INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue IV, April 2025
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Analysis of Digital Transformation Strategies Based on an
Integrated Approach of Analytical Tools
Khakimova M.F., Saidmurodzoda L.Kh.
Tajik National University
DOI: https://doi.org/10.51583/IJLTEMAS.2025.140400079
Received: 23 April 2025; Accepted: 04 May 2025; Published: 16 May 2025
Abstract: In the context of rapid development of digital technologies, organizations increasingly turn to the concept of digital
transformation to improve their competitiveness and efficient operations. In this regard, the development and application of
suitable tools for choosing a digital transformation strategy is one of the important applied tasks of each organization. This fact
indicates the need to select advanced analytical tools. In the article, the author conducts a comprehensive study aimed at creating
an innovative model for assessing digital transformation strategies. The model combines innovative methods of multi-criteria
analysis, in particular multi-criteria decision making, fuzzy logic and other mathematical approaches that allow for a
comprehensive analysis and development of the most optimal strategies for the development of an organization. Based on the
results of the study, the optimal strategy for the object under study was determined for the purpose of successful digital
transformation. Further, the main conclusions and promising areas for further research are formulated.
Key words: digital transformation, strategy selection, decision making, cybernetic systematicity, multicriterial decision making,
fuzzy logic, analytical process from the standpoint of hierarchy, axiomatic design.
I. Introduction
The Industry 4.0 concept, which emerged at the turn of the decade, has become a driver of innovation in various industries today.
Digital transformation, as an integral part of this concept, allows organizations to implement new technologies, create intelligent
products and services, and optimize business processes. The interaction of people, machines, and data in real time opens up new
horizons for innovation and allows companies to take leading positions in the market.
Digital transformation affects all aspects of a company’s activities: from business models to corporate culture. In these conditions,
developing an effective strategy becomes a complex multifactorial task. Analyzing digital transformation strategies requires the
use of special analytical and applied methods that allow evaluating various options and choosing the most optimal one. Despite
the growing interest in digital transformation, there is a lack of research in the scientific literature devoted to the development of
such methods and models. Based on this, the cornerstone focus is the development of analytical tools that will allow organizations
to objectively evaluate strategic alternatives and make informed decisions in the context of digital transformation.
It should be noted that when choosing the right development strategy, each organization must take into account a huge number of
factors. This process is also complicated by the presence of enormous uncertainty, which is characteristic of modern global
economic development [4]. At the same time, the modern economic system is characterized by its complexity and emergence.
Based on this, we propose a comprehensive approach based on the methods of fuzzy logic and multi-criteria decision-making,
which would allow taking into account these two phenomena. In this case, the fuzzy logic method is reduced to reflecting the
uncertainty and vagueness in the assessment of criteria and alternatives, while the multi-criteria decision-making method takes
into account the totality of the criteria and factors themselves.
This approach was implemented using certain stages. First, a decision-making model is built taking into account the multifactorial
nature and uncertainty of development. Second, a research methodology is implemented based on the tools of the fuzzy analytical
process from the standpoint of hierarchy and fuzzy axiomatic design. Factor weights are determined by the method of fuzzy
analytical process from the standpoint of hierarchy, and the most appropriate strategy is selected using fuzzy analytical design.
The advantages of using this method are its convenience and consistency, in particular its ability to group criteria from the
standpoint of hierarchy. In turn, the fuzzy analytical design method is attractive due to its ability to measure the compliance of
system characteristics with its functional requirements. In addition, it excludes alternative strategies that do not meet the
functional requirements of the system.
Digital transformation is radically changing the business landscape, requiring companies to constantly adapt and rethink their
strategies. Successful digital transformation involves not only the implementation of new technologies, but also a comprehensive
transformation of business processes, structures, and cultural norms [1-3]. According to research, effective digital transformation
requires aligning four key aspects: technological, economic, organizational, and cultural.
Digital transformation creates both new opportunities and significant challenges for organizations (Figure 1).
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue IV, April 2025
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Fig. 1. The role of digital transformation
Source: developed by the author based on research
There are a number of publications in the scientific literature devoted to the impact of digital transformation at the micro and
macro levels. Thus, Porter’s classic work is devoted to analysis of the competitive advantages of companies through the use of
technology. McCaffrey and Brynjolfsson examine the impact of digital technologies on the economy and society, emphasizing
the importance of innovation and adaptation to new conditions. For practical recommendations, the work of Westerman is
interesting, where he offers practical recommendations for managers seeking to successfully implement digital transformation in
their companies.
Research in the field of digital strategy development is actively developing in the academic world. Early works laid the
foundations for a systemic approach to digital strategies. Later, studies by Bharadwaj and Bleischer deepened the understanding
of key aspects of companies' digital transformation strategy. Lerner investigated the integration of digital strategy into the overall
business strategy, while Limani in his works substantiated the need for flexible and adaptable approaches to the study of company
strategies. Hyvonen contributed to the study of the specific features of digital transformation in Scandinavian countries.
Existing studies, such as Yeh's work, demonstrate the potential of multi-criteria decision-making methods in choosing digital
transformation strategies. However, it should be noted that the approach presented by Yeh does not provide an opportunity to take
into account the uncertainty factor, which is the driver of modern development. In order to fill this gap in scientific circles, we
propose an integrated approach based on the above-mentioned tools. This integrated approach will allow companies to more
effectively choose optimal digital transformation strategies for organizations in the face of uncertainty and complexity of
development.
Model specification.
The developed model is based on the synthesis of fundamental scientific research [5,6,9], industry reports [7,8,12] and expert
opinions. The conceptual decision-making scheme is presented in Fig.2.
•New business models: Digital technologies enable innovative business models
based on data, subscriptions and platform solutions.
•Improving customer experience: Personalization, automation and omnichannel
customer interactions are becoming key factors for success.
•Increased efficiency: Digital tools can streamline business processes, reduce costs
and increase productivity.
Possibilities
•Uncertainty: Rapid technological change creates a high degree of uncertainty and
difficulty in predicting the future.
•Resistance to Change: The introduction of new technologies often encounters
resistance from employees and management.
•Cybersecurity: Digitalization increases the risks of cyberattacks and requires
stronger security measures.
Challenges
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Fig. 2. Conceptual diagram of decision making
Source: developed by the author based on research
The analysis also examines four alternative digital transformation strategies: a customer- and people-focused strategy; a profit-
focused strategy; a strategy focused on the integration and alignment of business processes; and a strategy focused on
collaboration and innovation.
A1. Customer and People-Oriented Strategy
This strategy is subjective and is aimed at taking into account the interests of customers and staff. In other words, the focus is on
the economic entity. To obtain more detailed information about consumer behavior and their interests, information about the
client, his tastes and preferences is used. Information about the purchasing power of VIP clients and their age characteristics is
also used for segmentation. In order to create an expanded client base, organizations need to think through the path and maintain
communication with clients throughout their lives.
To ensure a successful digital transformation, it is essential not only to implement new technologies, but also to transform the
corporate culture. This requires creating an open and trusting atmosphere, delegating authority to employees, developing their
digital competencies and using digital technologies to determine their behavior.
A2. Profit-oriented strategy.
The value-based approach requires companies to rethink their business models and create new value propositions that meet
changing customer needs. Digital technologies, as well-known companies like Walmart and Zara have shown, namely the
integration of digital supply chains into their operations, can significantly improve efficiency and create new business
opportunities. The emergence of innovative business solutions, such as consulting services and free apps, highlights the need to
constantly search for new sources of income and adapt to changing market conditions. To develop successfully, organizations
must be flexible and able to change quickly.
A3. Strategy focused on integration and alignment of business processes
An effective integration and alignment strategy involves creating a unified ecosystem where all elements of the company work in
concert to achieve common goals. In other words, all strategies, capabilities, resources, and management systems of the company
must be aligned and coordinated with each other to support the organization’s goals. Communication plays a key role in this
process. The lack of clear communication and coordination between different departments can lead to decreased efficiency and
the inability of the organization to adapt to a rapidly changing environment. In this case, organizations become part of a larger
ecosystem with integrated value chains. Digital technologies here enable organizations to collaborate more closely with external
entities (partners).
A4. Strategy focused on collaboration and innovation
A strategy focused on collaboration and innovation requires the creation of integrated platforms and the development of a
corporate culture. In the era of digital transformation, where the integrated form of business prevails, only corporate efforts can
achieve success. Digital transformation requires more than technology; it must be balanced with a culture of knowledge sharing.
Technology teams and subject matter experts must come together early on. Personnel must work in cross-functional teams, and
open learning mechanisms must be adopted throughout the company. Only companies that work quickly and in real time have a
better chance of innovating faster and being successful.
Determining the most optimal digital
transformation strategy
Talent (C1)
digital
learning (C11)
Digital Human
Resources
(C12)
employee
engagement
(C13)
Management (C2)
Analytical
skills (C21)
Quality of
information
(C22)
Data and
Content
Management
(C23)
Preparedness for
emerging trends
(C3)
Digitalization
process (C31)
Changing
Culture (C32)
Leadership
Qualities
(C33)
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II. Research methodology
The research methodology consists of three consecutive stages, schematically presented in Figure 3. The first stage is aimed at
formulating the research problem and identifying the key factors influencing the choice of digital transformation strategy. At the
second stage, using the fuzzy analytical hierarchy process method, the weights of the factors are determined, allowing us to
determine their relative importance. The final stage of the research is devoted to choosing the optimal strategy using the fuzzy
analytical network method. This method allows us to take into account the relationships between various criteria and alternatives,
which increases the accuracy of decisions made.
Fig.3. Methodological approach.
Source: developed by the author based on research
Saaty 's Analytical Hierarchy Process is a powerful tool for multi-criteria decision making, allowing alternatives to be ranked
according to their degree of preference [10]. The flexibility of this method lies in the fuzzy ( chaotic ) environment, which
describes well the chaotic behavior of the subjects of the study. This, in turn, allows the toolkit to be successfully applied in
various fields of science, including economics, engineering, and social sciences.
The process of applying the fuzzy hierarchy process analysis toolkit includes the following steps:
Building a Hierarchical Structure: Breaking down the problem into hierarchical levels, including the goal, criteria, and
alternatives.
Pairwise comparison of elements: Comparison of elements of each level in pairs using linguistic variables (see Table 1).
Table 1. Linguistic scale of analysis of fuzzy analytical hierarchy process.
Linguistic expression
Abbreviation
Three-dimensional fuzzy numbers
Equal
E
(1,1,1)
Equally significant
E.I.
(0,5,1,1,5)
Low degree of significance
WMI
(1,1,5,2)
Medium level of significance
SMI
(1,5,2,2,5)
High degree of significance
VSMI
(2,2,5,3)
Extremely high degree of significance
AMI
(2,5,3,3,5)
Problem
formulation stage
•Defining the research objective based on
a multi-criteria decision making
approach;
•Definition of selection criteria and
strategic alternatives.
Stage of
development and
implementation of
the toolkit
•Building a decision-making hierarchy;
•Application of the stages of the analytical
hierarchy process toolkit;
•Evaluation of criteria weights.
Stage of choosing
the optimal
strategy
•Development of digital
transformation strategies;
•Application of the stages of
the fuzzy axiomatic design
toolkit;
•Selecting the optimal
strategy.
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Construction of fuzzy matrices of pairwise comparisons: Transformation of linguistic evaluations into fuzzy numbers for each
comparison. For this purpose, the membership function Ã=( l,m ,u ) is used , which can be expressed as follows:
(1)
Calculation of weights of criteria and alternatives: For calculating the weights of elements at each level of the hierarchy Fuzzy
mathematics methods are used.
Synthesis of results: At this stage, the weights of the criteria and alternatives are combined to determine the overall ranking of
the alternatives.
It should be noted that the toolkit of the fuzzy hierarchy process allows one to take into account the uncertainty and subjectivity
of assessments, which makes it especially useful for solving complex problems related to decision-making under emergent
conditions .
axiomatic design (AD) method used in this paper and proposed by Suh in 1990 has become a widely used method in engineering
disciplines [11]. It is based on two key axioms - independence of functional requirements and minimization of the information
content of the system. The fuzzy modification of the axiomatic design allows taking into account the uncertainty of estimates by
representing them as triangular fuzzy numbers. The design process is carried out as follows: first, an evaluation matrix is
constructed by collecting the selected policy decisions. Next, a paired comparison of elements is made using linguistic
expressions (see Table 2). In the third stage, the linguistic expressions are transformed into three-dimensional fuzzy numbers.
Table 2. Linguistic scale of fuzzy axiomatic design analysis.
Linguistic
expression
Abbreviation - atura
Three-dimensional fuzzy numbers
Very bad
VB
(0,5,1,1,5)
Bad
B
( 1,1,5,2 )
Average
M
( 1,5,2,2,5 )
Good
G
( 2,2,5,3 )
Very good
VG
(2,5,3,3,5 )
Next, the aggregation of the assessments of the decisions made and three-dimensional fuzzy numbers is carried out using the
following equation:
(2),
where K is the number of decisions taken,
are the ranks of alternatives of political decisions with the i
th
alternative and
j
th
criterion.
Next, the functional scope for each criterion is determined, followed by the calculation of the information content ( I ) for each
alternative. The basic concept of axiomatic design is to find the intersection region between the design range and the system range,
as shown in Fig. 4. In the fuzzy case, the system range and the design range are determined using three-dimensional fuzzy
numbers, as shown in Fig. 5.
Fig.4. Research ranges based on criteria
Source: developed by the author based on research
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Fig.5. General area of research
Source: developed by the author based on research
Based on the results obtained, the most optimal strategies are determined based on the following equations:
. (2)
(3)
Practical application of the model.
The implementation of the model is demonstrated using the example of a commercial bank in Tajikistan.
1
The banking sector was
chosen due to its high dynamism and the need for constant adaptation to new technologies. The model was built to determine the
optimal digital transformation strategy for the selected commercial bank in order to maximize profits. To determine the weights
of the criteria using the above stages. The results are presented in Table 3.
Table 3. Criteria weights
1
For confidentiality reasons, the name of the commercial bank is not provided.
Type of criterion
Weight
Secondary criterion
Internal weight
Weight
Rank
From
1
0.316
From
11
0.358
0.113
5
From
12
0.387
0.122
3
From
13
0.255
0,080
9
From
2
0.374
From
21
0.406
0.152
1
From
22
0.326
0.122
4
From
23
0.267
0,100
6
From
3
From
31
0.315
0.098
7
System range
Projecting range
0
1
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Source: calculated by the author based on research
The results of the table show that the most significant criteria selected after the entire evaluation procedure are C
21
- analytical
skills, C
32
- cultural modification, C
12
- digital human resources. Then, based on the obtained results, expert and functional
assessments were carried out, the results of which are presented in Table 4. The calculations were carried out as follows: for each
alternative and each criterion, the average value of expert assessments was calculated. Then the correspondence between the
obtained value and the functional requirement was determined. Based on these data, the information content of each alternative
was calculated, presented in Table 5.
Table 4. Evaluation of strategy alternatives
A
i
From
11
From
12
From
13
From
21
From
22
From
23
From
31
C
32
C
33
A
1
VB
B
M
B
M
G
M
B
VB
A
2
G
M
B
M
B
M
G
M
B
A
3
M
G
M
G
M
G
M
G
M
A
4
G
VG
G
G
VG
G
M
VG
VG
FRs
M
M
VB
B
B
M
VB
VB
B
Source: calculated by the author based on research
Table 5. Ranking of strategy alternatives
C
j
A
1
A
2
Area of the
system
Total area
I
Area of the system
Total area
I
C
1
From
11
0.500
0.055
3.173
0.500
0.222
1.168
From
12
0.500
0.500
0.000
0.500
0.500
0.000
From
13
0.500
0.222
1.168
0.500
0.222
1.168
C
2
From
21
0.500
0.014
5.164
0.500
0.222
1.168
From
22
0.500
0.347
0.527
0.500
0.222
1.168
From
23
0.500
0.125
2.000
0.500
0.222
1.168
C
3
From
31
0.500
0.500
0.000
0.500
0.347
0.527
C
32
0.500
0.125
2.000
0.500
0.125
2.000
C
33
0.500
0.000
inf
0.500
0.222
1.168
C
j
A
1
A
2
Total area
I
Area of the
system
Total area
I
C
1
From
11
0.500
0.125
2.000
0.500
0.055
3.173
From
12
0.500
0.222
1.168
0.500
0.014
5.164
From
13
0.500
0.347
0.527
0.500
0.500
0.000
C
2
From
21
0.500
0.500
0.000
0.500
0.347
0.527
From
22
0.500
0.222
1.168
0.500
0.125
2.000
From
23
0.500
0.125
2.000
0.500
0.222
1.168
0.310
From
32
0.408
0.126
2
From
33
0.277
0.086
8
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C
3
From
31
0.500
0.347
0.527
0.500
0.347
0.527
C
32
0.500
0.125
2.000
0.500
0.222
1.168
C
33
0.500
0.347
0.527
0.500
0.222
1.168
Source: calculated by the author based on research
Based on the analysis, in order to achieve maximum efficiency, the commercial bank under study must move to a customer- and
staff-oriented strategy. This will require significant changes in the bank’s business model, including a review of the product
portfolio, optimization of customer service processes, and the development of new digital channels. In addition, it will be
necessary to adapt the organizational structure so that it supports new business processes and promotes flexibility and innovation
in processes.
In summary, it should be noted that the developed model for assessing digital transformation strategies, based on fuzzy multi-
criteria assessment methods, allows for the effective consideration of uncertainty and subjectivity in decision-making. Testing the
model using a service enterprise (commercial bank) as an example confirmed its operability. Future research will be aimed at
improving the model and expanding its functionality.
Literature:
1. Muminova , F. M. Approach to index assessment of readiness for innovation in the regions of the Republic of Tajikistan
/ F. M. Muminova // Economy of Tajikistan. - 2019. - No. 2. - P. 173-178. - EDN XLWDCF.
2. Rakhimov, Sh. M. Digital technologies and their impact on economic development / Sh. M. Rakhimov // Economy of
Tajikistan. - 2023. - No. 4. - P. 9-13. - EDN WNUZPY.
3. Tabarzoda , O. S. Tatbiki technologies rakami dar modelsosia ravandhoi macroiqtisodi / O. S. Tabarzoda //
Muammohoi Guzarish ba iktisodi rakami dar of umchuria TOKIKISTON: vazhi kununi va durnamo : MAVODI
CONFERENCEI UMGURIYAVII ILMIYU AMAL DAR MAVZUI, Dushanbe, October 30 – 07, 2023. – Dushanbe:
Tajik National University, 2023. – P . 50-55. – EDN FVYDTG .
4. Khakimova, M. F. Dynamic sustainability as a dominant paradigm of modern development of the national economy of
the Republic of Tajikistan / M. F. Khakimova // Bulletin of the Tajik National University. Series of socio-economic and
social sciences. - 2021. - No. 3-2. - P. 152-157. - EDN ECKKFR.
5. Bharadwaj, A., El Sawy , O., Pavlou , P., and Venkatraman , N. (2013), Digital business strategy: toward a next
generation of insights, MIS Quarterly, Digital Business Strategy, 37(2), 471-482.
6. Bleicher , J., & Stanley, H. (2016), Digitization as a catalyst for business model innovation a three-step approach to
facilitating economic success, Journal of Business Management, 12, 62-72.
7. Deloitte, (2017), Future of Work, Work & Place Readiness Assessment.
8. Forrester Report, (2016), The Digital Maturity Model 4.0.
9. Hyvönen , J. (2018), Strategic leading of digital transformation in large established companies–a multiple case-study
10. Saaty , T. L. (1980). The Analytical Hierarchy Process, Planning, Priority. Resource Allocation . RWS Publications ,
USA.
11. Suh, N. P. (1990). The principles of design, Oxford University Press on Demand.
12. Telkom, (2016), Digital Readiness Assessment, Why a Digital Readiness Assessment is important for your business.
