Review

Exploring the Critical Success Factors Influencing the Outcome of

Digital Transformation Initiatives in Government Organizations
Abdalla Al Maazmi * , Sujan Piya and Zehra Canan Araci

Industrial Engineering and Engineering Management Department, University of Sharjah, Sharjah P.O. Box 27272,
United Arab Emirates; spiya@sharjah.ac.ae (S.P.); zaraci@sharjah.ac.ae (Z.C.A.)
* Correspondence: u20105089@sharjah.ac.ae; Tel.: +971-505333600

Abstract: This study investigates the previous studies on successful digital transformation initiatives
in government organizations and deduces the tangible and intangible benefits to showcase some
real-life examples and evidence. This article provides a thorough evaluation of the available literature
on successful digital transformation initiatives. It analyzes 53 important success elements grouped
across seven dimensions, giving a conceptual framework for executing digital transformation in
government organizations. The research identifies key success elements that are crucial for digital
transformation, emphasizing the importance of clear planning, flexibility, agility, and robust data
security measures. This study provides practical insights for organizations aiming to undertake
digital transformation initiatives, highlighting strategies to overcome hurdles and maximize benefits.
This study contributes a proposed conceptual framework and empirical evidence to guide academics,
professionals, and decision-makers in effectively navigating and leveraging digital transformation in
a rapidly evolving digital landscape.

Keywords: digital transformation; critical success factors; organizational challenges; literature review;
comprehensive framework; government organization

Citation: Al Maazmi, A.; Piya, S.; 1. Introduction

Araci, Z.C. Exploring the Critical
In the digital age, digital transformation (DT) functions as a strategic driver that uses
Success Factors Influencing the
the potential of digital technology to revolutionize the ways in which organizations operate,
Outcome of Digital Transformation
compete, and are structured [1–3]. DT embodies the digitalization of all components of an
Initiatives in Government
organization, namely, the digital transformation of processes, products, services, culture,
Organizations. Systems 2024, 12, 524.
and organization. These digital transformations enhance organizational performance
https://doi.org/10.3390/
systems12120524
and result in better customer experience and an increase in the competitiveness of the
system. This paper further expands upon the fast-moving and interdisciplinary nature of
Academic Editor: Mitsuru Kodama digital transformation research, which thrives in the fields of technology, entrepreneurship,
Received: 28 September 2024
strategic management, operations, marketing, and organizational science, enhancing the
Revised: 30 October 2024 global value chain position [4–7]. The changing environment in which digital technologies
Accepted: 15 November 2024 are embedded fundamentally shapes the activities of contemporary organizations. The
Published: 26 November 2024 technologies affect organizational processes, hierarchies, and various relationships with
stakeholders such as partners, suppliers, and customers [3–5]. The origins of DT trace back
to the 1980s and 1990s and were defined by the enormous impact of information technology
adoption on organizational structures, innovation, and performance [6–9]. The 1990s was a
Copyright: © 2024 by the authors. period of the growing prevalence of ICT-based managerial innovations, which was due to
Licensee MDPI, Basel, Switzerland.
the arrival of computer technology and the increasing number of internet users [8].
This article is an open access article
In the continuum of DT, public and private-sector entities face the same problems,
distributed under the terms and
though their approaches and challenges differ. Whereas private organizations, in par-
conditions of the Creative Commons
ticular, put profit maximization and competitive markets at the forefront of their digital
Attribution (CC BY) license (https://
transformation (DT) schemes, public organizations are more broadly mandated to ensure
creativecommons.org/licenses/by/
4.0/).
that the delivery of services is efficient, transparent, and accountable to citizens [8]. The

Systems 2024, 12, 524. https://doi.org/10.3390/systems12120524 https://www.mdpi.com/journal/systems

Systems 2024, 12, 524 2 of 26

consequence of this is that the goals, priorities, and limitations of public organizations
during their DT journey differ significantly from those of their private partners. In a
substantial portion of research on the topic, the goals of successful DT adoption are iden-
tified as the following: to improve operating efficiency, customer experience, business
models, and business culture [9–13]. While there is a rich literature on digital DT, what
remains largely unclear is the way digital technologies change organizational structures.
This is an attempt to bridge the knowledge gap on the organizational structural changes
accompanying DT, which have not yet been sufficiently investigated, particularly in the
face of numerous failed initiatives in the field of DT [10,11]. Just one out of every eight
digital transformation attempts is likely to fulfill its stated goals, implying an approximate
failure rate of 87.5% [14–16]. Digital transformation failures are frequently caused by a lack
of ownership and accountability, ineffective alignment between business and technology
leaders, insufficient in-house technological competence, poorly defined joint objectives,
inadequate cultural integration, a disregard for ambidextrous leadership development, and
a failure to maintain continuous improvement and momentum [15–18]. General Electric
(GE) is an example of failed digital transformation, as it struggled to match its imple-
mentation roadmap with its digital strategy objectives, citing rushed implementation and
inadequate co-ordination as having contributed to the failure of its digital transformation
efforts [19–24]. This line of research study is based on the discussion of critical success
factors (CSFs) for successful DT implementation, the indication of their relationship with
achievement rates, and the proposal of a comprehensive CSF framework for guiding orga-
nizations during this transformative process. The following work follows the systematic
review approach and examines all problems connected with the organizational changes
during DT in order to learn how to overcome implementation barriers. Moreover, the
outcome of this investigation reveals the significance of these factors in the execution of
DT initiatives and the trends that are transforming the field. The present study proceeds
from the conclusions of previous literature reviews undertaken [12–15]. It adopts a holistic
perspective on the essential conditions of successful digital transformation (DT) implemen-
tation. The domain of decision-making in DT is well covered by existing studies that have
examined the impact on diverse areas, including both public and private organizations.
By means of the investigations carried out in [12–14], it was determined that what digital
technologies are able to change is dependent on the different areas within the organization.
Nevertheless, given this extensive range of research, a significant discrepancy between the
study of these specific nuances of digitalization and their unique problems and techniques
in the public sector still exists. The research is aimed at bridging the gap by emphasizing
digital transformation within the public sector, in particular. This study aims to offer
valuable conclusions via an in-depth analysis of the obstacles, possibilities, and strategies
existing in the stated context. This will assist policymakers, public administrators, and
scholars when formulating policies. Understanding the different intricacies of how public
organizations digitally transform their processes is of utmost importance to improve the
delivery of public services and promote citizen engagement, consequently augmenting the
effectiveness of governance in line with the new digital age. The remainder of this paper is
structured as follows: In Section 2, the methodology of this research is discussed with the
foundation of the conceptual background. These were the CSFs identified and discussed
as CSFs. Section 3 mentions the model of the concept and briefly gives critical points in
the form of thematic categories. Finally, in Section 4, we conclude this work with possible
paths outlined for further research.

2. Materials and Methods

The approach that was taken to the DT literature followed a deductive method,
which is in line with the systematic review. Topics connected to DT within firms were
systematically derived from the articles. The procedure laid down [16] for systematic
literature reviews was followed, and the SPAR-4-SLR protocol listed [15] was employed. To
begin with, the scope of the review was well established by the authors as they conducted
Systems 2024, 12, 524 3 of 26

a thorough review of the literature and examined several survey articles, as stated in the
Introduction section. In addition, an appropriate search strategy helped to produce a
wide range of references, the vast majority of which were relevant to our research. Third,
the inclusion of contributions was decided upon by clearly defined standards, which are
explained in more detail in the parts that follow. Fourth, a content analysis was carried out
on publications about DT projects and implementations both inside and between articles,
and the material was arranged based on the critical success aspects of the suggested
conceptual framework. Lastly, conclusions for theory and practice were drawn from
the deep analysis that yielded these discoveries. Furthermore, prospective directions for
further investigation were suggested in light of the results. The Scopus and Web of Science
databases were used for the literature search because they are well known for their efficiency
in handling research related to economics and business [16–20] and for offering extensive
interdisciplinary coverage [20–27]. To guarantee credibility, only peer-reviewed journal
articles were taken into consideration, leaving out other formats such as books or conference
proceedings. This strict criterion was meant to protect integrity by removing materials that
did not undergo rigorous peer review. Keywords, titles, and abstracts containing terms
such as “digital transformation” AND “digitalization” or “digitalisation”, AND “Digital
Transformation”, as well as “success factors” and “frameworks”, were used in the search. By
means of digitalization initiatives and strategic transformations for organizational changes,
DT improves corporate operations [26–30]. These related ideas highlight how equally
important they are to the sample plan. The search was limited in time and took place
between 2013 and 2024. The period from 2013 to 2024 was chosen because it marks the rise
of digital transformation as a significant research topic, capturing its evolution and the latest
advancements. Starting in 2013 and ending in 2024, this reflects when DT began gaining
substantial academic attention, including the most recent studies, ensuring relevance
and comprehensiveness. This timeframe provides a balance between capturing recent
developments and maintaining a robust scope of available research. The [31] inclusion and
exclusion criteria were adhered to, and 2306 articles were found at first. The database’s
research field filters eliminated irrelevant topics such as “environmental sciences” and
“surgery” in favor of pertinent categories. Figure 1 illustrates the selection procedure
used in the study. Initially, 703 publications were considered. Some unrelated or distantly
related items were excluded from the dataset after further refinement that was focused
solely on articles related to digital transformation (DT). This approach produced a final
selection of 303 articles. To be more precise, 115 articles were removed since they either
completely ignored DT or focused mainly on specific technology applications. After
removing publications that were not pertinent to the study’s objectives, a final database of
104 articles was created for thorough examination.
Systems 2024, 2024,
Systems 12, x FOR PEER REVIEW
12, 524 4 of 28
4 of 26

Figure 1. Illustrates
Figure the process
1. Illustrates of searching
the process and selecting
of searching journal
and selecting articles.
journal articles.

3. Findings
3. Findings of theofLiterature
the Literature
ReviewReview
Figure
Figure 1 shows
1 shows the distribution
the distribution of articles
of articles acrossacross different
different subject
subject areasareas that deal
that deal with with
issues in the field of DT. Most of the reviewed publications were
issues in the field of DT. Most of the reviewed publications were published in journalspublished in journals
pertaining to general management and technology management.
pertaining to general management and technology management. From a disciplinary per- From a disciplinary
perspective,
spective, a significant
a significant portion ofportion of thesefell
these articles articles
withinfell withindomains
research researchsuch
domains such as
as man-
agement information systems, knowledge management, innovation management, and and
management information systems, knowledge management, innovation management,
production
production and operations.
and operations. The examination
The examination of 104 of articles
104 articles reveals
reveals a broad
a broad distribution
distribution
across academic areas, with Computer Science being the most prominent category (33 arti-
across academic areas, with Computer Science being the most prominent category (33
cles). Decision Sciences, Social Sciences, and Business Management and Accounting follow
articles). Decision Sciences, Social Sciences, and Business Management and Accounting
closely behind, with each having 14, 13, and 12 articles, respectively. Engineering contains
follow closely behind, with each having 14, 13, and 12 articles, respectively. Engineering
24 articles, while Economics, Econometrics, and Finance each have 8 articles. Figure 2
contains 24 articles, while Economics, Econometrics, and Finance each have 8 articles. Fig-
depicts the interdisciplinary nature of digital transformation research, with a strong em-
ure 2phasis
depictsonthe interdisciplinary
computer science andnature of digital
significant transformation
contributions fromresearch,
Business with a strong and
Management
emphasis on computer
Accounting, Decisionscience and Social
Sciences, significant contributions
Sciences, Engineering,fromandBusiness Management
Economics, Econometrics,
and Accounting, Decision Sciences, Social Sciences, Engineering, and
and Finance. This distribution emphasizes the comprehensive and multifaceted Economics, Econo-
approach
metrics, and Finance. This distribution emphasizes the comprehensive
to studying and executing digital transformation across multiple fields of study. and multifaceted
approachThree
to studying
separateandsections—the
executing digital transformation
organizational across
core, multiple fields
organizational of study. and
hierarchies,
success factors of DT initiatives—were created to provide the content analysis findings.
The theme topics found in the analysis are succinctly summarized in this format, with more
in-depth discussion to come in the parts that follow.
 2024, 12,
Systems 2024, 12, 524
x FOR PEER REVIEW 55of 28
of 26

35

30

25
Number of articles

20

15

10

0
Computer Engineering Decision Social Sciences Business Economics,
Science Sciences Management Econometrics,
and Accounting Finance
Subject Area

Figure 2.
Figure 2. Articles
Articles across subject areas
across subject areas in
in digital
digital transformation
transformation success
success research.
research.

3.1. Organizational
Three separate Hierarchy
sections—the organizational core, organizational hierarchies, and
success factors of DT
The literature review initiatives—were
revealed thatcreated
previous toresearch
provide articles
the content
haveanalysis
emphasized findings.
that
organizational structure is crucial for achieving digital transformation. It was foundwith
The theme topics found in the analysis are succinctly summarized in this format, that
moretop-down
both in-depth discussion
and bottom-upto come in the parts
strategies that follow.
can coexist with varying degrees of prominence
in the redesign of organizations for digital transformation [31–36]. In order to facilitate the
3.1. Organizational
integration Hierarchy
of bottom-up initiatives for quick adaptation, rigid hierarchies may not be well
suited to the adaptable character
The literature review revealed needed
that for successful
previous distributed
research articlestechnology
have emphasized[37]. Organi-
that
zations establish independent units, such as innovation laboratories,
organizational structure is crucial for achieving digital transformation. It was found and digital business
that
units to facilitate
both top-down andthebottom-up
adoption strategies
of digital technologies,
can coexist with which increases
varying degrees agility [38,39]. In
of prominence
order to manage
in the redesign of digital projects,
organizations fortop-down reorganization
digital transformation involves
[31–36]. developing
In order C-suite
to facilitate the
positions
integration such as Chief Digital
of bottom-up Officer
initiatives for(CDO)
quickor Chief Information
adaptation, Officer (CIO)
rigid hierarchies may [38,39]. On
not be well
the other hand, problems can result from disagreements and an abundance
suited to the adaptable character needed for successful distributed technology [37]. Or- of knowledge,
which can diminish
ganizations establishvalue [40]. Bothunits,
independent formal andasinformal
such innovation changes are brought
laboratories, and about
digitalby DT,
busi-
and knowledge exchange is made easier by collaborative networks that
ness units to facilitate the adoption of digital technologies, which increases agility [38,39].develop from the
bottom up [41,42]. Formal structure misalignments can prevent DT
In order to manage digital projects, top-down reorganization involves developing C-suite from progressing. It is
anticipated
positions such thatasplatforms and artificial
Chief Digital Officer intelligence
(CDO) or Chief (AI) will cause businesses,
Information Officer (CIO)whether top-
[38,39].
down or bottom-up, to become networked, decentralized communication
On the other hand, problems can result from disagreements and an abundance of channels [43–47].
According
knowledge,towhich [48], can
governance
diminishstructures
value [40].areBoth essential
formal andfor facilitating the production
informal changes are brought of
value because they underscore the importance of dedication and
about by DT, and knowledge exchange is made easier by collaborative networks that de-confidence.
velopInfrom
DT, the
organizational culture Formal
bottom up [41,42]. serves as a helper
structure as well as a hindrance.
misalignments can preventTherefore,
DT from
success requires an environment that welcomes change [46].
progressing. It is anticipated that platforms and artificial intelligence (AI) Even while DTwill
is crucial, core
cause busi-
elements of a company’s culture frequently do not change [47–50]. In order to overcome
nesses, whether top-down or bottom-up, to become networked, decentralized communi-
obstacles such as organizational opposition and individual inertia and to synchronize
cation channels [43–47]. According to [48], governance structures are essential for facili-
changes across all departments, stakeholder involvement is essential [51–56]. Managing
tating the production of value because they underscore the importance of dedication and
multiple cultures is essential in digital ecosystems, as mismatched cultural norms can have
confidence.
negative implications [57–60]. Particularly in supply chain management, marketing, and
In DT, organizational culture serves as a helper as well as a hindrance. Therefore,
advertising, digital technologies require process adaptation with distinct evolutionary paths
success requires an environment that welcomes change [46]. Even while DT is crucial, core
for processes [61–63]. According to [64,65], digitalization improves audience engagement,
elements of a company’s culture frequently do not change [47–50]. In order to overcome
personalized content distribution, and the codification of hitherto informal processes.
obstacles such as organizational opposition and individual inertia and to synchronize
Through digital processes, DT promotes improvements in internal and customer-facing
changes across all departments, stakeholder involvement is essential [51–56]. Managing
processes for quicker decision-making and co-ordination [64–68]. IT solutions that promote
multiple cultures is essential in digital ecosystems, as mismatched cultural norms can
gradual replacement and an ambidextrous approach, such as ERP, MES, and SCADA,
have negative
improve implications
transparency and [57-60]. Particularly
decision-making in supply
[69]. chain management,
Agile methods are emphasized marketing,
in the
and advertising, digital technologies require process adaptation
literature as a means of helping organizations adapt to changes in technology [70]. with distinct evolution-
ary paths for processes [61–63]. According to [64,65], digitalization improves audience
Systems 2024, 12, 524 6 of 26

3.2. Engagement in Digital Infrastructure

DT is a dynamic process that emphasizes the relationship between software, services,
and goods while pushing companies away from conventional product-centric models and
toward integrated systems [71–74]. The notion of “digital servitization”, which is at the
center of this revolution, involves manufacturing companies going through a fundamental
shift in order to add value by growing the range of digital services they offer that are
connected to physical products [75]. Although mass customization and differentiation offer
benefits, there are drawbacks, such as the servitization dilemma, which makes it uncertain
whether productivity improvements will materialize quickly [76–80]. Businesses that are
starting their path toward digital servitization frequently work together to co-create value
while overcoming obstacles pertaining to interoperability and the crucial requirement for
chances for data-sharing, especially on digital platforms [81].
According to [82], digital platforms play a crucial role in promoting co-operative
knowledge exchange and producing economic benefits. These platforms are distinguished
by multisided market structures that maximize user engagement and are impacted by
direct or indirect network effects. The emergence of digital ecosystems and an intricate
web of interconnected ecosystems changes the nature of traditional connections and calls
for creative managerial strategies based on self-governance and orchestration [83–85]. A
significant change is taking place in the dynamic between organizations and customers
as a result of digital transformation. Customers are now active co-creators of digital
solutions rather than passive customers, strengthening the symbiotic relationship that
boosts business revenues and increases customer bargaining power [86]. Fostering value
co-creation becomes critical in this changing environment, as businesses use tactics such as
rewards, community service, and loyalty-building exercises. Within the ecosystem of the
company, these initiatives foster closeness and a sense of belonging [87–89]. According to
the authors of [89], the subjective nature of competitive advantages in the digital economy
highlights the importance of trust, reciprocity, and reputation as relational assets. According
to the findings in [90], this transformative journey realigns power dynamics, encouraging
enterprises to emphasize customer engagement over old product-centric models and
acknowledging customers’ improved negotiating leverage in the DT landscape.

4. Critical Success Factors for Digital Transformation

4.1. Definition of Success in Digital Transformation
DT entails the comprehensive integration of digital technology into an organization’s
processes, fundamentally reshaping how value is delivered to stakeholders. Defining
“success” in DT is essential, especially for government organizations, where success extends
beyond technical implementation to include the achievement of strategic, stakeholder-
aligned goals. Success in this context means realizing well-defined objectives that enhance
organizational performance, service quality, and public satisfaction [24]. A DT initiative
may be technically sound, but without clear goals or alignment between these goals and
the implemented solutions, it may not yield success [45]. Misalignment often leads to
challenges in meeting public expectations and can result in unfulfilled transformation
objectives [23].

4.2. Critical Elements for Successful Digital Transformation

For DT to be successful, certain critical elements must be present, such as strong lead-
ership with a distinct vision [90–92]. Establishing a culture that embraces change, fostering
strong support networks, and equipping staff with tools to adapt is vital in managing
operational changes [93]. Moreover, investments in technology, skill development, and
a data-driven culture are crucial for leveraging data analytics effectively [94]. Adaptabil-
ity, agility, and a customer-centric approach are necessary to respond to evolving market
dynamics and technological advancements [95]. Employee engagement and participa-
tion are also key, with training and open communication serving as enablers of active
involvement [96,97].
Systems 2024, 12, 524 7 of 26

4.3. Complex Environment of Digital Transformation

The complex environment surrounding DT also requires attention to security, regula-
tory compliance, and collaborative partnerships, which collectively contribute to sustain-
able success [36]. Table 1 provides an overview of previous research accomplishments on
digital transformation initiatives’ success (DTIS), offering insights into various methodolo-
gies and approaches. These studies highlight the diverse nature of DT across industries,
contributing to a nuanced understanding of DTIS. For instance, the authors of [30] pro-
posed a CSF management framework for digital business solutions, focusing on CRM
digitalization. They validated their framework with a national internet and TV service
provider, underscoring the importance of tailored approaches to managing CSFs effectively.

Table 1. DTIS in previous studies.

Authors (Year) Contributions Findings/Conclusions

The study develops a DBS framework through a structured review
Digital business strategy
[55] of industry reports, offering specific actions for DBS and digital
(DBS) framework
business model design.
Digital businesses integrate technologies such as social, mobile,
[23] Opportunities of DT in business analytics/big data, and cloud to transform how they work. A clear
digital strategy and risk-taking are crucial for success.
Reasons for failure in digital One of the main reasons why DT fails is the lack of clearly defined
[13]
transformation goals and a detailed digital strategy.
Factors for the perceived benefit of DT Collaboration and cultural shifts are key for DT success, involving
[33]
in manufacturing customers, suppliers, and peers.
By reviewing empirical contributions, the study offers insights into
[32] Influencing factors for the success of DT
why organizations undergo DT, how to achieve it, and its impact.
The study, based on interviews with public sector leaders, identifies
Key success factors for digitalization in
[24] key success factors for digitalization in public organizations,
public organizations
offering valuable insights for effective DT initiatives.
A list of seven success factors and 23 subfactors emerged from the
Success factors for successful
[56] thematic groupings, constituting the initial steps toward building a
DT practices
DT framework.
The study unveils the factors that influence the success of DT,
Influencing factors for the success of DT
[98] focusing on modernizing backend systems and the threat
in financial services
of BigTech.
The study underscores five key success factors: lean customer
[99] Key success factors for DT start-ups orientation, entrepreneurial culture, ecosystem participation,
third-party tech integration, and capital acquisition.
The study identifies key success factors in the business model of
Six key success factors for platform-based
[100] digital platforms in the metal and steel industry using qualitative
business models
interviews and modified concepts.
Through a literature review, the study identifies strategies for
[101] Factors influencing business innovation business innovation amid uncertainties, exploring critical success
factors for valuable insights.
The study shows case companies’ approaches to DT in their
Approaches to DT in international
[102] networks, emphasizing the importance of a holistic perspective and
manufacturing networks
identifying challenges.
The proposed framework provides practical guidance for
[103] CRM digitalization framework identification, monitoring, and maximizing benefits. It is validated
via a national internet and TV service company.
As a result of surveying German companies, key DT project CSFs
Ten significant critical success factors for
[72] are identified: corporate organization, technology, emphasizing
digital transformation
culture, top management support, and unified digital strategy.
Systems 2024, 12, 524 8 of 26

Table 1. Cont.

Authors (Year) Contributions Findings/Conclusions

Digital partnering necessitates trust, management support, careful
Critical success factors for successful selection, common goals, commitment, communication, conflict
[104]
digital partnering resolution, and digital training, improving survival in the dynamic
digital landscape, especially in developing countries.
The study focuses on factors influencing DT in a globalized context,
[105] Factors influencing digital transformation
highlighting the difference between digitalization and digitization.
The study aggregates information on drivers, success factors, and
[102] Factors affecting DT in public healthcare challenges in public healthcare DT, providing a starting point for
future research.
Factors for DT success in the Greek The study lists drivers and barriers that hinder DT success in the
[106]
public sector Greek public sector.
The study identifies 11 key factors, including communication,
[107] Factors for DT success in the public sector leadership skills, and resistance to change, for DT success in the
public sector.
[108] Start-up survival factors in the digital era Found the importance of innovation and risk management.
Crucial factors in ERP deployment for
[109] Insights for success or failure in ERP adoption.
enterprise business
Influence of digital transformation in
[81] Found impact on operational efficiency and risk management.
Islamic banking

4.4. Failure in Digital Transformation Initiatives

DT is often heralded as a pathway to innovation, efficiency, and enhanced service
delivery. However, the sobering reality is that a significant percentage of DT initiatives fail
to meet their intended objectives, with failure rates estimated to range between 70% across
various sectors, including government organizations and smart city projects [14]. Under-
standing the reasons behind these failures is crucial for organizations aiming to navigate
the complexities of digital transformation successfully. One primary factor contributing to
the failure of DT initiatives is the lack of clearly defined strategic goals [110–114]. Many
organizations embark on digital transformation without a coherent vision or a thorough
understanding of their desired outcomes [115–117]. This ambiguity can lead to misaligned
projects that do not address the specific needs of stakeholders. As noted by Qiao [29], a
misalignment between technology solutions and organizational objectives often results in
wasted resources and missed opportunities for value creation.

4.5. The Role of Organizational Culture

Organizational culture plays a significant role in the success or failure of DT efforts.
A culture resistant to change can hinder the implementation of new technologies and
processes [118–122]. Research has shown that organizations with rigid hierarchies and
established ways of working are less likely to embrace digital transformation, which often
requires agility and adaptability [13–15]. In contrast, organizations that foster a culture of
innovation, where experimentation is encouraged, tend to navigate the challenges of DT
more successfully [123–127].

4.6. Technological Risks and Challenges

Moreover, the technological landscape itself poses risks for digital transformation.
Rapid advancements in technology can render existing solutions obsolete before they
can be effectively implemented [128–131]. Organizations that do not invest adequately
in current and future technologies may find their initiatives failing to deliver expected
benefits [37]. Additionally, security and compliance issues can complicate the integration
Systems 2024, 12, 524 9 of 26

of new technologies, leading to project delays and budget overruns, further contributing to
failure [132–135].

4.7. Broader Implications of Digital Transformation Failures

Failures in DT are not merely individual occurrences but can have broader implications
for the organizations involved [136–140]. They can result in financial losses, diminished
stakeholder trust, and a tarnished reputation. The lessons learned from these failures
underscore the importance of adopting a comprehensive approach that considers both
the success factors and potential pitfalls [141–145]. Organizations must conduct thorough
risk assessments, define clear objectives, engage stakeholders, and cultivate a supportive
organizational culture to increase the likelihood of successful digital transformation [32].

4.8. Perspectives on Critical Success Factors

In contrast, the authors of [66] surveyed German companies to identify key CSFs for
DT projects, emphasizing corporate organization, technology, culture, and unified digital
strategy. This discrepancy suggests varying perspectives on the essential factors driving
digital transformation success [84]. The authors of [59] delved into the superiority of digital
platforms in the metal and steel industry, identifying four key success factors, including
value, delivery, capture, and digital transformation. Their qualitative approach of using
interviews provides a rich understanding of platform-based business models. Conversely,
the work in [44] explored strategies for innovative business models, emphasizing value cre-
ation, innovativeness, resilience, and sustainability. This combination of industry-specific
success determinants and larger strategy concerns highlights the complexities of digital
transformation. The authors of [58] employed a qualitative approach by using the Del-
phi method to analyze critical success factors for digital partnering in the South African
construction sector. Their emphasis on collaboration and expert opinions contrasts with
another study [88], which identified the key success factors for digitalization in public
organizations through interviews with public sector leaders [146–152]. The authors of [90]
highlighted the necessity of adjustments in business and IT strategies, organizational struc-
ture, and processes for successful digital transformations [34]. The work in [79] provided a
theoretical perspective on the differences between DT and digitization, focusing on the role
of digitalization in converting physical products/services into digital formats [153,154].
This contrasts with the findings in [13], which explored the reasons behind DT failures,
emphasizing the importance of clear goals and detailed digital strategies. These differing
viewpoints shed light on the theoretical underpinnings and practical challenges of digital
transformation. The authors of [55] developed a digital business strategy (DBS) framework
based on a structured review of industry reports, highlighting 40 critical success factors
(CSFs) for DBS and digital business model design. Their comprehensive approach contrasts
with that in [99], where the opportunities of DT in business were explored, focusing on
the changes brought by digital technology. This debate between specific CSFs and broader
strategic insights offers a comprehensive view of digital transformation [94].
The authors of [24] identified key drivers, success factors, and challenges in DT in
public healthcare through a literature review and case studies [155–158]. Their findings are
juxtaposed with those in [99], which assessed the status of DT in Greece, comparing success
factors and obstacles across different European countries. This comparison highlights the
contextual nuances and challenges in implementing digital transformation strategies [100].
The authors of [101] explored the drivers, objectives, success factors, and implications of
DT based on a systematic literature review. In contrast, the work in [102] was focused
on challenges in DT for manufacturing networks, emphasizing a holistic approach and
cultural shift. This debate between broad strategies and industry-specific challenges pro-
vides a comprehensive understanding of digital transformation [103]. The authors of [106]
identified key success factors for DT start-ups, focusing on customer orientation and tech-
nological integration. Conversely, the authors of [32] investigated factors influencing DT in
the public sector, highlighting communication, leadership skills, and resistance to change.
Systems 2024, 12, 524 10 of 26

This debate between start-up strategies and public sector challenges offers insights into the
diverse landscapes of digital transformation [66]. The work in [58] analyzed the challenges
in the financial services sector using a PEST-model and Porter’s five forces, highlighting
the responses of incumbents to digital disruption. This contrasts with an econometric
analysis [33] of start-up survival factors, emphasizing innovation, risk management, and
effective HR management. These contrasting viewpoints provide a holistic view of digital
transformation from both established institutions and emerging start-ups [55]. The authors
of [22] identified the crucial success and failure factors in ERP deployment for enterprise
business, offering insights for stakeholders and ERP service providers. In contrast, the
authors of [83] conducted a poll on employees from Jordanian Islamic banks, highlighting
the substantial influence of digital transformation on operational efficiency and risk man-
agement. This comparison offers insights into the diverse impacts of digital transformation
across different sectors and organizations [77].
The findings of the research reported in Table 1 show that critical CSFs include strong
leadership, effective change management, data analytics capabilities, flexibility and agility,
customer-centricity, employee engagement, security and compliance, and partnerships
and co-operation. For example, Leyh et al. [72] emphasized the significance of corporate
organization, technology, and a unified digital strategy, whereas Rohn et al. [114] identified
specific success factors for platform-based business models in the metal and steel industries.
Furthermore, the authors of [133] identified common issues in the financial services sector,
underlining the importance of a systematic approach to updating backend systems in
response to the threat of BigTech market entry. Table 2 contains a thorough list of the
critical success factors (CSFs) required for DTIS, as well as the related authors who have
contributed to understanding these factors. A comprehensive examination of these CSFs
yields important insights into the need for successful digital transformation programs.
Smith and Beretta [129] and Purwanto et al. [108] identify “innovation culture” as a core
CSF. This element emphasizes the importance of corporations creating settings that promote
creativity and adaptation. Embracing innovation allows businesses to remain competitive
and responsive in today’s fast-changing digital market. Vial [144], Şimşek et al. [126],
Rohn et al. [114], and Leyh et al. [72] all highlight the “Learning and Development” CSF.
Continuous investment in workforce development and upskilling is critical for firms to
ensure their staff have the skills needed to effectively move digital initiatives forward.
This element acknowledges that knowledgeable and adaptable staff represents a critical
component of effective digital transformation.

Table 2. CSFs of DTIS.

No Critical Success Factors for Digital Transformation Authors

1 Innovation culture
2 Embracing change [22–27]
3 Collaboration and teamwork
4 Learning and development
5 Customer-centricity
[16,19,44,56,63]
6 Clear communication
7 Data-driven decision-making
8 Digital-minded leadership
9 Stakeholders involvment
[33,55]
10 Stakeholder identification
11 Active engagement
12 Collaboration and partnership
[21,64,73]
13 Inclusion
Systems 2024, 12, 524 11 of 26

Table 2. Cont.

No Critical Success Factors for Digital Transformation Authors

14 Continuous engagement
[21,64,73]
15 Measuring progress and evaluating success
16 Effective change management
17 Clear governance structure [44,86]
18 Executive sponsorship
19 Risk management
20 Compliance with laws and regulations
21 Data governance
[33–35,65]
22 Compliance with industry standards
23 IT governance
24 Performance measurement
25 Funding
26 Technology
[103–107,112–114]
27 Skilled workforce
28 Infrastructure
29 Training and development
30 Data analytics
31 Cloud computing [13,15,19,27]
32 Security
33 Adaptability
34 Agile methodologies
35 Continuous improvement
36 Innovation [76,83]
37 Scalability
38 Resilience
39 Cross-functional teams
40 Flexible resources
[14,18,77,82]
41 Willingness to embrace technological change
42 Employee digital skills and competencies
43 Organizational adaptability
44 Resource allocation for digital initiatives
45 Stakeholder involvement and support [23,63,88]
46 Organizational change management
47 Achievement of objectives
48 User satisfaction and adoption
49 Impact on operational efficiency
[56,66,73]
50 Business performance enhancements
51 Digital integration and connectivity
52 Employee empowerment and collaboration
[22,36,72]
53 Long-term sustainability and adaptability
Systems 2024, 12, 524 12 of 26

“Digital-minded leadership” emphasizes the importance of leaders who are forward-

thinking, embrace digital methods, and set the tone for their organization’s digital trans-
formation. Strong leadership is essential for inspiring and organizing the workforce to
achieve digital goals. The data-driven decision-making element emphasizes the necessity
of using data analytics to make educated judgments [133]. To effectively drive their digital
strategy, organizations must build robust data collection, analysis, and leveraging pro-
cesses. Data-driven insights allow businesses to discover trends, make predictions, and
enhance their operations. Change management is another crucial issue, as stressed by
Gurbaxani and Dunkle [48], where “Effective change management” is critical for dealing
with the human aspects of digital efforts. To achieve successful digital transformation,
organizations must overcome resistance, get stakeholder support, and ensure smooth tran-
sitions. Palaskas [102] emphasized the importance of risk mitigation and compliance in
digital efforts. The terms “risk management” and “compliance with laws and regulations”
emphasize the importance of firms identifying and mitigating risks connected with digital
projects while adhering to legal and regulatory frameworks. This factor acknowledges that
a proactive approach to risk management is critical for protecting digital ventures.
Tischlinger and Van Wordragen [136] underlined the need for technological prepared-
ness and infrastructure for digital transformation. Organizations must emphasize “tech-
nology”, “infrastructure”, and “IT governance” in order to properly support their digital
efforts. Investing in the proper technology and developing a strong infrastructure are the
foundations of successful digital transformation initiatives. According to Palaskas [102],
successful digital efforts rely heavily on user pleasure and uptake. “User Satisfaction and
Adoption” emphasizes the necessity of optimizing user experience and ensuring that new
digital tools and procedures are adopted by employees and stakeholders. This element
underlines the need for enterprises to prioritize user wants and preferences throughout
the transformation process in order to drive acceptance and optimize benefits. Successful
digital transformation requires employee skills and empowerment, as stressed by Manfreda
and Štemberger [79] and other researchers. “Employee digital Skills and Competencies”
emphasizes the significance of upskilling employees to ensure they have the appropriate
digital skills to properly use new technologies. Empowering people to accept digital tools
and procedures is critical to driving digital transformation ahead.

4.9. Empirical Research Insights

The comprehensive assessment of many studies on DTIS has provided useful insights
into the key success factors (CSFs) for successful digital transformation initiatives in diverse
industries. This analysis identified over 53 CSFs that have a substantial impact on the
success of DT activities, as shown in Table 2. The selection of the 7 CSFs from the initial
53 was conducted through a systematic and rigorous process to ensure their relevance
and applicability to DT success. The process began with a thorough literature review,
where each of the 53 CSFs was evaluated for its relevance to DT, based on the frequency
and quality of its appearance in high-quality, peer-reviewed studies. This was followed
by expert consultation involving a panel of digital transformation experts who provided
valuable insights on the most impactful CSFs. Their feedback was instrumental in refining
and prioritizing the factors according to practical experience and theoretical importance.
Additionally, the data quality and consistency of each CSF were scrutinized, with factors less
supported by empirical evidence or found in fewer reputable sources being deprioritized.
This methodical approach ensured that the final selection of CSFs was both theoretically
robust and practically significant. These aspects include strong leadership, effective change
management, data analytics capabilities, flexibility and agility (FA), customer-centricity,
employee engagement, security and compliance, and partnerships and co-operation. Based
on the findings of this research, it is clear that these criteria have continually emerged
as critical components for firms looking to manage the obstacles and opportunities of
digital transformation.
Systems 2024, 12, 524 13 of 26

5. Critical Success Factors for Conceptual Modeling

According to Teixeira et al. [134], conceptual model development is the act of putting a
system or concept into a visual representation that shows how its various components relate
to one another. With disciplines such as system design, engineering, and management, it is
a technique that aids with the understanding and communication of complicated concepts
and systems [44]. The process of developing a thorough model that shows the connections
between the various CSFs and the overall performance of DT is referred to as conceptual
model development in the context of CSFs for DT. The aforementioned model functions
as a framework for comprehending and executing digital transformation endeavors, con-
sidering the distinct obstacles and prospects involved. As indicated in Table 2, the first
step in developing a conceptual model for DT success is identifying the crucial elements.
For DT projects to be driven and ensured to be in line with the organization’s aims and
objectives, strong leadership and a clear vision are essential [67]. This includes directing
the group, bringing everyone together, and making difficult decisions. According to Cor-
reani et al. [24], digital transformation (DT) frequently entails substantial modifications
to an organization’s systems and procedures. Effective change management is crucial to
guarantee that these changes are properly adopted and executed. The technique involves
recognizing and conveying the modifications, educating the staff, and offering continuous
assistance and direction to guarantee the new procedures are embraced and integrated into
the company. Making educated decisions and advancing DT projects require the ability
to gather, process, and utilize data. Data security, data governance, data quality, and data
analytics skills are all included in this. According to Mehadjebia et al. [84], organizations
must possess flexibility and agility in order to promptly adjust to evolving market con-
ditions and client demands. This involves the capacity to change course swiftly and the
risk-free experimentation and testing of novel concepts. In order to meet their needs and
enhance the entire customer experience, DT efforts should be created with the needs of the
consumer in mind. This entails figuring out what the needs of the consumer are, creating
solutions to suit those needs, getting feedback frequently, and modifying the solutions
in response to that input. To guarantee that DT activities are successfully executed and
embraced by the workforce, employee involvement is essential [77]. Employee engagement
is increased through motivating employees to participate, giving them chances to learn
and develop, and creating an innovative work environment. Since DT projects frequently
use technology and sensitive data, it is crucial to make sure they are safe and adhere to all
applicable laws and standards [23]. In order to effectively share resources and information,
collaboration and partnerships are frequently necessary for digital transformation. Follow-
ing their identification, the CSFs are arranged in a conceptual model that shows how they
relate to the overall performance of DT.

6. Aggregating and Modeling CSFs

Grouping, classification, clustering, and modeling are all techniques used in data
science to analyze and make predictions about data [44]. These techniques can be applied
to the CSFs of DT to help organizations understand and optimize their digital initiatives.
An organization may group its DT initiatives by department, project type, or budget.
Classification is the process of categorizing data into predefined groups based on specific
characteristics. For example, an organization may classify its DT initiatives as “high-
priority”, “medium-priority”, or “low-priority” based on their expected impact on the
business. Clustering is a technique used to identify patterns or groups within a dataset. For
instance, an organization may use clustering to identify similarities between different DT
initiatives or to identify the key drivers of success for a particular initiative. Modeling is the
process of creating a mathematical representation of a system or process [33]. In particular,
an organization may use a predictive model to forecast the impact of a DT initiative on
key performance indicators such as revenue or customer satisfaction. Overall, grouping,
classification, clustering, and modeling are powerful techniques that can help organizations
better understand and optimize their DT initiatives. Modeling is the process of creating
Systems 2024, 12, 524 14 of 26

a mathematical representation of a system or process. As an illustration, an organization

may use a predictive model to forecast the impact of a DT initiative on key performance
indicators such as revenue or customer satisfaction [23]. Overall, grouping and modeling
are powerful techniques that can help organizations better understand the CSFs of DT,
identify areas for improvement, and make more informed decisions about their digital
initiatives [90]. While grouping the CSFs mentioned in the CSFs section, it was observed
that only 53 of them were applicable to the study’s scope. Consequently, in this study, the
53 valid CSFs were organized into seven categories, as depicted in Table 3. The groups
were named based on the relevance of the CSFs to the suggested name. This approach aids
in identifying patterns and trends in CSFs, facilitating the development of a comprehensive
framework for DT initiatives. Following a meticulous evaluation and grouping process,
this study identified seven clusters, which are discussed in subsequent sections. This
research builds upon and extends the existing body of literature on DT by offering a
more nuanced and integrated perspective on CSFs and their impact. Previous studies
have explored various dimensions of DT separately, often focusing on specific aspects
such as organizational culture or stakeholder engagement [77]. This research synthesizes
these disparate elements into seven comprehensive theoretical viewpoints: OCCI, SE, RG,
AR, FA, RORDT, and DTIS. By integrating these perspectives, the study provides a more
holistic understanding of the factors influencing DT success, aligning with and expanding
upon earlier work. While the existing literature has highlighted the importance of factors
such as innovation culture and effective governance, it often lacks a detailed examination
of the practical challenges and interplay between these factors [66,88]. This research
addresses these gaps by exploring the specific difficulties organizations face in maintaining
an innovation culture, engaging stakeholders, and balancing governance with flexibility.
This deeper analysis adds to the existing knowledge by providing a clearer picture of the
complexities involved in DT. Building on prior research that emphasizes the theoretical
aspects of DT, this study provides practical implications and actionable recommendations
for organizations [55]. For example, it highlights the importance of continuous learning
and effective stakeholder engagement as critical for DT success, echoing but expanding
upon earlier findings. The practical recommendations offered are informed by empirical
evidence and theoretical analysis, bridging the gap between theory and practice. The
study’s development of a comprehensive conceptual model that includes OCCI, SE, RG,
AR, and FA as mediators, with DT impact on DTIS as the dependent variable, extends
previous theoretical frameworks [22]. This model builds on earlier work by integrating
the multiple dimensions of DT into a cohesive structure, providing a more detailed and
practical framework for understanding and implementing digital transformation.

Table 3. Classified CSFs affecting DT success.

Dimension Description CSFs Affecting DT Success

1. Innovation culture: An organizational culture that encourages
OCCI in DT initiatives experimentation and innovation can help drive DT success.
cultivate a culture of 2. Embracing change: A culture that is open to change and
continuous learning, adaptable to new technologies can help the organization to
innovation, and successfully implement DT.
Organizational culture of
adaptability. This culture 3. Collaboration and teamwork: A culture that promotes
continuous improvement
encourages employees to collaboration and teamwork can help foster a sense of shared
(OCCI)
actively contribute to the ownership and buy-in for DT initiatives.
transformation process, 4. Learning and development: A culture that encourages
fostering enhanced agility continuous learning and development can help ensure that the
and long-term success organization has the necessary skills to successfully
implement DT.
Systems 2024, 12, 524 15 of 26

Table 3. Cont.

Dimension Description CSFs Affecting DT Success

5. Customer-centricity: A culture that prioritizes the needs of
OCCI in DT initiatives customers can help ensure that DT initiatives are aligned with
cultivate a culture of customer needs and goals.
continuous learning, 6. Clear communication: A culture of clear and transparent
innovation, and communication can help ensure that all stakeholders are
Organizational culture of
adaptability. This culture informed and engaged in the DT process.
continuous improvement
encourages employees to 7. Data-driven decision-making: A culture that values data and
(OCCI)
actively contribute to the encourages data-driven decision-making can help ensure that DT
transformation process, initiatives are informed by data and analysis.
fostering enhanced agility 8. Digital-minded leadership: A culture of digital-minded
and long-term success leadership can help ensure that the organization has the
necessary vision and leadership to drive DT forward.
1. Stakeholders can help keep everyone informed and engaged in
the DT process.
2. Stakeholder identification: Identifying all relevant stakeholders
and understanding their needs and concerns is essential to align
DT initiatives with their goals.
3. Active engagement: Actively engaging stakeholders through
workshops, focus groups, and consultations helps gather
SE in DT initiatives entail valuable feedback for informed DT decision-making.
active involvement and 4. Collaboration and partnership: Building collaboration and
collaboration with relevant partnerships with stakeholders fosters a sense of shared
stakeholders. This ensures ownership and commitment to DT initiatives.
Stakeholder engagement (SE) their feedback is 5. Inclusion: Involving stakeholders from diverse backgrounds and
considered, fostering perspectives ensures that DT initiatives are inclusive and
support, shared considerate of different needs.
ownership, and driving 6. Continuous engagement: Consistently involving stakeholders
sustainable transformation throughout the DT process enables real-time adjustments and
addresses their evolving needs and concerns.
7. Measuring progress and evaluating success: Regularly measuring
and evaluating the progress of DT initiatives allows organizations
to make data-driven decisions and adjustments as needed.
8. Effective change management: Managing and communicating
changes effectively to all stakeholders eases the transition to a
digital environment and minimizes resistance.
1. Clear governance structure: Having a clear governance structure
ensures that DT initiatives align with organizational goals
RG in DT involve strong, and priorities.
effective leadership 2. Executive sponsorship: Strong executive sponsorship provides
guiding the organization’s the necessary support and resources for successful
digital journey with clear DT implementation.
goals and strategies. It 3. Risk management: Identifying and mitigating potential risks
fosters a shared vision, associated with DT is crucial for ensuring its success.
commitment to change, 4. Compliance with laws and regulations: Ensuring DT initiatives
Robust leadership comply with relevant laws and regulations ensures their
and effective challenge
governance (RG) sustainability and long-term success.
and risk management. RG
promotes alignment, 5. Data governance: Robust data governance ensures data is used
accountability, and and shared in a compliant, secure, and responsible manner.
collaboration for a smooth 6. Compliance with industry standards: Adhering to industry
DT implementation, standards ensures DT initiatives are aligned with best practices.
delivering value and 7. IT governance: IT governance ensures DT initiatives align with IT
sustainable transformation strategy and are efficiently and effectively delivered.
8. Performance measurement: Measuring performance and
evaluating the success of DT initiatives allows for informed
decision-making and adjustments as needed.
Systems 2024, 12, 524 16 of 26

Table 3. Cont.

Dimension Description CSFs Affecting DT Success

1. Funding: Adequate funding is necessary to ensure that DT
ARL for DT initiatives initiatives have the necessary resources to be successful.
involve allocating 2. Technology: Access to the latest technology and tools is necessary
sufficient financial and for successful DT.
human resources and 3. Skilled workforce: A skilled workforce with the necessary
focusing on continuous knowledge, skills, and experience to implement DT is essential.
learning and development. 4. Infrastructure: Having the necessary infrastructure in place, such
This includes investing in as a robust IT network, is crucial for successful DT.
technology, training in 5. Training and development: Providing training and development
Adequate resources and opportunities to employees can help ensure that they have the
digital skills, and
learning (ARL) necessary skills to successfully implement DT.
providing ongoing
learning opportunities to 6. Data and analytics: Having access to high-quality data and
ensure the organization analytics tools can help organizations make informed decisions
has the capabilities and and optimize their DT initiatives.
knowledge to drive 7. Cloud computing: Adopting cloud computing can help ensure
innovation and achieve that DT initiatives have the necessary scalability and flexibility to
sustainable growth in the be successful.
digital era 8. Security: Having robust security measures in place can help
ensure that DT initiatives are protected from cyber threats and
data breaches.
1. Adaptability: Being able to adapt to change and be flexible in the
face of challenges is critical for successfully implementing DT.
2. Agile methodologies: Adopting agile methodologies can help
organizations respond quickly to changes and deliver DT
FA in DT initiatives reflect initiatives in a more iterative and flexible way.
an organization’s capacity 3. Continuous improvement: Continuously improving processes
to swiftly adapt to and technologies can help ensure that DT initiatives stay
evolving challenges and up-to-date and competitive.
opportunities. It fosters a 4. Innovation: Encouraging innovation and thinking outside the
culture of experimentation, box can help organizations find new and creative solutions to
change, and innovation, DT challenges.
Flexibility and agility (FA)
enabling quick 5. Scalability: Having the ability to scale DT initiatives up or down
adjustments to strategies, as needed can help ensure that they are flexible and responsive to
processes, and changing needs.
technologies, ensuring 6. Resilience: Having a resilient organization that can quickly
competitiveness and recover from disruptions or failures can help ensure that DT
readiness in a rapidly initiatives stay on track.
changing digital landscape 7. Cross-functional teams: Building cross-functional teams can help
organizations respond to changes more quickly and effectively.
8. Flexible resources: Having flexible resources such as cloud-based
services can help organizations respond to changes quickly
and efficiently.
ORDT assesses an 1. Willingness to embrace technological change.
organization’s 2. Employee digital skills and competencies.
Role of organizational
preparedness for DT, 3. Organizational adaptability.
readiness for digital
ensuring a smooth 4. Resource allocation for digital initiatives.
transformation (RORDT)
transition and enabling 5. Stakeholder involvement and support.
innovation 6. Organizational communication and change management.

DT initiatives integrate 1. Achievement of objectives.

advanced technologies for 2. User satisfaction and adoption.
Digital transformation growth, efficiency, and 3. Impact on operational efficiency.
initiatives success improved customer 4. Business performance enhancements.
experiences through AI, 5. Digital integration and connectivity.
analytics, and automation 6. Employee empowerment and collaboration.
7. Long-term sustainability and adaptability.
Systems 2024, 12, 524 17 of 26

6.1. Organizational Culture of Continuous Improvement (OCCI)

A key factor contributing to DT performance is organizational culture, which is defined
as the common beliefs and norms influencing both employee conduct and corporate
operations [11]. Eight CSFs are highlighted in Table 3 to emphasize the importance of
company culture to DT success. It is believed that an innovative and experimental culture
is essential to enabling staff members to participate in the DT process [22]. In order to
create a common vision for DT and implement broad change throughout the company,
co-operation and teamwork—which are fostered by a positive culture—are essential. On
the other hand, cultures that are distrustful or isolated or that are reluctant to change can
impede the adoption of digital transformation (DT) [33]. Leaders play a crucial role in
forming the culture of their organizations. They should set a clear example, encourage
learning and innovation, and convey the DT vision. In summary, leaders have a crucial
role in cultivating a positive corporate culture that fosters creativity, co-operation, and
learning, as it has been shown to be a major element determining the success of digital
transformation [56].

6.2. Stakeholder Engagement (SE)

Among the five CSFs included in the DT effort, SE is a key factor in determining its
effectiveness. Stakeholders include employees, consumers, suppliers, shareholders, and
other impacted parties. Stakeholders are individuals or groups with a vested interest in
the initiative’s result [55]. In order to minimize resistance and promote buy-in, effective
SE is essential since it guarantees that the initiative and stakeholders’ requirements are
aligned [44]. The results are more likely to be understood, supported, and successful when
stakeholders are actively involved. A clear vision for DT that emphasizes its connection to
stakeholder involvement is crucial for leaders who play a critical role in promoting effective
SE. To ensure alignment with overall corporate objectives and achieve desired outcomes, it
is imperative to create an environment that values stakeholder input, as noted by Cichosz
et al. [22].

6.3. Robust Leadership Governance (RG)

Effective governance ensures that initiatives are aligned with the organization’s goals
and objectives and that they are carried out in a controlled and consistent manner. It
also promotes ethical and responsible execution and ensures compliance with legal and
regulatory requirements [66]. They provide clear policies, processes, and guidelines for
DT efforts to ensure successful communication with stakeholders. Leaders also ensure
that the required processes and controls are in place to monitor and manage DT activities,
which are constantly reviewed and updated as needed. To summarize, RG is a vital
component of successful DT programs [22]. It guarantees that organizational goals are
met, that implementation is regulated, that compliance is followed, and that execution is
carried out responsibly. Leaders play an important role in implementing RG to support DT
success [64].

6.4. Adequate Resources and Learning (ARL)

Financial, technological, and human resources are essential for launching and main-
taining digital transformation (DT) efforts. ARL is critical to DT performance; without
them, DT activities may be delayed or fail entirely [12]. Leaders must ensure that they
have access to the necessary technology, tools, and resources to effectively implement
DT projects. Additionally, securing a sufficient budget to fund these initiatives and hav-
ing the appropriate personnel to manage and execute them is crucial [22]. Learning is
a vital component of this dimension, as it fosters a culture of continuous improvement
and adaptability within the organization. By prioritizing learning and development, or-
ganizations empower their teams to acquire new skills and knowledge that are essential
for navigating the complexities of digital transformation. This proactive approach not
only enhances employee competencies but also ensures that the organization can swiftly
Systems 2024, 12, 524 18 of 26

adapt to evolving market demands and technological advancements. Effective resource

management, therefore, extends beyond allocation to include robust learning opportunities
that prepare employees to tackle challenges and innovate solutions. Additionally, leaders
should implement strategies for ongoing training and professional development, ensuring
that employees are well equipped to drive successful DT initiatives. Backup strategies
must also be developed to address any unanticipated incidents that may disrupt DT efforts,
reinforcing the organization’s agility and resilience in the face of change.

6.5. Flexibility and Agility (FA)

Flexibility is the ability to adapt to change and respond to new possibilities or prob-
lems, whereas agility is the ability to react rapidly and efficiently to changes in the envi-
ronment [33]. In the context of DT, FA are crucial because they enable firms to adapt to
new technologies and trends while also responding rapidly to market changes. When a
company is adaptable and nimble, it is better prepared to seize new opportunities and
mitigate the impact of potential risks. Leaders play an important role in promoting FA
inside their firms [33]. They must build an environment that welcomes change and stim-
ulates experimentation [25]. They should also aggressively seek out new technologies
and trends and make sure their teams have the resources they need to test and deploy
them. Furthermore, they should create a culture that encourages the team to learn and
adapt fast [42]. Flexible and agile organizations are better positioned to capitalize on new
possibilities while minimizing the effect of possible dangers.

6.6. Role of Organizational Readiness for Digital Transformation (ORDT)

Alkhamery [3] strongly advocated for the significant impact of organizational readi-
ness on the success of DT initiatives. Through extensive study, he highlighted the crucial
role that a well-prepared and adaptive organizational structure plays in the effective imple-
mentation of digital transformation strategies. The research underscores the importance of
developing specific capabilities within organizations to navigate the challenges posed by
digital disruption and to ensure a smoother and more successful transition to the digital
era. It aligns with the study’s focus on “DT and its Critical Success Factors”. The inclusion
of OCCI, SE, RG, AR, and FA as independent variables (IVs) is crucial in understanding
their impact on ORDT. OCCI fosters a change-friendly culture, SE involves stakeholders,
RG supports DT vision, AR provides learning opportunities, and FA encourages adapt-
ability. The proposed model, depicting relationships between IVs, ORDT as the mediator,
and the dependent variable (DV) as DTIS, allows for comprehensive analysis. Overall,
selecting ORDT as the mediator enhances understanding of how CSFs impact the success
of DT initiatives in public organizations. The conceptual framework is shown in Figure 3.
Organizational preparedness also plays a major role in the digital transformation of govern-
ments. For instance, the preparedness of EU Member States for a socially equitable digital
transformation is measured across four important dimensions: labor market, digital skills,
social protection, and digital infrastructure [136].
Table 4 summarizes the key dimensions identified in the framework for successful
digital transformation initiatives. Each dimension represents a critical area that organi-
zations must address to foster an effective transformation process. By categorizing these
dimensions, we can clearly see how they function as independent variables influencing the
overall success of DTIS. Additionally, ORDT acts as a mediator in this framework, high-
lighting its importance in linking the independent dimensions to the outcomes of digital
transformation. This structured presentation aims to clarify the relationships between the
various components and enhance the understanding of how organizations can effectively
navigate their digital transformation journeys.
 ORDT as the mediator, and the dependent variable (DV) as DTIS, allows for comprehen-
sive analysis. Overall, selecting ORDT as the mediator enhances understanding of how
CSFs impact the success of DT initiatives in public organizations. The conceptual frame-
work is shown in Figure 3. Organizational preparedness also plays a major role in the
Systems 2024, 12, 524 digital transformation of governments. For instance, the preparedness of EU Member 19 of 26
States for a socially equitable digital transformation is measured across four important
dimensions: labor market, digital skills, social protection, and digital infrastructure [136].

Organizational Culture of
Flexibility and Agility (FA)
continuous improvement
(OCCI)

Stakeholder Engagement Role of Organi-

(SE) zational Readi-
ness for Digital
Transformation Adequate Resources and
(ORDT) learning (AR)
Robust leadership
Governance (RG)

DT initiatives success (DTIS)

Figure 3. Conceptual model of the effect of CSFs on DT initiative success.

Table 4. Dimensions of digital transformation and their roles in the success of the initiative transfor-
Table 4 summarizes the key dimensions identified in the framework for successful
mation framework.
digital transformation initiatives. Each dimension represents a critical area that organiza-
tions must address to foster an effective transformation process. By categorizing these di-
Dimension Description Role in the Model
mensions, we can clearly see how they function as independent variables influencing the
Organizational culture of continuous Cultivating an environment that promotes ongoing
overall success of DTIS. Additionally, ORDT acts as a mediator inIndependent
this framework, high-
variable
improvement (OCCI) improvement and innovation.
lighting its importance in linking the independent dimensions to the outcomes of digital
Actively involving stakeholders in the transformation
Stakeholder engagement (SE) transformation. This structured presentation aims to clarify the relationships
Independentbetween
variable the
process to ensure buy-in and support.
various components and enhance the understanding of how organizations can effectively
Establishing
navigate their digital strong leadership journeys.
transformation and governance structures to
Robust leadership governance (RG) Independent variable
guide transformation efforts.
Table 4. Dimensions
The ability of
of digital transformation
an organization and
to adapt their roles
quickly in theinsuccess of the initiative trans-
to changes
Flexibility and agility (FA) Independent variable
formation framework.
the environment or market.
Ensuring that sufficient resources (financial, human,
Adequate resources and learning (AR) technological) are available while promoting continuous Independent variable
learning.
Role of organizational readiness for The extent to which an organization is prepared for digital
Mediator
digital transformation (ORDT) transformation initiatives.
Digital transformation initiatives The outcomes of digital transformation efforts, measuring
Dependent variable
success (DTIS) their success.

7. Research Implications and Contributions

This study illuminates seven distinct theoretical viewpoints on DT and the CSFs that
influence its success, offering valuable insights into the multifaceted nature of DT projects.
The OCCI viewpoint emphasizes the importance of fostering an organizational culture
centered on continuous learning, innovation, and adaptability, which requires ongoing
commitment from all levels of the organization. Future research should explore strategies
for sustaining an innovation culture and overcoming the challenges of long-term imple-
mentation. The SE perspective highlights the necessity of active stakeholder participation
throughout the DT process. Effective stakeholder engagement is crucial, but identifying
and fully involving all relevant stakeholders remains complex, warranting further investi-
Systems 2024, 12, 524 20 of 26

gation into enhancing engagement, addressing diverse demands, and maintaining effective
communication. The RG dimension underscores the critical role of leadership in guiding
DT efforts, where balancing governance with flexibility is essential. Overemphasis on gov-
ernance can lead to rigidity, impeding adaptability. Research should focus on achieving this
balance and integrating leadership practices into DT strategies. AR highlights the need for
efficient resource allocation and continuous learning. Managing resources while promoting
development presents a key challenge, and future studies should explore how to optimize
resource allocation and learning opportunities. FA reflects an organization’s ability to
respond swiftly to evolving challenges, though maintaining agility within larger organi-
zations can be difficult. Research should address how to sustain agility while balancing
stability in dynamic environments. Lastly, the ORDT perspective evaluates organizational
readiness for DT initiatives, stressing the importance of thorough data collection and anal-
ysis to measure the impact of DT on operational efficiency. Overall, these perspectives
offer a comprehensive understanding of the critical success factors in DT, though the com-
plexities involved must not be underestimated. Achieving DT success requires a balanced
and context-specific approach aligned with each organization’s unique characteristics. A
significant contribution of this research is the development of a detailed conceptual model
incorporating OCCI, SE, RG, AR, FA, and ORDT as mediators, with DTIS as the dependent
variable. This model elucidates the interplay between these factors and their collective
impact on DT outcomes, offering a structured approach to analyzing and implementing DT
strategies. The study also identifies gaps in the current literature, proposing future research
directions such as examining the effects of DT-induced decentralization on productivity and
performance and employing SEM and AMOS to explore the relationships between CSFs
and DT outcomes. These insights and recommendations enrich the theoretical framework
of DT and provide practical guidance for organizations aiming to enhance their DT efforts.

8. Conclusions and Future Investigation

This study conducted a thorough literature analysis to evaluate the impact of success
variables on the advancement of digital technology projects within corporations, as well
as how this influences corporate strategy and the theoretical underpinnings of these or-
ganizations. Initially, we discovered and examined a collection of published studies on
DT inside organizations, resulting in three main dimensions. Subsequently, we created a
comprehensive conceptual model that comprises eight important constructs: OCCI, SE, RG,
AR, FA as IVs, and the role of ORDT as a mediator, with DTIS as the DV. This paradigm
revealed various theoretical implications that can help guide future studies. We argue
that enterprises engaging in DT efforts frequently face competing dynamics during their
implementation. By addressing the CSFs that are important to our created conceptual
model, policymakers and decision-makers should be able to make educated decisions
before implementing DT in their companies. Notably, this movement undermines the
traditional demarcations of business borders, as organizations progressively extend to
include external actors. This growth is primarily driven by the profound relationships and
exchanges that digital communication channels enable with the outside world. As a result,
this change represents a divergence from the traditional concept of independent enterprises
in favor of the emergence of interconnected networks of firms. These networks collaborate,
share resources, and function in a dispersed context to generate value collectively. In
essence, our findings highlight the urgent need for a comprehensive rethinking of the
traditional notion of enterprises in the context of DT. Examining the influence of DT on
dual inclinations and CSFs is crucial, indicating a divergence from typical conflict-oriented
viewpoints. Exploring interactions between coexisting forces yields prospective results
such as substitution, conflict, or moderation dynamics. Future studies should look at the
impact of DT-induced decentralization on company productivity and performance. This
includes investigating how formal and informal acts fit within the DT landscape. Given
that DT contradicts conventional theories, new methodologies are required to examine how
companies adapt to the digital economy. In digital ecosystems, where power dynamics are
Systems 2024, 12, 524 21 of 26

based on technological control and relational centrality, in-depth investigations become

critical. Theories should be developed by assessing organizational power while taking
cognitive and behavioral implications into account. A thorough investigation of the impact
of technology affordances on power distribution within ecosystems is necessary. To fit with
the principles of achieving DT success in today’s changing digital ecosystem, theoretical
frameworks should change to a greater emphasis on interorganizational linkages. Future
research should focus on modeling the CSFs found in this study and implementing them
in case studies within organizations. The use of structural equation modeling (SEM) and
analysis of moment structures (AMOS) is recommended to thoroughly explore the links
and impacts within the context of the organizations under consideration.

Author Contributions: The research was conceptualized, designed, and performed by A.A.M., S.P.
and Z.C.A. The original draft of the paper was written by A.A.M. Finally, the paper was reviewed,
edited, and improved by S.P. and Z.C.A. All authors have read and agreed to the published version
of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.

References
1. Åkesson, M.; Sorensen, C.; Eriksson, C.I. Ambidexterity under digitalization: A tale of two decades of new media at a Swedish
newspaper. Scand. J. Manag. 2018, 34, 276–288. [CrossRef]
2. Alhassan, I.; Sammon, D.; Daly, M. Critical success factors for data governance: A telecommunications case study. J. Decis. Syst.
2019, 28, 41–61. [CrossRef]
3. Alkhamery, N.; Anwar Zainol, F.; Al-Nashmi, M. Conceptualizing the Role of Organizational Capabilities in Enhancing Firms
Readiness for Digital Business Transformation. Int. J. Manag. 2020, 11, 785–797. [CrossRef]
4. Alos-Simo, L.; Verdu-Jover, A.J.; Gomez-Gras, J.M. How transformational leadership facilitates e-business adoption. Ind. Manag.
Data Syst. 2017, 117, 382–397. [CrossRef]
5. Andriole, S.J. Five myths about digital transformation. MIT Sloan Manag. Rev. 2017, 58, 20–22.
6. Li, Z.; Lai, Q.; He, J. Does digital technology enhance the global value chain position? Borsa Istanb. Rev. 2024, 24, 856–868.
[CrossRef]
7. Baiyere, A.; Salmela, H.; Tapanainen, T. Digital transformation and the new logics of business process management. Eur. J. Inf.
Syst. 2020, 29, 238–259. [CrossRef]
8. Banalieva, E.R.; Dhanaraj, C. Internalization theory for the digital economy. J. Int. Bus. Stud. 2019, 50, 1372–1387. [CrossRef]
9. Barann, B.; Hermann, A.; Cordes, A.K.; Chasin, F.; Becker, J. Supporting digital transformation in small and medium-sized
enterprises: A procedure model involving publicly funded support units. In Proceedings of the 52nd Hawaii International
Conference on System Sciences, Maui, HI, USA, 8–11 January 2019.
10. Hämäläinen, M. A framework for a smart city design: Digital transformation in the Helsinki smart city. In Entrepreneurship and
the Community: A Multidisciplinary Perspective on Creativity, Social Challenges, and Business; Springer: Berlin/Heidelberg, Germany,
2020; pp. 63–86.
11. Benavides, L.C.; Arias, J.T.; Burgos, D. Mistakes that limit the success of digital transformation in higher education institutions.
In EDULEARN21 Proceedings, Proceedings of the 13th International Conference on Education and New Learning Technologies, Online
Conference, 5–6 July 2021; IATED: Valencia, Spain; pp. 609–617.
12. Anthony Jnr, B. Managing digital transformation of smart cities through enterprise architecture–A review and research agenda.
Enterp. Inf. Syst. 2021, 15, 299–331. [CrossRef]
13. Bonanomi, M.M.; Hall, D.M.; Staub-French, S.; Tucker, A.; Talamo, C.M.L. The impact of digital transformation on formal and
informal organizational structures of large architecture and engineering firms. Eng. Constr. Archit. Manag. 2019, 27, 872–892.
[CrossRef]
14. Saldanha, T. Why Digital Transformations Fail: The Surprising Disciplines of How to Take off and Stay Ahead; Berrett-Koehler Publishers:
Oakland, CA, USA, 2019.
15. Brock, J.K.U.; Von Wangenheim, F. Demystifying AI: What digital transformation leaders can teach you about realistic artificial
intelligence. Calif. Manag. Rev. 2019, 61, 110–134. [CrossRef]
16. Buhl, A.; Schmidt-Keilich, M.; Muster, V.; Blazejewski, S.; Schrader, U.; Harrach, C.; Süßbauer, E. Design thinking for sustainability:
Why and how design thinking can foster sustainability-oriented innovation development. J. Clean. Prod. 2019, 231, 1248–1257.
[CrossRef]
Systems 2024, 12, 524 22 of 26

17. Bürger, O.; Häckel, B.; Karnebogen, P.; Töppel, J. Estimating the impact of IT security incidents in digitized production
environments. Decis. Support Syst. 2019, 127, 113–144. [CrossRef]
18. Canhoto, A.I.; Clear, F. Artificial intelligence and machine learning as business tools: A framework for diagnosing value
destruction potential. Bus. Horiz. 2020, 63, 183–193. [CrossRef]
19. Caputo, A.; Pizzi, S.; Pellegrini, M.M.; Dabic, M. Digitalization and business models: Where are we going? A science map of the
field. J. Bus. Res. 2021, 123, 489–501. [CrossRef]
20. Cennamo, C. Competing in digital markets: A platform-based perspective. Acad. Manag. Perspect. 2021, 35, 265–291. [CrossRef]
21. Bloomfield, B.P.; Coombs, R. Centralization and decentralization debate revisited. J. Manag. Stud. 1992, 29, 459. [CrossRef]
22. Cichosz, M.; Wallenburg, C.M.; Knemeyer, A.M. Digital transformation at logistics service providers: Barriers, success factors and
leading practices. Int. J. Logist. Manag. 2020, 31, 209–238. [CrossRef]
23. Marzenna, C. Digitalization and competitiveness in the logistics service industry. E-mentor 2018, 77, 73–82.
24. Correani, A.; De Massis, A.; Frattini, F.; Petruzzelli, A.M.; Natalicchio, A. Implementing a digital strategy: Learning from the
experience of three digital transformation projects. Calif. Manag. Rev. 2020, 62, 37–56. [CrossRef]
25. Crittenden, A.B.; Crittenden, V.L.; Crittenden, W.F. The digitalization triumvirate: How incumbents survive. Bus. Horiz. 2019, 62,
259–266. [CrossRef]
26. Crupi, A.; Del Sarto, N.; Di Minin, A.; Gregori, G.L.; Lepore, D.; Marinelli, L.; Spigarelli, F. The digital transformation of SMEs–a
new knowledge broker called the digital innovation hub. J. Knowl. Manag. 2020, 24, 1263–1288. [CrossRef]
27. Das, A.; Dey, S. Global manufacturing value networks: Assessing the critical roles of platform ecosystems and industry 4.0.
J. Manuf. Technol. Manag. 2021, 32, 1290–1311. [CrossRef]
28. Dean, H.; Larsen, G.; Ford, J.; Akram, M. Female entrepreneurship and the metanarrative of economic growth: A critical review
of underlying assumptions. Int. J. Manag. Rev. 2019, 21, 24–49. [CrossRef]
29. Qiao, W.; Ju, Y.; Dong, P.; Tiong, R.L.K. How to realize value creation of digital transformation? A system dynamics model. Expert
Syst. Appl. 2024, 244, 122667. [CrossRef]
30. Di Vaio, A.; Palladino, R.; Pezzi, A.; Kalisz, D.E. The role of digital innovation in knowledge management systems: A systematic
literature review. J. Bus. Res. 2021, 123, 220–231. [CrossRef]
31. Srai, J.S.; Graham, G.; Hennelly, P.; Phillips, W.; Kapletia, D.; Lorentz, H. Distributed manufacturing: A new form of localised
production? Int. J. Oper. Prod. Manag. 2020, 40, 697–727. [CrossRef]
32. Do Vale, G.; Collin-Lachaud, I.; Lecocq, X. Micro-level practices of bricolage during business model innovation process: The case
of digital transformation towards omni-channel retailing. Scand. J. Manag. 2021, 37, 101154. [CrossRef]
33. Dremel, C.; Herterich, M.M.; Wulf, J.; Waizmann, J.C.; Brenner, W. How AUDI AG established big data analytics in its digital
transformation. MIS Q. Exec. 2017, 16, 81–100.
34. Eden, R.; Jones, A.B.; Casey, V.; Draheim, M. Digital transformation requires workforce transformation. MIS Q. Exec. 2018, 18,
1–17. [CrossRef]
35. El Sawy, O.A.; Malhotra, A.; Gosain, S.; Young, K.M. IT-Intensive value innovation in the electronic economy: Insights from
marshall industries. MIS Q. 1999, 23, 305–335. [CrossRef]
36. Eller, R.; Alford, P.; Kallmunzer, A.; Peters, M. Antecedents, consequences, and challenges of small and medium-sized enterprise
digitalization. J. Bus. Res. 2020, 112, 119–127. [CrossRef]
37. Magistretti, S.; Sanasi, S.; Dell’Era, C.; Ghezzi, A. Entrepreneurship as design: A design process for the emergence and
development of entrepreneurial opportunities. Creat. Innov. Manag. 2023, 32, 5–21. [CrossRef]
38. Franke, H.; Foerstl, K. Fostering integrated research on organizational politics and conflict in teams: A cross-phenomenal review.
Eur. Manag. J. 2018, 36, 593–607. [CrossRef]
39. Gebauer, H.; Fleisch, E.; Lamprecht, C.; Wortmann, F. Growth paths for overcoming the digitalization paradox. Bus. Horiz. 2020,
63, 313–323. [CrossRef]
40. Gertzen, W.M.; Van Der Lingen, E.; Steyn, H. Goals and benefits of digital transformation projects: Insights into project selection
criteria. S. Afr. J. Econ. Manag. Sci. 2022, 25, 4158. [CrossRef]
41. Ghobakhloo, M. Determinants of information and digital technology implementation for smart manufacturing. Int. J. Prod. Res.
2020, 58, 2384–2405. [CrossRef]
42. Gong, Y.; Yang, J.; Shi, X. Towards a comprehensive understanding of digital transformation in government: Analysis of flexibility
and enterprise architecture. Gov. Inf. Q. 2020, 37, 101487. [CrossRef]
43. Gong, Y.; Yang, X. Understanding strategies for digital government transformation: A strategic action fields perspective. Int. J. Inf.
Manag. 2024, 76, 102766. [CrossRef]
44. Govindarajan, V.; Immelt, J.R. The manufacturers only way can survive. MIT Sloan Manag. Rev. 2019, 60, 24–33.
45. Grabher, G.; Van Tuijl, E. Uber-production: From global networks to digital platforms. Environ. Plan. A 2020, 52, 1005–1016.
[CrossRef]
46. Gregory, R.W.; Kaganer, E.; Henfridsson, O.; Ruch, T.J. IT consumerization and the transformation of IT governance. MIS Q. 2018,
42, 1225–1253. [CrossRef]
47. Guenzi, P.; Nijssen, E.J. The impact of digital transformation on salespeople: An empirical investigation using the JD-R model.
J. Pers. Sell. Sales Manag. 2021, 41, 130–149. [CrossRef]
Systems 2024, 12, 524 23 of 26

48. Guinan, P.J.; Parise, S.; Langowitz, N. Creating an innovative digital project team: Levers to enable digital transformation. Bus.
Horiz. 2019, 62, 717–727. [CrossRef]
49. Gurbaxani, V.; Dunkle, D. Gearing up for successful digital transformation. MIS Q. Exec. 2019, 18, 6. [CrossRef]
50. Gurcan, F.; Ayaz, A.; Menekse Dalveren, G.G.; Derawi, M. Business Intelligence Strategies, Best Practices, and Latest Trends:
Analysis of Scientometric Data from 2003 to 2023 Using Machine Learning. Sustainability 2023, 15, 9854. [CrossRef]
51. Gurcan, F.; Boztas, G.D.; Dalveren, G.G.M.; Derawi, M. Digital Transformation Strategies, Practices, and Trends: A Large-Scale
Retrospective Study Based on Machine Learning. Sustainability 2023, 15, 7496. [CrossRef]
52. Hai, T.N.; Van, Q.N.; Thi Tuyet, M.N. Digital transformation: Opportunities and challenges for leaders in the emerging countries
in response to COVID-19 pandemic. Emerg. Sci. J. 2021, 5, 21–36. [CrossRef]
53. Hanelt, A.; Bohnsack, R.; Marz, D.; Marante, C.A. A systematic review of the literature on digital transformation: Insights and
implications for strategy and organizational change. J. Manag. Stud. 2021, 58, 39. [CrossRef]
54. Alonso, S.L.N. Can Central Bank Digital currencies be green and sustainable? Green Financ. 2023, 5, 603–623. [CrossRef]
55. Henfridsson, O.; Mathiassen, L.; Svahn, F. Managing technological change in the digital age: The role of architectural frames.
J. Inf. Technol. 2014, 29, 27–43. [CrossRef]
56. Heracleous, L.; Gledhill, D. Why Digital Transformation May Fail—And What Can Be Done About It. J. Appl. Behav. Sci. 2023, 60,
215–219. [CrossRef]
57. Holotiuk, F.; Beimborn, D. Critical success factors of digital business strategy. In Proceedings of the International Conference on
Wirtschaftsinformatik, St. Gallen, Switzerland, 12–15 February 2017.
58. Horváth, D.; Szabó, R.Z. Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies
have equal opportunities? Technol. Forecast. Soc. Chang. 2019, 146, 119–132. [CrossRef]
59. Jonathan, G.M. Digital transformation in the public sector: Identifying critical success factors. In Proceedings of the Information
Systems: 16th European, Mediterranean, and Middle Eastern Conference, EMCIS 2019, Dubai, United Arab Emirates, 9–10 December 2019;
Springer International Publishing: Berlin/Heidelberg, Germany, 2020; Proceedings 16; pp. 223–235.
60. Jones, M.D.; Hutcheson, S.; Camba, J.D. Past, present, and future barriers to digital transformation in manufacturing: A review.
J. Manuf. Syst. 2021, 60, 936–948. [CrossRef]
61. Kamalaldin, A.; Linde, L.; Sjödin, D.; Parida, V. Transforming provider- customer relationships in digital servitization: A relational
view on digitalization. Ind. Mark. Manag. 2020, 89, 306–325. [CrossRef]
62. Li, C.; Long, G.; Li, S. Research on measurement and disequilibrium of manufacturing digital transformation: Based on the text
mining data of A-share listed companies. Data Sci. Financ. Econ 2023, 3, 30–54. [CrossRef]
63. Kazim, F.A. Digital transformation and leadership style: A multiple case study. ISM J. Int. Bus. 2019, 3, 24–33.
64. Kohli, R.; Melville, N.P. Digital innovation: A review and synthesis. Inf. Syst. J. 2019, 29, 200–223. [CrossRef]
65. Kohtamäki, M.; Parida, V.; Patel, P.C.; Gebauer, H. The relationship between digitalization and servitization: The role of
servitization in capturing the financial potential of digitalization. Technol. Forecast. Soc. Chang. 2020, 151, 119804. [CrossRef]
66. Kolloch, M.; Dellermann, D. Digital innovation in the energy industry: The impact of controversies on the evolution of innovation
ecosystems. Technol. Forecast. Soc. Chang. 2018, 136, 254–264. [CrossRef]
67. Kraus, S.; Schiavone, F.; Pluzhnikova, A.; Invernizzi, A.C. Digital transformation in healthcare: Analyzing the current state-of-
research. J. Bus. Res. 2021, 123, 557–567. [CrossRef]
68. Kretschmer, T.; Khashabi, P. Digital transformation and organization design: An integrated approach. Calif. Manag. Rev. 2020, 62,
86–104. [CrossRef]
69. Kretschmer, T.; Leiponen, A.; Schilling, M.; Vasudeva, G. Platform ecosystems as meta-organizations: Implications for platform
strategies. Strateg. Manag. J. 2022, 43, 405–424. [CrossRef]
70. Kumar, M.; Tsolakis, N.; Agarwal, A.; Srai, J.S. Developing distributed manufacturing strategies from the perspective of a
product-process matrix. Int. J. Prod. Econ. 2020, 219, 1–17. [CrossRef]
71. Kutnjak, A.; Pihiri, I.; Furjan, M.T. Digital transformation case studies across industries—Literature review. In Proceedings of the
2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija,
Croatia, 20–24 May 2019; IEEE: New York, NY, USA; pp. 1293–1298.
72. Lanzolla, G.; Pesce, D.; Tucci, C.L. The digital transformation of search and recombination in the innovation function: Tensions
and an integrative framework. J. Prod. Innov. Manag. 2021, 38, 90–113. [CrossRef]
73. Legner, C.; Eymann, T.; Hess, T.; Matt, C.; Böhmann, T.; Drews, P.; Mädche, A.; Urbach, N.; Ahlemann, F. Digitalization:
Opportunity and challenge for the business and information systems engineering community. Bus. Inf. Syst. Eng. 2017, 59,
301–308. [CrossRef]
74. Leyh, C.; Köppel, K.; Neuschl, S.; Pentrack, M. Analysis of Critical Success Factors for Successfully Conducting Digitalization
Projects. In Information Technology for Management: Business and Social Issues, Proceedings of the 16th Conference, ISM 2021, and
FedCSIS-AIST 2021 Track, Held as Part of FedCSIS 2021, Virtual Event, 2–5 September 2021; Springer: Cham, Switzerland, 2022;
pp. 3–25.
75. Linde, L.; Frishammar, J.; Parida, V. Revenue models for digital servitization: A value capture framework for designing,
developing, and scaling digital services. IEEE Trans. Eng. Manag. 2021, 70, 82–97. [CrossRef]
76. Loebbecke, C.; Picot, A. Reflections on societal and business model transformation arising from digitization and big data analytics:
A research agenda. J. Strateg. Inf. Syst. 2015, 24, 149–157. [CrossRef]
Systems 2024, 12, 524 24 of 26

77. Lumineau, F.; Wang, W.; Schilke, O. Blockchain governance—A new way of organizing collaborations? Organ. Sci. 2021, 32,
500–521. [CrossRef]
78. Magistretti, S.; Dell’Era, C.; Petruzzelli, A.M. How intelligent is watson? Enabling digital transformation through artificial
intelligence. Bus. Horiz. 2019, 62, 819–829. [CrossRef]
79. Majumder, M.; Gupta, S.; Paul, J. Perceived usefulness of online customer reviews: A review mining approach using machine
learning & exploratory data analysis. J. Bus. Res. 2022, 150, 147–164. [CrossRef]
80. Guenzi, P.; Habel, J. Mastering the digital transformation of sales. Calif. Manag. Rev. 2020, 62, 57–85. [CrossRef]
81. Manfreda, A.; Štemberger, M.I. Establishing a partnership between top and IT managers: A necessity in an era of digital
transformation. Inf. Technol. People 2018, 32, 948–972. [CrossRef]
82. Martín-Martín, A.; Orduna-Malea, E.; Thelwall, M.; Delgado López-Cózar, E. Google scholar, Web of science, and Scopus: A
systematic comparison of citations in 252 subject categories. J. Informetr. 2018, 12, 1160–1177. [CrossRef]
83. Shehadeh, M.; Atta, A.; Barrak, T.; Lutfi, A.; Alrawad, M. Digital transformation: An empirical analysis of operational efficiency,
customer experience, and competitive advantage in Jordanian Islamic banks. Uncertain Supply Chain Manag. 2024, 12, 695–708.
[CrossRef]
84. Matarazzo, M.; Penco, L.; Profumo, G.; Quaglia, R. Digital transformation and customer value creation in made in Italy SMEs: A
dynamic capabilities perspective. J. Bus. Res. 2021, 123, 642–656. [CrossRef]
85. Mathieu, J.; Maynard, T.M.; Rapp, T.; Gilson, L. Team effectiveness 1997–2007: A review of recent advancements and a glimpse
into the future. J. Manag. 2008, 34, 410–476. [CrossRef]
86. Mehadjebia, N.; Halimi, L.; Mekerkeb, A.T. The UAE’s Strategy to Improve the Attractiveness of the Investment Climate. Tech.
Soc. Sci. J. 2023, 39, 336–349. [CrossRef]
87. Mendes, J.A.J.; Carvalho, N.G.P.; Mourarias, M.N.; Careta, C.B.; Zuin, V.G.; Gerolamo, M.C. Dimensions of digital transformation
in the context of modern agriculture. Sustain. Prod. Consum. 2022, 34, 613–637. [CrossRef]
88. Menz, M.; Kunisch, S.; Birkinshaw, J.; Collis, D.J.; Foss, N.J.; Hoskisson, R.E.; Prescott, J.E. Corporate strategy and the theory of
the firm in the digital age. J. Manag. Stud. 2021, 58, 1695–1720. [CrossRef]
89. Met, İ.; Uysal, E.U.; Özkaya, K.S.; Orç, E. Key success factors for strategic management in digital business. In Digital Business
Strategies in Blockchain Ecosystems; Springer: Cham, Switzerland, 2020; pp. 283–304.
90. Mia, M.M.; Majri, Y.; Rahman, I.K.A. Covariance based-structural equation modeling (CB-SEM) using AMOS in management
research. J. Bus. Manag. 2019, 21, 56–61.
91. Moktadir, M.A.; Kumar, A.; Ali, S.M.; Paul, S.K.; Sultana, R.; Rezaei, J. Critical success factors for a circular economy: Implications
for business strategy and the environment. Bus. Strategy Environ. 2020, 29, 3611–3635. [CrossRef]
92. Mongeon, P.; Paul-Hus, A. The journal coverage of Web of science and Scopus: A comparative analysis. Scientometrics 2016, 106,
213–228. [CrossRef]
93. Morakanyane, R.; O’Reilly, P.; McAvoy, J.; Grace, A. Determining digital transformation success factors. In Proceedings of the
53rd Hawaii International Conference on System Sciences, Maui, HI, USA, 7–10 January 2020.
94. Mosch, P.; Schweikl, S.; Obermaier, R. Trapped in the supply chain? Digital servitization strategies and power relations in the case
of an industrial technology supplier. Int. J. Prod. Econ. 2021, 236, 108141. [CrossRef]
95. Rueckel, D.; Muehlburger, M.; Koch, S. An updated framework of factors enabling digital transformation. Pac. Asia J. Assoc. Inf.
Syst. 2020, 12, 1.
96. Nachit, H.; Jaafari, M.; El Fikri, I.; Belhcen, L. Digital transformation in the Moroccan public sector: Drivers and barriers. SSRN
2021, 1–26. [CrossRef]
97. Nagaraj, A. Information seeding and knowledge production in online communities: Evidence from openstreetmap. Manag. Sci.
2021, 67, 4908–4934. [CrossRef]
98. Nambisan, S.; Lyytinen, K.; Majchrzak, A.; Song, M. Digital innovation management: Reinventing innovation management
research in a digital world. MIS Q. 2017, 41, 223–238. [CrossRef]
99. Nambisan, S.; Wright, M.; Feldman, M. The digital transformation of innovation and entrepreneurship: Progress, challenges and
key themes. Res. Policy 2019, 48, 103773. [CrossRef]
100. Nell, P.C.; Foss, N.J.; Klein, P.G.; Schmitt, J. Avoiding digitalization traps: Tools for top managers. Bus. Horiz. 2021, 64, 163–169.
[CrossRef]
101. Nonet, G.; Gössling, T.; Van Tulder, R.; Bryson, J.M. Multi-stakeholder Engagement for the Sustainable Development Goals:
Introduction to the Special Issue. J. Bus. Ethics 2022, 180, 945–957. [CrossRef] [PubMed]
102. Oludapo, S.; Carroll, N.; Helfert, M. Why do so many digital transformations fail? A bibliometric analysis and future research
agenda. J. Bus. Res. 2024, 174, 114528. [CrossRef]
103. Osmundsen, K.; Iden, J.; Bygstad, B. Digital Transformation: Drivers, Success Factors, and Implications. In MCIS 2018 Proceedings;
MCIS: Petaling Jaya, Malaysia, 2018; p. 37.
104. Paiola, M.; Gebauer, H. Internet of things technologies, digital servitization and business model innovation in BtoB manufacturing
firms. Ind. Mark. Manag. 2020, 89, 245–264. [CrossRef]
105. Palaskas, A. Exploring Digital Transformation in Public Healthcare: Drivers, Success Factors, and Challenges. Master’s Thesis,
International Hellenic University, Nea Moudania, Greece, 2022.
Systems 2024, 12, 524 25 of 26

106. Panchyrz, L.; Karumuri, M. Challenges with Organizing for Digital Transformation in International Manufacturing Networks.
Master’s Thesis, Mälardalens University, Västerås, Sweden, 2021. Available online: https://www.diva-portal.org/smash/get/
diva2:1597248/FULLTEXT01 (accessed on 25 October 2024).
107. Park, Y.; Pavlou, P.A.; Saraf, N. Configurations for achieving organizational ambidexterity with digitization. Inf. Syst. Res. 2020,
31, 1376–1397. [CrossRef]
108. Paul, J.; Criado, A.R. The art of writing literature review: What do we know and what do we need to know? Int. Bus. Rev. 2020, 9,
101717. [CrossRef]
109. Paul, J.; Lim, W.M.; O’Cass, A.; Hao, A.W.; Bresciani, S. Scientific procedures and rationales for systematic literature reviews
(SPAR-4-SLR). Int. J. Consum. Stud. 2021, 45, O1–O16. [CrossRef]
110. Pesce, D.; Neirotti, P.; Paolucci, E. When culture meets digital platforms: Value creation and stakeholders’ alignment in big data
use. Curr. Issues Tour. 2019, 22, 1883–1903. [CrossRef]
111. Purwanto, A.; Purba, J.T.; Sijabat, R.; Bernarto, I. The role of transformational leadership, organizational citizenship behaviour,
innovative work behaviour, quality work life, digital transformation and leader member exchange on universities performance.
Linguist. Antverp. 2021, 2908–2932.
112. Rahmati, P.; Tafti, A.; Westland, J.C.; Hidalgo, C. When all products are digital: Complexity and intangible value in the ecosystem
of digitizing firms. MIS Q. 2021, 45, 1025–1058. [CrossRef]
113. Sikki, N.; Aripin, Z.; Fitrianti, N.G. Business innovation and critical Success factors in digital transformation and challenging
times: An econometric analysis of startup viability and success. Kriez Acad. J. Dev. Community Serv. 2024, 1, 1–15.
114. Mandava, H. Critical Success Factors of Cloud ERP in the Enterprise Business. Univers. J. Comput. Sci. Commun. 2024, 3, 1–5.
[CrossRef]
115. Reis, J.; Amorim, M.; Melão, N.; Matos, P. Digital transformation: A literature review and guidelines for future research. In
Advances in Intelligent Systems and Computing; Springer: Berlin, Germany, 2018; Volume 745, pp. 411–421.
116. Rodriguez-Lluesma, C.; García-Ruiz, P.; Pinto-Garay, J. The digital transformation of work: A relational view. Bus. Ethics 2021, 30,
157–167. [CrossRef]
117. Rohn, D.; Bican, P.M.; Brem, A.; Kraus, S.; Clauss, T. Digital platform-based business models–An exploration of critical success
factors. J. Eng. Technol. Manag. 2021, 60, 101625. [CrossRef]
118. Rossi, M.; Festa, G.; Devalle, A.; Mueller, J. When corporations get disruptive, the disruptive get corporate: Financing disruptive
technologies through corporate venture capital. J. Bus. Res. 2020, 118, 378–388. [CrossRef]
119. Rossini, M.; Cifone, F.D.; Kassem, B.; Costa, F.; Portioli-Staudacher, A. Being lean: How to shape digital transformation in the
manufacturing sector. J. Manuf. Technol. Manag. 2021, 32, 239–259. [CrossRef]
120. Saarikko, T.; Westergren, U.H.; Blomquist, T. Digital transformation: Five recommendations for the digitally conscious firm. Bus.
Horiz. 2020, 63, 825–839. [CrossRef]
121. Sadowski, J. The Internet of landlords: Digital platforms and new mechanisms of rentier capitalism. Antipode 2020, 52, 562–580.
[CrossRef]
122. Schallmo, D.; Williams, C.A.; Boardman, L. Digital transformation of business models—Best practice, enablers, and roadmap. In
Digital Disruptive Innovation; World Scientific Pub Co., Inc.: Singapore, 2020; pp. 119–138.
123. Schallmo, D.; Williams, C.A.; Boardman, L. Digital transformation of business models-best practice, enablers, and roadmap. Int. J.
Innov. Manag. 2017, 21, 1–17. [CrossRef]
124. Schneider, P. Managerial challenges of industry 4.0: An empirically backed research agenda for a nascent field. Rev. Manag. Sci.
2018, 12, 803–848. [CrossRef]
125. Schorsch, T.; Wallenburg, C.M.; Wieland, A. The human factor in SCM: Introducing a meta-theory of behavioral supply chain
management. Int. J. Phys. Distrib. Logist. Manag. 2017, 47, 238–262. [CrossRef]
126. Schwertner, K. Digital transformation of business. Trakia J. Sci. 2017, 15, 388–393. [CrossRef]
127. Seran, T.; Bez, S.M. Open innovation’s “multiunit back-end problem”: How corporations can overcome business unit rivalry.
Calif. Manag. Rev. 2021, 63, 135–157. [CrossRef]
128. Siachou, E.; Vrontis, D.; Trichina, E. Can traditional organizations be digitally transformed by themselves? The moderating role of
absorptive capacity and strategic interdependence. J. Bus. Res. 2021, 124, 408–421. [CrossRef]
129. Şimşek, T.; Öner, M.A.; Kunday, Ö.; Olcay, G.A. A journey towards a digital platform business model: A case study in a global
tech-company. Technol. Forecast. Soc. Chang. 2022, 175, 121372. [CrossRef]
130. Sjödin, D.; Parida, V.; Kohtamäki, M.; Wincent, J. An agile co-creation process for digital servitization: A micro-service innovation
approach. J. Bus. Res. 2020, 112, 478–491. [CrossRef]
131. Sklyar, A.; Kowalkowski, C.; Tronvoll, B.; Sorhammar, D. Organizing for digital servitization: A service ecosystem perspective.
J. Bus. Res. 2019, 104, 450–460. [CrossRef]
132. Smith, P.; Beretta, M. The gordian knot of practicing digital transformation: Coping with emergent paradoxes in ambidextrous
organizing structures. J. Prod. Innov. Manag. 2021, 38, 166–191. [CrossRef]
133. Sousa, M.J.; Rocha, Á. Digital learning: Developing skills for digital transformation of organizations. Future Gener. Comput. Syst.
2019, 91, 327–334. [CrossRef]
134. Srai, J.S.; Harrington, T.S.; Tiwari, M.K. Characteristics of redistributed manufacturing systems: A comparative study of emerging
industry supply networks. Int. J. Prod. Res. 2016, 54, 6936–6955. [CrossRef]
Systems 2024, 12, 524 26 of 26

135. Struyf, B.; Galvani, S.; Matthyssens, P.; Bocconcelli, R. Toward a multilevel perspective on digital servitization. Int. J. Oper. Prod.
Manag. 2021, 41, 668–693. [CrossRef]
136. Study on Poverty and Income Inequality in the Context of the Digital Transformation. 25 March 2024. Publications Office of the
European Union. Retrieved 30 October 2024. Available online: https://op.europa.eu/en/publication-detail/-/publication/320f5
f62-eb3b-11ee-bf53-01aa75ed71a1/language-en (accessed on 1 November 2024).
137. Teichert, R. Digital transformation maturity: A systematic review of literature. Acta Univ. Agric. Silvic. Mendel. Brun. 2019, 67,
1673–1687. [CrossRef]
138. Teixeira, A.C.; Tenório, N.; Pinto, D.; Matta, N.; da Cruz Urpia, A.G.B. The critical success factors’ investigation during knowledge
management implementation within SME enterprises: A Participatory Design opportunity. SN Comput. Sci. 2023, 4, 25. [CrossRef]
[PubMed]
139. Tijan, E.; Jović, M.; Aksentijević, S.; Pucihar, A. Digital transformation in the maritime transport sector. Technol. Forecast. Soc.
Chang. 2021, 170, 120879. [CrossRef]
140. Sreenivasan, A.; Suresh, M. Digital transformation in start-ups: A bibliometric analysis. Digit. Transform. Soc. 2023, 2, 276–292.
[CrossRef]
141. Tortorella, G.L.; Fogliatto, F.S.; Tlapa Mendoza, D.; Pepper, M.; Capurro, D. Digital transformation of health services: A value
stream-oriented approach. Int. J. Prod. Res. 2023, 61, 1814–1828.
142. Trantopoulos, K.; Von Krogh, G.; Wallin, M.; Woerter, M. External knowledge and information technology: Implications for
process innovation performance. MIS Q. 2017, 41, 287–300. [CrossRef]
143. Tronvoll, B.; Sklyar, A.; Sörhammar, D.; Kowalkowski, C. Transformational shifts through digital servitization. Ind. Mark. Manag.
2020, 89, 293–305. [CrossRef]
144. Tsolkanakis, D. Digital Transformation in the Greek Public Sector-Identifying Drivers and Barriers. Master’s Thesis, International
Hellenic University, Thermi, Greece, 2022. Available online: https://repository.ihu.edu.gr/xmlui/bitstream/handle/11544/29
976/Digital%20Transformation%20in%20the%20Greek%20public%20sector-Identifying%20drivers%20and%20barriers.pdf?
sequence=1 (accessed on 25 October 2024).
145. van Dyk, R.; Van Belle, J.P. Drivers and challenges for digital transformation in the South African retail industry. In Information
Technology for Management: Current Research and Future Directions: Proceedings of the 17th Conference, AITM 2019, and 14th Conference,
ISM 2019, Held as Part of FedCSIS, Leipzig, Germany, 1–4 September 2019; Springer: Cham, Switzerland, 2020; pp. 42–62.
146. Vanneste, B.S.; Puranam, P.; Kretschmer, T. Trust over time in exchange relationships: Meta-analysis and theory. Strateg. Manag. J.
2014, 35, 1891–1902. [CrossRef]
147. Verhoef, P.C.; Broekhuizen, T.; Bart, Y.; Bhattacharya, A.; Qi Dong, J.; Fabian, N.; Haenlein, M. Digital transformation: A
multidisciplinary reflection and research agenda. J. Bus. Res. 2021, 122, 889–901. [CrossRef]
148. Schumacher, C.; Eggers, F.; Verhoef, P.C.; Maas, P. Data analysis for digital transformation: The example of willingness to
share personal information. In A Research Agenda for Digital Transformation; Edward Elgar Publishing: Cheltenham, UK, 2024;
pp. 281–326.
149. Vial, G. Understanding digital transformation: A review and a research agenda. In Managing Digital Transformation; Routledge:
London, UK, 2019.
150. Vogelsang, K.; Liere-Netheler, K.; Packmohr, S.; Hoppe, U. Success factors for fostering a digital transformation in manufacturing
companies. J. Enterp. Transform. 2018, 8, 121–142. [CrossRef]
151. Volberda, H.W.; Khanagha, S.; Baden-Fuller, C.; Mihalache, O.R.; Birkinshaw, J. Strategizing in a digital world: Overcoming
cognitive barriers, reconfiguring routines and introducing new organizational forms. Long Range Plan. 2021, 54, 102110. [CrossRef]
152. Warner, K.S.R.; Wäger, M. Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal.
Long Range Plan. 2019, 52, 326–349. [CrossRef]
153. Weking, J.; Stöcker, M.; Kowalkiewicz, M.; Böhm, M.; Krcmar, H. Leveraging industry 4.0—A business model pattern framework.
Int. J. Prod. Econ. 2020, 225, 107588. [CrossRef]
154. Werth, O.; Schwarzbach, C.; Rodríguez Cardona, D.; Breitner, M.H.; Graf von der Schulenburg, J.M. Influencing factors for the
digital transformation in the financial services sector. Z. Für Die Gesamte Versicherungswissenschaft 2020, 109, 155–179. [CrossRef]
155. Westerman, G.; Bonnet, D. Revamping your business through digital transformation. MIT Sloan Manag. Rev. 2015, 56, 2–5.
156. Westerman, G.; Soule, D.L.; Eswaran, A. Building digital-ready culture in traditional organizations. MIT Sloan Manag. Rev. 2019,
60, 59–68.
157. Wieland, A.; Handfield, R.B.; Durach, C.F. Mapping the landscape of future research themes in supply chain management. J. Bus.
Logist. 2016, 37, 205–212. [CrossRef]
158. Wiesböck, F.; Hess, T. Digital innovations: Embedding in organizations. Electron. Mark. 2020, 30, 75–86. [CrossRef]

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.