Journal of Entrepreneurship, Management and Innovation (2026)
Volume 22 Issue 4: 25-45
DOI: https://doi.org/10.7341/20262242
JEL Codes: M15, O33, O32, Q56, L25, M10, C83
Khodor Shatila, Assistant Professor at IPAG Business School, 10/12 rue du Théâtre, 75015 Paris, France, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. 
Abstract
PURPOSE: This study examines how digital transformation (DT) influences sustainable business growth (SBG) in Middle Eastern firms, proposing and empirically testing a parallel mediation model in which innovation, organizational agility, and digital resilience serve as transmitting organizational capabilities. Grounded in the resource-based view and dynamic capabilities theory, the study addresses the theoretical gap arising from the fragmented treatment of digitalization, innovation, agility, and resilience as isolated constructs, and the empirical gap stemming from the near-exclusive focus of existing research on Western and manufacturing-dominated contexts. METHODOLOGY: A structured online questionnaire was administered between May and August 2025 to managers and senior professionals in firms actively engaged in digital transformation initiatives across five Middle Eastern economies: Lebanon, Jordan, the United Arab Emirates, the Kingdom of Saudi Arabia, and Kuwait. Of 410 distributed invitations, 339 responses were received, and following the removal of 15 duplicate entries and incomplete responses, a final sample of 324 valid responses was retained. Data was analyzed using partial least squares structural equation modeling (PLS-SEM) via SmartPLS 4.0.9.6, with bootstrapping based on 5,000 resamples to assess mediation effects. FINDINGS: Digital transformation exerts significant positive effects on innovation (β = 0.666), agility (β = 0.613), and digital resilience (β = 0.530), as well as a direct effect on sustainable business growth (β = 0.191). Agility (β = 0.305) and digital resilience (β = 0.209) significantly mediate the DT–SBG relationship, whereas the mediation path through innovation is non-significant (β = 0.044). The total indirect effect amounts to 0.327, yielding a Variance Accounted For of 63.1%, confirming partial mediation. IMPLICATIONS: Theoretically, the findings extend dynamic capabilities theory by empirically demonstrating that digital transformation functions as an enabling infrastructure rather than a direct performance mechanism, with agility and resilience constituting the primary conversion pathways to sustainable growth. Practically, the results advise managers and policymakers on digitally transforming economies to prioritize the development of adaptive responsiveness and resilience-oriented governance alongside technological investment, as technology adoption alone does not guarantee sustainability outcomes. ORIGINALITY & VALUE: This study is among the first to integrate innovation, agility, and digital resilience into a unified parallel mediation framework linking digital transformation to sustainable business growth, empirically validated in a multi-country Middle Eastern context, a setting underrepresented in the extant digital transformation literature.
Keywords: digital transformation, sustainable business growth, innovation, agility, digital resilience, resource-based view, RBV, sustainable business performance, organizational agility, digital resilience, innovation capability, dynamic capabilities, parallel mediation, PLS-SEM, Middle East, emerging markets.
INTRODUCTION
The rapid pace of digital transformation has significantly altered competitive dynamics across industries, reshaping how firms create value, innovate, and sustain performance. Rather than simply adopting digital tools, digital transformation entails integrating advanced technologies into organizational processes, business models, and strategic choices (Wang et al., 2024; Varzaru & Bocean, 2024). Digitalization, in this respect, affects not just operational efficiency but also the structural foundations of competitiveness. Meanwhile, studies are also finding a connection between digital transformation and larger economic revitalization and sustainability agenda (Bindeeba et al., 2025; Sun et al., 2024). Nevertheless, the processes by which digital transformation can ensure sustainable business results are not well incorporated into a consistent theoretical framework.
In spite of the fact that digital entrepreneurship has been mentioned as the recognition and exploitation of the opportunities provided by digital technologies (Bindeeba et al., 2025; Sun et al., 2024), the majority of literature addresses digitalization as either a technological phenomenon or a business model innovation process considered in isolation (Liu et al., 2023; Zhao et al., 2024). This fragmentation limits understanding of the interplay between technological integration and organizational capabilities for creating long-term, sustainable performance. In addition, the critical schools of thought warn of the unjustified positive impact of digitalization, with possible unintended effects including increased energy consumption, technological lock-in, and an unequal distribution of values (Usai et al., 2021). These strains imply that digital transformation does not necessarily lead to sustainable growth; rather, its impact depends on complementary strategic capabilities.
This paper conceptualizes digital transformation as a facilitating capability that enhances firms’ ability to be innovative, adaptable, and resilient to disruption, drawing on the resource-based view and dynamic capabilities theory (Teece et al., 2016). In this context, innovation, organizational agility, and digital resilience are discussed as mediating variables that digital transformation can affect sustainable development of business. Innovation is also a measure of a firm’s ability to create and introduce new ideas and processes (Zhao et al., 2024; Gaglio et al., 2022). Agility refers to the ability to detect environmental changes and quickly rearrange resources (Zhang et al., 2025; Xu et al., 2024). Digital resilience is the organization’s ability to absorb shocks, continue, and learn from disruptions (Duchek, 2020; He et al., 2023). These capabilities are united in an interdependent structure that could transform digital investments into performance results aimed at sustainability.
These dimensions have been studied independently in previous research. It has been demonstrated that digital transformation can help to boost innovation performance (Chen et al., 2024; Liu et al., 2023), enhance sustainability-related performance (Wang et al., 2024; Bindeeba et al., 2025), and strengthen adaptive capabilities like agility (Rafi et al., 2022; Hwang et al., 2025) and resilience (Duchek, 2020; Ye et al., 2024). Nevertheless, these streams are still very detached. Very little empirical research integrates digital transformation, innovation, agility, and resilience into a single structural representation to serve as a principle of sustainable business development. Moreover, the current body of evidence is largely based on single-industry settings or developed markets (Gaglio et al., 2022; Parrilli et al., 2023), leaving emerging and transitional markets relatively unexplored.
This study addresses these gaps by developing and empirically testing an integrative model linking digital transformation to sustainable business development through innovation, agility, and digital resilience. Instead of redefining digital entrepreneurship as another higher-order phenomenon, the study focuses on the firm-level processes of digital transformation and how they relate to dynamic capabilities to generate sustainability-oriented outcomes. By doing so, it sheds light on the systemic aspects of digital capability building and, in turn, contributes to a more consistent vision of the combined roles of technological integration, strategic responsiveness, and organizational resilience in long-term performance.
The study is empirically based on data from firms operating in Lebanon, Jordan, the United Arab Emirates, the Kingdom of Saudi Arabia, and Kuwait, in environments characterized by continuous digitalization and economic diversification. The study expands digital transformation and sustainability research by analyzing companies across sectors in these Middle Eastern economies, rather than focusing on Western, manufacturing-oriented societies.
Theoretically, the study is valuable in three aspects. To start, it contributes to the literature on digital transformation by abandoning direct-effect assumptions and testing a parallel mediation model grounded in dynamic capabilities theory (Teece et al., 2016). Second, it integrates innovation, agility, and digital resilience into a single empirical framework, thereby overcoming fragmentation in prior research. Third, it reconceptualizes sustainable business development as a multidimensional performance framework encompassing economic stability, environmental responsibility, and social considerations (Wang et al., 2024; Dangelico et al., 2022; Awwad et al., 2026), rather than financial expansion.
In practice, the results can guide managers and policymakers who wish to reconcile digital projects with sustainability goals. The findings indicate that long-term effectiveness in digitally transforming environments does not rely solely on adopting technology, but also on the establishment of complementary organizational competences that foster innovativeness, dynamic responsiveness, and endurance. In the context of digital transformation, the present study offers a theoretically grounded and empirically supported answer to the question of how technological integration leads to sustainable business development.
The remainder of the paper proceeds as follows. The next section reviews the theoretical background and develops the research hypotheses. This is followed by the methodology, measurement model assessment, structural model assessment, discussion of findings, and concluding remarks, including limitations and future research directions.
LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT
The primary sources of the present study are the RBV and dynamic capabilities theory, as they provide well-grounded explanations of the mechanisms by which firms achieve sustained competitive advantage in dynamic environments. The RBV holds that an organization’s success depends on the establishment and use of valuable, rare, inimitable, and non-substitutable (VRIN) resources that deliver long-term value. Digital transformation is a resource configuration approach in the digital age that enables corporations to strategically design their resources, leveraging technological, human, and organizational resources to achieve maximum performance (Bindeeba et al., 2025; Sun et al., 2024). However, they are insufficient because markets are more volatile and technology is evolving rapidly; the main characteristic of successful firms is the ability to renew and repackage resources in a dynamic environment, as described in the theory of dynamic capabilities (Teece et al., 2016). In that regard, innovation, agility, and digital resilience are considered the organization’s major dynamic capabilities, enabling firms to identify opportunities, exploit them, and restructure their resource base to sustain growth (Warner & Wäger, 2019). Innovation may also be linked to the Schumpeterian approach, which focuses on creative destruction, in which technological combinations can lead to the rebirth of competition (Zhao et al., 2024). The concept of agility as an outcome of dynamic capability suggests the company’s ability to respond swiftly to market shocks and implement strategy changes on the fly (Zhang et al., 2025). Digital resilience, on its part, enhances this flexibility by introducing technological resilience and learning mechanisms that enable a firm not only to bounce back after disruption but also to become more resilient (Duchek, 2020; He et al., 2023).
Digital transformation has been gradually integrated into modern business strategy and has affected how companies strive to achieve competitive advantage and sustainability in digitally intensive business settings. Digital transformation can be defined as a strategic process based on the resource-based view and dynamic capabilities theory and is viewed as the ability of firms to re-organize resources, redirect competencies, and integrate technological infrastructures to enable the creation of long-term values (Teece et al., 2016; Bindeeba et al., 2025; Warner, & Wäger., 2019; Shatila et al., 2025; Khodor et al., 2024). Instead of focusing solely on technology, digital transformation involves organizational alignment, process redesign, and the development of capabilities to accelerate efficiency, innovation, and flexibility. An emerging body of literature indicates that digital transformation positively correlates with better sustainability-oriented performance. For example, in a meta-analysis of 44 empirical studies, Bindeeba et al. (2025) found that digital transformation is positively associated with the economic, environmental, and social dimensions of sustainability. On the same note, Sun et al. (2024) believe that digital transformation can support sustainable growth by enhancing firms’ dynamic capabilities, thereby enabling continued adaptation and resilience. Chen et al. (2024) also relate the digital transformation to market-oriented business model innovation, which can lead to sustainability-oriented outcomes. Further evidence from Wang et al. (2024) and Liu et al. (2023) suggests that digital transformation has the potential to increase transparency, governance, and stakeholder involvement, which may be associated with long-term organizational stability. Nevertheless, the correlation between digital transformation and sustainability is not necessarily good everywhere. Critical views emphasize the possibility of uncompensated environmental costs arising from digitalization, as it can lead to higher energy use, greater technological reliance, and greater complexity in coordination (Usai et al., 2021). In addition, digital investments cannot deliver sustainable returns when they lack proper alignment with organizational capabilities or strategic purpose. Such contradictory results imply that digital transformation is not necessarily a source of sustainable growth; instead, it has different impacts depending on the integration of digital technologies into organizational processes and strategic purpose. In terms of dynamic capabilities, digital transformation could be considered an enabling infrastructure that raises the opportunities for sensing, resource reconfiguration, and technology initiatives, as well as long-term alignment capabilities of firms (Teece et al., 2016; Kulichyova et al., 2025). Digital capabilities can be used for sustainability, both in the short term for operational efficiency and in the long term for strategic goals when applied as part of value-creation models. To that end, despite considering the risks and contingencies of the situation, the current study hypothesizes that digital transformation is positively linked to sustainable business development at the firm level. This led to the development of the following hypothesis:
H1: Digital transformation has a positive and significant effect on sustainable business growth.
Innovation is often considered one of the main outcomes of digital transformation, but the nature of this interdependence should be carefully discussed in theoretical terms. In the Schumpeterian view, technological change facilitates the recombination of resources and information to create new products, services, and business models. In a similar vein, the dynamic capabilities theory would imply that digital transformation can help a firm increase its capacity to perceive opportunities and reconfigure assets in ways that trigger innovation (Teece et al., 2016). Information asymmetries can be minimized, cross-functional coordination will be possible, and the speed of experimentation will increase with the help of digital infrastructures (Zhao et al., 2024). Digital transformation, in this sense, can provide the structural conditions that are favorable to both exploratory and exploitative innovation (Shatila et al., 2025).
There is generally empirical evidence of a positive correlation between digital transformation and innovation performance. Zhao et al. (2024) demonstrate that digitalization increases the firms’ innovative potential by improving decision quality and reducing informational constraints. Liu et al. (2023) also state that digital transformation promotes a learning-oriented, calculated risk-taking organizational culture that can drive innovation. Gaglio et al. (2022) emphasize that digital technologies enhance knowledge management and inter-organizational relationships, enabling knowledge diffusion and co-creation processes to be conducted more rapidly. Other research suggests that advanced digital systems accelerate product development and support customization (Varzaru & Bocean, 2024; Li et al., 2023). The relationship, however, is not linear.
Without strategic alignment with organizational processes, digital transformation may further introduce complexity, coordination issues, and technological inflexibility. Innovative outcomes may be constrained when digital infrastructure is overinvested without the corresponding development of capabilities (Usai et al., 2021; Shatila et al., 2026). Moreover, digital tools are not sufficient to ensure novelty. Innovation will be determined by firms’ ability to unleash technological resources within the broader strategic and cultural frameworks. These reflections indicate that the digital transformation does not necessarily lead to innovation but can heighten the organization’s innovative potential when incorporated into adaptive organizational structures. In dynamic capabilities terms, digital transformation can be viewed as an enabling infrastructure that increases firms’ capacity to access, recombine, and use knowledge resources. Digital systems could reduce barriers to innovation and support incremental and radical initiatives by increasing transparency, interconnectivity. In this respect, therefore, despite considering the contextual contingencies, the theoretical rationale and the existing empirical evidence suggest that digital change enhances innovative capability in firms. This led to the development of the following hypothesis:
H2: Digital transformation has a positive and significant effect on innovation.
Organizational agility is the ability of a firm to detect environmental changes, leverage new opportunities, and realign resources to address uncertainty. In the dynamic capabilities model, the concept of agility captures the implementation of sensing and reconfiguration processes that help companies adjust to unstable environments (Teece et al., 2016). Digital transformation and agility are typically linked in theory, as digital systems can enable improved information processing, coordination, and real-time visibility, potentially enabling quick adjustments (Zhang et al., 2025). Nonetheless, there is still a question of whether digital transformation automatically equates to agility. There is considerable empirical evidence of a positive correlation between digital capabilities and organizational responsiveness. According to Zhang et al. (2025), the more digitally integrated a firm is, the more flexible it becomes in responding to external disruptions, in part because it allows the firm to respond more swiftly and efficiently. On the same note, Shatila et al. (2024) assert that digital transformation fosters adaptive cultures and simplifies communication, thereby enabling greater flexibility. Xu et al. (2024) also argue that the digital transformation reinforces analytical and data-processing abilities that, in turn, promote learning and foresight, which are the main elements of agility. Further support from Moarefi and Mortezaei (2025) and Bux et al. (2025) indicates that digital platforms and automation systems can be used to make faster, evidence-based decisions and to collaborate effectively across functions, both of which are attributes of adaptive responsiveness. However, the digital transformation does not consistently create agility. Such technological integration may create structural complexity, raise coordination costs, or introduce technological rigidity if systems are poorly aligned with organizational operations. Companies can also implement digital tools without establishing decentralized decision-making and adaptive leadership, thereby restraining their agile capabilities. These reflections suggest that digital transformation alone is not enough. It requires agility, which is achieved by integrating digital infrastructures into organizational routines and governance structures. In terms of dynamic capabilities, digital transformation can be conceptualized as an enabling infrastructure that enhances the firm’s ability to process information, coordinate resources, and make rapid adjustments. The operationalization of agile behaviors can be achieved through digital systems by reducing communication time lags and enhancing the proactive power of analysis. In line with this, although the contextual contingencies and potential implementation difficulties are recognized, the theoretical rationale and existing empirical evidence indicate that the digital transformation enhances organizational agility at the firm level. This led to the development of the following hypothesis:
H3: Digital transformation has a positive and significant effect on agility.
The term digital transformation has also come to be associated with building digital resilience, especially in an environment of technological turbulence and systemic uncertainty. Modern conceptualizations of resilience have gone beyond the notion of disruption recovery and focus on the ability to adapt, reorganize, and transform in response to perpetual change (Boh et al., 2026). In the dynamic capabilities framework, resilience can be viewed as a higher-order capability that helps firms perceive environmental instability, reallocate resources, and maintain performance in unfavorable circumstances (Duchek, 2020). In this regard, digital transformation can strengthen resilience by introducing sophisticated new technologies into organizational workflows and decision-making models. Generally, empirical studies provide evidence of a positive relationship between digital capabilities and organizational resilience. He et al. (2023) demonstrate that companies that invest in online platforms and knowledge-sharing systems exhibit more adaptive responses to technological upheavals, in part because of improved coordination and data transparency. Equally, Dolgui and Ivanov (2022) indicate that digitalized production and supply chain systems are resilient systems because they provide flexibility and real-time data access. Ye et al. (2024) argue that digital resilience is underpinned by information technology capabilities that enable the dynamic reallocation of technological resources in response to environmental change. Other studies by Parrilli et al. (2023) and Chen et al. (2024) emphasize that resilience changes with digital maturity, suggesting that integrating technology facilitates ongoing learning and dynamic development of the system structure. Mourtzis and Panopoulos (2022) also emphasize that digital transformation, and thus interoperability, redundancy, and automation, can make systems robust. Nonetheless, digital transformation does not necessarily generate resiliency. The absence of governance and risk management capabilities may result in cyber vulnerabilities and systemic fragility or technological lock-in when overreliance on interconnected digital systems is not achieved (Boh et al., 2026). Digital integration can also expose users to platform dependency and infrastructure risk. The factors to consider indicate that resilience is determined by more than technological investment; it is shaped by how digital systems are strategically managed and integrated into organizational practices. According to the dynamic capabilities approach, digital transformation may be considered an enabling infrastructure that facilitates firms’ capacity to sustain continuity and adjust during disruption. Digital systems could aid the creation of adaptive resilience mechanisms by enhancing transparency, coordination, and analytical foresight. To this end, the theoretical rationale and current empirical evidence suggest that, despite the risks of implementation and the contingent specifics of situations, the digital transformation is likely to have a positive impact on digital resilience at the firm level. This led to the development of the following hypothesis:
H4: Digital transformation has a positive and significant effect on digital resilience.
Innovation has been considered one of the driving forces of organizational revival and continued competitiveness. In Schumpeterian terminology, innovation can be viewed as the process by which companies reassemble knowledge and resources to create new value, which, in turn, drives economic evolution and industrial change. In the modern market, where rapid technological growth and environmental uncertainty are driving rapid change, innovation is becoming not only a competitive advantage but a strategic need (Teece et al., 2016). It also helps companies balance short-term and long-term operational flexibility by continually updating products, processes, and business models. There is evidence that innovation and SOP are positively correlated, and this is supported by empirical research. As Rashid et al. (2020) illustrate, innovation capabilities are linked to non-financial and financial performance indicators, such as profitability and market share. According to Awwad et al. (2026), innovation will help achieve sustainability by improving resource efficiency and creating environmentally friendly products. On the same note, Dangelico et al. (2022) recognize green innovation as a strategy through which a company can attain a competitive advantage and tackle environmental goals. Watson et al. (2018) also argue that innovation has been found to increase stakeholder interest and legitimacy that can help bolster reputational and financial performance in the long term. Other studies indicate that innovation enhances organizations’ flexibility, enabling companies to integrate sustainability into emerging business paradigms (Song et al., 2023; Oliveira-Dias et al., 2022). However, not every innovation-sustainability relationship is linear. Innovation may require significant investment in resources, risk, and implementation complexity, which can limit short-term performance or cause unintended trade-offs with the environment without careful management. Additionally, not every type of innovation is sustainable; companies might seek technological novelty without integrating sustainability principles into their strategic course. These reflections imply that the role of innovation in sustainable business development is to align innovation activities with environmental, social, and governance goals. Within the dynamic capabilities conceptualization, innovation is one of the fundamental mechanisms by which companies regenerate competences and evolve as stakeholder expectations change (Teece et al., 2016). Innovation can contribute to competitiveness and advance sustainability goals when strategically oriented towards long-term value creation. With that said, despite situational contingencies and implementation difficulties, the theoretical rationale and existing empirical data support the idea that innovation has a positive impact on sustainable business development. This led to the development of the following hypothesis:
H5: Innovation has a positive and significant effect on sustainable business growth.
Organizational agility also relates to individualization as a dynamic skill, enabling firms to sense environmental change, exploit newly introduced opportunities, and realign resources with volatility. The dynamic capabilities framework has three dimensions that operationalize responsiveness and adaptability in the face of uncertainty as agility (Teece et al., 2016). Agility, rather than mere speed, is a company’s ability to restructure and realign its processes and strategy in response to technological turbulence, competitive discontinuity, or regulatory shifts. Agility may be an instrument that links temporary responsiveness with sustainability over time in digitally heavy environments where technological cycles are shortened and market requirements are evolving rapidly. The overall testimony of the empirical studies is that agility relates to the enhanced organizational performance and dynamic outcomes. Rafi et al. (2022) define that more intense innovation and financial performance typify agile companies and are partially evident in the rapid resource redistribution and decentralized decision-making processes. According to Hwang et al. (2025), agility can help firms manage market uncertainty and maintain their competitive positioning. It can also be suggested that there may be a relationship between strategic results and technological investment via agility, whereby responsiveness may be associated with technological investment (Chen et al., 2024). Similarly, Nakandala et al. (2024) state that agility promotes sustainability by increasing resource utilization efficiency and fostering lifelong organizational learning. Agility, according to Jabbour et al. (2020), enables the transition to circular and sustainable business models by facilitating the incorporation of stakeholder feedback and technological innovation within a limited time frame. Roy et al. (2024) expand on this point, noting that digital agility enables short-term flexibility and a consistent long-term course of action. Mueller-Saegebrecht and Walter (2025) even suggest conceptualizing agility as a meta-capability, i.e., a stratum, an operational level, and a learning level.
Nevertheless, there is a close relationship between agility and sustainable growth. Agility can enhance responsiveness, but excessive agility may lead to coordination issues or a lack of strategy when it is not aligned with long-term objectives. Moreover, agility does not always mean sustainability; agile principles can be applied by companies seeking to seize a market quickly, without integrating environmental and social issues into their strategic decisions. Such considerations suggest that the role of agility in sustainable business growth is founded on a strategic orientation and alignment with sustainability objectives. It is possible to consider agility through the lens of dynamic capabilities, which hold that companies constantly renew and refresh their competencies and reallocate resources in response to evolving stakeholder demands (Teece et al., 2016). In a balanced economic, environmental, and social performance, agility can be employed to enable strategic integration with sustainability-focused goals. Subsequently, organizational agility will have a positive impact on the growth of sustainable businesses, as, despite contingent circumstances and governance concerns, theoretical arguments and available empirical evidence support the expectation of such a trajectory. This led to the development of the following hypothesis:
H6: Agility has a positive and significant effect on sustainable business growth.
Digital resilience has become a key organizational capability in digitally intensive settings being influenced by fast-paced technological change. Drawing on resilience theory and the resource-based view, digital resilience can be defined as a firm’s ability to withstand technological shocks, adapt to digital uncertainty, and sustain long-term performance (Duchek, 2020). Notably, resilience in the digital economy is not just about recovery but also about anticipating risks, reconfiguring in response to them, and learning to use disruption to create value and drive renovation. Within this sense, resilience is not just a state of stability, but adaptive robustness in changing technological ecosystems. Generally, empirical research indicates that digital resilience promotes the longevity of organizational performance. According to the report by Kumar et al., firms with high digital resilience perform better, especially when technological disruption is involved (Kumar et al., 2024). According to Saeed et al. (2023), IT governance and cybersecurity play a crucial role in securing business continuity, as robust digital security mechanisms have been shown to minimize recovery time and reduce operational susceptibility to disruption. According to Parrilli et al. (2023), resilient organizations are better able to rise to the challenge of crises and turn them into an innovative opportunity, thereby ensuring increased competitiveness in the long term. On the same note, Nair et al. (2024) reveal that the ability to maintain revenue and productivity stability during a period of systemic disruption is more evident in firms with well-structured digital resilience frameworks. Awad et al. (2024) go further and argue that digital resilience enhances stakeholder trust and governance transparency, which, in turn, can indirectly improve sustainability-oriented performance. Clement and Rivera (2017) underline that resilience promotes adaptive learning processes that help organizations transform adversity into strategic benefit. Duchek’s (2020) conceptualization of resilience as a dynamic capability that evolves alongside digital transformation and innovation confirms its strategic relevance.
However, digital resilience is not a sure-footed stepping stone to sustainable growth. Cybersecurity, redundancy and digital infrastructure investments may be expensive and overconfidence in risk mitigation can lead to a lack of strategic experimentation. In addition, resilience processes can cushion operational continuity without needing to be geared towards environmental or social sustainability goals, unless those goals are set. These reflections indicate that the connection between digital resilience and sustainable business development relies on how resilience capabilities can be incorporated into broader strategy and governance systems. In the context of dynamic capabilities, digital resilience can increase firms performance in uncertain circumstances by maintaining continuity and facilitating adaptive change (Duchek, 2020). Resilience can help maintain economic stability, stakeholder trust, and long-term value creation when appropriately harmonized with sustainability-focused goals. In line with this, contextual contingencies and possible trade-offs notwithstanding, there is theoretical reasoning and current empirical evidence that counter the expectation that digital resilience contributes positively to sustainable business development. This led to the development of the following hypothesis:
H7: Digital resilience has a positive and significant effect on sustainable business growth.
The research parallel mediation model, highlighting the research hypotheses, was represented in Figure 1.
Figure 1. Research model
METHODOLOGY
The paper employs a quantitative, cross-sectional research design to examine the structural relationships among digital transformation, innovation, agility, digital resilience, and sustainable business growth at the firm level. The aim is explanatory: to examine how digital transformation has direct and indirect relationships with sustainability-oriented performance outcomes, based on organizational capabilities. Since the proposed framework is complex and the relationships among variables are multiple, PLS-SEM was used with SmartPLS 4.0.9.6. PLS-SEM is especially appropriate for predictive-focused studies and for models that emphasize explaining variance in endogenous variables. This method also allows robust estimation of indirect effects in parallel mediation models without multivariate normality assumptions.
Measurement of constructs
All constructs were operationalized using multi-item Likert-type scales adapted from established empirical studies to ensure content validity and theoretical coherence. A five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”) was employed to capture respondents’ evaluations of their organizations’ digital and strategic capabilities.
DT was measured using items adapted from Bindeeba et al. (2025), Sun et al. (2024), and Liu et al. (2023), capturing the extent of digital integration across organizational processes, strategic alignment of digital initiatives, data-driven decision-making, and technology-enabled business model transformation. INN was assessed using scales adapted from Zhao et al. (2024) and Gaglio et al. (2022) that reflect both exploratory and exploitative innovation capabilities, including new product and service development, process improvement, and knowledge-based learning, consistent with Schumpeterian innovation logic. AG was operationalized using measures drawn from Zhang et al. (2025), Xu et al. (2024), and Rafi et al. (2022), capturing strategic flexibility, rapid decision-making, and resource reconfiguration in response to environmental turbulence. Digital resilience (DR) was measured based on Duchek (2020), He et al. (2023), and Ye et al. (2024), emphasizing absorptive capacity, adaptive learning, technological robustness, and recovery capabilities that enable continuity under disruption. SBG was assessed using multidimensional indicators adapted from Wang et al. (2024), Dangelico et al. (2022), and Awwad et al. (2026), encompassing economic performance, social responsibility, and environmental stewardship, thereby reflecting an integrated sustainability-oriented performance construct.
Sampling and data collection
Data were collected between May and August 2025 through a structured online questionnaire administered to managers and senior professionals in organizations actively engaged in digital transformation initiatives across five Middle Eastern economies: Lebanon, Jordan, the United Arab Emirates, the Kingdom of Saudi Arabia, and Kuwait. These countries were selected to represent a regional context characterized by varying degrees of digital maturity, economic diversification, and institutional development, thereby enabling cross-contextual analytical richness within a coherent geopolitical setting. A purposive non-probability sampling strategy was employed to ensure that all respondents possessed direct managerial knowledge of digital transformation processes and sustainability-related organizational practices. Eligibility was restricted to individuals holding managerial, executive, or strategic roles who were actively involved in digital transformation projects at the time of the survey. To enforce the firm-level unit of analysis and prevent multiple responses from the same organization, only one respondent per firm was targeted, and questionnaires were distributed through professional networks and direct organizational contact rather than open-access platforms. A total of 410 questionnaire invitations were distributed across the five countries. Of these, 339 responses were received, yielding an initial response rate of 82.7%. Following data screening, 15 responses were removed due to duplication identified in an additional set of incomplete or internally inconsistent responses, resulting in a final usable sample of 324 valid responses, representing an effective usable response rate of 79.0%.
Prior to full deployment, the questionnaire underwent a pilot testing phase with a small group of academics and practitioners familiar with digital transformation research, who reviewed the instrument for content clarity, item comprehensibility, and face validity. Minor wording adjustments were made based on their feedback before the survey was finalized. To assess the potential threat of nonresponse bias, an extrapolation test was conducted comparing early and late respondents across key demographic variables (firm size, industry, and country), following the procedure recommended by Armstrong and Overton (1977). No statistically significant differences were detected between early and late respondents, providing reasonable assurance that nonresponse bias does not materially threaten the representativeness of the collected data.
Due to the purposive, non-probabilistic nature of the sampling design, the findings cannot be statistically generalized to the broader population of firms in the region. Rather, the results provide analytical insight into the structural associations among the studied constructs within the specific regional and organizational context under investigation. Possible selection bias and context-specific effects are acknowledged as methodological limitations and discussed further in the limitations section.
Table 1. Sample profiling
|
Variable |
Category |
Frequency |
Percent (%) |
Cumulative (%) |
|
Firm size |
Large (≥ 250 employees) |
134 |
41.4 |
41.4 |
|
Medium (50–249 employees) |
119 |
36.7 |
78.1 |
|
|
Small (< 50 employees) |
71 |
21.9 |
100 |
|
|
Firm age |
Less than 5 years |
38 |
11.7 |
11.7 |
|
5–10 years |
74 |
22.8 |
34.5 |
|
|
11–20 years |
75 |
23.1 |
57.6 |
|
|
More than 20 years |
137 |
42.3 |
100 |
|
|
Industry type |
Finance and Banking |
36 |
11.1 |
11.1 |
|
Information Technology |
90 |
27.8 |
38.9 |
|
|
Manufacturing |
40 |
12.3 |
51.2 |
|
|
Services |
92 |
28.4 |
79.6 |
|
|
Other (Logistics, Retail, etc.) |
66 |
20.4 |
100 |
|
|
Ownership structure |
Private |
241 |
74.4 |
74.4 |
|
Public |
83 |
25.6 |
100 |
|
|
Digital maturity level |
Early Stage (initial adoption) |
40 |
12.3 |
12.3 |
|
Developing Stage (moderate adoption) |
126 |
38.9 |
51.2 |
|
|
Mature Stage (high integration) |
158 |
48.8 |
100 |
Source: Author’s work based on SPSS Version 17.
Table 1 sample represents a comparatively diversified organizational structure. There is a significant representation of established corporate bodies, with large firms (41.4%), medium-sized firms (36.7%), and small firms (21.9%) being the majority. In terms of firm age, most organizations have been in operation for over 20 years (42.3), indicating a mature business environment, and young firms (under 10 years) constitute about a third of the sample. Information Technology (27.8) and Services (28.4) are leading sectors, indicating a high concentration of the digitally focused, service-based industry. The majority of firms are privately owned (74.4%), compared with 25.6% of publicly listed corporations. Last but not least, digital maturity also seems to be rather advanced, with almost half of the organizations (48.8) operating at a mature phase of digital integration, 38.9 percent at the developing phase, and 12.3 percent at the early stage.
Data analysis procedure
The data analysis followed a two-step strategy, as the PLS-SEM guidelines dictate. In the first part, the measurement model was evaluated to determine the indicators’ reliability, internal consistency, and convergent and discriminant validity. In the second phase, the structural model was tested to assess the hypothesized direct and indirect relationships between the constructs. Bootstrapping with 5000 subsamples was used to assess the statistical significance of path coefficients and mediation effects. The resulting empirical standard errors and confidence intervals are obtained through this resampling process, without making any rigid distributional assumptions, thereby strengthening inference. The tool of 5000 bootstrap resamples is stable and reliable in giving estimates of indirect effects in the mediation framework.
Measurement model assessment
The model used to measure it was tested for outer loading, internal consistency reliability, and convergent and discriminant validity. To determine the reliability of the indicators, outer loadings were used, and all were above the minimum acceptable level of 0.50, with most above 0.70. This implies that the indicators have adequate variance with their constructs. Internal consistency was assessed using Cronbach’s alpha and composite reliability, both with high values exceeding recommended levels. The used values of Average Variance Extracted (AVE) over 0.50 supported convergent validity because each construct can be used to explain more than half of the variance of its indicators.
Both the Fornell-Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio were used to assess discriminant validity. In the Fornell-Larcker measure, the square root of the AVE of the individual constructs was higher than its correlation with the other ones. Moreover, all HTMT values were lower than the conservative threshold of 0.85, indicating that the constructs are empirically distinct. Outer Variance Inflation Factor (VIF) values were used to test collinearity at the indicator level, and the results indicated values below the recommended cut-off; thus, collinearity does not pose a threat to the estimation of measurement models.
Structural model assessment
The structural model was evaluated by examining path coefficients, coefficients of determination (R²), predictive relevance (Q²), and collinearity diagnostics at the construct level. The R² value for Sustainable Business Growth was 0.402, indicating moderate explanatory power within the studied sample. This suggests that digital transformation, innovation, agility, and digital resilience collectively explain a meaningful proportion of variance in sustainability-oriented performance outcomes.
Predictive relevance was assessed using the blindfolding procedure with an omission distance of 7, consistent with the recommendation that the omission distance should divide the number of observations without remainder and fall between 5 and 10 (Hair et al., 2019). The Q² value for Sustainable Business Growth was greater than zero, indicating that the model is predictive of the endogenous construct. It should be noted that Q² obtained through the blindfolding procedure evaluates predictive relevance within the sample, rather than constituting true out-of-sample prediction; the latter would require additional procedures such as PLSpredict, cross-validation, or a holdout sample (Shmueli et al., 2019). Construct-level collinearity was assessed through inner model VIF values to ensure that multicollinearity among predictors did not distort structural estimates. The VIF values for predictors remained below the conservative threshold of 3.3. These results indicate that multicollinearity does not bias the interpretation of structural relationships.
Mediation analysis
Bootstrapped specific indirect effects were used to analyze the mediating effect of innovation, agility, and digital resilience on the relationship between digital transformation and sustainable business growth based on 5000 resamples. Indirect effects of digital transformation on sustainable business development via each mediator were found to be statistically significant, suggesting that these organizational abilities convey a portion of the impact of digital transformation on sustainability-based performance.
Bootstrapped specific indirect effects were used to analyze the mediating role of innovation, agility, and digital resilience in the relationship between digital transformation and sustainable business growth, based on 5,000 resamples. The direct effect of digital transformation on sustainable business growth remained statistically significant after the inclusion of the three mediators (β = 0.191, p = 0.024), confirming partial mediation. The total indirect effect across the three pathways amounts to 0.327, while the total effect of digital transformation on sustainable business growth is 0.518. The resulting Variance Accounted For (VAF) is 63.1%, indicating that the majority of digital transformation’s influence on sustainable business growth is transmitted through the mediating capabilities of agility and digital resilience, while a meaningful direct effect persists. Among the three indirect paths, DT → AG → SBG (0.187) and DT → DR → SBG (0.111) are statistically significant, whereas DT → INN → SBG (0.029) is non-significant, suggesting that innovation does not function as an active mediating channel in this sample.
Common method bias
Given the cross-sectional and single-respondent nature of the data collection, potential common-method bias was assessed. Full collinearity VIF values remained below 3.3, providing further evidence that common method bias does not materially threaten the validity of the structural relationships. Nonetheless, the cross-sectional and self-reported nature of the data is acknowledged as a methodological limitation.
RESULTS
Measurement model assessment
Table 2 reports the outer loadings for all indicator variables across the five reflective constructs: Agility (AG), Digital Resilience (DR), Digital Transformation (DT), Innovation (INN), and Sustainable Business Growth (SBG). In PLS-SEM, indicator loadings are considered acceptable when they exceed 0.708, as this indicates that the construct explains more than 50% of the indicator’s variance (Hair et al., 2019). The AG indicators yield loadings ranging from 0.812 to 0.836, and the INN indicators record the highest individual loading in the model at 0.861 (INN4), both sets reflecting strong construct-indicator relationships. DT indicators range from 0.737 to 0.820, and SBG indicators from 0.686 to 0.861, all demonstrating satisfactory convergence with their respective constructs. The DR indicators present slightly more variation, with DR1 recording the lowest loading in the model at 0.684, marginally below the conventional threshold; however, given that its removal would not substantially improve the AVE and that the remaining indicators load strongly, its retention is methodologically justifiable (Hair et al., 2017).
Table 2. Factor loadings
|
AG |
DR |
DT |
INN |
SBG | |
|---|---|---|---|---|---|
|
AG1 |
0.836 |
||||
|
AG2 |
0.817 |
||||
|
AG3 |
0.821 |
||||
|
AG4 |
0.812 |
||||
|
DR1 |
0.684 |
||||
|
DR2 |
0.803 |
||||
|
DR3 |
0.795 |
||||
|
DR4 |
0.807 |
||||
|
DT1 |
0.763 |
||||
|
DT2 |
0.737 |
||||
|
DT3 |
0.820 |
||||
|
DT4 |
0.798 |
||||
|
INN1 |
0.782 |
||||
|
INN2 |
0.778 |
||||
|
INN3 |
0.833 |
||||
|
INN4 |
0.861 |
||||
|
SBG1 |
0.686 |
||||
|
SBG2 |
0.824 |
||||
|
SBG3 |
0.770 |
||||
|
SBG4 |
0.861 |
Source: Author’s work based on Smart PLS Version 4.0.9.6.
Table 3 shows the internal consistency and convergent validity values of all constructs. The alpha values for Cronbach are between 0.778 (DR) and 0.839 (AG), which exceed the suggested minimum of 0.70, indicating good internal consistency. The composite reliability values (rho c) range from 0.856 to 0.892, which also supports the construct reliability. Furthermore, all AVEs fall within the range of 0.599-0.675, indicating that both constructs explain more than 50% of the variance in their indicators.
Table 3. Reliability analysis
|
Cronbach’s alpha |
Composite reliability (rho_a) |
Composite reliability (rho_c) |
Average variance extracted (AVE) |
|
|
AG |
0.839 |
0.841 |
0.892 |
0.675 |
|
DR |
0.778 |
0.791 |
0.856 |
0.599 |
|
DT |
0.785 |
0.787 |
0.861 |
0.608 |
|
INN |
0.830 |
0.832 |
0.887 |
0.663 |
|
SBG |
0.794 |
0.807 |
0.867 |
0.621 |
Source: Author’s work based on Smart PLS Version 4.0.9.6.
Table 4 indicates that all HTMT values are below the conservative cut-off of 0.85, supporting the discriminant validity of the constructs. The maximum value of HTMT lies between AG and INN (0.834), indicating a rather good conceptual correlation, but it falls within acceptable ranges. Other inter-construct HTMT ratios range from 0.610 to 0.765, indicating empirical differentiation amongst variables. These results confirm that the constructs are not highly correlated and highlight distinct theoretical concepts in the model.
Table 4. Heterotrait-Monotrait Ratio (HTMT)
|
AG |
DR |
DT |
INN |
|
|
DR |
0.751 |
|||
|
DT |
0.754 |
0.669 |
||
|
INN |
0.834 |
0.676 |
0.823 |
|
|
SBG |
0.703 |
0.648 |
0.629 |
0.610 |
Source: Author’s work based on Smart PLS Version 4.0.9.6.
Table 5 also affirms discriminant validity on the Fornell-Larcker criterion. The inter-construct correlations are lower as compared to the square root of AVE of each construct (diagonal elements). As an example, the square root of AVE of AG (0.821) has higher value than the highest correlation with INN (0.695), and the square root of AVE of SBG (0.788) is higher than the correlation with other constructs. It means that each construct will have greater variance among its indicators than among indicators of other latent variables, thereby certifying the uniqueness of the constructs in the structural model.
Table 5. Fornell-Lacker discriminant validity
|
AG |
DR |
DT |
INN |
SBG |
|
|
AG |
0.821 |
||||
|
DR |
0.615 |
0.774 |
|||
|
DT |
0.613 |
0.530 |
0.780 |
||
|
INN |
0.695 |
0.552 |
0.666 |
0.814 |
|
|
SBG |
0.581 |
0.522 |
0.516 |
0.499 |
0.788 |
Source: Author’s work based on Smart PLS Version 4.0.9.6.
Table 6. Multi-collinearity statistics
|
VIF |
|
|
AG1 |
2.036 |
|
AG2 |
1.928 |
|
AG3 |
1.854 |
|
AG4 |
1.879 |
|
DR1 |
1.415 |
|
DR2 |
1.624 |
|
DR3 |
1.598 |
|
DR4 |
1.633 |
|
DT1 |
1.564 |
|
DT2 |
1.383 |
|
DT3 |
1.693 |
|
DT4 |
1.686 |
|
INN1 |
1.627 |
|
INN2 |
1.635 |
|
INN3 |
2.028 |
|
INN4 |
2.174 |
|
SBG1 |
1.278 |
|
SBG2 |
1.804 |
|
SBG3 |
1.695 |
|
SBG4 |
2.054 |
Source: Author’s work based on Smart PLS Version 4.0.9.6.
Table 6 presents the VIF values for all indicator variables across the five latent constructs, namely Agility (AG), Digital Resilience (DR), Digital Transformation (DT), Innovation (INN), and Sustainable Business Growth (SBG), assessed to diagnose potential multicollinearity concerns within the measurement model. In PLS-SEM, multicollinearity is considered problematic when VIF values exceed the threshold of 5.0, with more conservative scholars recommending a ceiling of 3.3 to ensure the stability and interpretability of the structural estimates (Hair et al., 2019; Diamantopoulos & Siguaw, 2006). As shown in Table 6, all indicator VIF values fall well below both thresholds, ranging from 1.278 (SBG1) to 2.174 (INN4), thereby confirming the absence of multicollinearity among the reflective indicators. The AG indicators range from 1.854 to 2.036, the DR indicators from 1.415 to 1.633, and the DT indicators from 1.383 to 1.693, all of which reflect acceptable and stable levels of inter-indicator independence. Similarly, the INN indicators yield VIF values between 1.627 and 2.174, while the SBG indicators range from 1.278 to 2.054, further corroborating the absence of redundancy across the measurement items. These results collectively indicate that each indicator contributes unique, non-overlapping variance to its respective construct, thereby satisfying a critical prerequisite for the validity and reliability of subsequent structural path estimations (Hair et al., 2017; Ringle et al., 2015).
Table 7. Inner model VIF
|
Predictor → SBG |
VIF |
|
AG |
2.372 |
|
DR |
1.743 |
|
DT |
2.010 |
|
INN |
2.399 |
Source: Author’s work based on Smart PLS Version 4.0.9.6.
Table 7 reports the VIF values for the inner model predictors of sustainable business growth. The VIF values range from 1.743 to 2.399, remaining well below the conservative threshold of 3.3 recommended in PLS-SEM literature (Hair et al., 2021). These results indicate the absence of multicollinearity among the predictor constructs (innovation, agility, digital resilience, and digital transformation), confirming that the structural path estimates are stable and not inflated by collinearity. Therefore, multicollinearity does not pose a threat to the validity of the structural model.
Table 8. R-Square
|
R-square |
R-square adjusted |
|
|
AG |
0.376 |
0.374 |
|
DR |
0.280 |
0.278 |
|
INN |
0.444 |
0.442 |
|
SBG |
0.408 |
0.400 |
Source: Author’s work based on Smart PLS Version 4.0.9.6.
The coefficient of determination (R²) results, presented in Table 8, reveal the explanatory power of the structural model across all endogenous constructs. Innovation (INN) demonstrates the highest R² value of 0.444 (adjusted R² = 0.442), indicating that approximately 44.4% of its variance is explained by the exogenous variable, which reflects a moderate-to-substantial predictive accuracy in line with established benchmarks in social science research (Hair et al., 2019). Sustainable Business Growth (SBG) follows with an R² of 0.408 (adjusted R² = 0.400), suggesting that the model accounts for roughly 40% of the variance in SBG, a noteworthy result given the complexity of the construct and the multifaceted nature of its antecedents. Agility (AG) yields an R² of 0.376 (adjusted R² = 0.374), while Digital Resilience (DR) records the lowest explanatory power at 0.280 (adjusted R² = 0.278). Collectively, these R² values confirm that the structural model possesses adequate predictive capacity across all endogenous constructs, with all values surpassing the commonly cited threshold of 0.10 considered meaningful in PLS-SEM applications (Hair et al., 2017).
Table 9. Q statistics
|
SSO |
SSE |
Q² (=1-SSE/SSO) |
|
|
AG |
1296.000 |
972.581 |
0.250 |
|
DR |
1296.000 |
1086.213 |
0.162 |
|
DT |
1296.000 |
1296.000 |
0.000 |
|
INN |
1296.000 |
921.411 |
0.289 |
|
SBG |
1296.000 |
982.412 |
0.242 |
Source: Author’s work based on Smart PLS Version 4.0.9.6.
Table 9 presents the Q² statistics derived from the blindfolding procedure, which assess the within-sample predictive relevance of the structural model for each endogenous construct (Cha, 1994). In general, Q² values greater than zero indicate that the model has predictive relevance for the construct (Hair et al., 2019). The results confirm satisfactory predictive relevance for all reflectively measured endogenous variables. Innovation (INN) records the highest Q² value (0.289), followed closely by Agility (AG) at 0.250 and Sustainable Business Growth (SBG) at 0.242, indicating that the model demonstrates meaningful predictive accuracy for these constructs. Digital Resilience (DR) yields a Q² of 0.162, which, while lower than the others, still confirms the model’s relevance in predicting this variable. Notably, Digital Transformation (DT), the sole exogenous construct in the model, yields a Q² of 0.000, as expected, given that blindfolding procedures apply exclusively to endogenous variables.
Table 10. F-Square
|
AG |
DR |
DT |
INN |
SBG |
|
|
AG |
0.066 |
||||
|
DR |
0.042 |
||||
|
DT |
0.602 |
0.390 |
0.799 |
0.031 |
|
|
INN |
0.001 |
||||
|
SBG |
Source: Author’s work based on Smart PLS Version 4.0.9.6.
Table 10 reports the f² effect size values, which assess the contribution of each predictor construct to the R² of the respective endogenous construct and thereby evaluate the relative practical significance of each structural path (Hair et al., 2021). Digital Transformation (DT) emerges as the most influential predictor across the model, exhibiting large effect sizes on Innovation (f² = 0.799) and Agility (f² = 0.602), and a moderate-to-large effect on Digital Resilience (f² = 0.390). These results underscore DT’s dominant and pervasive role as the primary driver of all three mediating constructs in the model. In contrast, DT exerts only a small effect on Sustainable Business Growth (f² = 0.031), suggesting that its direct influence on SBG is relatively limited when the mediators are accounted for. Among the mediators, Agility (AG) demonstrates a small-to-moderate effect on SBG (f² = 0.066), while Digital Resilience (DR) yields a small effect (f² = 0.042), both of which are considered meaningful in behavioral research contexts (Hair et al., 2017). Innovation (INN), however, exhibits a negligible effect on SBG (f² = 0.001), which aligns with and contextually explains the non-significant path coefficient reported in the direct effects analysis.
Mediation analysis
The direct path coefficients derived from the PLS-SEM analysis are reported in Table 11, providing insight into the nature and significance of each hypothesized structural relationship. Digital Transformation (DT) demonstrates a strong and statistically significant positive effect on all three mediating constructs: Innovation (β = 0.666, T = 15.646, p < 0.001), Agility (β = 0.613, T = 11.476, p < 0.001), and Digital Resilience (β = 0.530, T = 10.391, p < 0.001), confirming that DT serves as a robust and multidimensional driver of organizational capabilities in the studied context. These results are consistent with the dynamic capabilities perspective, which posits that digital transformation enables firms to reconfigure their internal processes and develop adaptive competencies (Teece et al., 1997).
Table 11. Path analysis
|
Original sample (O) |
Sample mean (M) |
Standard deviation (STDEV) |
T statistics (|O/STDEV|) |
P values |
|
|
AG SBG |
0.305 |
0.307 |
0.078 |
3.915 |
0.000 |
|
DR SBG |
0.209 |
0.207 |
0.068 |
3.059 |
0.002 |
|
DT AG |
0.613 |
0.615 |
0.053 |
11.476 |
0.000 |
|
DT DR |
0.530 |
0.534 |
0.051 |
10.391 |
0.000 |
|
DT INN |
0.666 |
0.669 |
0.043 |
15.646 |
0.000 |
|
DT SBG |
0.191 |
0.194 |
0.085 |
2.255 |
0.024 |
|
INN SBG |
0.044 |
0.045 |
0.077 |
0.570 |
0.568 |
Source: Author’s work based onSmart PLS Version 4.0.9.6.
Furthermore, DT exerts a statistically significant direct effect on Sustainable Business Growth (β = 0.191, T = 2.255, p = 0.024), suggesting that, beyond its indirect pathways through the mediators, digital transformation also contributes directly to SBG. Among the mediating constructs, Agility (AG) demonstrates a significant positive influence on SBG (β = 0.305, T = 3.915, p < 0.001), as does Digital Resilience (DR) (β = 0.209, T = 3.059, p = 0.002). In contrast, the path from Innovation (INN) to SBG yields a non-significant coefficient (β = 0.044, T = 0.570, p = 0.568), indicating that innovation alone does not directly translate into sustainable business growth in this sample, and that its contribution may instead be contingent upon other mediating or moderating mechanisms not captured in the present model.
Table 12. Indirect effects
|
Original sample (O) |
Sample mean (M) |
Standard deviation (STDEV) |
T statistics (|O/STDEV|) |
P values |
|
|
DT INN SBG |
0.029 |
0.030 |
0.052 |
0.563 |
0.574 |
|
DT DR SBG |
0.111 |
0.109 |
0.036 |
3.073 |
0.002 |
|
DT AG SBG |
0.187 |
0.188 |
0.047 |
3.941 |
0.000 |
Source: Author work based on Smart PLS Version 4.0.9.6.
Table 12 presents the results of the mediation analysis through bootstrapped indirect effects, examining whether Agility (AG), Digital Resilience (DR), and Innovation (INN) serve as significant transmission mechanisms between Digital Transformation (DT) and Sustainable Business Growth (SBG). The indirect path DT → AG → SBG yields the strongest and most statistically significant indirect effect (β = 0.187, T = 3.941, p < 0.001), confirming that organizational agility plays a pivotal mediating role in translating the benefits of digital transformation into sustainable business outcomes. This finding is theoretically coherent, as agility enables organizations to rapidly adapt their structures and strategies in response to digital disruptions, thereby generating competitive advantages that support long-term growth (Teece et al., 1997; Sambamurthy et al., 2003). The indirect path DT → DR → SBG also attains statistical significance (β = 0.111, T = 3.073, p = 0.002), indicating that digital resilience constitutes a meaningful channel through which DT contributes to SBG, reinforcing the notion that firms capable of withstanding and recovering from digital adversities are better positioned to sustain their growth trajectories. In contrast, the indirect path DT → INN → SBG is non-significant (β = 0.029, T = 0.563, p = 0.574), a result consistent with the non-significant direct effect of INN on SBG reported in Table 11. This suggests that while digital transformation effectively stimulates innovation, innovation does not independently mediate the DT–SBG relationship in the present context, potentially pointing to boundary conditions such as industry type, market maturity, or firm size that moderate the innovation-to-growth conversion process.
DISCUSSION
The results of this research will help to continue the discussion about whether the digital transformation is the direct cause of sustainable business performance or whether its effect is mediated by the organizational capabilities. Instead of corroborating a deterministic perspective that views digitalization as a self-sufficient source of sustainable advantage, the findings tend to support a different more subtle interpretation based on the dynamic capabilities framework (Teece et al., 2016). The digital transformation seems more like an enabling infrastructure that reinforces organizational capabilities, which translate into sustainability-oriented results. Such understanding can be opposed to the efficiency-driven approaches that view digitalization as a direct performance-optimization mechanism (Dolgui and Ivanov, 2022) but place digital transformation within a broader capability-development logic.
The results confirm that digital transformation significantly enhances innovation capability, which is consistent with Schumpeterian theory and with prior empirical evidence suggesting that digital infrastructures facilitate knowledge recombination, reduce information asymmetries, and accelerate both exploratory and exploitative innovation processes (Zhao et al., 2024; Liu et al., 2023; Gaglio et al., 2022). However, the mediation path from innovation to sustainable business growth is non-significant, indicating that while digital transformation effectively stimulates innovation as an organizational capability, innovation does not independently function as a transmission mechanism linking digital transformation to sustainability-oriented performance in this sample. This finding, though unexpected given the prominence of innovation in the sustainability literature, is not without theoretical grounding. In contexts characterized by high environmental uncertainty, structural resource constraints, and institutional volatility as is the case across the Middle Eastern economies examined the conversion of innovation outputs into measurable sustainability performance may be contingent upon additional boundary conditions not captured in the present model, such as market maturity, absorptive capacity, or the depth of digital-innovation strategic alignment (Usai et al., 2021; Parrilli et al., 2023; Cosa and Torelli, 2024). Moreover, the non-significance of this path may reflect a temporal lag: innovation investments often require extended periods before materializing into observable sustainability outcomes, a dynamic that cross-sectional data are structurally unable to detect.
The connection between digital transformation and organizational agility further strengthens the dynamic capability theory. Although past literature has discussed agility as a precursor or an outcome of digitalization (Zhang et al., 2025; Xu et al., 2024), the current research has found out that digital infrastructure improves sensing and responding capacities of firms. Digital transformation reinforces adaptive responsiveness by facilitating real-time information processing, cross-functional coordination and quick resource reconfiguration. Notably, agility is not merely an outcome of digital maturity but a focal point through which digital initiatives can be transformed into sustainability-oriented growth. This corroborates the recent agility of work positioning as a mediating capacity between technological inputs and strategic outcomes (Chen et al., 2024; Nakandala et al., 2024), and it makes sense to explain that its role is in the introduction of strategic flexibility in situations of uncertainty. The ability to rapidly restructure operations can be a more direct route to sustainable performance than innovation in turbulent regional contexts, as exemplified in the sample.
Another dynamic capability that is supported by the study is digital resilience. Based on the conceptualization of resilience as a developmental process rather than a response mechanism, as suggested by Duchek (2020), the results suggest that the digital transformation enhances firms’ capacity to absorb shocks, sustain operations, and adapt to disruption. This is also in line with He et al. (2023) and Ye et al. (2024), who have highlighted the significance of digital preparedness, cybersecurity systems, and knowledge-sharing infrastructures in strengthening organizations. Nonetheless, the findings indicate that resilience is not so much a growth accelerator as a stabilizer. Unlike innovation and agility, which spur proactive expansion, resilience safeguards current value-creation operations and ensures sustainability in the long run. This difference advances the resilience literature by situating it within a protective capability within a wider sustainability framework (Clement and Rivera, 2017; Nair et al., 2024).
Collectively, the findings demonstrate that digital transformation influences sustainable business growth primarily by strengthening organizational agility and digital resilience, both of which serve as significant, substantively important mediating capabilities in this parallel mediation framework. While digital transformation also significantly enhances innovation capability, innovation does not emerge as a statistically significant mediating pathway to sustainable business growth in this sample, suggesting the context-contingent and potentially time-lagged nature of the innovation-to-sustainability conversion process. This integrative pattern empirically supports the dynamic capabilities perspective, which holds that sustained competitiveness derives not from resource endowment alone but from the continuous reconfiguration of capabilities in adaptive response to environmental demands (Teece et al., 2016).
Notably, the results also restrain overly positive accounts about digital transformation. Although digitalization has a positive effect on sustainability-oriented performance, its impact depends on the alignment of strategy and the development of capabilities. This observation is echoed by those who hold critical views, warning against the notion that digital transformation is inherently good (Usai et al., 2021), arguing that technology creates value only when incorporated into proper organizational practices and institutions. In this regard, the research transcends technological determinism and places the digital transformation in a systemic capacity arrangement.
Theoretically, the findings strengthen the link between the resource-based logic and the dynamic capabilities theory. Unlike the resource-based view, which emphasizes the significance of valuable, rare, and inimitable resources, the current results emphasize that digital resources alone cannot drive sustainable growth. Competitive advantage emerges through the dynamic recombination of digital resources into agile decision-making processes and resilience-building practices, with innovation representing an important capability that requires additional contextual enablers to convert into observable sustainability outcomes
CONCLUSION
This research has explored the connection between digital transformation and sustainable business growth using innovation, organizational agility, and digital resilience as mediating variables. Based on the resource-based persp.ective and dynamic capabilities theory, the conclusions show that digital transformation has contributed to sustainability-oriented performance not only through technology but also by creating complementary organizational capabilities. This supports the view that digital assets create value when thoughtfully embedded and continually repurposed within adaptive organizational systems.
By empirically combining innovation, agility, and digital resilience within a structural framework, the study contributes to understanding how companies implement technological integration and its multidimensional performance effects. The findings indicate that sustainable business growth is primarily driven by the interaction between digital transformation and two organizational capabilities agility and digital resilience, which serve as the principal transmission mechanisms through which digital investments are converted into sustainability-oriented performance outcomes. Although digital transformation significantly enhances innovation, it does not emerge as a statistically significant mediating pathway in the present sample, suggesting that the innovation-to-sustainability link is subject to boundary conditions that future research should examine. Digital transformation thus functions as an enabling platform, while the effectiveness of digital investments in generating long-term performance is contingent upon the development of adaptive responsiveness and resilience-oriented organizational capacity.
Digital transformation is therefore a facilitating platform, and dynamic capabilities define the effectiveness of digital investments in translating into long-term performance.
The research is relevant to the literature because it goes beyond individual analyses of digitalization, innovation, or resilience and illustrates their interrelatedness in shaping sustainability-oriented outcomes. The study also extends the empirical inquiry to Middle Eastern economies, providing insight into digitally transforming companies operating in an emerging, transitional setting. In terms of management, the findings highlight the need to develop technological capacity, strategic agility, and resilience-focused governance systems to remain competitive in long-term, digitally dynamic environments. Organizations that align digital investment with strategic agility and resilience are better positioned to navigate uncertainty and sustain long-term growth.
There are a few constraints to consider when interpreting the results of this research. To start with, the study used a purposive, non-probability sampling approach. Though this strategy ensured that respondents had relevant managerial experience and direct participation in digital transformation initiatives, it restricts the statistical extrapolation of the findings. The results are thus a product of relationship analysis of the analyzed sample as opposed to inference based on a population. Second, the empirical data were gathered from companies operating in Lebanon, Jordan, the United Arab Emirates, the Kingdom of Saudi Arabia, and Kuwait. Although such a regional focus provides a great deal of data on the digital transformation of the Middle East, institutional structures, regulatory conditions, and levels of digital maturity are region-specific. These contextual attributes might influence the processes of capability development and sustainability of organizational outcomes, thus limiting the generalizability of the results. Third, causal interpretation is limited by the cross-sectional design. The hypothesized relationships are theoretically grounded in the dynamic capabilities framework, but the cross-sectional datasets fail to capture evolution over time. Organizational capabilities and digital transformation are dynamic processes that are a matter of time. Fourth, the research is based on self-reports of perceptual measures from single respondents within each firm. Even though the statistical diagnostics indicate that common method bias is not a major issue, perceptual measures may still introduce subjectivity or response bias. Lastly, sustainable business development was operationalized as a multidimensional sustainability-based performance construct that fused economic stability, environmental responsibility, and social concerns. Although this methodology captures the full picture of sustainability outcomes, it fails to separate the potentially distinct impacts of digital capabilities on each performance dimension.
These results can be extended and enhanced in several aspects in future research. To start with, longitudinal research designs would enable scholars to study the interactions among digital transformation, innovation, agility, and resilience over time. These designs may explain a temporal course of development of capabilities and give more robust causal judgment. Second, the moderating role of institutional environments on the relationship between digital transformation and sustainability-oriented performance could be investigated by comparative cross-regional studies. It would be worthwhile to scrutinize companies in developed and emerging economies to understand the contingencies in their respective contexts better. Third, objective performance measures could be introduced in future research to complement perceptual measures and enhance measurement quality. Fourth, additional studies can separate the notion of sustainable business growth into its economic, environmental, and social dimensions to determine whether innovation, agility, and resilience have differentiated impacts across the three dimensions. Fifth, other moderating or mediating variables, e.g., organizational culture, leadership style, quality of governance, or level of digital maturity, might be considered by scholars to further advance theoretical knowledge of how technological transformation can be translated into long-term, sustainable performance. Sixth, the non-significant mediation path through innovation warrants targeted investigation. Future studies should explore moderating conditions, such as market maturity, firm absorptive capacity, strategic alignment, and digital governance quality, under which innovation translates digitalization investments into sustainable growth outcomes. Longitudinal designs would be particularly valuable in detecting the time-lagged effects of innovation on sustainability performance that cross-sectional data cannot capture.
References
Awwad, A., Anouze, A. L. M., & Elbanna, S. (2026). Green product innovation: Influences on environmental sustainability performance. Management Decision, 64(3), 935–958. https://doi.org/10.1108/MD-06-2024-1366
Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402. https://doi.org/10.1177/002224377701400320
Awad, J., & Martín-Rojas, R. (2024). Enhancing social responsibility and resilience through entrepreneurship and digital environment. Corporate Social Responsibility and Environmental Management, 31(3), 1688–1704. https://doi.org/10.1002/csr.2655
Bindeeba, D. S., Tukamushaba, E. K., & Bakashaba, R. (2025). Digital transformation and its multidimensional impact on sustainable business performance: Evidence from a meta-analytic review. Future Business Journal, 11(1), Article 90. https://doi.org/10.1186/s43093-025-00511-z
Boh, W. F., Melville, N. P., Baptista, J., Chasin, F., Horita, F., Ixmeier, A., Johnson, S. L., Sarker, S., Ketter, W., Kranz, J., Miranda, S., Nan, N., Pentland, B. T., Recker, J., Sadeghi, S., Sarker, S., Sutanto, J., Wang, P., & Wilopo, W. (2026). Digital resilience for the climate crisis: A multi-perspective analysis. MIS Quarterly, 50(1), 1–34. https://doi.org/10.25300/MISQ/2025/18779
Bux, A., Zhu, Y., & Devi, S. (2025). Enhancing organizational agility through knowledge sharing and open innovation: The role of transformational leadership in digital transformation. Sustainability, 17(15), Article 6765. https://doi.org/10.3390/su17156765
Clément, V., & Rivera, J. (2017). From adaptation to transformation: An extended research agenda for organizational resilience to adversity in the natural environment. Organization & Environment, 30(4), 346–365. https://doi.org/10.1177/1086026616658333
Cha, J. (1994). Partial least squares. Adv. Methods Mark. Res, 407, 52-78.
Chen, A., Li, L., & Shahid, W. (2024). Digital transformation as the driving force for sustainable business performance: A moderated mediation model of market-driven business model innovation and digital leadership capabilities. Heliyon, 10(8), Article e29509. https://doi.org/10.1016/j.heliyon.2024.e29509
Cosa, M., & Torelli, R. (2024). Digital transformation and flexible performance management: A systematic literature review of the evolution of performance measurement systems. Global Journal of Flexible Systems Management, 25(3), 445-466. https://doi.org/10.1007/s40171-024-00409-9
Dangelico, R. M., Schiaroli, V., & Fraccascia, L. (2022). Is Covid-19 changing sustainable consumer behavior? A survey of Italian consumers. Sustainable Development, 30(6), 1477–1496. https://doi.org/10.1002/sd.2322
Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British journal of management, 17(4), 263–282. https://doi.org/10.1111/j.1467-8551.2006.00500.x
Dolgui, A., & Ivanov, D. (2022). 5G in digital supply chain and operations management: Fostering flexibility, end-to-end connectivity and real-time visibility through internet-of-everything. International Journal of Production Research, 60(2), 442–451. https://doi.org/10.1080/00207543.2021.2002969
Duchek, S. (2020). Organizational resilience: A capability-based conceptualization. Business Research, 13(1), 215–246. https://doi.org/10.1007/s40685-019-0085-7
Gaglio, C., Kraemer-Mbula, E., & Lorenz, E. (2022). The effects of digital transformation on innovation and productivity: Firm-level evidence of South African manufacturing micro and small enterprises. Technological Forecasting and Social Change, 182, Article 121785. https://doi.org/10.1016/j.techfore.2022.121785
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). An introduction to structural equation modeling. In Partial least squares structural equation modeling (PLS-SEM) using R: A workbook (pp. 1-29). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7_1
Hair Jr, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. https://doi.org/10.1504/IJMDA.2017.087624
He, Z., Huang, H., Choi, H., & Bilgihan, A. (2023). Building organizational resilience with digital transformation. Journal of Service Management, 34(1), 147–171. https://doi.org/10.1108/JOSM-06-2021-0216
Hwang, B.-N., Jitanugoon, S., & Puntha, P. (2025). Unraveling the impact of IT resources on firm performance: A mediated-moderated model. European Journal of Innovation Management. Advance online publication. https://doi.org/10.1108/EJIM-08-2024-0895
Jabbour, C. J. C., Seuring, S., de Sousa Jabbour, A. B. L., Jugend, D., Fiorini, P. D. C., Latan, H., & Izeppi, W. C. (2020). Stakeholders, innovative business models for the circular economy and sustainable performance of firms in an emerging economy facing institutional voids. Journal of Environmental Management, 264, Article 110416. https://doi.org/10.1016/j.jenvman.2020.110416
Khodor, S., Aránega, A. Y., & Ramadani, V. (2024). Impact of digitalization and innovation in women’s entrepreneurial orientation on sustainable start-up intention. Sustainable Technology and Entrepreneurship, 3(3), Article 100078. https://doi.org/10.1016/j.stae.2024.100078
Kulichyova, A., Kazantsev, N., White, L., & Islam, N. (2025). Digital transformation in large established organisations: Four restructuring dilemmas based on dynamic capabilities. International Journal of Management Reviews, 27(3), 420–450. https://doi.org/10.1111/ijmr.12395
Kumar, V., Sindhwani, R., Behl, A., Kaur, A., & Pereira, V. (2024). Modelling and analysing the enablers of digital resilience for small and medium enterprises. Journal of Enterprise Information Management, 37(5), 1677–1708. https://doi.org/10.1108/JEIM-01-2023-0002
Li, S., Gao, L., Han, C., Gupta, B., Alhalabi, W., & Almakdi, S. (2023). Exploring the effect of digital transformation on firms’ innovation performance. Journal of Innovation & Knowledge, 8(1), Article 100317. https://doi.org/10.1016/j.jik.2023.100317
Liu, M., Li, C., Wang, S., & Li, Q. (2023). Digital transformation, risk-taking, and innovation: Evidence from data on listed enterprises in China. Journal of Innovation & Knowledge, 8(1), Article 100332. https://doi.org/10.1016/j.jik.2023.100332
Moarefi, M., & Mortezaei, G. (2025). The impact of digital transformation and customer focus on agile management. Journal of Technology in Entrepreneurship and Strategic Management, 4(2), 1–12. https://doi.org/10.61838/kman.jtesm.4.2.13
Mourtzis, D., & Panopoulos, N. (2022). Digital transformation process towards resilient production systems and networks. In A. Dolgui, D. Ivanov, & B. Sokolov (Eds.), Supply network dynamics and control (pp. 11–42). Springer. https://doi.org/10.1007/978-3-031-09179-7_2
Mueller-Saegebrecht, S., & Walter, A.-T. (2025). Strategic agility—An urgent capability for successful business model innovation? A conceptual process model and theoretical framework. Strategic Change, 34(3), 407–428. https://doi.org/10.1002/jsc.2645
Nakandala, D., Elias, A., & Hurriyet, H. (2024). The role of lean, agility and learning ambidexterity in Industry 4.0 implementations. Technological Forecasting and Social Change, 206, Article 123533. https://doi.org/10.1016/j.techfore.2024.123533
Nair, A. J., Manohar, S., & Mittal, A. (2024). Reconfiguration and transformation for resilience: Building service organizations towards sustainability. Journal of Services Marketing, 38(4), 404–425. https://doi.org/10.1108/JSM-04-2023-0144
Oliveira-Dias, D., Kneipp, J. M., Bichueti, R. S., & Gomes, C. M. (2022). Fostering business model innovation for sustainability: A dynamic capabilities perspective. Management Decision, 60(13), 105–129. https://doi.org/10.1108/MD-05-2021-0590
Parrilli, M. D., Balavac-Orlić, M., & Radicic, D. (2023). Environmental innovation across SMEs in Europe. Technovation, 119, Article 102541. https://doi.org/10.1016/j.technovation.2022.102541
Rafi, N., Ahmed, A., Shafique, I., & Kalyar, M. N. (2022). Knowledge management capabilities and organizational agility as liaisons of business performance. South Asian Journal of Business Studies, 11(4), 397–417. https://doi.org/10.1108/SAJBS-05-2020-0145
Ringle, C., Da Silva, D., & Bido, D. (2015). Structural equation modeling with the SmartPLS. Brazilian Journal of Marketing, 13(2). https://ssrn.com/abstract=2676422
Roy, V., Schoenherr, T., & Jayaram, J. (2024). Digital enabled agility: Industry 4.0 unlocking real-time information processing, traceability, and visibility to unleash the next extent of agility. International Journal of Production Research, 62(14), 5127–5148. https://doi.org/10.1080/00207543.2023.2284835
Rashid, M. H. U., Nurunnabi, M., Rahman, M., & Masud, M. A. K. (2020). Exploring the relationship between customer loyalty and financial performance of banks: Customer open innovation perspective. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), Article 108. https://doi.org/10.3390/joitmc6040108
Saeed, S., Altamimi, S. A., Alkayyal, N. A., Alshehri, E., & Alabbad, D. A. (2023). Digital transformation and cybersecurity challenges for businesses resilience: Issues and recommendations. Sensors, 23(15), Article 6666. https://doi.org/10.3390/s23156666
Sambamurthy, V., Bharadwaj, A., & Grover, V. (2003). Shaping agility through digital options: Reconceptualizing the role of information technology in contemporary Firms1. MIS quarterly, 27(2), 237-263. https://doi.org/10.2307/30036530
Song, Y., Dhariwal, P., Chen, M., & Sutskever, I. (2023). Consistency models. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 32211–32252). PMLR. https://proceedings.mlr.press/v202/song23a.html
Shatila, K., Aránega, A. Y., Soga, L. R., & Hernández-Lara, A. B. (2025). Digital literacy, digital accessibility, human capital, and entrepreneurial resilience: A case for dynamic business ecosystems. Journal of Innovation & Knowledge, 10(3), Article 100709. https://doi.org/10.1016/j.jik.2025.100709
Shatila, K., Nigam, N., & Mbarek, S. (2025). Entrepreneurial resilience in turbulent times: The role of entrepreneurial orientation and innovation in the Middle East. Journal of Enterprising Communities: People and Places in the Global Economy, 19(5), 1255–1280. https://doi.org/10.1108/JEC-10-2024-0211
Shatila, K., Hernández-Lara, A. B., & Gburová, J. (2026). Digital literacy, entrepreneurial networking, and sustainable innovation: Economic and cultural determinants of entrepreneurial success in the Middle East. Sustainable Technology and Entrepreneurship, 5(2), Article 100129. https://doi.org/10.1016/j.stae.2026.100129
Shatila, K., Nigam, N., & Mbarek, S. (2024). Seeds of change: Nurturing entrepreneurial ecosystems for sustainable enterprises in Lebanon and Jordan. The Journal of Entrepreneurship, 33(4), 897–924. https://doi.org/10.1177/09713557241307728
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189
Sun, Z., Hu, D., & Lou, X. (2024). The impact of digital transformation on the sustainable growth of specialized, refined, differentiated, and innovative enterprises: Based on the perspective of dynamic capability theory. Sustainability, 16(17), Article 7823. https://doi.org/10.3390/su16177823
Teece, D., Peteraf, M., & Leih, S. (2016). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 58(4), 13–35. https://doi.org/10.1525/cmr.2016.58.4.13
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7%3C509::AID-SMJ882%3E3.0.CO;2-Z
Usai, A., Fiano, F., Petruzzelli, A. M., Paoloni, P., Briamonte, M. F., & Orlando, B. (2021). Unveiling the impact of the adoption of digital technologies on firms’ innovation performance. Journal of Business Research, 133, 327-336. https://doi.org/10.1016/j.jbusres.2021.04.035
Vărzaru, A. A., & Bocean, C. G. (2024). Digital transformation and innovation: The influence of digital technologies on turnover from innovation activities and types of innovation. Systems, 12(9), Article 359. https://doi.org/10.3390/systems12090359
Watson, R., Wilson, H. N., Smart, P., & Macdonald, E. K. (2018). Harnessing difference: A capability-based framework for stakeholder engagement in environmental innovation. Journal of Product Innovation Management, 35(2), 254–279. https://doi.org/10.1111/jpim.12394
Wang, H., Lu, L., Fu, Y., & Li, Q. (2024). An empirical assessment of the influence of digital transformation on sports corporate sustainability. PLOS ONE, 19(4), Article e0297659. https://doi.org/10.1371/journal.pone.0297659
Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001
Xu, M., Zhang, Y., Sun, H., Tang, Y., & Li, J. (2024). How digital transformation enhances corporate innovation performance: The mediating roles of big data capabilities and organizational agility. Heliyon, 10(14), Article e34905. https://doi.org/10.1016/j.heliyon.2024.e34905
Ye, F., Ke, M., Ouyang, Y., Li, Y., Li, L., Zhan, Y., & Zhang, M. (2024). Impact of digital technology usage on firm resilience: A dynamic capability perspective. Supply Chain Management: An International Journal, 29(1), 162–175. https://doi.org/10.1108/SCM-12-2022-0480
Zhang, J., Zhang, S., Tan, X., & Zhao, H. (2025). The impact of digitalization on organizational agility: Evidence from the enterprise survey for innovation and entrepreneurship in China. Humanities and Social Sciences Communications, 12, Article 917. https://doi.org/10.1057/s41599-025-05256-2
Zhao, X., Chen, Q.-A., Yuan, X., Yu, Y., & Zhang, H. (2024). Study on the impact of digital transformation on the innovation potential based on evidence from Chinese listed companies. Scientific Reports, 14, Article 6183. https://doi.org/10.1038/s41598-024-56345-2
Biographical note
Khodor Shatila is an Assistant Professor of Entrepreneurship and Business Strategy at IPAG Business School (France). He holds a Ph.D. from ICN Business School (France) and a second PhD from Universitat Rovira i Virgili (Spain). His research interests include digital transformation, entrepreneurial resilience, innovation management, sustainable business development, organizational agility, and strategic management in emerging economies. He has published widely in internationally recognized peer-reviewed journals. Dr. Shatila brings interdisciplinary expertise in quantitative research methods, structural equation modeling, and configurational analysis, with a particular focus on business contexts in the Middle East and the Mediterranean.
Author contribution statement
Khodor Shatila is the sole author of this manuscript and is fully responsible for all aspects of the work, including conceptualization, theoretical framework development, research design, data collection, statistical analysis using PLS-SEM, interpretation of findings, and manuscript writing. The author read and approved the final version of the manuscript.
Conflicts of interest
The author declares no conflicts of interest.
Citation (APA style)
Shatila, K. (2026). Digital transformation and sustainable business growth: The mediating role of innovation, agility, and digital resilience. Journal of Entrepreneurship, Management and Innovation, 22(4), 25-45. https://doi.org/10.7341/20262242
Received 13 October 2025; Revised 12 February 2026, 17 March 2026; Accepted 7 April 2026.
This is an open-access paper under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode).