Evaluating Barriers to Blockchain Adoption in the Insurance Sector using
Interval-Valued Intuitionistic Fuzzy TOPSIS
FATIMA ZAHRA MESKINI, YOUSSEF LAMRANI ALAOUI, RAJAE ABOULAICH
Ife Lab, Laboratory of Studies and Research in Applied Mathematics,
Mohammadia School of Engineering (EMI) - Mohammed V University in Rabat,
Rabat,
MOROCCO
Abstract: - In this work, we suggest studying the barriers that prevent from using blockchain technology and smart
contracts in the insurance sector. It is possible to improve many services, by introducing "Fintech" information
technologies which will ensure maximum transparency and speed. The goal of our paper is to answer two main
questions: What obstacles stand in the way of the successful use of blockchain technology throughout the insurance
sector? Which of them are the most notable obstacles that require decision-makers consideration?. We opt for an
analysis of the barriers to blockchain adoption using fuzzy logic for the following reasons. In many realistic
situations, it is difficult to gather the exact assessment data; the assessment is based mainly on the decision makers'
knowledge and their experiences using linguistic terms or sentences in a natural or artificial language. The idea is
to transform the linguistic variables into fuzzy sets using appropriate membership functions. In other words, fuzzy
logic allows a better representation of the uncertainty and subjectivity of decision-makers. In our study, we analyze
the answers of twenty experts, - highly skilled professionals with advanced knowledge acquired through education
and experience-, about the most significant barriers to blockchain adoption in an interval-valued intuitionistic fuzzy
environment. Then, by making use of decision-making tools such as IVIF TOPSIS, we make a ranking of barriers
according to their importance to find the most important factors that influence the adoption of blockchain
technology. This study's goal is to propose a model for identifying and tracking the crucial elements that influence
managers' decisions on whether to adopt a financial technology like blockchain in their businesses or not. In the
end, we conclude with some recommendations and suggestions to overcome the most important barriers and face
future challenges.
Key-Words: - Blockchain, insurance, interval-valued intuitionistic fuzzy logic, decision making, TOPSIS method,
simulations.
Received: October 28, 2023. Revised: May 17, 2024. Accepted: June 19, 2024. Published: July 12, 2024.
1 Introduction
Thousands of companies around the world are very
excited about blockchain technology, they are actively
thinking about the possible applications of this
technology that can improve their products or
services, [1]. Others think that this new technology is
facing many challenges that should be studied and
solved to benefit from this powerful tool without
illusions, [2]. Actually, no one can predict what and
when blockchain technology is going to change
businesses.
The emerging potential of this technology
motivates us to study the barriers to blockchain
adoption in the insurance sector, as an important field
in the financial industry. The concept is to consult
managers and professionals with extensive experience
who are knowledgeable about the key factors that may
encourage or discourage the adoption of blockchain
technology. The answers given by experts are in the
form of linguistic terms explaining their evaluation of
each barrier. Thus, to have a convenient interpretation
of this survey, we consider uncertainties by making
use of an intuitionistic interval-valued fuzzy approach,
[3]. Then, we opt for the TOPSIS method, which
aggregates the various answers of all experts to rank
the most important barriers.
Blockchain, as a new technology of data
transmission, is revolutionizing various aspects of the
insurance industry, [4]. By providing a secure and
transparent ledger system, it ensures the integrity of
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policyholder data and reduces fraudulent activities,
[5]. Smart contracts automate claims processing,
speeding up settlements and enhancing customer
satisfaction, [6]. Blockchain's decentralized nature
enables seamless data sharing among insurers,
reinsurers, and other stakeholders, leading to more
accurate risk assessment and improved underwriting
processes, [7]. With optimized operations, enhanced
data security, and increased trust, blockchain plays a
crucial role in transforming the insurance sector,
benefiting both insurers and policyholders, [8].
The Blockchain adoption in insurance is analyzed
in this paper, through decision-making tools. We opt
for interval-valued intuitionistic fuzzy TOPSIS, which
evaluates alternatives by comparing them to an ideal
solution and an anti-ideal solution and then assigns
scores to rank the options based on their proximity to
the ideal, [9]. It is an extension of the traditional
TOPSIS method, considering the hesitancy and non-
membership degrees in assessing alternatives. This
additional information provides a better understanding
of vague and imprecise data.
The goal of our research is to evaluate whether
insurance companies are ready or not to adopt
blockchain technology, by selecting the main barriers
and determining the importance of each one by using
MCDM methods in an intuitionistic interval-valued
fuzzy environment.
The paper will be structured as follows. The first
section will review the most significant obstacles to
blockchain technology in the insurance industry. The
second part presents the environment and tools
required in our study. For this purpose, we explain
intuitionistic interval-valued fuzzy logic and multi-
criteria decision-making tools such as the TOPSIS
method. Then, we present in detail the proposed
methodology, followed by our case study. The
outcomes of the application are presented in the last
section, along with interpretation and results analysis.
Finally, we offer some suggestions for overcoming
adoption-related obstacles for blockchain technology.
2 Barriers to Blockchain Adoption in
Insurance
To evaluate whether blockchain technology is adapted
to the Moroccan insurance sector, we have chosen to
analyze the reasons that may not encourage
companies to switch to smart insurance based on
blockchain technology.
To answer this question, we survey twenty experts
in the insurance sector and blockchain. In our work,
we mean by experts, in finance or blockchain, highly
skilled professionals with advanced knowledge in
their respective domains. We asked directors in the
financial sector who had compelling backgrounds and
extensive expertise in the insurance industry, such as
many directors in the largest insurance firms in
Morocco. For Blockchain and IT experts, we
considered experienced professionals and researchers
in those fields, who could provide consistent answers
to our investigation. Their insights contribute to well-
informed decision-making, shaping strategies in
finance or blockchain-related industries.
The answers are collected and then transformed
into fuzzy sets reflecting uncertainties and hesitation
degrees for a better analysis. Each expert is supposed
to evaluate the importance of each criterion on a
specific scale, through linguistic variable that reflects
his point of view.
In the following paragraphs, we make a literature
review on the most important barriers to justify the
main criteria selected in our study.
EFFICIENCY: To adopt a new technology or not,
we need to make sure of its efficiency or its added
value in improving the existing process. The power of
blockchain is that it is a distributed ledger, shared by
everyone but does not belong to anyone. Technically,
all participants agree on a set of rules called
"consensus" and work by this agreement, [10]. Indeed,
many customers have lost trust in many financial
institutions; this technology can be the proof of a new
period of full transparency. It will enable
organizations to focus on more important things, such
as improving their services and creating new products.
[11], as for smart contracts combined with blockchain,
they are very efficient for making processes faster,
Actually, actions are automatically executed if the
terms of the virtual contract are fulfilled without any
intermediary. No manual verification is required, as
long as oracles are efficient in verifying the related
conditions.
In other words, trust, transparency, and rapidity
are the characteristics that may add more efficiency to
insurance products based on blockchain. But, every
expert can evaluate the efficiency of this technology
from his perspective.
COST: The cost of the project of integrating
fintech in the insurance sector is important, the
financial situation of the company and the budget
dedicated to innovation play a role in encouraging the
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adoption or not of fintech. We all know that, like any
new project, it can be costly at the beginning as an
initial investment but after that, costs will reduce
significantly. It depends if the company is ready to
spend or invest money in a new experience or not.
The implementation of blockchain technology can
be costly due to several factors. Firstly, the
development and deployment of blockchain solutions
require specialized technical expertise. Additionally,
the infrastructure required to support a blockchain
network, including servers, nodes, and storage, can be
expensive to set up and maintain, let alone the high
consumption of energy, [12]. Adding to this, we need
a huge budget to train employees and initiate them to
fintech or even recruit new skills, which leads us to
the next criteria.
SKILLS AVAILABILITY: There is still a lack of
skills related to blockchain and a lack of a masters or
thesis on this topic. But, it is possible to make efforts
to train employees and adapt them to this new
technology, [13]. No one can deny that blockchain
promises very attractive applications, but this
attractiveness should not push organizations to
integrate this tool without really understanding it,
[14]. The number of people who master this
technology is quite low, adding IoT into the mix, the
qualified human capital may not be enough. That’s
why we should try to understand the core of this
technology and train people in this field, to avoid
potential financial loss. It will also be interesting to
develop more developer-friendly APIs for developers
because the current interfaces are not easy to use,
[15]. Last but not least, preparing the human capital
with relevant skills is an important factor before using
fintech in the insurance sector.
SECURITY: The structure of the blockchain
demonstrates that it is made of encrypted and
immutable code blocks. A great number of servers or
nodes are storing the same amount of data at the same
time instead of a centralized entity, [16]. This is a
strong point because it means that to hack a
blockchain, you have to hack so many servers at the
same moment, which is almost impossible. Even if
some gaps should be filled such as the "51% attack",
which means that the majority may attack the network
to manipulate and take control of the blockchain. But,
generally, security remains one of the main
advantages of blockchain technology, [17].
UNTESTED TECHNOLOGY: We all know that
Blockchain technology is still in its early stages,
which means that it is still not sufficiently tested. But,
governments, global banks, and international
organizations - who are very interested in blockchain
applications- are risk-averse, and may not be ready to
put their sensitive data in an unreliable system. These
institutions are quite slow to innovate and need to rely
on a system tested and approved for a long period. For
insurance companies, the fact that this technology has
not yet been tested by many big organizations may be
discouraging, but others may consider it a challenge to
be among the leaders, [18].
INNOVATION STRATEGY: Innovation is
paramount for insurance companies as it enables them
to stay competitive in a rapidly evolving industry by
offering new and customized products and services to
meet changing customer needs. Embracing
technological advancements and data analytics allows
insurers to enhance risk assessment, streamline
operations, and improve profitability. Nevertheless,
the willingness of companies to modernize their
processes, to look for new methods to satisfy their
clients depends on the management strategy. Some
companies have powerful innovation strategies. They
are more interested in research and development than
others, which pushes them to be more flexible in
adopting recent technologies and renewing their
processes, [19].
STANDARDISATION: At the moment, the
blockchain is neither regulated nor standardized, there
is no legal code or compliance to follow. This
limitless field may scare some organizations from
taking a step in this very open world without any law
to protect them or regulation to define their limits.
But, at the same time, it would be a mistake to tighten
the regulations related to blockchain before fully
understanding its potential. Even for engineers and
programmers, the lack of standardization makes it
difficult for blockchain participants to communicate
and work together effectively, [20].
PRIVACY: There are some particular concerns
regarding privacy in blockchain. For instance, bitcoin
as a blockchain may not have a good reputation
because of the misuse of this currency. Many people
around the world profit from the fact that transactions
are anonymous, to use it in some illegal fields such as
drug dealing and blackmailing, [21]. In general, if
one malicious user engages in illicit behavior, this
level of anonymity could be detrimental and
damaging to all users. On the other hand, in a private
blockchain network, the nobility is blind to one
another's precise identity and relies on consensus for
all transactions. Therefore, a blockchain may function
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as a private place instead of being a clear platform for
plain and recognizable operations, [22].
INTEGRATION PROBLEMS: It costs money
and takes time to integrate blockchain technology
with the already existing processes. To connect new
blockchain consortium apps with legacy systems,
application programming interface (API) gateways are
still needed, [23].
REGULATION ISSUES: Another barrier to
blockchain adoption is related to regulation issues. We
need to deal with the emergence of some technology-
based methods and concepts, such as cryptographic
signatures and intelligent contracts, which are not
clarified in the existing regulations, [24]. Insurance
companies may be reticent about adopting a
technology with aspects that need regulations from
governments to protect both service providers and
customers. This aspect needs to be evaluated by
companies to prevent any conflicts or non-conformity
with the existing laws.
PROCESSING POWER AND TIME: There are
some issues related to the processing power of
blockchain. This technological aspect may lead us to
think about the real potential of blockchain when used
massively. Actually, with the current means, the
transactions in the Bitcoin blockchain network do not
exceed 7 transactions per second, which is not adapted
to high-frequency trading. VISA allows 2000 tps
while Twitter reaches 5,000 tps. Adding to this, the
size of a block in the Bitcoin blockchain is limited to
1 MB, and a block needs ten minutes to be mined.
But, if we want larger blocks, we will need more
storage space and more time, which leads us to the
next challenge of storage, [25].
STORAGE: As a decentralized system, data in
blockchains are not stored in one central unit, but, at
all the nodes of the network. This issue may create
some problems because the amount of stored data will
be huge over time, especially since what is written
cannot be erased (immutability of blockchains), [26].
This issue can be very challenging for many devices
with low storage capacities such as sensors.
3 Environment and Tools
The literature review we provided earlier, outlines the
obstacles to blockchain adoption in the insurance
industry. In an interval-valued intuitionistic fuzzy
environment, we have decided to use an MCDM
strategy to rank those obstacles from the most
significant to the least important. The required tools
for our investigation are presented in the following
section.
3.1 Interval-valued Intuitionistic Fuzzy
Environment
Fuzzy logic is a mathematical framework that deals
with reasoning and decision-making in situations
where uncertainty and imprecision are present. Fuzzy
logic is different from classical logic, the latter is
based mainly on two aspects either true or false. On
the other hand, fuzzy logic is based on a membership
function that represents degrees of truth with reflect
uncertainties contained in data.
3.1.1 Introduction to Interval-Valued
Intuitionistic Fuzzy Logic
In the 1960s, was the emergence of fuzzy logic as a
method to deal with ambiguities and imprecise
information. It is especially helpful in areas where
human thinking is involved because it can replicate
the adaptability and tolerance for error that people
frequently express.
Fuzzy sets are important to understand fuzzy logic
theory. A fuzzy set is a group of items having various
degrees of membership, represented by a function that
assigns to each object a grade of membership ranging
from zero to one, indicating the degree to which it
belongs to the set. This representation enables a better
understanding of many real-world phenomena.
To model human reasoning and decision-making
processes, we can make use of fuzzy logic also uses
linguistic variables associated with fuzzy rules.
Linguistic variables allow us to characterize concepts
using natural language phrases, transformed to fuzzy
sets.
Fuzzy logic has been applied in a variety of
domains, including Information systems, Data
analysis, Imagery enhancement, Sonar, Radar,
medical applications, genetics, Control theory, and
computer science. As we mentioned earlier, it is an
effective technique for dealing with uncertain and
imprecise information, allowing for resilient and
adaptable solutions to complicated issues, [27]. To
summarize, fuzzy logic offers a mathematical
framework for thinking about uncertainty and
imprecision. It provides a more sophisticated
approach to decision-making and problem-solving
when simple binary reasoning may be insufficient or
irrelevant.
Since decision-making is frequently done under
certain conditions, such as lack of information and
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expertise, lack of consensus among decision-makers,
and time constraints, the rising complexity of socio-
economic communities generates complexity and
ambiguity in the priorities of decision-makers, [28].
Therefore, it would be practical to make decisions in
such a case using an interval-valued fuzzy
environment. The membership functions would be an
interval instead of a precise number, which is the key
characteristic of adopting an interval-valued fuzzy
environment. It is challenging to fully convey an idea
or linguistic variable by an integer number in the
range [0, 1] in fuzzy set theory. Therefore, it would be
more reasonable to describe the degree of confidence
by an interval of [0, 1].
3.1.2 Some Definitions
Some fundamental IFS and IVIFS notions are briefly
explained in the following paragraphs. Those
definitions are essential to understanding the
operations of fuzzy numbers using MCDM methods
in the next sections.
Definition 1. Fuzzy set
Let be a set.
A fuzzy set in is defined as follows:
where is the membership function
and is the membership degree of .
Definition 2. Intuitionistic fuzzy set
Let be a set, an intuitionistic
fuzzy set (IFS)
A” is defined as:
〈
〉
where and are the membership degree
and non-membership degree of x to A, respectively,
with the following conditions:
If the IFS A is an ordinary fuzzy
set.
Definition 3. Interval-valued intuitionistic fuzzy set
Suppose that X is a non-empty set, an interval-valued
intuitionistic fuzzy set (IVIFS) ˜A is defined as:
〈
〉
represent membership interval and non-membership
interval of the element x X to A, respectively,
satisfying
is the hesitation interval of x to
A,
Definition 4. Operations on IVIF numbers
Assume that
then the basic operations of IVIF are expressed by the
following formulas:
Definition 5. Score function and accuracy function
α = ([a, b], [c, d]) is an interval-valued
intuitionistic fuzzy number (IVIFN) satisfying that 0 ≤
a ≤ b ≤ 1, 0 ≤ c ≤ d ≤ 1 and b + d ≤ 1. The score
function S(α) and the accuracy function H(α) of α are
presented as follows:
Definition 6. Comparison between IVIF numbers
Assume that
and
are two IVIFNs, then the
comparison operations between IVIFNs are presented
as below:
3.2 Multicriteria Decision Making Tools
Multiple Criteria Decision Making, or MCDM for
short, is a branch of research that examines the
process of making decisions when several competing
criteria must be taken into account. MCDM offers a
systematic way to assist decision-makers in evaluating
options and choosing the best course of action in a
variety of real-world settings where they must make
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complicated decisions with many objectives. Multiple
factors, including cost, time, quality, risk, and
environmental effects, are intended to be included in
the decision-making process using MCDM
methodologies. The choice problem is intrinsically
difficult since these criteria frequently have multiple
dimensions, units of measurement, and levels of
relevance
3.2.1 Why MDCM, Use and Applications
Giving decision-makers a systematic framework for
evaluating and contrasting choices according to how
well they perform against several criteria is one of the
most important goals of multicriteria decision-making
methods (MCDM), [29]. The process of solving an
MCDM problem requires some essential milestones
such as defining the problem structure, identifying the
most relevant criteria, identifying and weighting them,
analyzing alternatives, and finally decision synthesis,
to efficiently find out the best solution, [30].
There are many multi-criteria decision-making
techniques developed to solve several problems in
various fields, let's mention some examples: AHP
(Analytic Hierarchy Process), TOPSIS (Technique for
Order of Preference by Similarity to Ideal Solution),
PROMETHEE (Preference Ranking Organization
Method for Enrichment Evaluations), and ELECTRE,
[31].
The MDCM mentioned above utilizes mainly
mathematical models, algorithms, and decision rules
to rank or prioritize alternatives. The final goal is to
provide decision-makers with meaningful information
about the best alternatives depending on their
priorities and preferences, [32]. We can also make use
of MCDM methods and at the same time, study the
impact of changes in criterion weights or input data,
which enables us to measure uncertainty and also
make a sensitivity analysis. The latter aims to identify
significant factors and analyze the stability of the
alternatives considered, [33].
The MDCM techniques mentioned earlier are
used in management fields, engineering, healthcare,
logistics, and public policy, but also education as well
as industry development. They can help decision-
makers in every stage from a project analysis to
concrete realization, [34]. These strategies offer a
systematic way to evaluate alternatives, classify
actions, and achieve optimum results in a variety of
disciplines where decisions require making important
choices and must take into account a wide range of
criteria, [35].
3.2.2 TOPSIS Method
After presenting what are MDCM techniques, now we
move to the TOPSIS method, or the Technique for
Order of Preference by Similarity to Ideal Solution as
an example, this method was developed in 1981 and
improved later. The principle of the TOPSIS method
is quite simple, it assumes that the chosen solution for
our problem is the option that is geometrically closest
to the positive ideal solution (PIS) and the furthest
away from the negative ideal solution (NIS), [36],
which is quite intuitive and logical. No need to be
precise that such a method has been utilized
extensively in a wide range of industries, including
manufacturing, financial analysis, quality evaluation,
technology management but also mission planning,
[37].
The TOPSIS method's key steps are the following.
The first step is to make the decision matrix
normalized. Then, determine the ideal solution matrix
of positive and negative ideal solutions by using this
formula:
Then, calculate the distance from the negative
ideal solution and the distance between the alternative
and the best condition. We can calculate the Euclidian
distance or hamming distance or any other suitable
formula of separation measurement. The last step is to
calculate the similarity to the worst condition:
if and only if the alternative solution has the best
condition; and if and only if the alternative
solution has the worst condition. This score enables us
to Rank the alternatives.
4 Case Study Application
In the following section, we explain the general
methodology and then we present the application to
our case study “Ranking barriers to blockchain
adoption in insurance” using Interval-valued
intuitionistic Fuzzy TOPSIS as an MCDM technique.
4.1 General Methodology
After giving an overview of the literature and
presenting the tools required in our study, we explain
in the following section, the steps required to filter
those factors. For this purpose, we make use of
decision-making techniques in an interval-valued
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fuzzy environment. The output is to define the most
significant among the selected barriers.
Step 1: The first step is to collect answers from
experts in the insurance sector in blockchain
technology to evaluate each one of the factors from
the literature review.
Step 2: The second step is to transform the answers
from linguistic variables into interval-valued fuzzy
numbers according to the predefined scale chosen in
the form, to obtain the decision-making matrix made
of an IVIF number.
Step 3: In this stage, we make use of decision-
making methods to make a classification of all the
criteria of the matrix using IVIF TOPSIS.
Step 4: We obtain the results and run many
scenarios to compare the results of each simulation
and analyze sensitivity.
Step 5: We explain the output by highlighting the
most important barriers to blockchain adoption that
should be considered by managers and decision-
makers.
4.2 Application of IVIF TOPSIS for
Identification of Blockchain Barriers
4.2.1 Algorithm and Steps of the Simulation
When dealing with multi-criteria decision issues
having uncertainties and taking into consideration the
preferences of the decision-makers, fuzzy TOPSIS is
an effective tool, [38]. The best alternative may be
found using this technique, [39].
The alternatives here represent the criteria of
blockchain adoption and the most important criterion
is the one with the highest scores in the answers of
experts. To use the TOPSIS method in our
application, we ask 20 experts about 12 barriers to
blockchain adoption and collect their answers in the
form of linguistic variables.
Step 1: The survey intitled Barriers to blockchain
adoption in the insurance sector, completed by each
expert will be as follows, Table 1 is an example of an
answer sheet.
Question: Based on your experience, what is the
importance of each factor in the blockchain adoption
in insurance?
VL: Very low, L: Low, M: Medium, H: High, VH:
Very high.
Table 1. Answer sheet example
Criteria/Importance
VL
L
M
H
VH
Efficiency
X
Cost
X
Skills availability
X
Security
X
Untested technology
X
Innovation strategy
X
Standardization
X
Privacy
X
Integration problems
X
Regulation issues
X
Processing power
X
Problem of storage
X
Step 2: We collect the answers and then transform
linguistic variables to IFIV numbers using the Table
2. Table 2. Transformation to IVIF numbers
Linguistic variable
Corresponding IFIV number
Very low
([0.3, 0.4], [0.4, 0.6])
Low
([0.5, 0.6], [0.3, 0.4])
Medium
([0.6, 0.7], [0.2, 0.3])
High
([0.7, 0.8], [0.1, 0.2])
Very High
([0.8, 0.9], [0.1, 0.2])
Step 3: We calculate the Positive Ideal solution
and Negative Ideal Solution given by the following
formula
:
Where :
Step 5: Calculating distances
There are various methods of distance
measurement used in different contexts, [34]. Some
commonly used ones include Euclidean distance,
Manhattan distance (also known as city block
distance), Cosine distance, Hamming distance, and
Jaccard distance. Each method has its properties and
applicability depending on the data and problem at
hand. In our case, we have chosen the normalized
hamming distance because of its simplicity but also
accuracy.
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Distances from Positive Ideal Solution D+
Distances from Negative Ideal Solution D-
Step 6: Calculate Similarity to worst condition:
The highest score of Si means that the barrier is
very far from the worst one. It means that ranking the
12 barriers according to the best scores will give the
most significant barriers to blockchain adoption.
The highest score of Si means that the barrier is
very far from the worst one. It means that ranking the
12 barriers according to the best scores will give the
most significant barriers to blockchain adoption.
4.2.2 Results Discussion and Recommendations
a. Results and findings
After calculating the positive distance, and
negative distance, from the IVIF matrix, results are
given by Table 3.
Table 3. Results of the simulation
D+
D-
Score
Rank
1
0,075
0,1475
0,662921
5
2
0,06
0,1725
0,741935
2
3
0,08
0,1425
0,640449
7
4
0,0525
0,175
0,769230
1
5
0,0775
0,145
0,651685
6
6
0,0625
0,16
0,719101
3
7
0,145
0,0875
0,376344
12
8
0,1425
0,09
0,387096
11
9
0,115
0,1075
0,483146
10
10
0,0725
0,15
0,674157
4
11
0,0875
0,14
0,615384
8
12
0,115
0,1175
0,505376
9
According to the findings, the following are the
most important barriers to blockchain implementation
in the insurance business, according to experts. The
first is regulation, as there is no blockchain-related
law in our nation and no legal framework to stimulate
the use of this new technology. The second issue is
cost. Switching from traditional technologies to a new
tool that requires powerful devices and particular
software may be pricey, and some businesses may not
be willing to invest a large budget on this transition.
Security is the third obstacle for financial
professionals. The insurance industry and financial
institutions in general may be hesitant to accept new
technologies due to security concerns as long as they
deal with sensitive data.
b. Sensitivity analysis
Sensitivity analysis is an important technique in
modeling, especially when it comes to risk and
decision-making. It involves determining how
changes to a model's inputs or parameters impact the
model's outputs. Sensitivity analysis in a model
should be done for several reasons. In particular,
making meaningful choices: Enables better-informed
judgments to be made by accounting for the model's
unpredictability and uncertainty. Particularly in the
fields of finance, project management, and strategic
planning, this might be helpful. Model validation is a
second significant factor. Sensitivity analysis can
highlight weaknesses or contradictions in the model
by pointing out connections that don't make sense. As
a result, model quality can be enhanced.
To carry out a sensitivity analysis of our model,
we will run several simulations. The weights of the
experts will vary in each scenario, hence the weighted
matrix will change each time. The weights assigned
are, in turn, interval-valued intuitionistic fuzzy
numbers, and the computation will be completed by
IVIF Topsis as previously mentioned. This change in
weights will cause a change in the matrix, and so in
the estimated distances, positive and negative, and
thus in the scores, resulting in a change in the barrier
ranking. Following that, we will compare the findings
generated in each case to assess the model's
sensitivity.
Simulation 1
In this scenario, we suppose that the weight of
each expert is very important. w = [0.8, 0.9], [0.1, 0.2]
The results of calculated distances, scores, and
ranking are given by Table 4.
Table 4. Results of simulation 1
D+
D-
Score
Rank
1
0,075
0,1475
0,662921
5
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DOI: 10.37394/23207.2024.21.129
Fatima Zahra Meskini,
Youssef Lamrani Alaoui, Rajae Aboulaich
E-ISSN: 2224-2899
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Volume 21, 2024
2
0,06
0,1725
0,741935
2
3
0,08
0,1425
0,640449
7
4
0,0525
0,175
0,769230
1
5
0,0775
0,145
0,651685
6
6
0,0625
0,16
0,719101
3
7
0,145
0,0875
0,376344
12
8
0,1425
0,09
0,387096
11
9
0,115
0,1075
0,483146
10
10
0,0725
0,15
0,674157
4
11
0,0875
0,14
0,615384
8
12
0,115
0,1175
0,505376
9
Simulation 2
In this simulation, we try to give more importance
to experts with 10 years of experience w1= [0.7,0.8],
[0.1,0.2] in comparison to experts with (5 to 10) years
of experience w2= [0.5, 0.6], [0.3,0.4] . Table 5 shows
the results of this scenario.
Table 5. Results of simulation 2
D+
D-
Score
Rank
1
0,051875
0,118375
0,695301
5
2
0,0495
0,12625
0,718349
3
3
0,0595
0,1135
0,656069
7
4
0,03475
0,13825
0,799132
1
5
0,05825
0,11475
0,663294
6
6
0,049
0,12675
0,721194
2
7
0,108625
0,061625
0,361967
12
8
0,1045
0,06575
0,386196
11
9
0,079625
0,090625
0,532305
9
10
0,04975
0,1205
0,707782
4
11
0,062125
0,110875
0,640895
8
12
0,08375
0,0865
0,508076
10
Simulation 3
To emphasize the financial point of view, we try
to give more importance to insurance professionals
w1= [0.7,0.8], [0.1,0.2] in comparison to blockchain
and IT experts w2= [0.5, 0.6], [0.3,0.4] . Table 6
shows the results of this scenario.
Table 6. Results of simulation 3
D+
D-
Score
Rank
1
0,039
0,101625
0,722666
3
2
0,036375
0,109
0,749785
2
3
0,049375
0,08975
0,645103
7
4
0,030375
0,111
0,785145
1
5
0,043875
0,1
0,695047
5
6
0,042875
0,099
0,697797
4
7
0,09075
0,055875
0,381074
12
8
0,087625
0,059
0,402387
11
9
0,06725
0,069875
0,509571
9
10
0,044625
0,09525
0,680965
6
11
0,055
0,089625
0,619706
8
12
0,072625
0,07325
0,502142
10
Simulation 4
In this simulation, we try to give more importance
to fintech experts w1= [0.7,0.8], [0.1,0.2] in
comparison to professionals of the financial sector
w2= [0.5, 0.6], [0.3,0.4], to see if this perspective is
going to change the findings. The results are
mentioned in the Table 7.
Table 7. Results of simulation 4
Criteria
D+
D-
Score
Rank
1
0,040875
0,102875
0,715652
3
2
0,03975
0,10775
0,730508
2
3
0,0515
0,095
0,648464
8
4
0,0315
0,11775
0,788944
1
5
0,046
0,10425
0,693843
6
6
0,0435
0,10675
0,710482
4
7
0,085125
0,058625
0,407826
11
8
0,086
0,05775
0,40173
12
9
0,072625
0,071125
0,494782
10
10
0,0445
0,102
0,696245
5
11
0,049625
0,096875
0,661262
7
12
0,06875
0,075
0,52173
9
The following graph presented in Figure 1 shows
the ranking result obtained by each simulation. We
can see that there is not a huge difference between the
findings of each scenario, the results are quite similar
which gives more accuracy and consistency to our
results.
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Youssef Lamrani Alaoui, Rajae Aboulaich
E-ISSN: 2224-2899
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Volume 21, 2024
Fig. 1: Sensitivity analysis
c. Results analysis
According to the previous findings, the top three
barriers to blockchain are regulation, cost, and
security. We discuss each barrier in the following
paragraph and then we compare our findings to
similar studies.
First of all, concerning security issues, we all
know that there are some risks such as 51% attack. It
happens when one entity controls over 50% of the
network's computational power, undermining its
security. This can lead to fraudulent actions like
double-spending, highlighting the importance of
strong security in blockchain networks. But, the
occurrence of this attack is very rare. Blockchain is
quite safe for the financial sector since it employs
cryptographic technologies and decentralized
consensus to maintain data integrity and prevent
tampering, [17]. Because of cryptographic hashing
and distributed validation nodes, malicious users find
it exceedingly difficult to alter financial data or
compromise network integrity. Smart contracts, in
large part, enable the automation of trustless financial
transactions while minimizing the danger of fraud. In
general, blockchain remains a robust and transparent
platform for secure digital transactions within the
financial industry
Secondly, although using blockchain technology
in the insurance industry might be expensive, the
significant advantages it provides make it worthwhile.
A new level of confidence and transparency is made
possible by blockchain's built-in mechanisms that
increase data accuracy. By streamlining claims
processing and reducing administrative expenses, this
improved efficiency also eliminates the need for
middlemen. Furthermore, blockchain records'
immutability and auditability provide a reliable record
of rules and claims history. Long-term cost
reductions, enhanced client experiences, and a more
robust and competitive insurance business are all
promised in exchange for the initial investment in
blockchain technology. That is why we should think
about the gain behind investing a certain budget in
such innovative technology.
Moreover, the lack of comprehensive regulation
in the blockchain business has both advantages and
downsides. On the one hand, it encourages innovation
by lowering administrative hurdles to the development
of new technologies and applications. This promotes
investment and entrepreneurship in the blockchain
sector. However, a lack of regulation exposes
investors and consumers to risks such as fraud and
market manipulation. People who do not fully
appreciate the risks involved, especially risks
incurring significant financial losses. But, in all cases,
we can say that it is just a matter of time and countries
are going to adopt, sooner or later, regulations to
benefit from the full potential of this technology
legally and safely.
To compare our findings, we conducted research
on relevant journals, that were also interested in
blockchain adoption barriers, using MCDM
techniques in an interval-valued intuitionistic fuzzy
environment. The chosen references are the following,
Prioritization of factors affecting the digitalization of
quality management using interval-valued
intuitionistic fuzzy Best-Worst Method [40], Research
on significant factors affecting the adoption of
blockchain technology for enterprise distributed
applications based on integrated MCDM FCEM-
MULTIMOORA-FG method [41], Expert oriented
approach for analyzing the blockchain adoption
barriers in humanitarian supply chain, [42]. We
selected these papers because of the similarities to our
work, in the goal but also the methodology of the
study.
According to reference [40], the interval-valued
intuitionistic fuzzy Best-Worst technique was used to
prioritize the aspects influencing the digitalization of
quality management. Using the proposed approach,
the most important significant criterion has been
identified. as a "Management" element. When
considering the sub-criteria, Digital skills, and talent,
Digital quality management culture, and the Existence
of digital strategy are ranked as the top three.
According to the second reference [41], research on
important variables influencing the adoption of
blockchain technology for business distributed
applications is based on an integrated MCDM
approach. The findings show that Scalability,
Performance, and Maintenance are the three primary
variables influencing the adoption of blockchain
technology for organizations' distributed systems.
Concerning the third reference [42], Regulatory
uncertainty, Lack of knowledge/employee training,
and high sustainability Costs are the three main
barriers. We can conclude that regulation, cost,
performance or efficiency, and skills, which are very
important according to our simulations, are the main
intersections with the findings of related studies.
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Fatima Zahra Meskini,
Youssef Lamrani Alaoui, Rajae Aboulaich
E-ISSN: 2224-2899
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Volume 21, 2024
Similar problems are covered in several additional
articles using other MCDM techniques. Depending on
the replies and viewpoints of the experts, the
outcomes can vary. In any event, it provides us with a
general understanding of the most crucial aspects on
which we should focus to successfully implement
blockchain effectively.
5 Conclusion
As we mentioned earlier, there are no legislative
limitations in the blockchain world, which scares
many organizations from taking the plunge. It is also
helpful to set standards to encourage more innovation,
and at the same time, enable organizations to
communicate on the systems they are developing
through some general guidelines and basic standards
to avoid ending up with incompatible systems.
Moreover, it is very helpful to associate a
technology with another instead of talking about every
technology alone. A new technology is not supposed
to eradicate the others, on the contrary, combining
them usually gives impressive results. For example,
AI can be of great benefit to the blockchain especially
when we talk about smart contracts. As the latter
relies on oracles, building oracles based on AI tools
will make it more efficient. The intelligent oracle will
learn from the outside and train itself and thus, is
going to solve the problems related to the
irreversibility of transactions.
Last but not least, to fully understand blockchain
technology, to discover its full potential, and to
develop interesting applications through it, we have to
train people, test this technology, and prove its
efficiency, and all these measures take time. It is
therefore time to fill the technical gaps of this
technology, to be prepared for the new era of
blockchain platforms, stay up to date on the news, and
try to take advantage of this powerful tool, without
overestimating it or underestimating its potential.
In the end, every scientific paper, no matter how
meticulously conducted, is inevitably bound by its
inherent limitations. These constraints may arise from
the scope of the study, available resources,
experimental design, or even the current state of
scientific knowledge. Recognizing and addressing
these limitations is a fundamental aspect of scholarly
integrity. Embracing the inherent limitations fosters a
culture of continuous improvement and encourages
future investigations to enhance our understanding of
the complexities within the scientific domain.
Our study has certain limitations, particularly due
to the limited number of specialists involved in the
research. Although the chosen sample is recognized
for its expertise, it inevitably falls short of reflecting
the full field of specialists. Potential errors and
misunderstandings in the experts' interpretations of
the submitted questions provide an extra degree of
restriction. These considerations, taken together,
highlight the importance of being careful when
extending our findings. This work serves as a basis for
future research that will overcome these issues and
contribute to a more complete knowledge of the issue.
The coming study will aim to use other multi-
criteria decision-making techniques such as Best
worst method, ELECTRE, and PROMETHEE… to
compare our findings and improve the consistency of
our study. We can also aggregate many MCDM
methods and create a combination to have more
significant results. It will be the aim of our future
research.
Declaration of Generative AI and AI-assisted
technologies in the writing process
During the preparation of this work the authors used
QuillBot in few paragraphs in order to improve the
readability and language of the manuscript. After
using this tool, the authors reviewed and edited the
content as needed and takes full responsibility for the
content of the publication.
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Contribution of Individual Authors to the Creation
of a Scientific Article (Ghostwriting Policy)
The authors equally contributed to the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare.
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US
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.129
Fatima Zahra Meskini,
Youssef Lamrani Alaoui, Rajae Aboulaich
E-ISSN: 2224-2899
1597
Volume 21, 2024
