

captured, next information related to supporting contracts can be added as a second phase

in the implementation/application evolution. In a next step the contacts involved could also

be coded as smart contracts leading to a higher degree of automation. This can improve

efficiency (see further) but can also have implications which are discussed in the next

paragraph.

• End-to-end traceability for customers and partners can be obtained by creating more

transparency in the supply chain. This requires collaboration and the physical good can be

traced by linking a digital passport with this good. Therefore, labelling technologies such as

RFID, barcode, DNA, etc. should be used to link the passport with the physical good.

The IoT technology could be used here as a plus to automate data addition serving

improved flow control, for example by adding information of the temperatures observed

during transportation. Traceability can already be reached when an industry associate

(NGOs, government, etc.) or ERP cloud software provider receives and controls all data in

a centralized or decentralized way. Yet, it is not (always) possible since not all data is stored

in the same, standardized manner due to organizations having different systems. Moreover,

to do it properly, the data needs to be centralized by one party that can be trusted.

• Firmly related to traceability and collaboration is the possibility of improved efficiency. Better

data registration and management can impact the inventory management in a positive way.

One example is tackling the bull whip effect. Smart contracts can automatically execute the

terms coded in the contract. Consequently, this can have an effect on the pricing of

products. Scalability is argued to be an extra challenge for blockchain, but this could be

resolved by storing raw data off-chain in combination with resource efficient consensus

mechanisms. It must be noted that there are already various ERP tools which support in

estimating product orders of one company.

• Blockchain also forces all stakeholders to adopt a blockchain governance by

standardization and defining rules or guidelines to manage the (IT) processes and the data

extracted from the processes. Standardization can also help to increase the efficiency of

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processes along the supply chain. However, a better governance needs to be developed

first. This is a challenging task in this complex ecosystem as discussed in the literature

review (Beck et al., 2018). IBM proposes that stakeholders create an ‘Advisory Council’.

The council, consisting of industry representatives, helps to set rules of engagement for the

community. Kamilaris, Prenafeta-Boldú, and Fonts (2018) argue that there would be less

influence by largest players. This can improve collaboration of smaller players. However, I

do not completely agree on this. In my opinion, a framework can indeed result in more

collaboration of all players. However, the largest players in the supply chain would still have

the most advantage in terms of influencing power. They could propose the most

experienced advisors when it comes to designing the rules and smart contracts.

Furthermore, they could still pressure (smaller) stakeholders in that supply chain to adopt

the technology – which is not necessary a bad thing if it results in overcoming current

challenges.

• Security is another topic argued to be impacted by the blockchain technology. The database

is decentralized, so less chance for malicious attacks or failures, and every transaction

added is tamperproof. Furthermore, data can also be encrypted to keep the details of the

transaction discrete. ‘Trust Anchors’ are also part of the ecosystem who have access to the

ledger to see encrypted hashes and to validate transactions. Although, they have access to

the ledger, they cannot decrypt data unless they have a public and private key pair. The

ecosystem may be ‘tamperproof’ after verification, but the entries into the ledger are still

prone to human errors or intentional manipulation. Thus, the first entry, for example linking

the physical good with the digital passport, is still vulnerable to fraud (Javier, 2018).

However, it could be easier to trace who made the mistake since it becomes more difficult

to tamper the transaction logs afterwards. Comparing with a simple cloud database, it can

be argued a regular cloud database is prone to manipulations from the first entry and

onwards.

• There is discussion ongoing about the speed of adopting the blockchain technology in the

industry. An article in the business magazine ‘Forbes’ is positive and argues that the

blockchain Food Trust framework can face the challenges of blockchain and digital adoption

argued above. For example, Carrefour and Walmart are already implementing the

technology (Stanley, 2018). Contrary, research in the UK fresh food industry concludes

that there is a (very) slow adoption due to the lack of knowledge, use cases, collaboration,

standardization of protocols, lack of driving force, etc. The disruptive character results in a

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barrier for IT integration while the company’s most important requirements are seemingly

met with current systems. However, the research also concluded that the producers might

adopt the technology when large retailers (or regulators) push the technology as they have

the most power (Osei, Canavari, & Hingley, 2018). Other researchers argue the technology

is promising but needs multiple years to mature. This is no surprise as this is argued over

most of the emerging technologies. The technology is a new means which has the

advantages covered in the literature after all stakeholders have trust in and more knowledge

of the technology. When the governance challenge is overcome, standardization can also

result in more companies to adopt the technology. Further integration in supply chain

management can transform relationships not only in the business-to-business, but also

business-to-customer and customer-to-customer contexts (Chang et al., 2019; Queiroz,

Telles, & Bonilla, 2019).

The following notes can already be made when comparing the information above with what authors

cited in the literature review like Morrison (2016) remarked:

A) Concerning governance, regulations may lack enforcement of smart contracts at this

moment. The smart contracts also face an enormous challenge for implementation because

once implemented, they are difficult to change. Thus, they should be coded to take into

account all possible cases in the complex business environment (Beck et al., 2018). The

‘Advisory Council’ can aid in the general governance of a supply chain by creating standards

for best practice amongst other things.

B) Business process and procedures may also change. Related to governance, it enables a

standardised way of sharing data and streamlining processes for all stakeholders.

C) The blockchain technology is still in its infancy and must compete with mature IT systems

such as cloud ERP and other track-and-trace systems. These mature technologies have

already proven to work and may cost less for development and implementation at this

moment.

Generalisation of this case to other (food) supply chains is possible. The poultry product can be

replaced by a various range of food and non-food products. Carrefour is planning to roll out the

blockchain technology to their other food products (Carrefour, n.d.-d). Furthermore, there are a

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number of start-ups providing an alternative to the IBM Food Trust platform such as

Provenance.org (Provenance, 2019).

The rest of this case focuses on the ‘free-range Auvergne Quality Line chickens’ sold by Carrefour.

Therefore, Carrefour partners with IBM so that all parties have access to the permissioned

blockchain ledger. Furthermore, the partners have authorisation to update the ledger with new

transactions of the products. Customers can scan the product’s QR code with a mobile software

application that is connected to the ledger. Consequently, customers receive more information

about the product’s origins and processing (Carrefour, 2018c; IBM, 2019b).

3.3.3 Business Model Canvas Carrefour

3.3.3.1 Discussion BMC models

The as-is and to-be BMCs are modelled based on the information gathered in the chapters 3.3.1

and 3.3.2. Figure 3.16 explains the as-is BMC model, while Figure 3.17 explains the to-be BMC

model (Osterwalder & Pigneur, 2010). Both models attempt to visual the business model of

Carrefour with a focus on the food supply chains. The impact on certain elements is shown in bold

and is discussed per building block in the paragraphs below.

Customer segments

Today, Carrefour focuses on the mass market. Through their channels, they approach customers

of all ages on an international level. Customers expect the (poultry) products to be of a certain

quality and ethically produced with care for the environment (Carrefour, n.d.-b). In a situation

without the blockchain technology there is no true guarantee that this expectation is truly fulfilled

but it is rather an assumption that such is the case. Otherwise, the retailer’s reputation would be

damaged, and customers will shop different products or worse, at a different retailer.

In the to-be model with blockchain, the focus remains on the mass market. According to the

interviewee, Carrefour aims to strengthen the bond with its existing customers and attract new

customers by being more transparent about the product’s authenticity and origin. Thus, improving

the assurance of the quality of their products. This is also found in the 19th ‘Act for Food’ on their

website (Carrefour, n.d.-d).

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Figure 3.16: The BMC of Carrefour with focus on poultry products (as-is)

Figure 3.17: The BMC of Carrefour with focus on poultry products on the blockchain

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Value propositions

Based on Carrefour’s ‘Acts for Food’, the most important value propositions are distinguished in

the as-is model. Carrefour wants to offer quality products at a competitive price while investing in

local economies. Furthermore, they want their products being offered in a responsible way by

showing respect to their employees, customers and the planet (Carrefour, n.d.-b).

With the Auvergne chickens on the blockchain, they want to fortify, assure and prove the value

proposition. The main reasons for introducing blockchain is to bring transparency and food safety.

Being more transparent and in this way assuring the quality of products fits in the Carrefour’s food

act and becoming a leader in food transition (Carrefour, 2018a). The overall customer experience

is impacted in a positive way as transparency is increased (F.-J. Roose, personal communication,

December 21, 2018).

Channels

In the as-is situation, the value proposition is delivered to customers through an omnichannel

approach. Furthermore, sales happen in the warehouses and online using the web shop (Carrefour,

n.d.-a).

In the to-be situation, the blockchain technology can create marketing opportunities providing the

possibility to improve the channels in the warehouses and online store (F.-J. Roose, personal

communication, December 21, 2018). The idea is that a (potential) customer gets an overview of

the supply chain the poultry product passed through just by scanning a QR code labelled on the

product (Carrefour, 2018c). A downside of this is that competitors also can see this information.

Thus, the data should better not be of any strategical importance (Osei et al., 2018).

Customer relationships

That Carrefour wants to be shopper-centric and wants to assist shoppers to enhance their shopping

experience can already be derived from the value propositions. Moreover, they want to retain

customers by using numerous customer advantages such as a customer card and other discount

deals. Customers also have self-service options, for example by using the self-scan. Marketing is

an important factor to create brand awareness for certain products. Furthermore, the marketing

around the ‘Acts for Food’ tries to create a self-aware community of customers which is also in line

with the value proposition (Carrefour, n.d.-b).

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The to-be model shows that the products on the blockchain can have an impact on the customer

relations. At first, more transparency is created resulting in better customer assistance and thus

also being more shopper-centric. Interested customers can easily find more information about the

product. Second, brand awareness is improved when products are on the blockchain by using

marketing techniques. It will be easier to distinguish certain features of the products after scanning

their QR code. Finally, the increased transparency in combination with the blockchain technology

incorporated in the ‘Acts for Food’ could leads to a community which is more self-aware of the

products they are buying (F.-J. Roose, personal communication, December 21, 2018).

Revenue streams

The most significant revenue comes from selling products in the warehouses and online.

Furthermore, there could also be income streams from delivering products or services to other

trading partners (Carrefour, n.d.-a).

With the blockchain technology, there can be an impact on the revenue stream thanks to the new

marketing opportunities according to the blockchain solution lead interviewee (F.-J. Roose,

personal communication, December 21, 2018). However, further research needs to be done to

measure if the investments lead to a significantly increased revenue stream. In the competitive

market landscape with care for the environment, animal friendliness of processes and the race for

the lowest prices, there is also the challenge to maintain current revenues and avoid declining

revenues due to competitor hopping.

Key resources

In the as-is situation, many resources are needed in order to sell food products such as poultry.

Most of the resources addressed in the business model canvas speak for themselves to manage

a retailing business. Furthermore, negotiation power is an important intangible asset to keep prices

low compared to competitors (Carrefour, n.d.-a).

Moving the Auvergne chicken on the blockchain in the to-be model, it can be stated that the prime

impact is the required blockchain R&D investments. Implementing a Blockchain approach requires

a new IT infrastructure together with improved labelling techniques and a software provider for the

app that scans the QR codes (F.-J. Roose, personal communication, December 21, 2018).

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Moreover, the personnel would probably need to be trained to use the new IT infrastructure in the

right way (Carrefour, n.d.-c).

Key activities

The key activities are the diverse activities involved in the supply chain (e.g. production and

processing, procurement, distribution of goods, etc.). Furthermore, there are other retail activities

that speak for themselves such as marketing, e-commerce, etc. as well as doing (obligatory) quality

checks to examine if products are safe for consumption (e.g. monitoring temperature) (Carrefour,

n.d.-a; Ericksen, 2008).

The to-be model shows the impact of putting products on the blockchain which results in a totally

new way of working. The name of the activity remains the same, but the procedure changes. Only

the way customers pay for the products doesn’t change. If the supply chain activities are connected

to the ledger in a proper way with smart contracts, this can impact the efficiency for all activities as

well as traceability and the security of the data (F.-J. Roose, personal communication, December

21, 2018). Smart programs can also be programmed to automatically check if conditions for quality

management are met. The latter could be when combining the technology with other automated

technologies such as IoT (Carrefour, n.d.-d).

Key partnerships

The supply chain requires current producers to collaborate. This often happens with contractual

agreements that outline the terms for delivery, payment and so on. Carrefour also has trading

partners to develop promotion strategies for example. Furthermore, they also have consulting

partners which help to solve internal problems. Other partners include food inspection agencies

and NGOs which provide a quality label to their products. Final, there are service partners assisting

in the current IT infrastructure of the Carrefour Group (Carrefour, n.d.-a).

With blockchain, an independent blockchain technology provider (e.g. IBM) should become an

important partner to help in setting up the new IT infrastructure and assisting in the introduction,

use and application of the new platform (IBM, 2019b). The producers in the supply chain remain

the same but require collaborating actively in the system. BMC in this case visualizes the partners

but fails to explain how these partners interact with the retailer and what their partner relation is.

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Cost structure

The as-is cost structure at least exists out of four kinds of costs, derived from the four key parts of

a supply chain (Ericksen, 2008). At first, there are costs for producing, processing food, distribution

and selling products lead to the typical costs of a supply chain. Adding to this, maintaining the

current IT infrastructure also bares costs. And finally, marketing costs are also significant and

needed to approach and attract potential customers (Carrefour, n.d.-a).

Blockchain technology impacts the development and maintenance costs for a new IT infrastructure.

Although a blockchain technology provider like IBM charges subscription costs for using modules

of the network, the interviewee argues that being more transparent has an effect on reducing

inventory costs as the bull whip effect is (partly) tackled depending on the level of safety stock (F.-

J. Roose, personal communication, December 21, 2018).

3.3.3.2 Conclusion BMC

Comparing the as-is and to-be model leads to the conclusion that blockchain impacts one or more

subjects within each segment of the Carrefour’s business model canvas. Only the customer

segment isn’t impacted.

Taking the objective of this master’s dissertation into consideration when drafting conclusions, it

can be stated that the overall impact on value proposition and customer relations when introducing

blockchain technology is positive:

• Traceability is impacted by the improved transparency. This can be seen in the value

proposition where quality can be impacted. This can also be seen in the customer relations.

Better transparency leads to an even more shopper-centric approach and shopping

assistance and fits into an ecological and climate sensitive customer. This way, a more self-

aware community of shoppers can be established. This is also in line with the current ‘Acts

for Food’. It must be noted that transparency can also be improved with an existing

centralized track-and-trace system. The difference is that there is no control by a centralized

third party in the case of blockchain. In combination with the immutability aspect, the retailer

could be perceived as more trustworthy. This fits in their overall marketing campaign where

they guarantee quality and food safety.

• Efficiency in processes within customer relations isn’t directly impacted. The impact on

current activities requires new IT resources but can lead to automation of current processes

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with the help of smart contracts. The optimisation of processes could lead to lower prices

for the same or even better quality and that way impacting the customer relations. It should

be noted that the partners are modelled but not the contractual agreements and the required

active collaboration which may lead to increased efficiency between partner’s activities.

• Improved security and trust can also be considered as an impact. Apart from enhanced

cybersecurity and no trusted third party fully controlling the network as in current cloud

solutions, the customers can also be assured of the quality of a poultry product by having

access to means serving as a customer to check authenticity and origin. This ties back to

the first parameter of traceability which can be seen in the value proposition and customer

relations.

A final note should be made here. Further research is necessary for quantifying the revenue

streams and costs. The technology can lead to improved processes and new marketing techniques,

but this comes at the cost of developing and implementing a new IT infrastructure to which every

actor of the supply chain must be willing to collaborate.

3.3.4 E³value model of the poultry supply chain

Before examining the e³value models, the following assumptions are made. First, the focus of this

model is on the poultry supply chain. A more complex model could be made where not only other

food supply chains but also interactions with governmental organizations (e.g. food inspection),

NGOs, certifiers and other stakeholders are modelled. Second, it is assumed that hatcheries,

farmers and slaughterhouses have agreements (e.g. a charter) with the Carrefour Group, but are

not part of the group itself. Final, it is also assumed that customers shop in the physical

warehouses.

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Figure 3.18: Value network of a poultry supply chain (as-is)

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3.3.4.1 As-is e³value model

The as-is model in Figure 3.18 is a value network of a current supply chain where the customers

wants to satisfy their need for food. In this abstraction, the focus lays on the different actors who

produce and process the meat. First in line are the Hatchery, Farmer and Slaughterhouse who

produce and process food. Second is the actor Transporter, which is assumed to be independent

from the composite actor Carrefour Group for transporting the Chicks, full grown Chickens and the

butchered Chicken meat. Carrefour Group covers Carrefour’s Manufacture plants, Distribution

centres, IT department and Warehouses. It is assumed that Carrefour Group also covers a

Transportation firm which transports and distributes the further processed meat (Carrefour, n.d.-a).

The Market segment Customer has a basic need for food which can be satisfied by shopping in

warehouses. Assumed is that customers want to buy safe and ethically produced food. The value

objects Food and Money are exchanged between Customer and Warehouse within the Carrefour

Group. All previous steps to get to the point of selling food requires value exchanges between the

previous actors, who add value to the product, and the mandatory transportation firms. It can be

stated that the value-adding actors are the customers to the previous link in this supply chain. More

actors such as NGO’s (e.g. Fairtrade), sector or government organizations could be added to this

model to interact with each existing actor. However, this would make this model more complex and

does not contribute to understand the supply chain itself (Carrefour, n.d.-a).

It is possible to trace down the authenticity and origin of a product to the hatcheries, but then this

value network requires that every stakeholder in this network could access another one’s data.

Although the industry is highly monitored, the separate actors could still enhance their transparency

and general collaboration as previously mentioned (Hegnsholt et al., 2018).

Moreover, Carrefour Group could manage and store all data of their goods’ steps prior to further

processing in a centralized or decentralized way in the already existing IT department. However,

such approach is more vulnerable to hijackers and problems such as the single point of failure,

which was already mentioned in the previous cases. All actors producing, processing or

transporting food are also responsible for managing and storing their own data. This leads to

security depending on the level of cybersecurity of each separate actor. It should also be mentioned

that trust and governance to exchange value objects also depend on the contractual agreements

between actors and -with extension beyond this abstraction- also NGO’s, government and sector

organizations (Carrefour, n.d.-a; Hegnsholt et al., 2018; Patnayakuni & Patnayakuni, 2014).

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3.3.4.2 To-be e³value model in a blockchain context

Figure 3.19: Value network of a poultry supply chain on the blockchain (to-be)

Looking at the to-be model in Figure 3.19, a new actor “IBM Food Trust” can be identified. This

actor is an abstraction of the IBM Food Trust platform with whom Carrefour partners with and which

represents the distributed ledger. The platform also involves possibilities to store data in side-

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databases. An important note must be made here: the platform is only an actor by approximation.

One could misread the model and conclude the platform is a centralized partner. At the moment of

writing this master’s dissertation, there is no alternative syntax to model decentralized blockchain

actors in e³value models. IBM only provides and maintains the P2P decentralized network built with

the platform as the tool for stakeholders to connect with each other (IBM, 2019b).

In this value network, it is assumed that all stakeholders in this supply chain could interact with the

distributed ledger by sending Data in return for increased Process support. Therefore, it is required

that all companies in the supply chain digitalize and adopt the technology. Instead of keeping data

records to their own, they share them on the ledger by the value exchange. The process support

is a copy of the ledger’s immutable transactions, with the goal of improving trust and transparency

for all stakeholders. It can also hold the automated execution of smart contracts. An overview of

the datasets sent to an received from the blockchain can be found in Attachment 2. The activity of

Carrefour’s IT department also changes. Instead of only storing and managing the data of products

and processes in Carrefour’s databases, data is now also exchanged with the ledger. IBM offers

modules of their platform in return for a certain subscription fee (IBM, 2019b). This leads to the

value exchange where increased Process support is perceived in return for sending Data and

paying the Subscription fee. It is an assumption that only the Carrefour Group pays subscription.

In reality, it could be that all stakeholders ranging from production to retailing have to pay a

subscription fee within a blockchain ecosystem.

Customers at the other hand are able to track the origin of the product and in return for this

information, they can become a new customer of the product that went through this blockchain

ecosystem (Carrefour, 2018c).

3.3.4.3 Comparison e³value models

The first conclusion that can be drawn when comparing the two e³value networks is that there is

no third party removed. In fact, an independent party becomes important to provide the protocol

and other IT support. They merely set up a P2P decentralized network using an application on the

Hyperledger Fabric. In return they charge a subscription fee from stakeholders in the supply chain

in order to maintain the network on the long term.

When comparing the two e³value models and with respect to the objective, the following points are

worth mentioning:

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• Addressing customers, they would not see much difference at first hand compared with a

centralized track-and-trace system in the cloud, since the interface would be the same. Only

the underlying technology is different and would be perceived as value-adding if customers

have knowledge about the blockchain technology and do not trust the data within a

centralized track-and-trace system at all. Customers should also be aware that errors in first

entries to the ledger could lead still lead to false trust.

• The parameter traceability is shown to be affected when looking at the ecosystems as a

whole. Increased by transparently sharing data to a common distributed ledger under the

assumption all producing actors want to collaborate and share (parts of) their data. For

producers and Carrefour, this leads to a better support of their processes. For customers,

this leads to the ability to get an overview of the product’s supply chain. The question

remains if an active collaboration of the whole supply chain is possible.

• Efficiency can be improved with transparency and smart contracts within the activity of

ledger referencing can automatically execute the terms of a negotiated contract. Moreover,

the shared data can be used to improve current processes. This is stated under the

assumption that the activity ledger referencing encompasses the tamperproof distributed

ledger technology and the side databases to store large amounts of data. Although the

impact can mainly happen within the processes of Carrefour and suppliers, there can be an

indirect impact on customer relations if customers are prone to topics such as food waste.

Lower food waste can also lead to lower production and inventory costs, thus meaning

either more profit for the seller or reduced price for the buyer. Hence, blockchain can

become an enabling technology to support better quality products at lower costs. There is

still a challenge to create a standard for best practice and coding all possible scenarios into

smart contracts.

• The aspect security shifts from all parties maintaining their own data storage in their

activities (or Carrefour group trying to maintain data from the whole supply chain) to the

blockchain platform validating and verifying transactions and thus storing the data of the

whole supply chain. All producers, processors, distributors and retailers can make changes

to the ledger after consensus. However, the customers can only read the data in the form

of the object Traceability. Compared to a centralized cloud database where they would also

be able to only read the data, the blockchain brings an extra layer of security as customers

cannot tamper anymore with the version they can read. Important note: with blockchain, it

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would also be necessary to secure the IT infrastructure connecting the actors to the ledger.

Also, the parties still need to secure their own activities.

• Concerning trust, it is difficult to model contractual agreements in both models. In the as-is

model, only the results of contractual agreements, i.e. the value objects, are modelled but

not the underlying terms. In the to-be model, the model can be made more complex by

modelling the ‘Trust Anchors’ who validate transactions, thus making it easier to deduce

trust. However, this doesn’t mean that an actor cannot send false data to the blockchain.

For example, a farmer could say that a chicken didn’t receive antibiotics while the opposite

could be true in reality. This could only be checked by the food inspection and not by the

smart contracts. The platform would only be able to tell which actors the transaction did so

it would be able to help the food inspection to trace down where the problem had begun.

Another way could be to make sure that sensitive claims such as “antibiotic free” are only

accepted by the blockchain as a qualifying label if they are certified by an independent

control organization just like in the current situation. Also, visualizing smart contracts with

e³value is difficult. The contractual agreements themselves could be programmed within

the smart contracts – making that trust can only modelled indirectly in the to-be model.

When focussing on customer relations, it is stated that the customer receives information

from a ledger which isn’t controlled by the retailer only.

The modelled to-be value network could be used as a basis for further research with focus on

quantitative data. This way a first investigation can be done to answer whether the value network

is economically sustainable on the long term.

3.3.5 Conclusion about chickens on the blockchain case

Keeping in mind the objective to answer the main research question within the predefined scope,

the following paragraphs attempts to answer and conclude the considerations of chapter 1.2 for

this case in particular.

Concluding from the interview with the blockchain solutions lead, the data referred to in the

literature review and the data of the involved companies found online, the technology can impact

the business models by facing the challenges such as food waste and customers demanding more

transparency about the products they want to buy. Cost could be cut on the longer term as efficiency

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improves thanks to smart contracts. The value propositions and customer relations can also be

impacted in a positive way. In the case of Carrefour, this fits in the picture of the ‘Acts for Food’.

One of the current challenges in the food supply chain, collaboration, is also a requirement to adopt

the technology.

The technology also brings other new challenges with it. All stakeholders should have enough

knowledge on how to use the technology when it matures in order to adopt it. It will be an important

task to code all situations in smart contracts. Whenever a situation is not coded, this could lead to

disputes that slow down the processes. Another challenge is governance since guidelines and

standards for best practices are still discussed. A final challenge is that the immature technology

must compete with mature (and proven) cloud ERP systems assuming that modifications and

enhancements to the more mature technology can be cheaper to develop and implement for certain

cases at this moment.

The chosen EM techniques can demonstrate the following impact:

• For the BMC models, the addressed impacts on customer relations are in line with the

argumentation that the IBM Food Trust platform could overcome current food supply chain

challenges. This is under the assumption that all partners of that particular supply chain are

willing to collaborate. In general, the to-be BMC model visualizes the impact in chapter 3.3.2

argued as well. However, concerning the key activities, some extra explanation is required

to understand the activities impacted in bold (e.g. standardization of existing activities).

Moreover, the supply chain’s other actors are explained in the section of the key partners,

but the BMC models do not give an overview of the actual supply chain, the required active

collaboration and the partner relationships.

• The e³value models give an overview of the supply chain’s value network. All steps and

partnerships between the actors of the supply chain are visible. More actors such as food

inspection authorities could be added without much hassle but would make the model more

complex to read. The modelled value chain requires active collaboration as modelled with

the value exchanges. The sum of all value exchanges can show the value proposition of

the BMC. It must be stated that the consensus mechanisms and smart contracts themselves

are hard to model and are assumed to function within the ledger’s activity and in the internal

processes of the actors. On first view, one could misread the to-be model and believe the

platform is a centralized party, such as a track-and-trace system in the cloud. Only the

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contents of the objects ‘Traceability’ and ‘Process support’ would be different. Thus, a

decentralized actor is something that should be developed in e³value modelling tools for

this case.

In this case, the impact is also noticed in the context of the qualitative parameters. When comparing

both types of EM models, the following becomes clear in the context of value offerings to customers:

• Traceability: Blockchain technology’s transparency in the supply chain leads to an impact

on traceability. In the BMC, this is explicitly modelled in the value propositions, key activities

and customer relations. In the e³value model, this can be deduced from the value

exchanges where datasets are exchanged for (improved) process support. Moreover, the

e³value network shows more explicitly the track that the poultry products follow. The sum of

the value exchanges can result in the value proposition argued in the BMC to-be model.

• Efficiency: No direct impact on customer relations in the BMC. However, optimisation of

processes could lead to less food waste and inventory costs and thus potentially lowering

price of products. The same could be said about the e³value network since the terms of a

contract could automatically be forced using the assumed smart contracts in the processes.

However, both models lead to the argument that further research is necessary on this

assumption that prices could be lowered.

• Security and trust: BMC models the impact on the quality of processes and products in

the value propositions, key activities and customer relations. This is closely related to the

traceability. The e³value model is partly able to model the impact on customer relations

concerning security and trust. This had to be deduced indirectly from the improved

traceability and the content of the ledger’s activity. It can be stated, however, that the

customers receive data from a distributed ledger which isn’t controlled by anyone in the

supply chain. Worth noticing about security for the producers and retailers: All kinds of

(de)centralized database systems are prone to false entries, but such should more likely

happen in a system that has less transparency and less traceability. Enhancing

transparency (and traceability) leads to a reduction of this threat as the chances that the

fraud is detected and exposed are increased.

If the arguments of Gordijn et al. (2019) would be taken into account, it can be concluded that a

type C case is not an ideal blockchain deployment case. Not all entities in this ecosystem are equal

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as the Carrefour group has more power to dictate the rules of the game. Apart from the extra

security layer, i.e. the immutability of records, it can be questioned whether a track-and-trace

system in the cloud would better for a large player since the blockchain technology and additional

governance hasn’t matured yet.

However, the models developed show a simplified abstraction of one particular food supply chain

ending at customers who shop at a large retailer. Minor adjustments to these abstractions can

result in the development of business models of (smaller) retailers in an ecosystem with more

market flexibility. Consequently, it can be generalized to food and other supply chains.

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4. Combined results of the cases

The purpose of this master’s dissertation is to get more insight in the possible impact of the

deployment of the blockchain technology beyond financial transactions. Therefore, the literature

review provides background information about the key concepts and the applicability of this

technology. In preparing an answer to the research question, answers to the considerations are

provided in the following paragraphs. The answers are based on the conclusions from the different

cases considered.

Consideration 1: How can the technology make an impact on the business model?

Concluding from the cases, an impact can be demonstrated on business models in general. Despite

literature arguing that the blockchain technology will render the third party obsolete, this is not a

general truth. Based on the research conducted and for the cases investigated the literature

argument is only valid for the CrowdBC case. It must be remarked that independent developers or

organisations are also necessary to keep the blockchain application up to date. The technology

can impact inefficiencies currently present in (interorganizational) business processes, especially

in the KYC and supply chain cases, providing that new challenges arising with it are overcome.

This is in line with the study performed by Holotiuk et al. (2017), stating that both new and existing

BMs can be impacted resulting in improving inefficient structures within financial services. However,

one should not forget that the technology is not a magical tool that solves all problems. No matter

what technology is deployed, it always will be a challenge to let all parties collaborate on a common

IT system. Furthermore, it will be a tremendous task, if not impossible, to code smart contracts to

cover or that cover all sorts of situations. The question can be raised if such is a true necessity for

cases such as the supply chain case and if it is not enough to have smart contracts covering the

main aspects. The principle of ‘garbage in, garbage out’ can also be applied here, since entries

with false data could be validated and added to the ledger. Further (academic) research is required

to assess the qualitative and quantitative aspects and the financial impact in specific cases.

Consideration 2: What impact(s) can be demonstrated using the chosen enterprise

modelling techniques?

The Business Model Canvas is unable to properly model the CrowdBC application, since it is just

a decentralized tool to connect requesters and workers. Apart from this, this technique quite models

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the impact discussed in the explanation of every case. This affects almost all building blocks. There

are small differences seen in “customer relations” for example, as this depends on the problem

domain of the case investigated. The BMC technique also models the collaborating partners but it

does not well suit to model how parties on the blockchain interact with each other. Consequently,

an extra building block describing the partner relationships can be a useful addition to the existing

technique in the context of blockchain.

The e³value model explains how actors exchange value with each other in their value network.

Depending on the case, it can map the transaction costs and other value objects linked to the

blockchain technology. However, it fails to model a decentralized blockchain application. In all

cases, one could misread the blockchain in the model as a centralized actor. Thus, the modelled

actors are only actors by approximation and a syntax for blockchain ‘actors’ should be developed

within this modelling technique. Furthermore, a business process modelling technique can be

useful to understand the underlying processes of the value exchanges such as smart contracts and

their sequence as they can only implicitly be derived for the models in the cases. Also, the value

proposition can only be deduced in the KYC and supply chain cases.

The specific impact demonstrated by the chosen techniques is given in the answer of the third

consideration (see next paragraph).

Consideration 3: How are qualitative parameters like “trust”, “security”, “traceability” and

“process efficiency” affected in the context of service offerings to customers?

How the parameters are affected is highly depending on the case examined and the technique

used. The remainder of this paragraph concludes the results of each case:

• Traceability: Compared to the second and third case where there is a positive impact, the

customers in the crowdsourcing case will not notice changes to “traceability” in both

modelling techniques. In the crowdsourcing case, the end result for a worker and his

requesters in the as-is and to-be model is the same, only the underlying mechanisms are

different. In the second and third case, the to-be BMC clearly models it in the building blocks

“value proposition” and/or “customer relationships” by the effect on transparency. The to-be

value network shows how “traceability” can be affected by following the value exchanges

compared to the as-is e³value network.

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• Efficiency: This parameter is positively affected in all cases under their (specific)

assumptions. The end result can be the automation of processes or a different way of

running the business within the investigated scope. This can lead to a financial impact such

as downscaling transaction costs. The BMC partly demonstrates this in the “value

proposition” and “customer relationships”. There is also impact on the “key activities” and

financial building blocks but the BMC cannot adequately model the promised efficiency

gains from the collaboration in the network. In contrast to the BMC, the e³value modelling

technique can demonstrate the network and the impact on the (financial) value objects. It

should be noted that the impact on the internal activities need to be implicitly deduced.

• Trust: In the to-be BMCs of the three cases, the positive impact can clearly be seen in the

value propositions to their respective customers. This is not the case when drawing

conclusions from the e³value networks. The reader can only implicitly deduce the impact by

having background knowledge. Only the to-be value network in the supply chain case

requires extra explanation. As argued above, “traceability” can be deduced. In fact, this

parameter is key to improve trust as it gives transparency to (potential) customers about the

product’s whereabouts. It must be remarked that “trust” shifts from the centralized party to

the validators and open blockchain protocol. Thus, a degree of trust is always required

within this context.

• Security: The blockchain technology can impact this parameter in a positive way by the

combination of existing technologies such as distributed storage and cryptography but does

not provide full security. Peers in the network still need to consider that private keys could

be stolen. Data could also be reversed when too many nodes are compromised. Concluding

from the cases, the same remarks stated in the discussion of “trust” can be applied here to

the BMC. In the e³value models this must be deduced implicitly by having more background

information about the blockchain’s integrity. Also, the security of data still depends on the

chosen key resources for the off-chain data storage and the security of the IT infrastructure

linked to the blockchain ledger.

Consideration 4: Is generalisation of the cases investigated possible?

It is impossible to generalise the cases to one general case. Therefore, more cases need to be

investigated. However, based on the required immutability and the equal entities in a flexible market

102

context argued by Gordijn et al. (2019), the three cases can separately be generalized within their

respective problem domain.

The first case, CrowdBC, is a DAO within the problem domain of crowdsourcing and can be

generalized within this problem domain as this is a basic and standard application. Typical in this

P2P network are the flexible market conditions as well as the immutability of data records required

for tracking tasks and reputation. Regulatory conditions are important for this type of case to

succeed.

The second case with blockchain boils down to some sort of ecosystem where FIs can ‘collaborate’

on financial services by sharing verified datasets and get a monetary reward in return for their effort.

Immutability is an important feature since the dataset is already verified. However, making changes

to the verified datasets in the blockchain is difficult but could be addressed by making additions

rather than actual changes

The third case can be generalised to other blockchain supply chain cases. In fact, retailers such as

Carrefour are already rolling out the technology to other (food) supply chains. However, Gordijn et

al. (2019) argue that it would be a more suitable and useful case in a flexible market context.

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5. Conclusion and further research

According to the general literature review, the blockchain technology is a means for peers to

transact digital goods or digital signatures/fingerprints of real goods in a decentralized P2P network

and without the necessity of a trusted middleman. The literature explains the key concepts and

researchers state that the technology has game-changing features for communicating and

transacting over electronic networks. This can influence how business is done. It can automate the

execution of contracts under certain conditions by implementation of smart contracts. Furthermore,

it can link physical goods with a digital identity on the decentralised ledger, especially in

combination with other emerging technologies such as IoT. The technology is emerging but is also

hyped in recent years. Consequently, there is discussion about real life applicability. The second

interviewee and the research of Lokøy and Nyberg (2018) amongst other researches remark that

blockchain as a solution shouldn’t be used for a problem that does not exist, nor to create a problem

to justify its use and be able to ride the hype train. The technology can be useful in different problem

domains but require a cautious consideration process before adopting the blockchain technology.

For example, applications can still require a degree of trust. Furthermore, organisations should not

forget that the blockchain technology also brings new challenges to businesses such as

governance, collaboration and scalability for development and implementation. There is also

discussion going on about the speed of adoption since there are only few use cases and

frameworks to support decision-making about adoption. At this moment, many organisations are

reluctant to overhaul their current IT systems. Although the discussions and challenges,

organisations should monitor new developments in order to not fall behind when the technology

matures.

The case study investigation comes to a similar conclusion. Every case starts with an explanation,

followed by the development of its the Business Model Canvas and e³value models in current (as-

is) and most likely (to-be) situations seeking to pave a path serving to draw conclusions about the

possible impact on business models. The interviewees see possibilities in this emerging

technology, but also point at the new challenges it brings along. Concluding from the cases, it can

be stated that the third party does not always becomes obsolete when adopting this technology.

Even when the third party is replaced, (and it can be questioned if such is a real must/need)

independent developers or organisations are needed instead to maintain and update the

decentralised application. This is in the context of both public and permissioned ledgers.

104

Furthermore, the cases are built on important assumptions such as collaboration and governance.

There is still discussion about the feasibility (issues with scalability, resources and costs) of setting

up a P2P network that can be linked to (distributed) servers to store data off-chain.

The research question “How can enterprise modelling techniques serve to analyse the impact

of the blockchain distributed ledger technology on business models and value of service

offering(s) to customers?” is split into four considerations which are answered in the previous

chapter by drawing conclusions from the individual cases.

In summary, both modelling techniques addressed in this research cannot fully model the impact.

Although the Business Model Canvas technique can demonstrate an impact on the building blocks

in the different cases, there are some major remarks. First, a blockchain application like CrowdBC

is rather a tool. Consequently, only the business model of a user (e.g. a freelancer using the

application as a tool) can be modelled in the to-be situation. Second, the technique fails to model

the interactions with and between partners in the P2P network since it focuses on the company

investigated but not so much on the position within the organisation’s (permissioned) network. The

e³value technique demonstrates this position and the interacting value exchanges, but most of the

impact must be deduced implicitly. Moreover, a new syntax must be developed for decentralized

(blockchain) actors as they can be mistaken for centralized actors or market segments.

When drawing conclusions, the reader must also keep in mind the limitations of this research. First,

the to-be models are not actually ex post but are argued to be the situation that it should most likely

be. Second, even though data of different sources was used, the cases can seem subjective since

they were based on only three different interviews. Hereby, the questions were mainly based on

the EM techniques, apart from some open questions and discussions. Third and as already stated,

assumptions were used in the cases. The effective real outcome can be different if the assumptions

are not met. Also, the reliability of the results changes as a function of time since features can

change.

This research contributes to how Enterprise Modelling techniques can be used to model

organisations in the context of blockchain. Further research should involve more interviews with

people involved at the core of new blockchain processes or changing processes. The focus can be

on a quantitative analysis which maps the impact on the financial parameters based on lower

105

transaction costs and automated processes amongst other things. Also, further research can

compare (existing) centralized technologies with cases where blockchain is successfully

implemented within the same application domain. More use cases can also help organisations in

deciding whether the blockchain technology can disrupt (or improve) business in their specific

application field.

X

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Attachment 1.1

7. Attachments

Attachment 1: A blockchain infographic (Morrison & Sinha, 2016)

Attachment 2.1

Attachment 2: Datasets of a chicken on the blockchain (Javier, 2018)