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Article

Managing Financial and Operational Risks Through Digital Transformation: The Mediating Influence of Information and Communication Technologies’ Adoption and Resistance to Change

by
Siham Slassi-sennou
1 and
Soufiane Elmouhib
1,2,*
1
ERMOT Laboratory, Faculty of Law, Economics and Social Sciences, Sidi Mohamed Ben Abdellah University, Fez 30060, Morocco
2
Multidisciplinary Research Laboratory LAREM, HECF Business School, Fez 30000, Morocco
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2025, 18(3), 128; https://doi.org/10.3390/jrfm18030128
Submission received: 27 January 2025 / Revised: 19 February 2025 / Accepted: 22 February 2025 / Published: 1 March 2025
(This article belongs to the Section Risk)
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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.

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).
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 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; N 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 (N 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).

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).

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 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 of innovation and integrating digital processes into financial risk management, digital transformation enables organizations to proactively anticipate and manage financial uncertainties (Ali & Govindan, 2023). Therefore, digital transformation mediates the relationship between resistance to change and financial risk (H7).
Similarly, resistance to change poses a significant barrier to operational risk management, as it can obstruct the adoption of advanced operational technologies and process improvements (Fiedler, 2010; Lines et al., 2015). This resistance often leads to inefficiencies, increased human errors, and vulnerabilities in compliance and system reliability (Rahman et al., 2024; Pleune, 2017). Digital transformation plays a crucial role in overcoming these obstacles by automating operations, enhancing real-time monitoring, and streamlining workflows to prevent operational disruptions (Ali & Govindan, 2023; Xu et al., 2017). Furthermore, digital transformation strengthens coordination and agility, ensuring that industrial regulations and safety standards are met while reducing risks related to system failures and process inefficiencies (Dokuchaev, 2020; Vorontsova & Baranov, 2021). Therefore, it mediates the relationship between resistance to change and operational risk (H8).

3. Research Methodology

3.1. Measurement

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 by Venkatesh and Bala (2008) based on determinants of technology adoption in the organizational context (Venkatesh & Bala, 2008). The scale for resistance to change was developed in the literature by Oreg (2003), focusing on behavioral and attitudinal resistance during organizational change (Oreg, 2003). Digital transformation was measured from the 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, we used proposed items capable of capturing managers’ perceptions (Figini et al., 2015; Gilliam et al., 2010).
To be more comprehensive, each construct is covered by more than one item. Survey items were developed to be linguistically neutral and contextually specific. To validate the items and ensure that all items were understood as intended, especially by Moroccan risk managers who are more flexible with French than English, a back-translation procedure was implemented in accordance with the recommendations presented in the literature (Brislin, 1986). This included translating the items into the local language and then having them back-translated into English by independent translators to ensure linguistic 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 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 responses (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).
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.
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.
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 and H2, respectively. This highlights the fact that digital transformation is an important requirement for risk mitigation at the enterprise level.
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.

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, 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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Jrfm 18 00128 g001
Figure 1. Research conceptual model.
Figure 1. Research conceptual model.
Jrfm 18 00128 g001
Table 1. Respondents’ characteristics (n = 768).
Table 1. Respondents’ characteristics (n = 768).
AttributesCharacteristicsNumber of ResponsesPercentage
Firm’s economic classificationVSE (very small enterprise)8411%
SMEs (small and medium enterprises)58476%
BE (big enterprise)10013%
SectorFinance16922%
Tourism14619%
Textiles23030%
Automotive699%
Other547%
Table 2. Convergent validity (n = 768).
Table 2. Convergent validity (n = 768).
ConstructsLoadingsCACRAVEConstructsLoadingsCACRAVE
Digital transformation 0.8860.8860.637Financial risk 0.8980.9000.662
DT10.787 FR10.810
DT20.791 FR20.822
DT30.810 FR30.832
DT40.804 FR40.809
DT50.798 FR50.809
DT60.797 FR60.800
ICT adoption 0.8840.8850.683Operational risk 0.9070.9100.643
ICTA10.843 0.828
ICTA20.835 0.757
ICTA30.842 0.792
ICTA40.805 0.790
ICTA50.806 0.826
Resistance to change 0.7990.8040.713 0.830
RtC10.861 0.790
RtC20.819
RtC30.852
Table 3. Heterotrait–Monotrait ratio (HTMT).
Table 3. Heterotrait–Monotrait ratio (HTMT).
Digital TransformationFinancial RiskICT AdoptionOperational RiskResistance to Change
Digital transformation
Financial risk0.661
ICT adoption0.7580.804
Operational risk0.7470.8950.896
Resistance to change0.8980.7160.8360.804
Table 4. Direct effect.
Table 4. Direct effect.
HypothesesRelationshipsFindingsResults
H1Digital transformation → financial riskNegative and statistically significant (β = −0.595; p < 0.01)Supported
H2Digital transformation → operational riskNegative and statistically significant (β = −0.672; p < 0.01)Supported
H3ICT adoption → digital transformationPositive and statistically significant (β = 0.279; p < 0.01)Supported
H4Resistance to change → digital transformationNegative and statistically significant (β = −0.565; p < 0.01)Supported
Table 5. Indirect effect.
Table 5. Indirect effect.
HypothesesIndependent VariableMediatorDependent VariableStd. BetaT-Valuep-ValueDecision
H5ICT adoptionDigital transformationFinancial risk−0.1667495p < 0.01Supported
H6ICT adoptionDigital transformationOperational risk−0.1887637p < 0.01Supported
H7Resistance to changeDigital transformationFinancial risk0.33616,879p < 0.01Supported
H8Resistance to changeDigital transformationOperational risk0.37918,534p < 0.01Supported
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Slassi-sennou, S.; Elmouhib, S. Managing Financial and Operational Risks Through Digital Transformation: The Mediating Influence of Information and Communication Technologies’ Adoption and Resistance to Change. J. Risk Financial Manag. 2025, 18, 128. https://doi.org/10.3390/jrfm18030128

AMA Style

Slassi-sennou S, Elmouhib S. 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. 2025; 18(3):128. https://doi.org/10.3390/jrfm18030128

Chicago/Turabian Style

Slassi-sennou, Siham, and Soufiane Elmouhib. 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, no. 3: 128. https://doi.org/10.3390/jrfm18030128

APA Style

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

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