Article
Managing Financial and Operational Risks Through Digital
Transformation: The Mediating Influence of Information and
Communication Technologies’ Adoption and Resistance
to Change
Siham Slassi-sennou 1 and Soufiane Elmouhib 1,2, *
1
2
*
Academic Editor: Thanasis Stengos
Received: 27 January 2025
ERMOT Laboratory, Faculty of Law, Economics and Social Sciences, Sidi Mohamed Ben Abdellah University,
Fez 30060, Morocco; sihamslassi@yahoo.fr
Multidisciplinary Research Laboratory LAREM, HECF Business School, Fez 30000, Morocco
Correspondence: soufiane.elmouhib@usmba.ac.ma or s.mouhib@hecfsup.com; Tel.: +212-605096796
Abstract: This study examines the relationships among ICT adoption, resistance to change,
and digital transformation, focusing on their influence on financial and operational risk
management. The research utilizes a quantitative design, drawing on data from 768 Moroccan professionals across multiple industries. Structural Equation Modeling (SEM) was
employed to analyze both direct and indirect effects within the proposed theoretical framework. The findings indicate that ICT adoption has a positive effect on digital transformation,
whereas resistance to change exerts a negative influence. Additionally, digital transformation mediates the impact of ICT adoption and resistance to change on financial and
operational risks, thereby reducing these risks. These results underscore the potential role
of change management in facilitating digital transformation and mitigating risk. From a
managerial and policy standpoint, the study highlights the importance of fostering an organizational environment supportive of ICT adoption and addressing resistance to change.
Integrating digital transformation into risk management strategies may also contribute to
organizational resilience. This research extends existing knowledge by clarifying how digital transformation mediates the relationship between technology adoption, organizational
behavior, and risk management.
Revised: 19 February 2025
Accepted: 22 February 2025
Published: 1 March 2025
Keywords: digital transformation; ICT adoption; risk management; resistance to change;
financial risk
Citation: Slassi-sennou, S., &
Elmouhib, S. (2025). Managing
Financial and Operational Risks
Through Digital Transformation: The
Mediating Influence of Information
and Communication Technologies’
Adoption and Resistance to Change.
Journal of Risk and Financial
Management, 18(3), 128. https://
doi.org/10.3390/jrfm18030128
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/
1. Introduction
In the dynamic, uncertain world we live in today, with the acceleration of digital
technologies, such as AI and machine learning, organizations need to evolve in order
to respond, compete, and manage risks. Digital transformation, the process of embedding digital technologies in every aspect of an organization, fundamentally reshapes how
entities function, make decisions, and generate value (Aboiron & Aboiron, 2022). It is
important to modern enterprises striving for a competitive advantage in fast-changing
markets, whether by enhancing operational efficiency or fostering innovation (Pchelintsev,
2024). While digital transformation presents challenges, maximizing digitization offers
solutions to these risks (Zaikovsky et al., 2021). Elements like the adoption of information
and communication technologies (ICT) and effective change management are critical to
successfully implementing digital initiatives (De-Pablos-Heredero, 2020).
licenses/by/4.0/).
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https://doi.org/10.3390/jrfm18030128
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Financial risks can cripple organizations and hinder growth in the future (You &
Zhao, 2023); this includes uncertainties surrounding liquidity, defaults in loans, and price
variations brought on by the market (Cao, 2023). Equally, operational risks (system failure,
human error, compliance) present threats to continuity and reputation (Ali & Govindan,
2023). With businesses adopting digital solutions to mitigate these risks, the impact of ICT
adoption and change management can become increasingly critical (De-Pablos-Heredero,
2020). Adopting ICT enables the use of new technologies, such as artificial intelligence,
big data analytics, and the Internet of Things (IoT), that provide tools for improved transparency, monitoring, and decision making (Parra et al., 2019). Conversely, reluctance
to change could sabotage transformation efforts, preventing the integration of new approaches and retarding innovation (Bagrationi et al., 2023). Therefore, ICT adoption, change
resistance, and digital transformation are interdependent and deserve further exploration,
especially when it comes to risk management.
While the literature widely highlights digital transformation and its purported ability
to solve challenges in organizations (Ali & Govindan, 2023; Cao, 2023; Parra et al., 2019), it
lacks a comprehensive understanding of how factors, such as difficulties combining ICT
adoption and resistance to change, interact to influence financial and operational risks. We
aim to fill this research gap by investigating the role of digital transformation as a mediator
in these relationships.
In order to fill in these gaps, this research examines the following research questions.
(1) To what extent does ICT adoption influence digital transformation? (2) What is the
impact of change resistance on digital transformation? (3) How does digital transformation
mediate the association between ICT adoption, resistance to change, and operational and
financial risk outcomes? This set of research questions aims to present a measurable scope
regarding the relationships falling under these constructs, thus highlighting statement
verification via rigorous quantifiable methods.
Drawing upon organizational and risk management theories, the present study suggests that the effect of ICT adoption on digital transformation is positive and that the effect
of resistance to change is negative. Furthermore, the study suggests digital transformation
as a mediator of the impact of ICT adoption and resistance to change on financial and
operational risks, thus establishing a model that complements our understanding of these
relationships. The present study aims to test these hypotheses by applying a quantitative
research design with the help of Structural Equation Modeling (SEM). We collect data
from practitioners in a wide range of industries who have experienced ICT adoption and
risk management, which facilitates the analysis of direct and indirect effects in the full
theoretical model.
This paper is organized as follows. Section 2 is a critical review of the relevant
literature, leading to hypotheses. Section 3 describes the research methodology, including
data collection and analysis methods. Section 4 describes the findings of the study, which is
followed in Section 5 by a discussion of their theoretical and practical implications, research
limitations, and directions for future research.
2. Literature Review and Hypothesis Development
2.1. Conceptual Framework
2.1.1. ICT Adoption
ICT adoption is defined as the use and implementation of information and communication technology to make organizations more efficient, to enable better decision making,
and to enhance risk management. The whole process includes the development, adoption,
and use of ICT platforms and tools to enhance efficiency, productivity, and the development
of some degree of risk identification, assessment, and mitigation capabilities. As researchers
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have concluded, ICT adoption is transformative, and technological innovations deliver
economically optimal solutions by meeting organizational vulnerabilities as well as needs
(Bam et al., 2022; Belanova, 2023; Varma & Khan, 2015; Qian et al., 2012).
Theoretical models and perspectives from the literature about ICT adoption in the
context of risk management provide some dimensions for the development of a critical discussion about the importance of ICT adoption as an enabler for an organization to manage
risks. A more general perspective sees the adoption of ICT as a multi-step process, involving stages like the phases of initiation, integration, and operationalization. These phases
help match technological capabilities with organizational objectives, thereby enabling risks
like time lag, technology failure, and cyber breaches to be mitigated effectively (Bam et al.,
2022; Belanova, 2023). In the aspect of supply chain and operational risk management, ICT
tools, such as Enterprise Resource Planning (ERP) systems, Radio Frequency Identification
(RFID), and automated data analytics, enhance visibility and robustness while reducing
risks of cybercrime and fraud. However, these tools also create new threat vectors, like data
breaches and unauthorized data access, that require investment in advanced cybersecurity
measures, such as cryptography and intrusion detection/prevention (Varma & Khan, 2015).
Theoretical lenses also bring out the dynamic nature of the reactive and proactive mechanism of managing risk. Reactive methods respond to problems after they occur, which
inevitably creates delayed reactions and interruptions within organizations. On the other
hand, proactive measures, like early investment in incident response, training of employees,
and governance frameworks that enable the adoption of ICT to be matched with building
resilience at the same time, prepare the organizations so that they can deal with the risks in
a more efficient manner (Qian et al., 2012).
2.1.2. Resistance to Change
Employees are reluctant to undertake necessary new internal initiatives, especially
strategic changes or technology implementation. It has cognitive, emotional, and behavioral
elements and is typically driven by fear, perceived threat, or differences of opinion about
the value of going through the change. Active responses include outright challenges or
mocking, while passive responses include withholding information or verbally agreeing
but not following through (Fiedler, 2010; Lines et al., 2015; Vrhovec et al., 2015).
Resistance to risk management change is a complex structure that represents risk and
opportunity for improvement in an organization. Different theoretical perspectives explain
resistance in terms of psychological or organizational factors or processes, including cognitive dissonance, emotional discomfort, fear of job displacement, and threat to established
practices, that exacerbate the challenges of executing the project, disrupt allocation of resources, and lead to postponed adoption of the technology (Rahman et al., 2024). However,
resistance also has a positive aspect; it may encourage a critical assessment and highlight
weaknesses in new systems or processes, allowing organizations to improve strategies
and make better decisions (Fiedler, 2010; Vrhovec et al., 2015). Resistance is considered an
organizational risk that needs to be treated using risk management frameworks, such as the
Organizational Risk Diagnosing model (Lines et al., 2015). Strategies, such as stakeholder
engagement, open communication, targeted training programs, and a phased approach
composed of identification, evaluation, planning, risk mitigation, resolution, and control,
enable better transitions and alignment with organizational goals (Fiedler, 2010; Lines
et al., 2015). Across transitions, change agents also play an important role in establishing
trust, creating clarity around benefits, alleviating concerns, and encouraging collaboration
(Lines et al., 2015).
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2.1.3. Digital Transformation
Digital transformation is the integration and strategic application of digital technologies to fundamentally change how organizations operate, interact with customers, and
deliver value. Such a transformation enables greater operational efficiency and better
decision making and fosters innovation at the same time, with inherent risks addressed. It
has influenced multiple specific contexts, such as competition, sustainability, and flexibility,
in fast-changing environments (Dokuchaev, 2020; D. S. Pashchenko & Komarov, 2021;
Vorontsova & Baranov, 2021; You & Zhao, 2023)
Digital transformation from a risk management perspective is considered the method
for identifying, assessing, and managing risks caused by technological changes and organizational changes (You & Zhao, 2023). Adoption of these technologies may increase data
transparency and reliability, which would contribute to risk identification and the development of pre-emptive solutions (Dokuchaev, 2020; You & Zhao, 2023). Although these
tools allow for greater resource allocation due to real-time monitoring and analysis, they
also reduce inefficiencies by optimizing routing patterns and eliminating delays and errors.
Big data analytics can develop financial tools to predict the occurrence of cash flow interruptions or other risks to an organization, thereby enabling timely and effective responses
(Dokuchaev, 2020). Also, digital transformation helps with the management of emerging
risks, like cybersecurity threats and data privacy concerns. Such frameworks as Continuous
Adaptive Risk and Trust Assessment (CARTA) and Governance, Risk, and Compliance
(GRC) systems allow for monitoring threats in real time and adjusting the outcome periodically (Dokuchaev, 2020; Vorontsova & Baranov, 2021). Digital transformation goes beyond
making the organization safer from particular threats; it helps in proactively dealing with
internal resistance to change, regulatory pressures, and market uncertainties through strategies like allocating financial reserves at nascent stages, gradually implementing changes,
and cross-functional integration (D. S. Pashchenko & Komarov, 2021).
2.1.4. Operational Risk
Operational risk encompasses a wide breadth of vulnerabilities, from human errors
and system failures to fraud and legal or regulatory non-compliance, and it is defined as
the risk of loss resulting from inadequate or failed internal processes, people, and systems
or from external events (Moosa, 2007; Pleune, 2017; Xu et al., 2017). Operational risk is
different from market and credit risks, and it is an essential factor in the complex domains
of daily operational management.
Operational risk is a multifaceted concept in risk management, but it has become
a relevant tool for facilitating ICT-oriented adoption and different levels of integration.
However, with the increasing reliance of organizations on technology, these risks become
exacerbated, with operational failures during ICT adoption being a threat to continuity,
efficiency, and reputation (Pleune, 2017; Xu et al., 2017). The character of the integration
of ICT poses additional challenges, especially at the phase of adoption, when lack of
understanding or a testing phase could lead to system vulnerabilities, such as automation
errors, data breaches, and system integration failures (Xu et al., 2017). Strategies for
comprehensive operational risk management include risk assessments, audits, testing, and
real-time tracking, accompanied by continuous training of personnel, to reduce the chance
of human error and enable smooth switchovers in technology (Xu et al., 2017). In fact,
frameworks, such as the Basel Committee’s Advanced Measurement Approach (AMA)
and the Standardized Measurement Approach (SMA), provide structured methods for
managing this risk through scenario analysis, operational loss tracking, and external loss
data (Moosa, 2007).
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2.1.5. Financial Risk
Financial risk is the possibility of losing money or being unstable financially as a
result of the inability to manage the financial activities of an organization (Jirásková, 2017;
Valaskova et al., 2018). It covers a wide spectrum of risks, such as default on repayment of
debts, poor usage of resources, regulatory non-compliance, and impact of market fluctuations, inflation, or movements in exchange rates on economic losses. The nature of this risk
is related to financial management decisions, and it is even more pronounced in periods of
technological implementation or economic instability (Jirásková, 2017; S. Pashchenko et al.,
2017; Valaskova et al., 2018).
Financial risk, a significant part of organizational risk, needs a systematic process of
identification, assessment, and mitigation to ensure sustainability. The risks associated
with technology adoption become magnified by significant upfront investments, uncertain
payoffs, and business interruptions (S. Pashchenko et al., 2017; Valaskova et al., 2018). These
may consist of, among other things, cost overruns, longer wait times for projected value
creation, and unanticipated system integration costs. In this respect, predictive tools based
on ratios like return on capital, cash ratios, and debt-to-equity ratios, function to determine
suspected financial distress so as to enable corrective action to take place before distress
(Valaskova et al., 2018). These tools provide consistency of sentiment between financial
planning and overall risk management strategies that include liquidity management and
the compatibility of technological investments.
2.2. Hypothesis Development
The empirical studies prove that financial risk may be reduced with the help of digital
transformation by improving operational efficiency and renovating the financial structure
(Afanasiev & Kandinskaia, 2021; Cao, 2023; You & Zhao, 2023). This transformation,
with an improvement in resource allocation and strengthening of liquidity management,
can assist organizations in reducing traditional risks and vulnerabilities with the use of
technologies including big data, artificial intelligence, and an enhanced risk evaluation
tool. In addition, through the advancement of digital infrastructures, risk can be accurately
identified, especially with advanced technologies, including ISM, Interpretative Structural
Modeling, and MICMAC, Cross-Impact Matrix Multiplication Applied to Classification
(Wang et al., 2022). The overall evidence suggests that the hypothesis regarding digital
transformation and its negative effect on financial risk can be confirmed (H1).
According to the literature, digital transformation, especially among Industry 4.0
technologies (I4Ts), can reduce operational risks related to poor traceability, visibility, and
predictability of events. In this regard, IoT-enabled systems, such as RFID and GPS, can
be used to sense and respond to disruption and to reduce operational uncertainties (Ali
& Govindan, 2023). Other technologies, such as big data analytics and cloud computing, assist in optimizing the supply–demand match with respect to quantity and quality
(Ali & Govindan, 2023). Also, structured frameworks, like the Strategic Risk Mitigation
Model (STRMM), offer many benefits by correlating data architecture with organizational
risk mitigation, reinforced with data analytics and monitoring (Masuda & Viswanathan,
2019). Therefore, from the above-mentioned approaches and technical benefits of digital
transformation, we can hypothesize that it effectively reduces operational risks (H2).
According to the literature, ICT adoption forms a key driver of digital transformation,
enabling the technological infrastructure and operational capabilities needed for organizational renewal. It is through the redesign of business processes, the development of
competencies, and alignment with strategic objectives in view of contemporary challenges
that the technologies of cloud computing, IoT, and AI foster productivity, innovation, and
competitive advantage (Arbaiza, 2018; Parra et al., 2019). Effective communication, focused
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training, user-friendly systems, and change management processes ensure the engagement
of stakeholders and their adaptation to fast-changing technologies, further strengthening
the role of ICT in fostering digital activities, such as e-commerce and automation (Bughin
et al., 2021). The empirical evidence seems to constantly demonstrate that ICT adoption
triggers strategic renewal, sustains the achievement of performance improvement, and
opens new avenues for growth, hence proving the hypothesis that the adoption of ICT is
positively related to digital transformation (H3).
Resistance to change is an important impediment to digital transformation based
on values like tradition and stability. It is revealed in emotional, cognitive, and systemic
oppositional behavior (Bagrationi et al., 2023; Scholkmann, 2021). Such individual or organizational resistance hampers new technology adoption, delays innovation, and misaligns
transformative programs with their underlying goals (Bagrationi et al., 2023; Scholkmann,
2021). Indeed, fear of the unknown holds back many a change effort, and in a bureaucracy
that includes bottlenecks caused by middle management fearful of losing influence or
autonomy, emotional factors seriously compound the challenge (Khanboubi & Boulmakoul,
2021). Nevertheless, initiatives, such as transparent communication, stakeholder cooperation, and aligning strategies with the values of the organization, can lessen resistance and
ease periodic transitions (Bagrationi et al., 2023; Khanboubi & Boulmakoul, 2021). The
above findings confirm the hypothesis of a negative relationship between resistance to
change and digital transformation (H4).
While ICT adoption improves efficiency via analytics, monitoring, and automation,
it does not decrease financial risk unless digital transformation integrates these emerging
technologies into the strategic process of organizations (You & Zhao, 2023). As mentioned
above, digital transformation allows organizations to better anticipate, prevent, and manage
financial risks by embedding digital processes in core practices, driving innovation, and
promoting adaptability. In addition, it can leverage the advantages of ICT adoption by
increasing transparency and ensuring regulatory compliance, and it can also help to nurture
a culture of risk awareness (Parra et al., 2019). That is, we can hypothesize the mediating
effect of digital transformation in the relationship between ICT adoption and financial
risk (H5).
ICT adoption increases the efficiency of the operation by automating the process,
improving communication, and enhancing efficient workflows; however, the impact of ICT
on operational risk is driven by the extent of digital transformation in embedding these
technologies in the strategy and operations of the organization (Khan et al., 2018). As we
mention in hypothesis 4, digital transformation can help organizations to identify, consider,
and limit operational risks more effectively by embedding digital processes into routine
practices, promoting data-driven decision making, and reducing obstacles to agility (Ali
& Govindan, 2023). Furthermore, this transformation enhances ICT adoption benefits by
having a positive impact on the resilience of systems, which can ensure that operational
standards are met to build a risk management culture for proactivity (Parra et al., 2019).
Such empirical studies offer evidence supporting the hypothesis that digital transformation
mediates the relationship between ICT adoption and operational risk (H6).
Resistance to change makes financial risk management a challenge, as it prevents
the early implementation of best practices, relevant technologies, and effective processes
(Vrhovec et al., 2015; Khanboubi & Boulmakoul, 2021). This resistance can delay crucial
financial decision making, hinder the adoption of risk-reducing financial tools, and create
inefficiencies in managing liquidity, credit, and investment risks (You & Zhao, 2023; Cao,
2023). However, digital transformation can mitigate these limitations by embedding digital solutions into financial strategies, enhancing transparency, and improving financial
forecasting capabilities (Bagrationi et al., 2023; You & Zhao, 2023). By fostering a culture
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of innovation and integrating digital processes into financial risk management, digital
uncertainties (Ali
& Govindan,
2023).toTherefore,
transformation
relatransformation
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anticipate
and managemediates
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uncertionship
between
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(H7).
tainties (Ali & Govindan, 2023). Therefore, digital transformation mediates the relationship
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as it canresistance
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Lines
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(Rahman et
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et
2024; obstacles
Pleune, 2017).
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(Ali & Govindan,
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al., 2017). Furthermore,
digital transformation
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enworkflows
to prevent operational
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& Govindan,
2023; Xu
et agility,
al., 2017).
Furthermore,
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and process
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nov,failures
2021). Therefore,
it mediates
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opTherefore,
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erational risk
(H8).
risk (H8).
3. Research Methodology
3. Research Methodology
3.1. Measurement
3.1. Measurement
The measures for all constructs in Figure 1 of the study were based on items adapted
The measures for all constructs in Figure 1 of the study were based on items adapted
from existing scales in the literature. The ICT adoption measurement scale was developed
from existing scales in the literature. The ICT adoption measurement scale was develby Venkatesh and Bala (2008) based on determinants of technology adoption in the organoped by Venkatesh and Bala (2008) based on determinants of technology adoption in the
izational context (Venkatesh & Bala, 2008). The scale for resistance to change was develorganizational context (Venkatesh & Bala, 2008). The scale for resistance to change was
oped in the literature by Oreg (2003), focusing on behavioral and attitudinal resistance
developed in the literature by Oreg (2003), focusing on behavioral and attitudinal resistance
during organizational change (Oreg, 2003). Digital transformation was measured from the
during organizational change (Oreg, 2003). Digital transformation was measured from the
perspective that digital change within organizations is a complex and multidimensional
perspective that digital change within organizations is a complex and multidimensional
process (Matt et al., 2015). In order to measure the levels of operational and financial risks,
process (Matt et al., 2015). In order to measure the levels of operational and financial risks,
we used proposed items capable of capturing managers’ perceptions (Figini et al., 2015;
we used proposed items capable of capturing managers’ perceptions (Figini et al., 2015;
Gilliam et al., 2010).
Gilliam et al., 2010).
Figure
Figure 1.
1. Research
Research conceptual
conceptualmodel.
model.
To
To be
be more
morecomprehensive,
comprehensive,each
eachconstruct
constructisiscovered
coveredby
bymore
morethan
thanone
oneitem.
item.Survey
Survey
items were developed to be linguistically neutral and contextually specific. To validate the
items were developed to be linguistically neutral and contextually specific. To validate
items and ensure that all items were understood as intended, especially by Moroccan risk
the items and ensure that all items were understood as intended, especially by Moroccan
managers who are more flexible with French than English, a back-translation procedure
risk managers who are more flexible with French than English, a back-translation procewas implemented in accordance with the recommendations presented in the literature
dure was implemented in accordance with the recommendations presented in the litera(Brislin, 1986). This included translating the items into the local language and then having
ture (Brislin, 1986). This included translating the items into the local language and then
them back-translated into English by independent translators to ensure linguistic and
having them back-translated into English by independent translators to ensure linguistic
conceptual equivalence between the two versions.
and conceptual equivalence between the two versions.
Respondents were asked about the extent to which they agreed with statements pertaining to each construct using a five-point Likert scale (1: Strongly Disagree to 5: Strongly
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Respondents were asked about the extent to which they agreed with statements
pertaining to each construct using a five-point Likert scale (1: Strongly Disagree to 5:
Strongly Agree) for every item in the questionnaire. Other demographic variables, including
age, gender, and professional role, were included to provide context to a diverse sample
that was drawn from all over the organizational landscape.
In order to test the research hypotheses and characterize various complex causal
relationships, including direct and indirect effects among the study’s dependent and independent variables, we employed the PLS–SEM method using SmartPLS-4 with confirmatory
factor analysis (CFA).
Multiple statistical techniques were used to confirm the reliability and validity of
the measurement items. Composite Reliability (CR) and Cronbach’s Alpha (CA) in the
study showed good quality of internal consistency, with all being greater than the minimum recommended threshold value of 0.7. All constructs were found to meet or exceed
the minimum 0.5 cut-off for Average Variance Extracted (AVE), indicating adequate convergent validity and, therefore, that the items measuring each theoretical construct were
capturing the underlying concepts of interest. Discriminant validity was assessed using
the Heterotrait–Monotrait (HTMT) ratio, and all of the HTMT values were less than 0.85,
establishing the uniqueness of the constructs. Furthermore, both the direct and indirect
relationships in the model were analyzed to evaluate their importance and strength.
3.2. Sample and Data Collection
In order to determine the sample and to obtain an appropriate number of respondents,
especially in a large population, Slovin’s formula was mobilized in this study (Anugraheni et al., 2023). Given that the study specifically explored organizations experiencing
digital transformation, the target population consisted of professionals responsible for
ICT adoption and risk management processes. Using this formula, 768 respondents were
chosen to accurately represent the population, with a margin of error of 5% at a confidence level of 95%. Participant selection was based on a Voluntary Selection Method,
and a self-administered online questionnaire was used as the main tool for data collection. The questionnaire was therefore shared among social media platforms like Facebook
and LinkedIn, as well as email lists of professional organizational networks that ensured
targeted respondents.
In advance of full-scale data collection, a pilot test was performed with a smaller
sample to verify the content, clarity, and availability of the questionnaire. Thus, data from
this test were excluded from the final analysis. The main data were collected during a
period of three months between May and July 2024. From this period, 768 responses (92.1%
of the total number of responses) were found to be valid after removing incomplete and/or
inconsistent responses, for a total of 834 responses. For the purpose of hypothesis testing,
the analysis was limited to the valid responses to maintain the reliability and validity of
the data.
This methodical procedure for sample selection and data collection ensured that the
data recorded are representative and robust enough to allow for accurate analysis of the
relationships between the model’s constructs, like ICT adoption, resistance to change,
digital transformation, and the operational and financial risks.
4. Research Results
4.1. Descriptive Statistics
Table 1 presents the descriptive statistics for the characteristics of the 768 respondents
in our sample. Regarding the economic classification of the firms, 584 (76%) responses
were provided by SMEs. Very small enterprises (VSEs) contributed the least at 84 re-
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sponses (11%), while big enterprises (BEs) provided 100 responses (13%), showing only
moderate representation. Such proportions mirror the structure of the Moroccan business
environment, where big firms and very small enterprises (VSEs) behave as complementary
elements, with SMEs occupying the major part at a rate of 97% (Imane & Fatima, 2024).
Table 1. Respondents’ characteristics (n = 768).
Attributes
Characteristics
Number of Responses
Percentage
Firm’s economic
classification
VSE (very small enterprise)
SMEs (small and medium enterprises)
BE (big enterprise)
84
584
100
11%
76%
13%
Sector
Finance
Tourism
Textiles
Automotive
Other
169
146
230
69
54
22%
19%
30%
9%
7%
In terms of sectoral distribution, the textiles sector had the largest share of responses,
with 230 respondents (30%). This result accounts for textiles as a significant part of Morocco’s industrial and export economy (Smouh et al., 2022). Next was the finance sector,
which, accounting for 169 responses (22%), is essential for providing funds for the business
operations and investments of nearly all other sectors. Tourism represented 146 answers
(19%), which further strengthens its position as a major contributor to the Moroccan economy. There were 69 responses (9%) from the automotive sector, which is representative
of the continuing contribution of this sector to the national economy, albeit smaller than
what it could be. Finally, the “other” category accounted for 54 (7%) responses, including
participants from underrepresented or other industries.
These results are consistent with the features of the Moroccan economic structure, in
which SMEs and the two major sectors represent the biggest contributors (Pouya et al.,
2021). The sample, therefore, is able to represent the overall population of Morocco, which
makes sense for the study considering the national economic context.
4.2. Assessment of the Measurement Model
As shown in Table 2, the Composite Reliabilities (CRs) and Cronbach’s Alphas (CAs)
were larger than 0.7 for all constructs and, therefore, further validate the reliability of
the constructs. For example, high reliability was observed for financial risk (CR = 0.900,
CA = 0.898) and operational risk (CR = 0.910, CA = 0.907). Likewise, constructs like digital
transformation (CR = 0.886, CA = 0.886) and ICT adoption (CR = 0.885, CA = 0.884) also
showed good internal reliability.
Although all of the indicators reached the minimum threshold of loading, equal
to 0.7 or higher, most of them exceeded this threshold, where the values ranged from
0.757 to 0.861. Importantly, resistance to change had the highest loading within its respective construct (0.861). Convergent validity was also established, because all AVE values
for constructs exceeded the recommended level of 0.5. For instance, ICT adoption at 0.683
and financial risk at 0.662 affirm that the variance is well-explained by the indicators in
these constructs.
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Table 2. Convergent validity (n = 768).
Constructs
Loadings
Digital
transformation
DT1
DT2
DT3
DT4
DT5
DT6
0.787
0.791
0.810
0.804
0.798
0.797
ICT adoption
ICTA1
ICTA2
ICTA3
ICTA4
ICTA5
0.843
0.835
0.842
0.805
0.806
Resistance to
change
RtC1
RtC2
RtC3
CA
CR
AVE
Constructs
0.886
0.886
0.637
Financial risk
FR1
FR2
FR3
FR4
FR5
FR6
0.884
0.885
0.683
Loadings
CA
CR
AVE
0.898
0.900
0.662
0.907
0.910
0.643
0.810
0.822
0.832
0.809
0.809
0.800
Operational risk
0.828
0.757
0.792
0.790
0.826
0.799
0.804
0.713
0.830
0.861
0.819
0.852
0.790
To check discriminant validity, the Heterotrait–Monotrait (HTMT) ratio criterion was
applied, as shown in Table 3. Each value was far less than the threshold of 0.9, indicating
that all constructs are distinct from one another. As an example, the HTMT value of
digital transformation and financial risk was 0.661, thus proving that these constructs were
conceptually different. Likewise, the HTMT ratios (0.804) of ICT adoption and operational
risk, as well as operational risk and resistance to change, provide further evidence for
constructs’ distinctiveness.
Table 3. Heterotrait–Monotrait ratio (HTMT).
Digital transformation
Financial risk
ICT adoption
Operational risk
Resistance to change
Digital
Transformation
Financial
Risk
ICT
Adoption
Operational
Risk
0.661
0.758
0.747
0.898
0.804
0.895
0.716
0.896
0.836
0.804
Resistance to
Change
Moreover, constructs like ICT adoption and financial risk (HTMT = 0.758) also exhibit
acceptable discriminant validity in the context of the theoretical model. Even the highest
HTMT ratio observed between digital transformation and resistance to change (0.898) did
not exceed the critical threshold, confirming that these constructs are different.
These findings confirm that the constructs are reliable and suitable for predicting
the factors influencing digital transformation, ICT adoption, resistance to change, and
associated risks within the proposed theoretical framework.
4.3. Direct Effect Analysis
The direct effect analysis according to Structural Equation Modeling (SEM) is summarized in Table 4. Digital transformation, as hypothesized, significantly reduces financial
risk (β = −0.595; p < 0.01) and operational risk, (β = −0.672, p < 0.01), thus supporting H1
J. Risk Financial Manag. 2025, 18, 128
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and H2, respectively. This highlights the fact that digital transformation is an important
requirement for risk mitigation at the enterprise level.
Table 4. Direct effect.
Hypotheses
Relationships
Findings
Results
H1
Digital transformation → financial risk
Negative and statistically significant
(β = −0.595; p < 0.01)
Supported
H2
Digital transformation → operational risk
Negative and statistically significant
(β = −0.672; p < 0.01)
Supported
H3
ICT adoption → digital transformation
Positive and statistically significant
(β = 0.279; p < 0.01)
Supported
H4
Resistance to change → digital
transformation
Negative and statistically significant
(β = −0.565; p < 0.01)
Supported
Moreover, ICT adoption positively and significantly affects digital transformation
(β = 0.279; p < 0.01), thus providing support for H3. This finding emphasizes that information and communication technologies have been used as a key component of digital
transformation initiatives.
Meanwhile, resistance to change has a negative and significant effect on digital transformation (β = −0.565; p < 0.01), thus fulfilling H4. This finding suggests that more
resistance to change is an impediment on the path to successful digital transformation. As
such, organizations seeking to implement digital transformation must manage resistance
to change, which is therefore critical.
Finally, the findings highlight the interrelationships among digital transformation,
ICT adoption, resistance to change, and risk management while further confirming the
validity of the theoretical model of this study. Organizations looking to enable their digital
transformation as a tool to mitigate risk and optimize operations may find them insightful.
4.4. Indirect Effect Analysis
Results of the indirect effects, shown in Table 5, further support the mediating effect of digital transformation on the relationships between ICT adoption, resistance to
change, and dependent variables (financial and operational risk). Similar to our hypotheses H5 and H6, ICT adoption, through digital transformation, diminishes financial risk
(Std. Beta = −0.166, t-Value = 7.495, p < 0.01) and operational risk (Std. Beta = −0.188,
t-Value = 7.637, p < 0.01). In the same way, resistance to change has an indirect effect
on financial risk (Std. Beta = 0.336, T-Value = 16.879, p = 0) and operational risk (Std.
Beta = 0.379, T-Value = 18.534, p = 0) through digital transformation, thus supporting H7
and H8. These findings stress that resistance amplifies risks (even when digital transformation is suspected to act as a mediator) and call for managing resistance while implementing
digital transformation programs.
These results validate the considerable mediating impact of digital transformation in
the theorized model. They highlight the traditional role of ICT adoption and resistance to
change as factors driving the effectiveness of digital transformation in preventing risks and
highlight that technology adoption and change management strategies are important in
the link between them and successful outcomes.
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Table 5. Indirect effect.
Hypotheses
Independent Variable
H5
ICT adoption
H6
ICT adoption
H7
Resistance to change
H8
Resistance to change
Mediator
Digital
transformation
Digital
transformation
Digital
transformation
Digital
transformation
Dependent
Variable
Std. Beta
T-Value
p-Value
Decision
Financial risk
−0.166
7495
p < 0.01
Supported
Operational risk
−0.188
7637
p < 0.01
Supported
Financial risk
0.336
16,879
p < 0.01
Supported
Operational risk
0.379
18,534
p < 0.01
Supported
5. Discussions and Conclusions
5.1. Theoretical Implications
The results of this study have theoretical implications related to the link between
digital transformation, ICT adoption, resistance to change, and risk management. By
demonstrating that digital transformation helps with the mitigation of financial and operational risk, the findings support digital transformation as a mechanism that addresses
organizational and financial liabilities (Ali & Govindan, 2023; Cao, 2023; You & Zhao,
2023). While this corroborates previous theories in risk management, it also invites further
exploration of the contexts in which digital transformation can be associated with these
kinds of effects.
The interaction between ICT adoption, resistance to change, and operational and
financial risk, mediated by digital transformation, provides evidence for the complexity
of technological and organizational interactions. ICT adoption seems to enhance digital
transformation, and resistance seems to hinder it. Thus, both variables need more in-depth
exploration of their interrelations in distinct organizational contexts (Bagrationi et al., 2023;
Denning, 2023).
In this study, the mediating role played by digital transformation adds to the available
literature by showing how its function lies between the model’s variables. Still, this
mediating role may be context-specific, a line that begs further research to identify the
“how” of these findings.
Resistance to change is also among the significant barriers affecting the outcomes
of risk management, as it inhibits digital transformation and increases risks indirectly
(Bagrationi et al., 2023; Fiedler, 2010; Khanboubi & Boulmakoul, 2021). While this important
finding underlines the need to manage resistance, additional research is needed to explore
these relationships across different industries and organizational types.
5.2. Practical Implications
This research reveals insights for organizations that aim to improve their risk management through digital transformation. The findings indicate that digital transformation is an
effective two-fold risk mitigator that in itself is a practical contribution as a strategic tool
for improving organizational resilience (Liu & Qi, 2024). This should serve as information
for managers that digital transformation initiatives should remain an instrument for risk
management strategies.
The digital transformation process is positively driven by ICT adoption, which emphasizes the importance for organizations to adapt to modern information and communication
technologies, meaning investing in these technologies to support proper digital transformation streamlining. The downside of resistance to change is that it can undermine
digital transformation, and, as such, addressing organizational resistance is just as important. Leaders suggest that specific change management strategies should be implemented,
J. Risk Financial Manag. 2025, 18, 128
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including building an agile-oriented culture, ensuring sufficient training, and early stakeholder engagement within the transformation process (Handani, 2024; Surianto et al., 2024).
Moreover, the implications of these findings are that if organizations do not prioritize their
resistance to change, it can weaken the success of digital transformation and pose more
financial and operational risks. Thus, it needs to be continuously watched and managed
using strategic approaches (Khanboubi & Boulmakoul, 2021).
5.3. Research Limitations
While this research on the role of digital transformation in reducing financial and
operational risks is informative, there are a number of limitations that are related to the
generalizability of the results. To begin with, it is important to note that the findings are
based on a narrow geographical and sector-based focus within Morocco and in sectors like
textiles and tourism. Although this context enables us to provide grounded insights, it also
restricts the generalizability of our findings beyond comparable sectors and to economies
with different contextual and organizational dynamics. However, these findings need to
be replicated and, if necessary, expanded with studies that include broader industries and
global contexts.
The cross-sectional design of the research is another limitation that refers to data
collection at a certain point in time. Such an approach does not take account of the fact that
digital transformation is neither static nor linear and that it unfolds, often in an iterative
way, in response to technological and organizational changes. Longitudinal studies may
achieve better insight into not only the causal relationship between digital transformation
and risk mitigation but also when the effect of digital transformation starts (Rutter, 1994).
In addition, this study references participants’ own reports, which may include subjective omissions or errors in reporting (Woodside & Wilson, 2002). This limitation could
be mitigated through future studies that gather objective metrics, such as financial results,
reports of incidents where risks came to fruition, and so on, which would allow for more
conclusive validation of the results.
Author Contributions: Conceptualization, S.S.-s. and S.E.; methodology, S.S.-s. and S.E.; software,
S.S.-s. and S.E.; validation, S.S.-s. and S.E.; formal analysis, S.S.-s. and S.E.; investigation, S.S.-s. and
S.E.; resources, S.S.-s. and S.E.; data curation, S.S.-s. and S.E.; writing—original draft preparation,
S.S.-s. and S.E.; writing—review and editing, S.S.-s. and S.E.; visualization, S.S.-s. and S.E.; supervision, S.S.-s. and S.E.; project administration, S.S.-s. and S.E. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki and approved by the Ethics Committee of HECF Business School.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: All the data used in this study is available at the link: https://
drive.google.com/file/d/1MuCJRYFm9OfFblrjeqSynm57DHhejRF2/view?usp=sharing, accessed
on 21 February 2025.
Conflicts of Interest: The authors declare no conflicts of interest.
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