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Article

Agility in the Digital Era: Bridging Transformation and Innovation in Supply Chains

by
Soufiane Elmouhib
* and
Zineb Youbi Idrissi
Multidisciplinary Research Laboratory LAREM, HECF Business School, Fez 30060, Morocco
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(8), 3462; https://doi.org/10.3390/su17083462
Submission received: 16 February 2025 / Revised: 6 April 2025 / Accepted: 9 April 2025 / Published: 13 April 2025
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Abstract

:
This study aims to examine how digital supply chain (DSC) dimensions—including digital performance measurement, information technology, digital suppliers, manufacturing, logistics and inventory, and digital client management—influence supply chain agility, and how agility subsequently impacts innovation performance within Moroccan industries. A cross-sectional quantitative research design was employed, collecting data via structured questionnaires from 634 supply chain professionals across sectors such as agri-food, textile, automotive, and aerospace in Morocco. Data were analyzed using structural equation modeling (SEM) with SmartPLS 4, evaluating direct and mediating relationships among variables. Results reveal that digital performance measurement, information technology, and logistics significantly enhance supply chain agility, which in turn strongly boosts innovation performance. Surprisingly, digital manufacturing negatively impacted agility, while digital suppliers and digital clients showed no significant direct effect. Theoretically, the study provides empirical evidence linking DSC dimensions to agility-mediated innovation performance, enriching dynamic capabilities and resource-based views. Practically, it advises managers to prioritize digital performance monitoring and IT integration to foster agility-driven innovation. This paper disaggregates digital supply chain dimensions, clarifying their distinct impacts on agility and innovation, thus addressing a research gap in digital transformation literature.

1. Introduction

The rapid evolution of digital technologies such as the Internet of Things (IoT), cloud computing, and big data analytics has significantly transformed traditional supply chain management practices [1,2]. These technological advancements have given rise to the concept of the digital supply chain (DSC), characterized by enhanced connectivity, responsiveness, and operational flexibility. In today’s highly volatile markets, adopting digital supply chain practices is not merely advantageous but essential for maintaining competitive agility and fostering innovation [1].
Despite substantial interest in digital transformation, the current literature remains fragmented regarding how distinct digital supply chain dimensions collectively influence supply chain agility and subsequent innovation performance [3,4,5,6,7]. Specifically, there is limited empirical understanding of how individual DSC elements—such as digital performance measurement, digital information technology, digital suppliers, digital manufacturing, digital logistics and inventory, and digital client management—interact to impact agility and innovation performance.
This study seeks to bridge this gap by examining how six digital supply chain dimensions impact supply chain agility, and how agility, in turn, fosters innovation performance. Through empirical validation, it demonstrates how digital integration enhances both agility and innovation.
The findings of this study offer contributions to both theoretical frameworks and managerial practice. Theoretically, it extends existing knowledge by providing empirical evidence on the mediating effects of agility in the digital transformation–innovation nexus. Practically, the study delivers actionable insights for managers aiming to prioritize digital capabilities effectively, thereby improving organizational responsiveness and innovation capacity.
The scope of this research is geographically concentrated on Moroccan industries, specifically examining sectors such as agri-food, textile, automotive, and aerospace over the data collection period from July to November 2024. Focusing on this regional and sectoral context allows for targeted insights relevant to organizations undergoing digital transformation within similar emerging market environments.
This paper is structured as follows: first, a literature review and hypotheses are developed to outline the conceptual framework. The research methodology and data collection approaches are subsequently detailed, followed by the analysis of empirical results. The paper concludes with a discussion of theoretical and managerial implications, along with limitations and potential directions for future research.

2. Literature Review and Hypothesis Development

2.1. Conceptual Framework

2.1.1. Digital Supply Chain

The concept of the digital supply chain (DSC) focuses on transforming traditional supply chains through emerging technologies such as the Internet, cloud computing, and the Internet of Things (IoT). The DSC is described as a “customer-centric platform model” [2] and a “smart, value-driven, efficient process” [8], designed to improve visibility, flexibility, and responsiveness in demand management while minimizing risk. By leveraging real-time data from multiple sources, the DSC enables organizations to optimize decision-making, coordination, and operational efficiency across internal functions and external partners [1,2,9,10].
Conceptualized as a system of interdependent digital dimensions, the DSC integrates several core components, each contributing uniquely to supply chain transformation. These include digital performance measurement, digital information technology, digital suppliers, digital manufacturing, digital logistics and inventory, and digital client management [11,12].
Digital performance measurement (DPM) involves the use of analytics, dashboards, IoT, and automated reporting systems to monitor key metrics—such as cost, quality, delivery, and flexibility—across the supply chain. It enables real-time monitoring, supports predictive analytics for disruption management, and strengthens strategic planning through data-driven insights [13,14].
Digital information technology (DIT) includes systems such as ERP platforms, big data analytics, and cloud-based tools that facilitate the efficient collection, sharing, and analysis of information. DIT enhances decision-making, improves visibility, and promotes agility by ensuring synchronized and timely communication throughout the supply chain [7,15].
Digital suppliers (DS) are partners who adopt digital tools—such as IoT integration, real-time data sharing, and supplier relationship management (SRM) systems—to improve procurement, coordination, and responsiveness in sourcing, production, and logistics [16,17].
Digital manufacturing (DM) applies technologies such as artificial intelligence, cyber-physical systems, and real-time analytics to optimize production processes. It enhances flexibility, enables predictive maintenance, and facilitates seamless coordination between physical operations and digital platforms, driving both innovation and efficiency [15,18].
Digital logistics and inventory (DLI) uses tools like IoT and cloud-based analytics to manage transportation, warehousing, and inventory in real time. These technologies increase visibility, automate processes, reduce lead times, and optimize stock levels [7,16].
Digital client (DC) improves customer engagement by leveraging digital tools to analyze preferences and feedback, allowing firms to align operations with customer expectations. This responsiveness supports competitiveness in increasingly personalized and demand-driven markets [19,20].

2.1.2. Supply Chain Agility

Supply chain agility is essential for responding swiftly to environmental changes, particularly in volatile markets where products have limited lifespans and demands shift constantly [21]. Agility refers to the strategic use of real-time data to quickly respond to demand changes. This capability can enhance a firm’s competitiveness and long-term viability by enabling prompt adaptation to evolving market conditions [22]. Unlike traditional forecast-based models, it allows supply chains to align operations more precisely with current demand. In highly customized markets, agility becomes especially valuable by transforming uncertainties into opportunities. It fosters flexibility and strengthens competitive positioning through the fulfillment of specific customer needs [23]. Analyzing supply chain agility from multiple perspectives highlights how its core elements interact to build a responsive and adaptable supply network.
Market sensitivity enables supply chains to remain aware of customer requirements and adjust accordingly to demand fluctuations. It is important for aligning operations with real-time market conditions [21,22]. Delivery speed and lead time reduction facilitate the prompt delivery of products, helping firms maintain competitiveness and customer satisfaction in dynamic environments [21,23]. Process integration and alignment promote seamless coordination across supply chain activities, reducing disruptions and enhancing collaborative initiatives [21,22]. Furthermore, new product introduction and rapid product development allow firms to bring innovations to market swiftly—a strategic advantage in industries with short product life cycles [21,23]. Customer service and satisfaction remain central by ensuring timely responses to client needs, fostering strong relationships, and enhancing competitive performance [23]. Lastly, virtualization and the use of IT tools support an open, data-driven environment that enables real-time information exchange and agile decision-making across the supply chain [21,22].

2.1.3. Innovation Performance

A firm’s ability to introduce new products, services, and technologies is important for gaining competitive advantage and enhancing operational efficiency, positioning innovation as a driver of both growth and effectiveness [24]. Measuring this capability involves financial and non-financial indicators that reflect how well a company aligns its innovation efforts with strategic goals, particularly in fostering organizational and supply chain transformation [25]. This comprehensive framework captures the diverse impacts of innovation on efficiency, value creation, and supply chain performance, helping firms leverage innovation to stay competitive and achieve long-term growth [26].
Innovation performance encompasses a range of dimensions that together highlight a firm’s ability to foster growth, efficiency, and competitive advantage. Product innovation focuses on developing unique products that meet market demands and enhance a company’s relevance and customer appeal [24]. Complementing this, process innovation drives internal efficiencies by improving methods of production and delivery, contributing to cost reduction and productivity, which is essential for streamlined operations across the supply chain [24,26].
The technology adoption dimension underscores the role of integrating advanced tools to strengthen a firm’s innovative capacity, supporting quicker, more agile responses to shifting market needs [24]. The market impact of these innovations is seen in increased market share, customer satisfaction, and profitability, demonstrating the tangible benefits of innovation for competitive positioning [24].
Reliability and customer focus reflect a commitment to consistent, dependable delivery and alignment with customer expectations. Reliability metrics, like on-time delivery rates, alongside customer satisfaction scores, emphasize the trust and value placed in a company’s offerings [25]. Similarly, customer satisfaction assesses the success of innovation in meeting customer needs, reinforcing the direct impact of innovation on customer value [26].
The dimensions of environmental friendliness and environmental and social impact recognize the importance of sustainability in innovation, including reduced emissions, eco-friendly materials, and socially responsible practices, aligning with corporate social responsibility goals [25,26].
Efficiency and operational efficiency measure the financial and operational gains from innovation, such as return on investment and optimized resource use, underscoring the economic viability of innovative projects [25,26]. Finally, financial performance assesses the revenue growth, profit margins, and cost savings resulting from innovation, solidifying its role in long-term profitability and market leadership [26].

2.2. Hypothesis Development

Digital performance measurement and digital information technology represent core internal digital capabilities that enable firms to monitor, analyze, and respond to operational data in real time [13,15]. Grounded in the resource-based view (RBV), these capabilities are considered strategic assets that provide a foundation for dynamic decision-making [27,28]. Furthermore, the dynamic capabilities theory emphasizes how firms must continuously integrate, build, and reconfigure internal competences to adapt to rapidly changing environments [29,30]. DPM allows organizations to assess key performance indicators with precision, while DIT facilitates real-time communication and data integration across functional units [13]. These capabilities enhance visibility, responsiveness, and coordination, all of which are essential for fostering supply chain agility [31]. Therefore, internal digital capabilities such as DPM and DIT are expected to positively influence supply chain agility.
Hence, we confirm that H1 and H2 are supported theoretically by the RBV and dynamic capabilities perspectives.
Digital collaboration with external partners is recognized as a key component of digital supply chain transformation [32,33]. Drawing on the network theory and supply chain integration (SCI) literature, the involvement of external actors—such as digital suppliers and digital clients—can enhance a firm’s responsiveness and real-time coordination capabilities [34,35]. According to network theory, firms are embedded in relational systems where the quality and digital maturity of inter-organizational connections affect operational flexibility and strategic agility [36]. Moreover, SCI emphasizes the role of seamless digital integration across organizational boundaries in fostering synchronized decision-making and rapid response to market changes [37]. Digital integration with suppliers enables more accurate forecasting, faster procurement cycles, and co-innovation potential, all of which directly contribute to increased supply chain agility [38]. Similarly, digitized interaction with clients enables firms to better align production and delivery with customer expectations, thereby reducing lead times and improving adaptability. Based on this theoretical grounding, we hypothesize that digital suppliers (H3) and digital clients (H6) have a positive and significant effect on supply chain agility.
In the context of supply chain management, digital manufacturing systems enable firms to adapt production processes in response to fluctuating demands or supply constraints [39]. Similarly, digital logistics and inventory systems can enhance end-to-end visibility and coordination across supply chain nodes [40]. Together, these technologies form the operational backbone that allows organizations to sense changes, make decisions swiftly, and reconfigure resources effectively—core components of supply chain agility [41].
From a theoretical perspective, this relationship is grounded in lean and agile manufacturing principles, which emphasize flexibility, and waste minimization [39,42]. The integration of digital tools in manufacturing and logistics enables the creation of agile production environments capable of absorbing shocks and responding to volatility [42]. Moreover, digital operations facilitate just-in-time responsiveness, shorter lead times, and improved synchronization with downstream and upstream partners, all of which are essential for an agile supply chain [43].
Recent studies have also confirmed that digitalization of internal operations significantly boosts an organization’s adaptive capacity and supply chain responsiveness [44,45].
Therefore, we hypothesize that digital manufacturing (H4) and digital logistics and inventory (H5) have a positive and significant effect on supply chain agility.
Agility is recognized as a mediator that translates digital investments into superior performance outcomes, particularly in the realm of innovation [7,46]. According to the dynamic capabilities theory, organizations need to acquire valuable digital assets and cultivate the agility to reconfigure these assets in response to rapid technological and market changes [47]. In this view, supply chain agility functions as a transformation mechanism allowing firms to convert digital capabilities into timely, innovative responses. Prior research highlights that digital capabilities alone do not guarantee innovation; rather, it is the firm’s ability to deploy those capabilities with speed, flexibility, and coordination that leads to enhanced innovation performance [48,49]. Agility supports this process by fostering dynamic sensing, swift reallocation of resources, and collaborative adaptation across the supply network [41]. Therefore, supply chain agility serves as a bridge that links digital supply chain capabilities to innovation outcomes.
Hence, we confirm that H7 is theoretically supported by the dynamic capabilities perspective, as it posits agility as a key mediating mechanism between digital supply chain and innovation performance.
The conceptual model in Figure 1 offers a multidimensional framing of digital supply chain components, and their joint influence on agility is more granular and comprehensive than existing models.
In contrast, many existing studies focus more narrowly on the influence of IT capabilities or supply chain digitalization as a whole on agility and innovation performance. For example, Khan et al. (2023) examined the mediating role of supply chain agility between general IT capabilities and innovation performance but did not disaggregate the components of digital capability into specific operational dimensions [15].
Similarly, Wang and Prajogo (2024) explored the effects of supply chain digitalization on firm performance mediated by agility and innovation capability. However, their model treats digitalization as a singular construct and does not account for the nuanced sub-dimensions like those in the current model [7].
Moreover, some models reverse the directionality, treating innovation capability as the mediator between digitalization and agility, rather than agility as a key mechanism enhancing innovation performance [6].

3. Research Methodology

3.1. Research Design

According to the conceptual model in Figure 1, the six dimensions of the digital supply chain—namely, digital performance measurement, digital information technology, digital suppliers, digital manufacturing, digital logistics and inventory, and digital clients—are hypothesized to positively influence supply chain agility, which subsequently drives innovation performance. In this framework, supply chain agility functions as an intermediary between the adoption of advanced digital capabilities and the achievement of innovative outcomes.
From a methodological standpoint, this study adopts a cross-sectional quantitative research design to test the proposed relationships. Data were collected at a single point in time to capture the perceptions of supply chain management professionals regarding their organizations’ level of digital adoption and its impact on agility. Grounded in a positivist paradigm, the research aims to confirm theoretical linkages through empirical analysis. In alignment with this approach, structural equation modeling (SEM) using SmartPLS 4 is employed for its ability to simultaneously assess multiple dependent and independent variables and to model complex relationships, offering a comprehensive understanding of both direct and mediating effects within the conceptual framework [50].

3.2. Sample and Data Collection

A structured questionnaire served as the primary instrument for data collection, preceded by a pilot test conducted in French to accommodate its widespread use in Morocco. This preliminary phase involved administering a subset of survey items to francophone supply chain professionals, who provided feedback on linguistic clarity and cultural relevance. Based on their input, minor revisions were made to ensure that the final version was both understandable and contextually appropriate.
Following this phase, the finalized questionnaire was distributed over a five-month period, from July to November 2024. The target population included supply chain management (SCM) professionals—such as logistics coordinators, procurement specialists, and operations analysts—ensuring that respondents had substantial expertise in digital supply chain practices and their impact on operational agility. Using Slovin’s formula, a target sample size of 500 valid responses was determined.
The survey was disseminated via professional social media groups, direct email, and in-person outreach at industry conferences, yielding 679 submissions. After eliminating 45 incomplete or invalid responses, 634 valid surveys remained. This statistically representative sample encompasses a variety of organizational types and sizes, enhancing the study’s external validity.
Respondents from diverse sectors such as agri-food, textile, automotive, aerospace, and others were analyzed collectively due to their engagement with similar digital supply chain components influencing agility and innovation performance. Although sectoral differences exist, all industries rely on capabilities such as digital information technology, digital logistics, and digital manufacturing to enhance responsiveness and competitiveness. Grouping respondents thus allows the identification of generalizable patterns regarding how digital supply chain elements foster agility and drive innovation outcomes.

3.3. Measurement Instruments

The survey instrument was developed based on established scales from prior research to ensure the validity and reliability of the constructs under investigation. As indicated in Table 1, all measurement items were drawn from the existing literature, capturing the core elements of digital supply chain dimensions, supply chain agility, and innovation performance. A similar approach was undertaken for supply chain agility, wherein established scales from previous research were adapted to measure the speed and flexibility of organizational responses to market changes. This emphasis on adaptability aligns with a growing consensus that agile supply chains can better capitalize on technological advancements and collaborative networks. Finally, innovation performance items were extracted from sources that have examined the design, implementation, and outcomes of innovative practices in diverse sectors.
Minor refinements were introduced to item wording to fit the specific organizational context of the participants, thus preserving clarity and relevance. These adjustments did not alter the underlying constructs but rather optimized their applicability to the research setting. The subsequent section on research results and discussion will present the reliability testing outcomes for these measures, along with a more detailed analysis of the overall measurement model.

4. Research Results

4.1. Descriptive Statistics

Table 2 presents the descriptive statistics from a questionnaire completed by 634 respondents, categorized by gender, age group, and sector of work. These demographic and professional characteristics provide context for interpreting the relationships tested in the conceptual model.
Among the respondents, 366 were male (57.73%) and 268 female (42.27%). This gender distribution suggests moderately higher participation of males, potentially reflecting the gender composition in supply chain roles within the sampled sectors.
The age distribution is divided into four groups, with the largest segment aged 35–45 years (230 respondents, 36.28%), followed by those aged 25–35 years (200 respondents, 31.55%) and 45–55 years (150 respondents, 23.66%). Participants over 55 years constitute the smallest group, with 54 respondents (8.52%). The predominance of middle-aged participants (25–45 years) suggests that the sample largely comprises professionally active individuals with substantial field experience.
Respondents represented five sectors: agri-food (211 participants, 33.28%), textile (169 participants, 26.66%), automotive (150 participants, 23.66%), aerospace (36 participants, 5.68%), and other sectors (68 participants, 10.73%). This distribution indicates a concentration in industries actively engaged in digital transformation, particularly within the agri-food and textile sectors.
Overall, the sample reflects a diverse demographic and sectoral profile, offering a relevant cross-section of supply chain professionals. The representation across key industries supports the study’s focus on digital supply chains and innovation performance in sectors where digitalization is a strategic priority.

4.2. Assessment of Measurement Model

Table 3 presents an evaluation of the convergent validity of the constructs in the conceptual model, based on factor loadings, Cronbach’s alpha (CA), composite reliability (CR), and average variance extracted (AVE). Each construct and its indicators are assessed to ensure the reliability and validity of the measurement model.
“Digital clients” is measured by four items (DC1–DC4), with factor loadings ranging from 0.716 to 0.893. The CA is 0.823, and the CR is 0.859, both exceeding the recommended threshold of 0.7. The AVE of 0.648 confirms satisfactory convergent validity.
“Digital information technology” includes three items (DIT1–DIT3), with loadings between 0.765 and 0.939. The CA (0.802) and CR (0.900) indicate strong internal consistency, while the AVE (0.716) reflects robust convergent validity.
“Digital logistics and inventory” consists of three items (DLI1–DLI3), with loadings ranging from 0.727 to 0.896. The CA (0.716) and CR (0.774) suggest moderate reliability, and the AVE of 0.635 supports acceptable convergent validity.
“Innovation performance” is assessed using four items (IP1–IP4), with loadings from 0.673 to 0.939. Although one loading falls slightly below the ideal threshold (0.7), it was retained due to its theoretical relevance. The CA (0.874), CR (0.914), and AVE (0.731) all demonstrate strong construct validity.
“Digital manufacturing” comprises four items (DM1–DM4), with loadings from 0.725 to 0.893. It exhibits high reliability (CA = 0.860, CR = 0.890) and good convergent validity (AVE = 0.710).
“Digital performance measurement” includes three items (DPM1–DPM3), with strong loadings (0.869 to 0.901). It demonstrates excellent reliability and validity, with CA and CR both at 0.860 and an AVE of 0.780.
“Digital suppliers” is measured by four items (DS1–DS4), showing loadings between 0.789 and 0.891. The CA (0.870), CR (0.890), and AVE (0.720) all confirm the construct’s internal consistency and convergent validity.
“Supply chain agility” includes five items (SCA1–SCA5), with loadings ranging from 0.866 to 0.933. The construct shows excellent internal consistency (CA and CR = 0.940) and strong convergent validity (AVE = 0.810).
Table 4 presents the heterotrait–monotrait ratio (HTMT) values for the constructs in the conceptual model, which are used to evaluate discriminant validity. The threshold for acceptable discriminant validity is an HTMT value below 0.9. All values in the table fall within this threshold, confirming adequate discriminant validity across all constructs.
For example, “digital clients (DC)” exhibits low HTMT values with “innovation performance (IP)” (0.266) and “supply chain agility (SCA)” (0.236), indicating strong distinctions. Similarly, “digital information technology (DIT)” has acceptable values with “digital logistics and inventory (DLI)” (0.213) and “digital manufacturing (DM)” (0.553), supporting clear separations among these constructs.
Even the highest HTMT value, observed between “digital performance measurement (DPM)” and “digital information technology (DIT)” (0.873), remains below the 0.9 threshold, signifying an acceptable level of discriminant validity. Other construct pairs, such as “digital manufacturing (DM)” with “digital clients (DC)” (0.880) and “digital suppliers (DS)” with “supply chain agility (SCA)” (0.715), are also comfortably within the acceptable range.
In summary, all HTMT values are within the acceptable threshold, confirming that the constructs in the model exhibit sufficient discriminant validity. This ensures the robustness of the model for further analysis and research.

4.3. Direct Relation Analysis

Table 5 and Figure 2 summarize the direct effects between the constructs in the conceptual model and present the statistical support for each hypothesis. The results highlight the key factors that significantly influence supply chain agility (SCA) and innovation performance (IP).
H1, examining the relationship between digital performance measurement (DPM) and SCA, is supported. The path coefficient is strong and positive (β = 0.499, p < 0.05), confirming DPM as a key driver of agility through enhanced monitoring and real-time evaluation.
H2 is also supported, with digital information technology (DIT) showing a moderate, statistically significant effect on SCA (β = 0.199, p < 0.05), highlighting its role in enabling responsive and data-integrated supply chain operations.
H4, testing the effect of digital manufacturing (DM) on SCA, is supported despite its negative coefficient (β = −0.122, p < 0.05). This counterintuitive result may suggest potential complexities or operational rigidity introduced through digital manufacturing technologies in certain contexts.
H5 shows that digital logistics and inventory (DLI) has a positive, though weaker, significant impact on SCA (β = 0.087, p < 0.05), indicating its contribution to agility through improved material flow and inventory visibility.
H3, which examines the effect of digital suppliers (DS) on SCA, is not supported (β = 0.025, p > 0.05). Similarly, H6, analyzing the role of digital clients (DC) in influencing SCA, is not supported (β = 0.013, p > 0.05). These findings suggest that external digital collaboration, while important, may not exert a direct influence on agility without other mediating or contextual factors.
Finally, H7 is supported, showing a strong and significant relationship between SCA and innovation performance (IP) (β = 0.600, p < 0.05). This confirms the role of agility as a key enabler of innovation.
The model explains a substantial proportion of variance in both dependent variables, with adjusted R2 values of 0.430 for SCA and 0.359 for IP. These values indicate a robust explanatory power of the proposed framework.

4.4. Indirect Relation Analysis

Table 6 illustrates how various independent variables influence innovation performance (IP) indirectly through supply chain agility (SCA). This mediation analysis sheds light on the mechanisms through which components of the digital supply chain contribute to innovation outcomes.
Digital clients (DC) exhibit a negligible and non-significant indirect effect on IP (β = 0.008, p > 0.05; T = 0.263), indicating no meaningful influence via SCA. While DC may affect other organizational outcomes, its role in driving innovation through agility appears limited in this model.
Digital information technology (DIT) shows a significant indirect effect on IP (β = 0.119, p < 0.05; T = 3.226). This underscores the importance of DIT in enhancing agility, which subsequently enables greater innovation performance. The relatively high T-value further supports the robustness of this relationship.
Digital logistics and inventory (DLI) also exert a significant indirect influence on IP (β = 0.052, p < 0.05; T = 2.765). Although its effect size is lower than that of DIT, the positive relationship confirms that efficient logistics and inventory management contribute to agility and, by extension, to innovation outcomes.
Digital manufacturing (DM) demonstrates a significant but negative indirect effect (β = −0.073, p < 0.05; T = 2.351). This suggests that potential inefficiencies or constraints within digital manufacturing processes may hinder supply chain agility, ultimately reducing innovation performance. This finding highlights a need to address operational challenges in digitally enabled manufacturing systems.
Digital performance measurement (DPM) presents the strongest and most significant indirect effect on IP (β = 0.299, p < 0.05; T = 8.041). This emphasizes the critical role of real-time performance monitoring and data-driven management in fostering agility and innovation.
In contrast, digital suppliers (DS) do not exhibit a significant indirect effect on IP (β = 0.015, p > 0.05; T = 0.532), indicating a limited mediating role through SCA in this context.
In summary, the results highlight the pivotal influence of DIT, DLI, and especially DPM in promoting innovation performance through their impact on supply chain agility.

5. Research Discussion and Limitation

5.1. Theoretical Implications

Digital performance measurement (DPM) enhances supply chain agility (SCA) by enabling real-time monitoring, informed decision-making, and rapid responsiveness. From a resource-based view, DPM represents a strategic digital capability that supports dynamic adjustment to internal and external changes, thereby fostering agility [7]. This agility, in turn, facilitates innovation by allowing firms to quickly reconfigure processes, integrate new technologies, and respond to market demands [6]. Prior research also suggests that DPM contributes to innovation indirectly by creating the operational flexibility and transparency required for adaptive and innovative behaviors across the supply chain [60].
The integration of digital information technology into supply chain operations facilitates real-time data sharing, automation, and process integration, which are critical enablers of agility in supply chain systems. From a dynamic capabilities perspective, DIT provides firms with the informational infrastructure needed to sense environmental changes and swiftly reconfigure operations accordingly, thereby improving responsiveness and flexibility [61]. Additionally, DIT supports digital performance management systems that enable continuous monitoring and performance feedback, further reinforcing agility through data-driven decision-making and adaptive planning [7]. The alignment of IT resources with agile supply chain processes thus plays an important role in enabling innovation and performance gains in dynamic and competitive environments [5].
The absence of a significant relationship between digital suppliers and supply chain agility may be attributed to the complex and indirect nature of supplier contributions to agility, which are often mediated by other organizational capabilities such as strategic sourcing and information sharing [62]. Supplier innovativeness alone does not automatically translate into agility unless firms have mechanisms in place to integrate supplier knowledge and adapt rapidly to changing demands [63]. Additionally, the influence of suppliers on agility tends to manifest through collaborative practices and trust-building over time, which may not be fully captured in direct linear relationships [62]. Empirical studies also suggest that the role of digital suppliers in enabling agility is contingent on the firm’s innovation capabilities and integration processes, acting more as enablers of innovation performance rather than direct contributors to agility [6].
Digital manufacturing integrates advanced technologies like automation, real-time analytics, and cyber-physical systems to improve efficiency and production quality. However, its implementation often involves high levels of system complexity, rigidity in standardized processes, and dependency on stable digital infrastructure. These factors can paradoxically reduce an organization’s agility by constraining the flexibility and responsiveness needed in volatile environments [3].
Moreover, digital manufacturing systems may require extensive integration efforts and process reengineering, which can slow down responsiveness and adaptation—key dimensions of supply chain agility [4,7]. This tension is especially evident when firms prioritize technological sophistication over dynamic capability development, leading to a misalignment between digital infrastructure and agile operational needs [6].
Additionally, institutional pressures and rigid compliance standards linked to digital systems can further hinder adaptive decision-making and limit quick reconfiguration of supply networks, particularly in highly regulated or industrialized sectors [3].
Digital logistics and inventory systems enhance supply chain agility by enabling real-time visibility, responsiveness, and flexibility in logistics operations. The integration of digital technologies allows firms to better manage inventory levels, optimize transportation routes, and quickly adapt to fluctuations in demand and supply, thereby fostering a more agile supply chain environment [64].
Furthermore, agility enabled by digital logistics practices acts as a conduit for innovation by reducing operational frictions, accelerating feedback loops, and supporting experimentation with new delivery models or production processes [6]. This mediating effect is explained by the dynamic capabilities theory, which posits that agility serves as an adaptive capability that transforms digital inputs into valuable innovation outcomes [3].
Digital client initiatives often enhance customer interaction and service personalization but do not inherently enable the internal flexibility and responsiveness that define supply chain agility. This is because agility typically depends on operational integration and internal coordination, which are less influenced by customer-facing technologies alone [7,61]. Without backend digital alignment or integration across the supply network, client digitalization efforts are unlikely to translate into agile capabilities [61]. Thus, the lack of significant impact from digital clients on agility and innovation can be attributed to this disconnect between front-end digitalization and internal dynamic capabilities [3].
Supply chain agility enhances a firm’s capacity to adapt quickly to market and technological changes, which is essential for innovation. It enables rapid reconfiguration of processes and resources, fostering responsiveness and experimentation [15]. Agile supply chains also support cross-functional integration and timely information flow, facilitating the development and implementation of new products, services, or processes [16]. From a dynamic capabilities lens, agility provides the structural flexibility needed to align innovation efforts with evolving external demands [17].

5.2. Practical Implications

The results of this study provide valuable insights to practitioners who aim to spur innovation through digitalizing supply chain processes. To begin with, the research establishes that digital performance measurement (DPM) contributes most to supply chain agility, highlighting the importance of real-time analytics, data-driven feedback loops, and performance monitoring systems. Organizations looking to increase agility and innovation would do well to assign high priority to the development of advanced digital dashboards and KPIs that facilitate ongoing evaluation and reactive decision-making.
Moreover, digital information technology (DIT) and digital logistics and inventory (DLI) were found to have significant effects on agility as well. These findings suggest that IT investments in enabling uninterrupted communication, transparency, and integration among supply chain activities can enable operational flexibility. In particular, logistics automation and inventory optimization technologies appear to play a crucial role in enabling agile responses to demand fluctuations and supply disruptions.
On the other hand, digital clients (DC) and digital suppliers (DS) did not have any direct or indirect influence on agility. This goes against general assumptions and implies that internal capabilities and integration mechanisms must be enhanced prior to responsiveness and adaptability being significantly influenced by external partnerships.
Finally, the study verifies supply chain agility as a key mediator of converting digital transformation into innovation performance. This points to the observation that adopting digital technologies is not sufficient; it is how these technologies enhance the organization’s capacity to learn rapidly and innovate rapidly. Thus, managers are warned to coordinate investments in digital with strategies based on agility, so organizational structure, culture, and processes enable ongoing learning, rapid reconfiguration, and market responsiveness.

5.3. Research Limitations

Despite the study’s empirical design and substantial sample size, several limitations should be acknowledged to contextualize the findings and guide future research.
First, the research adopts a cross-sectional design, capturing data at a single point in time. While this approach enables the assessment of relationships between digital supply chain components, agility, and innovation, it inherently limits causal inference. Longitudinal studies would be better suited to examine how these relationships evolve over time, particularly as firms undergo digital transformation at different paces and under varying market conditions.
Second, the study’s empirical setting is restricted to Moroccan industries, which may limit the external validity of the findings. Cultural, infrastructural, and economic factors specific to the Moroccan context could shape digital adoption behaviors and agility outcomes in ways that differ from other regions. As such, the generalizability of the results to global or even regional supply chains should be approached with caution.
Third, the data were collected using self-reported measures from supply chain professionals, which introduces the possibility of common method bias and response subjectivity. Participants may have overestimated their organization’s digital maturity or agility capabilities due to social desirability or perception bias, potentially inflating some of the observed relationships.
Fourth, the sampling method may introduce selection bias. Respondents were likely more digitally engaged or professionally active, which could skew the sample toward firms with greater digital maturity or interest in innovation. This non-random sampling approach, while practical for targeting supply chain experts, may limit the representativeness of the findings across the broader population of Moroccan businesses or across varying digital maturity levels.
Finally, while most constructs demonstrated strong reliability and convergent validity, one measurement item (IP2) exhibited a factor loading slightly below the commonly accepted 0.70 threshold. Although retained due to its theoretical relevance, this marginal deviation could affect the construct’s overall psychometric robustness. Future studies are encouraged to refine measurement instruments and reassess construct validity using alternative or mixed methods approaches.

6. Conclusions

This study examined the influence of digital supply chain components on supply chain agility and its impact on innovation performance. Six dimensions of the digital supply chain were analyzed: digital performance measurement, digital information technology, digital suppliers, digital manufacturing, digital logistics and inventory, and digital clients. The study also explored the mediating role of supply chain agility in linking digital adoption to innovation outcomes.
The findings indicate that digital performance measurement had the strongest impact on supply chain agility, emphasizing its role in real-time decision-making and process optimization. Digital information technology and logistics and inventory management also contributed positively, highlighting their significance in enhancing supply chain responsiveness and flexibility [65,66,67]. In contrast, digital manufacturing exhibited a weaker and, in some cases, negative effect, suggesting the presence of challenges that require further analysis [68,69,70]. Digital clients and digital suppliers did not show a significant direct impact on supply chain agility, indicating that their influence may be indirect or dependent on contextual factors.
The study confirmed the mediating role of supply chain agility, demonstrating its contribution to innovation performance [71]. Digital performance measurement, information technology, and logistics and inventory management showed significant indirect effects on innovation performance through agility, reinforcing their role in digital supply chain frameworks.
These findings highlight the strategic value of digital supply chain components in fostering agility and innovation. They bridge theoretical gaps by offering empirical evidence of the pathways through which digital adoption enhances innovation performance. Practically, the findings provide actionable insights for supply chain professionals, emphasizing the need to prioritize performance measurement and information technology while addressing challenges in manufacturing processes. By advancing the understanding of digital supply chain dynamics, this study contributes to both academic literature and industry practices, paving the way for future research into emerging technologies and their implications for supply chain management.

Author Contributions

Conceptualization, Z.Y.I. and S.E.; methodology, Z.Y.I. and S.E.; software, Z.Y.I. and S.E.; validation, Z.Y.I. and S.E.; formal analysis, Z.Y.I. and S.E.; investigation, Z.Y.I. and S.E.; resources, Z.Y.I. and S.E.; data curation, Z.Y.I. and S.E.; writing—original draft preparation, Z.Y.I. and S.E.; writing—review and editing, Z.Y.I. and S.E.; visualization, Z.Y.I. and S.E.; supervision, Z.Y.I. and S.E.; project ad-ministration, Z.Y.I. and S.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of HECF Business School.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data used in this manuscript can be found at the following link: https://drive.google.com/file/d/15UyVcvT5Nlwa9npyZ4Er_fRpYW8TMOot/view?usp=sharing.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ageron, B.; Bentahar, O.; Gunasekaran, A. Digital Supply Chain: Challenges and Future Directions. Supply Chain Forum Int. J. 2020, 21, 133–138. [Google Scholar] [CrossRef]
  2. Nowicka, K. Trust in Digital Supply Chain Management. Logist. Transp. 2018, 39, 59–64. [Google Scholar] [CrossRef]
  3. Alyasein, O.I.Y.; Ojha, D.; Sadeghi, R.K. Supply Chain Digitalization, Innovation Capability, and Organizational Agility: The Moderating Role of Institutionalization and Supply Chain Integration. Ind. Mark. Manag. 2025, 125, 215–225. [Google Scholar] [CrossRef]
  4. Rana, J.; Daultani, Y.; Goswami, M.; Kumar, S. Exploring the Impact of Supply Chain Digital Transformation on Supply Chain Performance: An Empirical Investigation. Bus. Strategy Environ. 2025, 34, 3497–3521. [Google Scholar] [CrossRef]
  5. Susitha, E.; Jayarathne, A.; Herath, R. Integrating Agility and Digital Capabilities: A New Paradigm for Competitive Supply Chain Performance in the Apparel Sector. J. Bus. Technol. 2024, 8, 65–98. [Google Scholar] [CrossRef]
  6. Wang, M.; Hill, A.; Liu, Y.; Hwang, K.; Lim, M.K. Supply Chain Digitalization and Agility: How Does Firm Innovation Matter in Companies? J. Bus. Logist. 2025, 46, e70007. [Google Scholar] [CrossRef]
  7. Wang, M.; Prajogo, D. The Effect of Supply Chain Digitalisation on a Firm’s Performance. Ind. Manag. Data Syst. 2024, 124, 1725–1745. [Google Scholar] [CrossRef]
  8. Büyüközkan, G.; Göçer, F. Digital Supply Chain: Literature Review and a Proposed Framework for Future Research. Comput. Ind. 2018, 97, 157–177. [Google Scholar] [CrossRef]
  9. Frederico, G.F.; Garza-Reyes, J.A.; Anosike, A.; Kumar, V. Supply Chain 4.0: Concepts, Maturity and Research Agenda. Supply Chain Manag. Int. J. 2019, 25, 262–282. [Google Scholar] [CrossRef]
  10. Lisitsa, V.; Mykhaylenko, O.; Rotenberg, O. Digital Supply Chains: Technologies, Trends and Directions of Development. Black Sea Econ. Stud. 2023, 81, 99–106. [Google Scholar] [CrossRef]
  11. Farahani, P.; Meier, C.; Wilke, J. Digital Supply Chain Management Agenda for the Automotive Supplier Industry. In Shaping the Digital Enterprise: Trends and Use Cases in Digital Innovation and Transformation; Springer: Berlin/Heidelberg, Germany, 2017; pp. 157–172. ISBN 978-3-319-40966-5. [Google Scholar]
  12. Rahamneh, A.a.A.; Alrawashdeh, S.T.; Bawaneh, A.; Alatyat, Z.; Mohammad, A.; Al-Hawary, S. The Effect of Digital Supply Chain on Lean Manufacturing: A Structural Equation Modelling Approach. Uncertain Supply Chain Manag. 2023, 11, 391–402. [Google Scholar] [CrossRef]
  13. Fauzi, A.F.; Hendayani, R.; Iskamto, D. The Role of Supply Chain Capability as Mediation Between Digitalization and Digital Connectivity on Operational Performance. J. World Sci. 2023, 2, 1517–1528. [Google Scholar] [CrossRef]
  14. Yu, W.; Chavez, R.; Liu, Q.; Cadden, T. Examining the Effects of Digital Supply Chain Practices on Supply Chain Viability and Operational Performance: A Practice-Based View. IEEE Trans. Eng. Manag. 2024, 71, 10413–10426. [Google Scholar] [CrossRef]
  15. Khan, K.U.; Atlas, F.; Ali, W.; Ghani, U.; Khan, F. The Nexus Between IT Capabilities and Firm Innovation Performance: The Mediating Effect of Supply Chain Agility. Sci. Technol. Soc. 2023, 28, 561–581. [Google Scholar] [CrossRef]
  16. Faturrahman, M.A.; Nursyamsiah, S. The Influence of Supply Chain Integration, Agility, and Innovation on Company Performance. Asian. J. Econ. Bus. Account. 2024, 24, 382–399. [Google Scholar] [CrossRef]
  17. Sunil, K. Supply Chain Innovation: The Core Capabilities Required and Expected Outcomes. J. Inf. Technol. Softw. Eng. 2019, 9, 256. [Google Scholar] [CrossRef]
  18. Zhang, B.; Zhao, S.; Fan, X.; Wang, S.; Shao, D. Green Supply Chain Integration, Supply Chain Agility and Green Innovation Performance: Evidence from Chinese Manufacturing Enterprises. Front. Environ. Sci. 2022, 10, 1045414. [Google Scholar] [CrossRef]
  19. Klaus, P. Towards Practical Relevance—Delivering Superior Firm Performance through Digital Customer Experience Strategies. Direct 2014, 15, 306–316. [Google Scholar] [CrossRef]
  20. Mihardjo, L.; Sasmoko, S.; Alamsjah, F. The Influence of Digital Customer Experience and Electronic Word of Mouth on Brand Image and Supply Chain Sustainable Performance. Uncertain Supply Chain Manag. 2019, 7, 691–702. [Google Scholar] [CrossRef]
  21. Agarwal, A.; Shankar, R.; Tiwari, M.K. Modeling Agility of Supply Chain. Ind. Mark. Manag. 2007, 36, 443–457. [Google Scholar] [CrossRef]
  22. Alzamendi-Ramirez, A.; Yoshida-Chiney, J.; Ramos-Palomino, E.; Mesia, R. Supply Chain Agility in Manufacturing Companies: A Literature Review. In Proceedings of the 2019 7th International Engineering, Sciences and Technology Conference (IESTEC), Panama, Panama, 9–11 October 2019; pp. 467–472. [Google Scholar] [CrossRef]
  23. Juneho, U. The Impact of Supply Chain Agility on Business Performance in a High Level Customization Environment. Oper. Manag. Res. 2017, 10, 10–19. [Google Scholar] [CrossRef]
  24. Chan, F.; Qi, H. An Innovative Performance Measurement Method for Supply Chain Management. Supply Chain Manag. 2003, 8, 209–223. [Google Scholar] [CrossRef]
  25. Barmuta, K.; Ivanova, E.; Orekhova, L.L. Measuring the Performance of Supply Chain Innovations. E3S Web Conf. 2022, 363, 01051. [Google Scholar] [CrossRef]
  26. Arabshahi, H.; Fazlollahtabar, H. Supply Chain Innovation Performance Evaluation Based on the Cross-Efficiency Concept: Case Study of Supply Chain Innovation Award’s Winners, Runner-Ups and Finalists. Int. J. Logist. Syst. Manag. 2020, 36, 343–369. [Google Scholar] [CrossRef]
  27. Chatterjee, S.; Mariani, M.; Ferraris, A. Digitalization of Supply Chain and Its Impact on Cost, Firm Performance, and Resilience: Technology Turbulence and Top Management Commitment as Moderator. IEEE Trans. Eng. Manag. 2024, 71, 10469–10484. [Google Scholar] [CrossRef]
  28. Ehie, I.; Ferreira, L.M.D.F. Conceptual Development of Supply Chain Digitalization Framework. IFAC-PapersOnLine 2019, 52, 2338–2342. [Google Scholar] [CrossRef]
  29. Ramos, E.; Patrucco, A.S.; Chavez, M. Dynamic Capabilities in the “New Normal”: A Study of Organizational Flexibility, Integration and Agility in the Peruvian Coffee Supply Chain. Supply Chain Manag. Int. J. 2023, 28, 55–73. [Google Scholar] [CrossRef]
  30. Roh, T.; Xiao, S. Extending the Research Agenda for Supply Chain Management in the Age of Disruption: The Multifaceted Role and Implications of Dynamic Capabilities. J. Gen. Manag. 2024, 50, 5–15. [Google Scholar] [CrossRef]
  31. Saputra, N. Bolstering Effect of Digital Capability on Firm Performance by Fostering Alliance Capability and Supply Chain Agility. In Proceedings of the 2024 International Conference on Information Management and Technology (ICIMTech), Bali, Indonesia, 28–29 August 2024; IEEE: New York, NY, USA; pp. 25–30. [Google Scholar]
  32. Garcia, F.; Grabot, B.; Paché, G. Creating and Sharing Interorganizational Knowledge Through a Supply Chain 4.0 Project: A Case Study. J. Glob. Inf. Manag. 2023, 31, 1–19. [Google Scholar] [CrossRef]
  33. Ma, C.; Gao, X.; Zhang, L.; Kang, W. Optimizing Timber Supply Chains: Exploring the Potential of Digital Collaboration. Sustainability 2024, 17, 15. [Google Scholar] [CrossRef]
  34. Danese, P.; Romano, P.; Formentini, M. The Impact of Supply Chain Integration on Responsiveness: The Moderating Effect of Using an International Supplier Network. Transp. Res. Part E Logist. Transp. Rev. 2013, 49, 125–140. [Google Scholar] [CrossRef]
  35. Landel, R. Coordination Module: Managing Supply Chains. SSRN Electron. J. 2017, 1–2. [Google Scholar] [CrossRef]
  36. Tazvivinga, K.; Samuels, A. Digital Transformation and Its Role in FMCG Supply Chain Resilience. Int. J. Bus. Ecosyst. Strategy 2687-2293 2024, 6, 23–38. [Google Scholar] [CrossRef]
  37. Jiang, F.; Isa, F.M.; Ng, S.P.; Bhatti, M. The Impact of Supply Chain Integration to Supply Chain Responsiveness in Chinese Electronics Manufacturing Companies. Sage Open 2023, 13, 21582440231219070. [Google Scholar] [CrossRef]
  38. Zhang, Q.; Feng, Y.; You, L. Research on the Impact of Supply Chain Integration on Supply Chain Resilience in NEV Manufacturing Enterprises. Sustainability 2024, 16, 8546. [Google Scholar] [CrossRef]
  39. He, Y.; Zheng, S.; Yuan, L. Dynamics of Fractional-Order Digital Manufacturing Supply Chain System and Its Control and Synchronization. Fractal Fract. 2021, 5, 128. [Google Scholar] [CrossRef]
  40. Lin, D.; Li, M.; Zhan, Q.; Song, X.; Yang, Y.; Li, H. Application of Intelligent Logistics Inventory Optimization Algorithm Based on Digital Supply Chain. Int. J. Emerg. Electr. Power Syst. 2023, 24, 61–72. [Google Scholar] [CrossRef]
  41. Khastoo, M.; Dori, B.; Raad, A. Impact of Agility Capabilities in the Supply Chain Network. Int. J. Sci. Study 2017, 5, 47–56. [Google Scholar]
  42. Hassani, Y.; Ceauşu, I.; Iordache, A. Lean and Agile Model Implementation for Managing the Supply Chain. Proc. Int. Conf. Bus. Excell. 2020, 14, 847–858. [Google Scholar] [CrossRef]
  43. Pasupuleti, V.; Thuraka, B.; Kodete, C.S.; Malisetty, S. Enhancing Supply Chain Agility and Sustainability through Machine Learning: Optimization Techniques for Logistics and Inventory Management. Logistics 2024, 8, 73. [Google Scholar] [CrossRef]
  44. Chen, Y.; Li, B.; Huo, B. Building Operational Resilience through Digitalization: The Roles of Supply Chain Network Position. Technol. Forecast. Soc. Change 2025, 211, 123918. [Google Scholar] [CrossRef]
  45. Sharma, A. Role of Digitalization in Improving Supply Chain Management: A Quantitative Investigation. Inf. Technol. Ind. 2018, 6, 57–61. [Google Scholar] [CrossRef]
  46. Mohaghegh, M.; Blasi, S.; Russo, I.; Baldi, B. Digital Transformation and Sustainable Performance: The Mediating Role of Triple-A Supply Chain Capabilities. J. Bus. Ind. Mark. 2024, 1–15. [Google Scholar] [CrossRef]
  47. Teece, D.; Peteraf, M.A.; Leih, S. Dynamic Capabilities and Organizational Agility: Risk, Uncertainty and Entrepreneurial Management in the Innovation Economy. SSRN Electron. J. 2016, 58, 13–35. [Google Scholar] [CrossRef]
  48. Benitez, J.; Arenas, A.; Castillo, A.; Esteves, J. Impact of Digital Leadership Capability on Innovation Performance: The Role of Platform Digitization Capability. Inf. Manag. 2022, 59, 103590. [Google Scholar] [CrossRef]
  49. Usai, A.; Fiano, F.; Messeni Petruzzelli, A.; Paoloni, P.; Farina Briamonte, M.; Orlando, B. Unveiling the Impact of the Adoption of Digital Technologies on Firms’ Innovation Performance. J. Bus. Res. 2021, 133, 327–336. [Google Scholar] [CrossRef]
  50. Wong, K. Partial Least Squares Structural Equation Modeling (PLS-SEM) Techniques Using SmartPLS. Mark. Bull. 2013, 24, 1–32. [Google Scholar]
  51. Najmi, A.; Makui, A. A Conceptual Model for Measuring Supply Chain’s Performance. Prod. Plan. Control 2012, 23, 694–706. [Google Scholar] [CrossRef]
  52. Garay-Rondero, C.L.; Martinez-Flores, J.L.; Smith, N.R.; Caballero Morales, S.O.; Aldrette-Malacara, A. Digital Supply Chain Model in Industry 4.0. J. Manuf. Technol. Manag. 2019, 31, 887–933. [Google Scholar] [CrossRef]
  53. Zou, P.; Zhang, H. A Measurement Model for Collaboration between Suppliers and Manufacturers. In Proceedings of the 2011 IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, 6–9 December 2011; IEEE: New York, NY, USA; pp. 529–533. [Google Scholar]
  54. Ahmad Amouei, M.; Valmohammadi, C.; Fathi, K. Developing and Validating an Instrument to Measure the Impact of Digital Supply Chain Activities on Sustainable Performance. J. Enterp. Inf. Manag. 2023, 36, 925–951. [Google Scholar] [CrossRef]
  55. Saad, S.M.; Bahadori, R. Logistics Capabilities Measurement in the Fractal Supply Network. Int. J. Logist. Syst. Manag. 2020, 36, 251. [Google Scholar] [CrossRef]
  56. Faisal, M.N.; Banwet, D.K.; Shankar, R. An Approach to Measure Supply Chain Agility. Int. J. Ind. Syst. Eng. 2007, 2, 79. [Google Scholar] [CrossRef]
  57. Li, X.; Goldsby, T.J.; Holsapple, C.W. Supply Chain Agility: Scale Development. Int. J. Logist. Manag. 2009, 20, 408–424. [Google Scholar] [CrossRef]
  58. Maharaja, R.; Devadasan, S.R.; Sakthivel, M.; Murugesh, R. Performance Measurement of the Leagile Supply Chain. Int. J. Serv. Oper. Manag. 2018, 31, 396. [Google Scholar] [CrossRef]
  59. Das, D. Development and Validation of a Scale for Measuring Sustainable Supply Chain Management Practices and Performance. J. Clean. Prod. 2017, 164, 1344–1362. [Google Scholar] [CrossRef]
  60. Abourokbah, S.H.; Mashat, R.M.; Salam, M.A. Role of Absorptive Capacity, Digital Capability, Agility, and Resilience in Supply Chain Innovation Performance. Sustainability 2023, 15, 3636. [Google Scholar] [CrossRef]
  61. Zhou, F.; Xu, S. How Digitalization and Information Technology Adoption Affect Firms’ Innovation Performance: Evidence from Chinese Automotive Firms. Eur. J. Innov. Manag. 2024. [Google Scholar] [CrossRef]
  62. Jermsittiparsert, K.; Rungsrisawat, S. Impact Strategic Sourcing, Supplier Innovativeness, and Information Sharing on Supply Chain Agility. Humanit. Soc. Sci. Rev. 2019, 7, 132–140. [Google Scholar] [CrossRef]
  63. Kim, M.; Chai, S. The Impact of Supplier Innovativeness, Information Sharing and Strategic Sourcing on Improving Supply Chain Agility: Global Supply Chain Perspective. Int. J. Prod. Econ. 2017, 187, 42–52. [Google Scholar] [CrossRef]
  64. Panichakarn, B.; Pochan, J.; Shafiq, M.; Saleem, I.; Wang, Y.; Nazeer, S. The Interplay of Digital Transformation, Agility, Environmental Volatility, and Innovation to Spur Enterprise Performance: Evidence from Chinese Electric Vehicle Firms. J. Open Innov. Technol. Mark. Complex. 2024, 10, 100408. [Google Scholar] [CrossRef]
  65. DeGroote, S.E.; Marx, T.G. The Impact of IT on Supply Chain Agility and Firm Performance: An Empirical Investigation. Int. J. Inf. Manag. 2013, 33, 909–916. [Google Scholar] [CrossRef]
  66. Swafford, P.M.; Ghosh, S.; Murthy, N. Achieving Supply Chain Agility through IT Integration and Flexibility. Int. J. Prod. Econ. 2008, 116, 288–297. [Google Scholar] [CrossRef]
  67. Tuominen, T.; Kitaygorodskaya, N.; Helo, P. Achieving Agility in Supply Chains with the Help of IT. Int. J. Electron. Cust. Relatsh. Manag. 2008, 1, 374. [Google Scholar] [CrossRef]
  68. Aninda, N.; Karyani, E. Supply Chain Digitalization and Operational Performance. Int. J. Asian Bus. Inf. Manag. 2022, 13, 1–16. [Google Scholar] [CrossRef]
  69. Ivanov, D.; Das, A.; Choi, T.-M. New Flexibility Drivers for Manufacturing, Supply Chain and Service Operations. Int. J. Prod. Res. 2018, 56, 3359–3368. [Google Scholar] [CrossRef]
  70. Najat, T.; Alaa Eddine, E.M. Digitalization and Business Automation for an Effective Supply Chain Integration: A Literature Review. In Proceedings of the 2024 IEEE 15th International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA), Sousse, Tunisia, 2–4 May 2024; IEEE: New York, NY, USA; pp. 1–7. [Google Scholar]
  71. Martinez-Sanchez, A.; Lahoz-Leo, F. Supply Chain Agility: A Mediator for Absorptive Capacity. Balt. J. Manag. 2018, 13, 264–278. [Google Scholar] [CrossRef]
Sustainability 17 03462 g001
Figure 1. Research conceptual model.
Figure 1. Research conceptual model.
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Sustainability 17 03462 g002
Figure 2. Structural model results: path coefficients and R2.
Figure 2. Structural model results: path coefficients and R2.
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Table 1. Research constructs and references for measurement item development.
Table 1. Research constructs and references for measurement item development.
Research ConstructsReferences for Measurement Items Development
Digital supply chainDigital performance measurement[7,8,45,51]
Digital information technology[7,8,52]
Digital suppliers[7,8,53]
Digital manufacturing[8,47,54]
Digital logistics and inventory[7,8,55]
Digital clients[7,8]
Supply chain agility[56,57,58]
Innovation performance[26,27,59]
Table 2. Descriptive statistics of respondent characteristics.
Table 2. Descriptive statistics of respondent characteristics.
AttributesCharacteristicsNumber of ResponsesPercentage
Respondent characteristicsMale36657.73%
Female26842.27%
Total634100%
Age25–35 years20031.55
35–45 years23036.28
45–55 years15023.66
>55 years548.52
Total634100%
SectorAgri-food sector21133.28
Textile16926.66
Automotive industry15023.66
Aerospace industry365.68
Other6810.73
Total634100%
Table 3. Convergent validity (n = 634).
Table 3. Convergent validity (n = 634).
ConstructsLoadingsCACRAVEConstructsLoadingsCACR AVE
Digital clients 0.8230.8590.648Digital manufacturing 0.8670.8950.716
DC10.815 DM10.871
DC20.893 DM20.725
DC30.716 DM30.893
DC40.786 DM40.883
Digital information technology 0.8020.9000.716Digital performance measurement 0.8630.8660.785
DIT10.939 DPM10.888
DIT20.765 DPM20.869
DIT30.824 DPM30.901
Digital logistics and inventory 0.7160.7740.635Digital suppliers 0.8740.8920.726
DLI10.896 DS10.891
DLI20.727 DS20.789
DLI30.757 DS30.884
DS40.840
Innovation performance 0.8740.9140.731Supply chain agility 0.9410.9430.811
IP10.939 SCA10.890
IP20.673 SCA20.909
IP30.894 SCA30.866
IP40.888 SCA40.933
SCA50.903
Table 4. Heterotrait-Monotrait ratio (HTMT).
Table 4. Heterotrait-Monotrait ratio (HTMT).
DCDITDLIDMDPMDSIPSCA
Digital Clients
Digital Information Technology0.494
Digital Logistics and Inventory0.4980.213
Digital Manufacturing0.8800.5530.493
Digital Performance Measurement0.3790.8730.1610.411
Digital Suppliers0.8220.5120.4410.8420.373
Innovation Performance0.2660.8320.0720.2540.8060.242
Supply Chain Agility0.2360.6500.1810.2340.7150.2480.648
Table 5. Direct effect.
Table 5. Direct effect.
HypothesesRelationshipsFindingsResultsAdjusted R2
H1DC → SCAp > 0.05, β = 0.013Not supported-
H2DIT → SCAp < 0.05, β = 0.199Supported-
H3DLI → SCAp < 0.05, β = 0.087Supported-
H4DM → SCAp < 0.05, β = −0.122Supported-
H5DPM → SCAp < 0.05, β = 0.499Supported-
H6DS → SCAp > 0.05, β = 0.025Not supported0.430
H7SCA → IPp < 0.05, β = 0.600Supported0.359
Table 6. Indirect effect.
Table 6. Indirect effect.
HypothesesIndependent VariableMediator Dependent VariableStd. BetaT-Valuep-ValueDecision
H7DCSupply chain agilityIP0.0080.263p > 0.05Not supported
H7DITSupply chain agilityIP0.1193.226p < 0.05Supported
H7DLISupply chain agilityIP0.0522.765p < 0.05Supported
H7DMSupply chain agilityIP−0.0732.351p < 0.05Supported
H7DPMSupply chain agilityIP0.2998.041p < 0.05Supported
H7DSSupply chain agilityIP0.0150.532p > 0.05Not supported
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Elmouhib, S.; Idrissi, Z.Y. Agility in the Digital Era: Bridging Transformation and Innovation in Supply Chains. Sustainability 2025, 17, 3462. https://doi.org/10.3390/su17083462

AMA Style

Elmouhib S, Idrissi ZY. Agility in the Digital Era: Bridging Transformation and Innovation in Supply Chains. Sustainability. 2025; 17(8):3462. https://doi.org/10.3390/su17083462

Chicago/Turabian Style

Elmouhib, Soufiane, and Zineb Youbi Idrissi. 2025. "Agility in the Digital Era: Bridging Transformation and Innovation in Supply Chains" Sustainability 17, no. 8: 3462. https://doi.org/10.3390/su17083462

APA Style

Elmouhib, S., & Idrissi, Z. Y. (2025). Agility in the Digital Era: Bridging Transformation and Innovation in Supply Chains. Sustainability, 17(8), 3462. https://doi.org/10.3390/su17083462

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