How Industry 4.0 Technologies Enhance Supply Chain Resilience: The Interplay of Agility, Adaptability, and Customer Integration in Manufacturing Firms

Abstract

This study examines how Industry 4.0 (I4.0) technologies enhance supply chain resilience (SCR) in manufacturing firms by testing the mediating roles of supply chain agility (SCAG), supply chain adaptability (SCAD) and the moderating effect of customer integration (CI). Grounded in the Resource-Based View (RBV) and Dynamic Capabilities View (DCV), the research conceptualizes digital technologies—such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI)—as both strategic resources and enablers of dynamic capabilities in turbulent environments. Survey data were collected from 273 manufacturing firms in Turkey, a context shaped by geopolitical and economic disruptions, and analyzed using structural equation modeling (SEM). The results indicate that I4.0 technologies positively affect SCR directly and indirectly through SCAG and SCAD. However, while agility consistently strengthens resilience, adaptability shows a negative mediating effect, suggesting context-specific constraints. CI significantly amplifies the positive impact of I4.0 on SCR, underscoring the importance of external relational capabilities. Theoretically, this research advances supply chain literature by integrating RBV and DCV to explain how digital transformation drives resilience through distinct dynamic capabilities. Practically, it offers guidance for managers to combine digital infrastructure with collaborative customer relationships to mitigate disruptions and secure long-term performance. Overall, the study provides an integrated framework for building resilient supply chains in the digital era.

1. Introduction

The manufacturing sector is undergoing a profound transformation driven by intensifying global uncertainties, digital disruption, and the need for more adaptive and resilient supply chains [1,2]. As geopolitical instability, pandemic-related shocks, and demand fluctuations continue to challenge supply chain continuity, firms increasingly prioritize supply chain resilience (SCR)—defined as the capability to anticipate, absorb, and recover from disruptions while maintaining or quickly regaining functionality [3,4]. Manufacturing firms, characterized by capital-intensive processes, long production cycles, and high interdependence across global networks, are especially vulnerable to external shocks [5,6]. Hence, building dynamic and sustainable SCR mechanisms has become a strategic imperative.

The rise of Industry 4.0 digital technologies (I4DT), including the Internet of Things (IoT), big data analytics (BDA), blockchain, and artificial intelligence (AI), offers manufacturing firms a promising avenue to enhance SCR [7,8]. These technologies enable end-to-end supply chain visibility, real-time responsiveness, and predictive capabilities that empower firms to mitigate risk and respond proactively to disruptions [9,10]. However, the presence of I4DT alone does not guarantee resilience. The transformation of digital tools into resilience outcomes depends critically on how firms deploy these tools to build operational and strategic capabilities [7,11].

In this context, two core dynamic capabilities—supply chain agility (SCAG) and supply chain adaptability (SCAD)—emerge as key mediators linking I4DT to SCR [12,13,14]. SCA refers to the firm’s capacity to respond quickly and effectively to short-term changes and disruptions, whereas SCAD denotes the ability to reconfigure supply chain strategies in alignment with evolving environmental demands [15,16,17]. Together, these capabilities reflect supply chain ambidexterity—the ability to balance operational responsiveness and long-term strategic adaptation in turbulent environments [18]. Their inclusion as dual mediating mechanisms is grounded in Lee’s [19] Triple-A framework, which identifies agility, adaptability, and alignment as the foundational elements of a resilient supply chain.

While prior studies often examine agility and adaptability independently, their simultaneous role in translating digital technology adoption into SCR remains underexplored [13,14,20]. Moreover, few studies integrate customer-centric enablers, such as customer integration (CI), despite their growing strategic relevance in digital supply chains [21,22]. CI refers to the degree to which firms incorporate customer feedback, preferences, and planning insights into operational and strategic decisions [23,24,25]. It enables downstream coordination, product customization, and real-time responsiveness—making it a critical moderator in the relationship between I4DT and SCR [26,27,28,29]. This study aligns CI with the “alignment” pillar in Lee’s [19] framework and positions it as a boundary condition that enhances the effectiveness of digital investments.

Theoretically, this study is grounded in the Resource-Based View (RBV) and Dynamic Capabilities View (DCV) [30,31]. RBV posits that I4DT constitutes valuable, rare, inimitable, and non-substitutable resources that can yield competitive advantages when appropriately deployed [32,33]. However, DCV emphasizes that firms must dynamically reconfigure their resources in response to environmental shifts [34]. Thus, SCA and SCAD are viewed as capability-based enablers that convert static digital tools into adaptive supply chain strategies [7,12]. This is particularly important in manufacturing contexts, where rigid structures and complex interdependencies often hinder rapid adaptation.

Despite rising scholarly interest, several critical gaps remain. First, the simultaneous mediating effects of SCA and SCAD in the I4DT–SCR relationship remain under-theorized and empirically untested in the manufacturing sector [13,35,36,37]. Second, the role of CI as a moderator—despite its significance in digitally enabled supply chains—has received limited attention in empirical models [22,38]. Third, most studies overlook the contextual idiosyncrasies of the manufacturing sector, particularly in emerging economies, where firms often face resource constraints and infrastructural rigidity [2,5,39]. This study addresses these gaps by developing a moderated mediation model that captures the interdependencies among I4DT, SCA, SCAD, CI, and SCR. To that end, the study seeks to answer the following research questions:

• How do Industry 4.0 digital technologies influence supply chain resilience in manufacturing firms?
• To what extent do supply chain agility and adaptability mediate the relationship between Industry 4.0 digital technologies and supply chain resilience?
• How does customer integration moderate the effects of Industry 4.0 digital technologies on agility, adaptability, and, ultimately, supply chain resilience?

This study contributes to supply chain and digital transformation literature in three key ways. First, it develops an integrative model that examines SCA and SCAD as dual mediators, thereby offering a holistic view of how digital technologies enhance resilience. Second, it introduces CI as a contextual moderator, bridging technological and relational mechanisms. Third, it contextualizes these relationships in the manufacturing sector, advancing theoretical and practical insights for digitally transforming firms operating under structural rigidity and external volatility.

The remaining sections are structured as follows: Section 2 reviews the relevant literature, Section 3 presents the hypotheses development, Section 4 describes the research methodology, Section 5 reports the results, Section 6 discusses the findings and their theoretical and managerial implications, and Section 7 concludes with limitations and suggestions for future research.

2. Literature Review

2.1. Underpinning Theories

The underpinning theory for this study is a combination of two complementary frameworks: the RBV and the DCV. These theoretical lenses provide a comprehensive foundation for examining how Industry 4.0 (I4.0) technologies contribute to enhancing SCR in manufacturing contexts.

The RBV posits that firms can gain and sustain competitive advantage by acquiring and effectively utilizing strategic resources that are valuable, rare, inimitable, and non-substitutable [32,40]. These resources include tangible assets such as infrastructure and equipment, and intangible resources such as knowledge, organizational routines, and inter-organizational trust [41,42]. Within the digital supply chain domain, I4.0 technologies—such as IoT, big data analytics, blockchain, and artificial intelligence—are conceptualized as strategic digital resources that enhance operational visibility, coordination, and transparency [7,33,43]. These digital enablers empower firms to proactively monitor disruptions, optimize inventory flows, and improve network-wide responsiveness, ultimately supporting the development of resilient supply chains [44]. However, while the RBV highlights the importance of possessing these digital assets, it does not fully explain how firms can dynamically deploy them to respond to change.

The DCV extends the RBV by emphasizing a firm’s capacity to integrate, develop, and reconfigure internal and external competencies to address rapidly changing environments [34,45]. Dynamic capabilities encompass sensing, seizing, and transforming activities that enable organizations to anticipate disruptions, capitalize on technological opportunities, and continually realign resources with environmental demands [30,31]. For instance, big data analytics enhances the firm’s sensing capability through predictive demand forecasting, while blockchain and AI technologies support rapid resource reconfiguration, reinforcing the seizing and transforming components of DCV [14,46,47]. This dynamic orchestration of resources is particularly vital for manufacturing firms navigating supply chain volatility in digitally connected ecosystems.

In this study, SCAG and SCAD are operationalized as dynamic capabilities through which digital resources are mobilized to enhance SCR [48]. SCAG refers to the firm’s ability to rapidly respond to short-term disruptions and volatile demand, while SCAD reflects the strategic capacity to redesign supply chain structures and processes in response to long-term environmental changes [12,15,17]. Both capabilities are central to transforming static resources into adaptive competencies that can ensure resilience under uncertainty and digital complexity.

Moreover, this study conceptualizes CI as a boundary-spanning dynamic capability that strengthens resilience by enabling downstream collaboration, demand sensing, and co-creation of solutions [31,46]. CI allows firms to align operational responses with evolving customer expectations, enhance real-time visibility, and adapt production priorities through close feedback loops [38]. This complements the agility and adaptability mechanisms and deepens the effect of I4.0 technologies on resilience.

By combining RBV and DCV, this theoretical framework captures both the possession of strategic digital resources and the capability to deploy them effectively through agility, adaptability, and integration. This dual-lens approach advances understanding of digital transformation in manufacturing supply chains and explains how firms not only build resilience but also navigate the potential tensions between flexibility and long-term performance [31]. Furthermore, the framework accommodates the nuanced insight that adaptability, if misaligned with strategic objectives, may have counterproductive effects on resilience—an emerging perspective warranting deeper exploration [35,44,49]. This study thus contributes a novel synthesis that links digital technology resources, dynamic supply chain capabilities, and resilience outcomes in a cohesive and theoretically grounded model.

2.2. I4.0 Digital Technologies

The conceptualization of I4.0 in this study emphasizes not only the presence of digital technologies but also their effective implementation across value chains to support organizational resilience. I4.0, originally coined in Germany in 2011, signifies a paradigm shift from traditional manufacturing to cyber–physical systems enabled by digital integration at multiple levels of production and supply [17,36]. Rather than merely cataloguing advanced tools, this study focuses on how these technologies are implemented to enable strategic transformation and enhance SCR. As such, I4.0 encompasses the deployment of smart technologies—including the Internet of Things (IoT), big data analytics, artificial intelligence, additive manufacturing, and blockchain—that connect physical and digital assets in real time [9,16,50,51].

Viewed from a value chain perspective, I4.0 technologies transform linear supply chains into Digital Supply Networks (DSNs), which are modular, interoperable, and responsive to disruptions. DSNs allow seamless integration among manufacturers, suppliers, distributors, and even customers, facilitating visibility, automation, and intelligence throughout the supply chain [36,52]. For example, IoT devices enable real-time tracking, blockchains foster transparency and trust, and big data analytics supports predictive decision-making to mitigate risks before they escalate [14,33,46]. These functionalities form the backbone of agile and adaptive supply chain operations, which are essential for resilience in volatile environments.

Crucially, this study adopts a technology implementation view by recognizing that the strategic value of I4.0 lies not in the possession of digital tools but in their integration into daily operational routines and supply chain processes. Implementation reflects an organization’s capability to use these technologies to foster coordination, automate workflows, and enhance data-driven responses to external shocks [7,44]. This distinction aligns with the DCV, emphasizing how digital enablers are mobilized through supply chain agility, adaptability, and customer integration to respond to environmental turbulence [12,38]. Therefore, the I4.0 framework in this study is not static but dynamic—shaped by organizational learning, employee involvement, and cross-functional alignment.

Moreover, the contribution of I4.0 extends beyond the firm level to influence global value chains and industrial ecosystems. Interdisciplinary studies have highlighted how the diffusion of I4.0 technologies enhances system-level flexibility, resilience, and sustainability [49]. By enabling end-to-end digitization, I4.0 supports real-time responsiveness and collaborative planning across the supply chain, providing critical infrastructure for firms navigating global uncertainty [39]. This broader perspective supports the integration of I4.0 into strategic resilience-building efforts in manufacturing.

In summary, I4.0 is conceptualized in this study as an implementation-oriented enabler of SCR, realized through the strategic use of digital technologies to support agility, adaptability, and customer integration. This approach avoids narrow technological determinism and instead focuses on the dynamic organizational processes that translate digital investments into operational continuity and competitive advantage.

2.3. Dynamic Supply Chain Capabilities

This study conceptualizes dynamic supply chain capabilities (DSCCs) through the integration of three interrelated dimensions: agility, adaptability, and customer integration. This alignment draws from Lee’s [19] Triple-A model, which posits that world-class supply chains require agility to respond to short-term changes, adaptability to adjust to long-term shifts, and alignment across partners [53]. Agility and adaptability thus represent core dynamic capabilities that jointly foster SCR.

Agility refers to the ability of the supply chain to respond quickly to unexpected changes in demand and supply conditions [54]. It involves flexibility in organizational structures, reconfigurable processes, and rapid decision-making under uncertainty [55]. Swafford et al. [56] argue that supply chain agility is a system-level capability that enables firms to seize opportunities in turbulent environments. Agility has been positioned as a dynamic capability that enhances not only operational responsiveness but also innovation and strategic adaptation [54].

In contrast, adaptability represents the capacity to reconfigure and transform supply chain design in response to longer-term market changes [19,53]. It involves the proactive redesign of networks, technologies, and partner roles to address evolving customer expectations and macroeconomic shifts. Dubey et al. [57] note that adaptability allows supply chain actors to sustain competitive advantage by anticipating and preparing for market evolution. However, this study challenges the common assumption that adaptability is always beneficial. In highly digitalized contexts, excessive adaptability may introduce complexity, dilute core competencies, and negatively affect SCR—an insight that adds to emerging critical literature on the dark side of dynamic capabilities [58].

Customer integration, while less emphasized in the traditional DSCC literature, is essential in the I4.0 environment where value creation is increasingly customer-driven and data-enabled [38]. Unlike supplier integration, which primarily enhances upstream efficiency, customer integration plays a pivotal role in synchronizing downstream demand with production and distribution processes [22]. Flynn et al. [23] define customer integration as the extent to which firms collaborate and share information with customers to improve demand forecasting, customization, and responsiveness. In digital supply chains, technologies such as AI and IoT amplify customer integration by enabling real-time data sharing, predictive analytics, and collaborative decision-making [59]. From a dynamic capabilities lens, this allows firms to continuously reconfigure customer-facing processes in response to shifting preferences, thereby supporting resilience and strategic agility.

Together, agility, adaptability, and customer integration form a triad of dynamic supply chain capabilities that reflect the sensing, seizing, and reconfiguring cycle outlined in the dynamic capabilities view [12,35,38,53]. These capabilities are not only interdependent but must be developed and deployed in coordination to enhance SCR. By integrating these elements into one cohesive framework, this study addresses a notable gap in the literature where they are often examined in isolation. Moreover, it introduces the novel proposition that adaptability may have context-dependent or even negative effects on resilience, especially in digitally turbulent environments—a contribution that advances both theory and practice.

2.4. Supply Chain Resilience in Manufacturing

SCR is broadly defined as the ability of a supply chain to anticipate, absorb, and recover from disruptions while maintaining or quickly restoring its functionality [50,60,61,62]. It comprises two interdependent components: resistance capacity, which minimizes the adverse effects of disruptions through proactive planning, and recovery capacity, which enables rapid restoration of operations post-disruption [63]. This dual framework has become central in understanding organizational preparedness in increasingly volatile environments.

In manufacturing firms, SCR is especially critical due to industry-specific structural constraints such as high capital intensity, fixed production assets, long lead times, and globally dispersed supplier networks [9,37,64]. These characteristics hinder swift adjustments and magnify the impact of supply chain disruptions. For instance, delays in a single supplier’s component delivery can stall entire production cycles, leading to cascading effects throughout the supply chain. Therefore, manufacturing contexts require tailored resilience strategies that account for operational rigidity and systemic interdependence.

From a theoretical perspective, the RBV positions digital technologies as valuable, rare, and inimitable resources that can enhance resilience capabilities [7]. Yet, the DCV complements this by emphasizing the firm’s ability to reconfigure these resources in response to external threats [12]. In the manufacturing context, dynamic capabilities such as agility, adaptability, and integration serve as enablers that convert static technological resources into responsive systems capable of managing supply chain shocks [5,13]. As Madrid-Guijarro et al. [64] argued, firms must cultivate routines that continuously improve quality, efficiency, and flexibility in turbulent conditions—an imperative that becomes even more pronounced in large-scale manufacturing environments. Recent reviews also highlight that I4.0 technologies are being reframed not only as efficiency drivers but as mechanisms for risk management, robustness, and resilience [65]. This perspective underscores the importance of balancing dynamic capabilities, as excessive reliance on adaptability may compromise robustness, a tension our study investigates.

Recent disruptions, including the COVID-19 pandemic and geopolitical instabilities, have intensified scholarly focus on SCR within manufacturing. These disruptions have revealed systemic fragilities in just-in-time models and emphasized the need for resilient, digitally enhanced supply chains [8,47]. As a result, emerging literature has identified a set of resilience-enhancing practices—such as supply chain visibility, multi-tier collaboration, and digital risk sensing—as essential for manufacturing firms [36]. Digital technologies like IoT, blockchain, and predictive analytics now act as resilience enablers by facilitating real-time monitoring, data-driven decision-making, and proactive risk mitigation [5,64].

Moreover, the evolving body of research reveals that SCR in manufacturing is not solely about technological sophistication, but also about strategic alignment and contextual sensitivity. Zouari et al. [66] emphasized that firms must align digital solutions with their production logic, logistics complexity, and customer responsiveness needs. This indicates that the successful implementation of SCR strategies depends on the firm’s ability to develop dynamic capabilities that match its structural realities [36]. For instance, while adaptability may enhance responsiveness in some contexts, it could generate inefficiencies or misalignment in rigid manufacturing systems—an insight this study investigates as part of its novel contribution. In line with Ismail et al. [65], this highlights the paradoxical nature of resilience, where robustness and adaptability must be carefully balanced to avoid capability trade-offs that erode stability.

Despite advancements, prior studies rarely capture how I4.0 technologies interact with dynamic supply chain capabilities to build resilience in manufacturing. By integrating digitalization with agility, adaptability, and customer integration, this study offers a comprehensive perspective on resilience mechanisms and introduces an underexplored paradox—the potential negative impact of over-adaptability on SCR. This theoretical advancement directly responds to recent calls for more contextualized and integrative models of SCR.

3. Conceptual Model and Hypotheses Development

3.1. I4.0 Digital Technologies and Supply Chain Resilience

I4.0 represents a transformative shift in manufacturing, driven by the integration of digital technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence (AI), and blockchain into supply chain operations [51,67]. From the RBV, these technologies are regarded as strategic assets that are valuable, rare, inimitable, and non-substitutable, thus offering firms the potential to gain sustained competitive advantage [7,32]. However, this study moves beyond merely identifying these technologies as resources and emphasizes their implementation—how firms actively embed and use them within supply chain processes to manage uncertainty and volatility.

While prior studies affirm that I4.0 technologies enhance visibility, coordination, and responsiveness [8,68], the mere possession of these tools is insufficient. The DCV complements the RBV by asserting that organizations must develop higher-order capabilities to sense environmental changes, seize opportunities, and transform operations accordingly [30,31,34]. In this context, IoT facilitates sensing by enabling real-time monitoring of supply chain nodes, while big data analytics and AI support seizing through predictive insights and scenario analysis [50,69]. Blockchain and cloud systems, in turn, facilitate transformation by enabling decentralized decision-making, enhancing traceability, and ensuring rapid reconfiguration in response to disruptions.

This integration of RBV and DCV offers a robust theoretical foundation for understanding how I4.0 technologies—when strategically implemented—function as enablers of supply chain resilience [5]. In manufacturing contexts, resilience refers to a firm’s ability to absorb, adapt to, and recover from disruptions while maintaining operational continuity and customer service levels [70]. Traditional strategies, such as holding excess inventory or diversifying suppliers, are no longer sufficient in today’s complex and globally connected supply chains [64]. Instead, proactive and technology-enabled capabilities are necessary for anticipating, responding to, and learning from disruptions.

Despite growing consensus on the transformative potential of I4.0, empirical research that systematically tests the relationship between its implementation and SCR—particularly through an integrated RBV-DCV lens—remains limited [8,35,47,71]. This study addresses this gap by examining how the implementation of I4.0 digital technologies enables firms to operationalize dynamic capabilities, thereby enhancing their resilience. The novelty of this research lies in its comprehensive framework that not only conceptualizes these technologies as resources but also explores their role in developing organizational capabilities that ensure long-term supply chain viability under turbulent conditions. Accordingly, the following hypothesis is proposed:

H1. 

The implementation of I4.0 digital technologies positively affects supply chain resilience.

3.2. I4.0 Digital Technologies, Supply Chain Agility, and Resilience

SCAG refers to a firm’s ability to respond rapidly, effectively, and efficiently to shifts in market demand and operational conditions [54]. It encompasses the dynamic capacity to readjust internal operations and collaborative processes to reduce lead times, enhance customer responsiveness, and navigate uncertainty [36,72]. From the lens of the DCV, SCAG reflects the “sensing” and “responding” capabilities that allow firms to detect and act upon market opportunities and threats [12]. In highly volatile manufacturing environments, agility is critical for survival and competitiveness.

I4.0 digital technologies—such as IoT, cloud computing, and AI—serve as enablers of agility by fostering real-time data exchange, predictive analytics, and automated decision-making [1]. This study emphasizes not just the presence of these technologies but their implementation across supply chain activities. This distinction is essential, as technological implementation reflects the degree to which these tools are operationalized to improve responsiveness and coordination [73]. Under the RBV, such implemented digital capabilities are valuable, rare, inimitable, and non-substitutable—thus offering the foundation for competitive advantage [26,40].

Three key mechanisms explain how I4.0 implementation strengthens agility. First, real-time visibility across supply chain nodes—enabled through cloud-based systems—improves partner coordination and responsiveness to fluctuating demand [16]. Second, decentralized decision-making supported by CPS and edge computing allows local entities to act autonomously but in alignment with strategic goals, enhancing decisiveness and speed [36]. Third, intelligent planning systems—such as AI-driven simulations and what-if scenario tools—enable dynamic and concurrent planning under disruption conditions [74].

Moreover, I4.0 implementation strengthens flexibility and reduces cycle times, enabling supply chains to adapt product features, delivery schedules, and service offerings to meet evolving customer expectations [75]. This adaptability is not static—it is continuously renewed through digital feedback loops and learning mechanisms, aligning with the DCV’s emphasis on renewing and reconfiguring resources [28]. As a result, digitally agile supply chains are better positioned to withstand volatility, drive customer satisfaction, and reinforce overall resilience.

Although prior research suggests a positive relationship between digital technologies and agility [5,13,14], few studies have empirically examined this link through the integrated lens of RBV and DCV, especially in the context of manufacturing firms. This study contributes novel insight by explicitly testing how the implementation of I4.0 digital technologies fosters supply chain agility, which in turn contributes to resilience. Therefore, the following hypotheses are proposed:

H2. 

The implementation of I4.0 digital technologies positively affects supply chain agility.

H3. 

Supply chain agility positively affects supply chain resilience.

3.3. I4.0 Digital Technologies, Supply Chain Adaptability, and Resilience

I4.0 digital technologies represent a transformative force that enables firms to respond proactively to volatile environments by enhancing their adaptive capacity across supply chain functions [1]. In this study, we conceptualize I4.0 not merely as a technological presence but as the implementation of digital technologies—including big data analytics, artificial intelligence, the Internet of Things (IoT), and digital twins—that are actively embedded within operational processes to support strategic responsiveness [73]. This operationalization reflects how firms leverage these technologies to improve visibility, control, and coordination across complex supply chains.

SCAD refers to an organization’s ability to restructure its supply chain design, processes, and strategies in response to changes in the external environment, such as market turbulence, supply shocks, or socio-political disruptions [15,35,58]. From the perspective of the DCV, adaptability is a “transform” capability that reflects an organization’s capacity to realign internal resources to meet external demands [53]. As such, adaptability enables firms to manage both expected and unforeseen challenges while maintaining performance continuity.

I4.0 technologies enhance this adaptive capacity by promoting modularity, real-time data flow, and decentralized decision-making. For example, AI and predictive analytics allow firms to anticipate disruptions, simulate multiple supply scenarios, and develop informed contingency plans [76,77]. Similarly, digital twins and IoT sensors provide granular insights into operational performance, enabling dynamic adjustments in procurement, production, or logistics activities [61,66]. These technologies also improve stakeholder coordination by increasing transparency and communication across the value chain, allowing firms to respond to disruptions collaboratively and efficiently [36,47].

While prior studies have confirmed the strategic role of digitalization in supporting adaptability [35,64,78], contextual factors such as the maturity of digital infrastructure, organizational readiness, and supply chain complexity can moderate this effect. Moreover, adaptability may yield context-dependent outcomes: while it is generally expected to enhance resilience under conditions of low operational rigidity and flexible resource availability, its effect may diminish—or even become counterproductive—under high manufacturing constraints, where frequent reconfiguration could disrupt stability [58]. Therefore, empirical investigation is necessary to test the robustness of this relationship across manufacturing environments [58]. This study addresses this gap by examining how the implementation of I4.0 technologies enhances adaptability in the context of the Turkish manufacturing sector. Grounded in the theoretical logic of the DCV and building on recent empirical insights, we propose the following hypotheses:

H4. 

The implementation of I4.0 digital technologies positively affects supply chain adaptability.

H5. 

Supply chain adaptability positively affects supply chain resilience under conditions of low operational rigidity, but its effect may weaken or even turn negative under high manufacturing constraints.

3.4. The Mediating Mechanism of Supply Chain Agility and Adaptability

SCAG and SCAD are conceptualized as distinct dynamic capabilities that mediate the relationship between the implementation of I4DT and SCR. Grounded in the DCV, these capabilities reflect a firm’s ability to sense environmental changes, seize opportunities, and reconfigure operational and structural assets to maintain competitiveness in the face of disruptions [12,13,14]. This distinction enhances theoretical clarity and avoids the common tendency in prior studies to conflate agility and adaptability [15,58], thereby addressing a critical gap in the supply chain literature.

SCAG represents a firm’s capacity to respond swiftly and flexibly to unexpected disruptions by modifying internal processes and realigning supply chain operations [15,16]. Enabled by I4DT tools such as cloud computing, IoT, and AI-based platforms, SCAG improves decision-making speed, operational visibility, and responsiveness across supply chain partners [36]. This responsiveness enhances resilience by allowing firms to absorb shocks, reduce lead times, and maintain service levels in volatile environments [72,79].

SCAD, in contrast, reflects a firm’s ability to reconfigure its supply chain design and strategies in response to long-term shifts in the external environment [35,58]. This includes restructuring supplier networks, diversifying distribution channels, and reallocating resources based on insights from digital systems [12]. I4DT supports SCAD by enabling real-time monitoring, predictive analytics, and scenario planning, which allow organizations to anticipate disruptions and proactively adapt their supply chain strategies [70,78]. Unlike agility, which deals with short-term operational changes, adaptability focuses on sustained strategic transformation, making it particularly valuable for building long-term resilience [47]. Consistent with the DCV, the value of adaptability is contingent upon environmental fit: it enhances resilience when external conditions require flexibility but may impose costs or inefficiencies when high structural constraints limit reconfiguration [35].

While previous research has disproportionately emphasized SCAG, this study contributes to the literature by treating SCAG and SCAD as distinct and complementary mediators between I4DT implementation and SCR. This distinction not only enhances conceptual rigor but also responds to calls from scholars such as Shi et al. [37] to include adaptability as a key intervening mechanism in digital supply chains. Moreover, by incorporating both mediators into a single model, this study advances our understanding of how I4DT influences resilience through multiple capability-building pathways [13,35].

The integration of the RBV and DCV further strengthens the conceptual foundation of this study. While RBV frames I4DT as a strategic asset, DCV explains how firms convert this digital resource into superior resilience outcomes through SCAG and SCAD [5,13,14,53]. This dual-theoretical lens addresses recent calls about insufficient integration and underscores the novelty of our framework. It also justifies the empirical testing of the proposed hypotheses despite prior empirical support, as the combined effect of SCAG and SCAD in the context of I4DT remains underexplored in the manufacturing sector. Accordingly, the following hypotheses are proposed to test the mediating roles of SCAG and SCAD:

H6. 

Supply chain agility positively mediates the relationship between the implementation of I4.0 digital technologies and supply chain resilience.

H7. 

Supply chain adaptability positively mediates the relationship between the implementation of I4.0 digital technologies and supply chain resilience.

3.5. The Moderating Role of Customer Integration

Customer integration serves as a critical dynamic capability in digitally enabled supply chains, facilitating synchronized responses to downstream demand variability and enhancing organizational resilience [23,80]. In the context of I4.0, digital technologies such as AI, IoT, and big data analytics enable real-time demand visibility and predictive insights, empowering firms to continuously align production and logistics with evolving customer needs [38]. This makes customer integration particularly valuable in volatile environments where rapid adaptation is key to resilience and agility.

While traditional supply chain integration research emphasizes internal, supplier, and customer integration, the present study focuses solely on customer integration due to its centrality in downstream responsiveness and its strong theoretical fit with the dynamic capabilities perspective [22]. Unlike supplier integration, which primarily supports upstream efficiency, customer integration directly contributes to sensing and seizing opportunities through enhanced market intelligence, collaborative planning, and innovation co-creation with end users [38]. This downstream focus is particularly pertinent in the I4.0 environment, where value creation is increasingly customer-driven and data-enabled.

Drawing on the RBV, customer integration constitutes an intangible, firm-specific resource that is difficult to imitate and valuable in achieving competitive advantage [25,26]. It reflects a firm’s ability to develop long-term relationships and co-create value with customers, which is essential for building resilient, market-oriented supply chains [29]. Moreover, from the DCV, customer integration supports the reconfiguration of supply chain processes in response to external shocks by facilitating the continuous flow of market knowledge [38]. This capability enables firms to realign internal resources, reprioritize production goals, and adapt delivery strategies to mitigate disruptions.

Customer integration also enhances the effects of I4.0 digital technologies on supply chain agility and adaptability by enabling seamless coordination between digital signals and operational execution [81]. For example, predictive analytics derived from customer data may only yield operational value when firms are embedded in tightly integrated information-sharing relationships with their customers [23,29,38]. Such integration ensures that the digital capabilities are matched with external responsiveness, thus amplifying their contribution to resilience.

Therefore, this study proposes that customer integration strengthens the positive effects of I4.0 digital technologies on supply chain agility, resilience, and adaptability by facilitating data-driven collaboration and downstream alignment. Based on these arguments, the following hypotheses are proposed:

H8. 

Customer integration moderates the relationship between the implementation of I4.0 digital technologies and supply chain agility.

H9. 

Customer integration moderates the relationship between the implementation of I4.0 digital technologies and supply chain resilience.

H10. 

Customer integration moderates the relationship between the implementation of I4.0 digital technologies and supply chain adaptability.

3.6. Conceptual Framework

Grounded in the RBV and DCV, this study develops a framework to examine how the implementation of I4.0 digital technologies enhances SCR through the mediating roles of SCAG and SCAD and the moderating role of CI. According to RBV, digital technologies represent strategic, valuable, and inimitable resources, but their value is only realized when activated through dynamic capabilities that allow firms to sense, seize, and transform in response to environmental shifts [30,31]. SCAG and SCAD are conceptualized as operationalizations of these dynamic capabilities, enabling short-term responsiveness and long-term structural realignment, respectively [12,13,14,53]. While prior studies have linked these capabilities to resilience [35,37,48,63], few have modeled their joint mediating role in digitally enabled supply chains, thereby justifying hypothesis testing. Furthermore, CI is positioned as a boundary-spanning moderator that enhances the effectiveness of these relationships by synchronizing downstream demand signals with internal supply chain processes [22,38]. Its exclusive inclusion—over internal or supplier integration—is theoretically justified by its centrality in I4.0’s customer-driven, data-enabled environments. The framework, depicted in Figure 1, presents a coherent model linking digital technology implementation with resilience through dynamic capabilities and relational integration, advancing understanding of how manufacturing firms can build sustainable, adaptive supply networks.

4. Methodology

4.1. Sample and Data Collection

The data for this study were gathered through a cross-sectional survey administered via an online questionnaire targeting representatives of Turkish manufacturing firms. Turkey was chosen as the focal point of this research because it is recognized as a global leader in manufacturing and a key innovator among developing countries. Prior studies emphasize Turkey’s prominence in supply chain research, given its strategic focus on integrating supply chain practices with innovation [39,77,82]. Additionally, Turkish manufacturing firms are known for their technological sophistication, particularly in the context of I4.0, positioning them as high-tech forerunners [51]. This makes Turkey an ideal context for examining how digital technologies bolster supply chain resilience.

The sampling strategy concentrated on medium and large manufacturing firms, each with over 50 people [83]. This criterion was essential to ensure that the selected firms had sufficient financial and intellectual capital to invest in digital technologies [40]. The initial sampling frame consisted of 1872 firms, retrieved from the TOBB (The Union of Chambers of Commerce, Industry, Maritime Trade, and Commodity Exchanges of Turkey) database. The TOBB database, a comprehensive source of information on Turkish firms, includes details on firm size, board members, and financial reports, making it a reliable foundation for sampling [84]. From this initial pool, we employed a random stratified sampling method, resulting in the selection of 900 firms.

The data collection process was conducted between October 2023 and January 2024. The questionnaire was designed using Google Forms owing to its user-friendly interface and flexibility, allowing for easy dissemination and completion by respondents. To enhance response rates, a two-step communication strategy was employed. First, all 900 firms were contacted by phone to introduce the study and gauge interest. Second, personalized emails were sent to CEOs, plant directors, operations managers, and supply chain managers. This multi-channel approach, recommended in survey-based research to increase engagement [85], was effective, as 769 firms responded to initial phone contact. However, 89 firms declined to participate due to scheduling conflicts or lack of involvement with I4.0. Eventually, 680 firms received an email questionnaire that included a cover letter detailing the study’s purpose and ensuring confidentiality. After two follow-up reminders, 279 responses were received, of which 273 were deemed valid, yielding a response rate of 30.3%.

To assess potential non-response bias, we followed the common procedure of comparing early and late respondents, as suggested in survey research [86]. Independent sample t-tests were conducted on key demographic variables (firm size and respondent position) and central study constructs. The results indicated no significant differences between the two groups (p > 0.05), suggesting that non-response bias is unlikely to have influenced the findings.

The respondents predominantly held senior managerial positions with responsibilities in logistics, plant operations, production, procurement, and supply chain management, ensuring that the data collected were reflective of individuals with substantial decision-making authority.

Table 1. Respondent Firms’ Characteristics.

Characteristics		Number	%
Individual-level characteristics
Respondent’s position	Logistic manager	36	13.2%
	Plant manager	31	11.4%
	Production manager	22	8.1%
	Procurement manager	18	6.6%
	Supply chain manager	150	54.9%
	Others	16	5.9%
Functional experience	<5 years	58	21.2%
	5–9	83	30.4%
	10–19	52	19.0%
	20–29	50	18.3%
	≥30 years	30	11.0%
Firm-level characteristics
Nature of business	Machinery and hardware	92	33.7%
	Electrical and IT	45	16.5%
	Plastic and rubber	36	13.2%
	Leather and garment	29	10.6%
	Food and beverages	22	8.1%
	Chemical and cosmetic	19	7.0%
	Pharmaceutical and medical	17	6.2%
	Other manufacturing	13	4.8%
Firm size (number of employees)	Medium-size (<250)	89	32.6%
	Large-size (≥250)	184	67.4%
Firm age (years of operation)	<5 years	7	2.6%
	5–14	102	37.4%
	15–24	116	42.5%
	25–34	30	11.0%
	≥35 years	18	6.5%
Total		273	100%

provides an overview of the sample characteristics, including firm size and respondent roles.

4.2. Instrumentation

To ensure the accuracy and validity of the study’s constructs, well-established measurement scales were adapted from the literature to operationalize the independent, dependent, mediating, moderating, and control variables (see Appendix A,

Table A1. Measurement items.

Industry 4.0 Digital Technologies (I4DT)

Using the five-point scale of never (1) to always (5), please indicate the frequency your company uses the Industry 4.0 digital technologies listed below.

I4DT1: Big data analytics.

I4DT2: Cloud-based e-procurement.

I4DT3: Internet of Things.

I4DT4: Artificial Intelligence.

Supply Chain Agility (SCAG)

Our organization:

SCAG1: Works hard to promote the flow of information with its suppliers and customers.

SCAG2: Works hard to develop collaborative relationships with suppliers.

SCAG3: Builds inventory buffers by maintaining a stockpile of inexpensive but key components.

SCAG4: Has a dependable logistics system or partner.

SCAG5: Draws up contingency plans and develops crisis management teams.

Supply Chain Adaptability (SCAD)

Our organization:

SCAD1: Monitors economies all over the world to spot new supply bases and markets.

SCAD2: Use of intermediaries to develop fresh suppliers and logistics infrastructure.

SCAD3: Evaluates needs of ultimate consumers—not just immediate customers.

SCAD4: Creates flexible product designs.

SCAD5: Determines where companies’ products stand in terms of technology cycles and product life cycles.

Customer Integration (CI)

For the past three years …

IC1: We have a high level of information sharing with major customers about market information.

IC2: We share information to major customers through information technologies.

IC3: We have a high degree of joint planning and forecasting with major customers to anticipate demand visibility.

IC4: Our customers provide information to us in the procurement and production processes.

IC5: Our customers are involved in our product development processes.

Supply Chain Resilience (SCR)

Please evaluate to what degree the following statements is valid for your company (1 = to a very low degree and 5 = to a very high degree)

SCR1: Our firm’s supply chain is able to adequately respond to unexpected disruptions by quickly restoring its product flow.

SCR2: Our firm’s supply chain can quickly return to its original state after being disrupted.

SCR3: Our firm’s supply chain can move to a new, more desirable state after being disrupted.

SCR4: Our firm’s supply chain is well prepared to deal with financial outcomes of supply chain disruptions.

SCR5: Our firm’s supply chain has the ability to maintain a desired level of control over structure and function at the time of disruption.

SCR6: Our firm’s supply chain has the ability to extract meaning and useful knowledge from disruptions and unexpected events.

). As the study was conducted in Turkey, the questionnaire was initially developed in English and then translated into Turkish by a bilingual supply chain and a manufacturing specialist. The translation process is essential in cross-cultural research to ensure linguistic and conceptual equivalence [87]. Additionally, a pilot test was conducted with 10 academics and five business professionals, leading to refinements in both the layout and wording of the questionnaire, further enhancing its clarity and precision.

4.2.1. Operationalization of I4.0 Digital Technologies

The independent variable, I4.0 digital technologies (I4DT), was measured using four items adapted from recent studies [9,16]. These items assess the extent of firms’ engagement with digital technologies, including artificial intelligence, cloud computing, big data analytics, and the Internet of Things (IoT). Participants rated their firm’s use of these technologies on a five-point Likert scale, ranging from “never” to “always.”

4.2.2. Supply Chain Agility and Adaptability

The mediating variables, SCAG and SCAD, were measured using five items adapted from previous studies [14]. SCAG captures the supply chain’s ability to respond swiftly and effectively to market changes, whereas SCAD measures the supply chain’s capacity to distribute risks, costs, and gains equitably among participants [57]. Both SCAG and SCAD represent dynamic capabilities, which are essential for understanding how firms develop resilience in their supply chains. In line with the dynamic capability theory, these variables highlight a firm’s ability to reconfigure resources in response to environmental changes, a critical factor in supply chain resilience [12,53].

4.2.3. Customer Integration

The moderating variable, CI, was measured using five items adapted from Salamah et al. [39] and Wong et al. [59]. These items capture the degree of collaboration between the focal firm and its customers in managing shared goals, supply chain activities, and the flow of products and information [23].

4.2.4. Supply Chain Resilience

The dependent variable, SCR, was measured using six items adapted from Stentoft et al. [88]. These items assess the firm’s ability to recover from and adapt to supply chain disruptions. The SCR indicators include the ability to reestablish product flows, return to normal operations, adapt to new conditions, handle financial impacts, retain control during disturbances, and learn from unexpected events [82].

4.2.5. Control Variables

Firm size and industry type were included as control variables to control for firm characteristics that might influence the implementation of digital technologies. Firm size was measured by the number of employees, whereas industry type was categorized according to the specific nature of the manufacturing sector. These control variables help account for differences in firms’ capacities to adopt and leverage digital technologies, ensuring that the relationships observed in this study are not confounded by underlying firm characteristics.

4.3. Common Method Bias Check

Several well-established remedies have been implemented to address potential concerns regarding common method bias (CMB). First, the Harman’s single-factor test was conducted [89]. Unrotated exploratory factor analysis revealed that the variance explained by a single factor was 36.3%, which was well below the 50% threshold, indicating that no single factor dominated the variance. Additionally, the full collinearity variance inflation factor (VIF) test showed values below 3.3, suggesting that collinearity was not an issue in the dataset [90]. These combined results confirm that CMB was unlikely to have influenced the study’s findings, providing strong support for the validity of the data and reliability of the conclusions drawn.

5. Analysis and Results

To test the hypothesized relationships, a covariance-based structural equation modeling (SEM) approach was employed following established guidelines in supply chain research [91]. The analysis proceeded in two stages. First, a measurement model was estimated to evaluate the fit of the latent constructs, confirming the model’s reliability and validity. In the second stage, the structural model was tested using AMOS 24.0, in which the path coefficients and factor loadings were assessed based on standardized coefficients [30]. To ensure the robustness of the results, especially given the sample size, a bootstrapping resampling technique was applied with 5000 bootstrap samples at a 95% confidence interval [92]. This approach mitigated potential biases and strengthened confidence in the findings, ensuring that the relationships between I4.0 digital technologies and SCR were captured reliably.

5.1. Confirmatory Factor Analysis

A confirmatory factor analysis (CFA) was conducted to evaluate the reliability and validity of the measurement model using AMOS 24.0 with maximum likelihood estimation [93]. CFA was selected over exploratory factor analysis (EFA) because of the study’s established theoretical assumptions regarding the relationships between observed variables and latent constructs, allowing for more precise testing of the measurement and structural models [94,95]. As presented in

Table 2. Factor Loadings, Composite Reliability (CR), and Average Variance Extracted (AVE).

Construct/Indicators	Mean	Std. Deviation	Factor Loadings	Cronbach’s α	CR	AVE
I4.0 Digital Technologies (I4DT)				0.834	0.861	0.615
I4DT1	3.08	1.177	0.845
I4DT2	3.18	1.214	0.652
I4DT3	2.80	1.145	0.771
I4DT4	2.69	1.196	0.920
Supply Chain Agility (SCAG)				0.863	0.884	0.607
SCAG1	3.01	1.080	0.685
SCAG2	3.30	1.100	0.841
SCAG3	3.41	1.084	0.792
SCAG4	3.43	1.031	0.790
SCAG5	3.40	1.130	0.857
Supply Chain Adaptability (SCAD)				0.893	0.910	0.670
SCAD1	3.32	1.140	0.873
SCAD2	3.26	1.068	0.832
SCAD3	3.28	1.198	0.733
SCAD4	3.30	1.086	0.787
SCAD5	3.36	1.059	0.861
Customer Integration (CI)				0.791	0.810	0.564
CI1	3.17	1.040	0.782
CI2	2.95	1.197	0.655
CI3	3.24	1.092	0.784
CI4	3.38	1.092	0.721
CI5	3.26	1.255	0.740
Supply Chain Resilience (SCR)				0.755	0.768	0.540
SCR1	3.72	1.060	0.680
SCR2	3.73	1.099	0.730
SCR3	3.55	1.003	0.692
SCR4	3.70	1.035	0.710
SCR5	3.16	1.133	0.647
SCR6	3.10	1.083	0.636

, the factor loadings for all items exceeded the recommended threshold of 0.7, with the exception of seven items that exceeded 0.6, ensuring composite reliability [96]. These results indicate a high level of internal consistency across the constructs. The factor loading values ranged from 0.636 to 0.920, confirming that all items contributed meaningfully to their respective constructs. Additionally, inspection of the cross-loading matrix revealed that no item loaded more than 0.5 on any unintended construct, thereby eliminating concerns about substantial cross-loadings [97]. Descriptive statistics were also reported, with mean scores for the items ranging from 2.69 to 3.73 and standard deviations between 1.03 and 1.25, reflecting moderate variability in responses.

Convergent validity was assessed by evaluating the model fit indices, which indicated a good fit for the measurement model. The CFA yielded the following fit statistics: χ² = 598.557, df = 261, χ²/df = 2.293, CFI = 0.910, IFI = 0.911, NFI = 0.935, TLI = 0.927, and RMSEA = 0.069. All indices were within the acceptable ranges for structural equation modeling, confirming the model’s overall fit [98,99]. Additionally, the composite reliability (CR) values reported in Table 2 exceeded the 0.70 threshold, and the average variance extracted (AVE) values surpassed 0.50, providing strong evidence of convergent validity [96,100].

Cronbach’s alpha values were calculated for all constructs to ensure reliability of the measurement model. As shown in Table 2, each construct demonstrated a Cronbach’s alpha of above 0.7, confirming that the items within each scale were consistent and reliable [39,101]. Discriminant validity was evaluated using the Fornell-Larcker criterion. As illustrated in

Table 3. Discriminant Validity: Fornell-Larcker Criterion.

Factors	I4DT	SCAG	SCAD	CI	SCR
I4.0 Digital Technologies (I4DT)	0.784
Supply Chain Agility (SCAG)	0.477 ***	0.894
Supply Chain Adaptability (SCAD)	0.392 ***	0.779 ***	0.819
Customer Integration (CI)	0.498 ***	0.596 ***	0.416 ***	0.682
Supply Chain Resilience (SCR)	0.500	0.585	0.429	0.637	0.583

Note(s): Diagonal (in bold) = square root of AVE; off the diagonal = inter-construct correlations; *** significant at the 0.001 level.

, the AVE values for each construct were greater than the corresponding squared correlations between constructs, signifying that the latent variables were distinct from each other, thus establishing discriminant validity [102]. Inter-construct correlations are reported in Table 3, supporting the distinctiveness of the constructs and their empirical separation. These results provide robust evidence that the measurement model is reliable and valid, making it suitable for further hypothesis testing and model evaluation.

5.2. Hypotheses Testing

The structural model developed to test the hypotheses was evaluated using AMOS 24.0, with bootstrapping techniques applied over 5000 samples at a 95% confidence interval to ensure robust results [93]. The model fit was deemed adequate, as indicated by the following key fit indices: χ² = 7.497, df = 4, χ²/df = 1.874, CFI = 0.979, IFI = 0.939, NFI = 0.949, TLI = 0.980, and RMSEA = 0.057. These values fell within acceptable thresholds, confirming that the model provided a good fit for the data [92,98]. Figure 2 presents the structural model results, with firm size and industry type controlled in all paths, including the moderation analysis involving customer integration.

The findings support Hypothesis 1, revealing that I4DT significantly and positively influences SCR (β = 0.191, p < 0.001). Hypothesis 2 was also confirmed, as I4DT demonstrated a significant positive relationship with SCAG (β = 0.176, p < 0.01), indicating that firms adopting digital technologies enhance their ability to respond swiftly to market changes. Furthermore, the results confirmed Hypothesis 3, showing that SCAG significantly and positively affects SCR (β = 0.581, p < 0.001), underscoring the importance of agility in resilient supply chains.

The analysis also supported Hypothesis 4, which revealed a positive link between I4DT and SCAD (β = 0.195, p < 0.01). However, Hypothesis 5 was not supported, as SCAD did not exhibit a significant positive effect on SCR (β = −0.319, p < 0.01), suggesting that adaptability alone may not directly contribute to supply chain resilience.

To assess the mediating effects of SCAG and SCAD, bootstrapping with 5000 iterations was conducted using a simple mediation analysis available in AMOS 24.0 [39]. SCAG partially mediated the relationship between I4DT and SCR (0.102**), with a 95% confidence interval ranging from 0.021 to 0.182, thus confirming Hypothesis 6. For Hypothesis 7, the indirect effect via SCAD was statistically significant (β = −0.062, p < 0.01) with a 95% confidence interval between −0.059 and −0.011; however, because the result was negative and contradicted the hypothesized positive mediation, H7 was rejected. These results are summarized in

Table 4. Hypotheses Testing Results and Standardized Path Coefficients.

Path	Hypothesis	β	Standard Error	t-Values	CI 95%		p-Values	Decision
					Lower	Upper
Direct effects
I4DT → SCR	H1	0.191 ***	0.019	4.401	0.119	0.263	0.001	Supported
I4DT → SCAG	H2	0.176 **	0.033	3.262	0.077	0.282	0.006	Supported
SCAG → SCR	H3	0.581 ***	0.092	4.586	0.390	0.800	0.001	Supported
I4DT → SCAD	H4	0.195 **	0.065	3.025	0.081	0.319	0.010	Supported
SCAD → SCR	H5	−0.319 **	0.074	−3.009	−0.524	−0.165	0.003	Not Supported
Indirect effects
I4DT → (SCAG) → SCR	H6	0.102 **	0.018	-	0.021	0.182	0.004	Supported
I4DT → (SCAD) → SCR	H7	−0.062 **	0.014	-	−0.059	−0.011	0.006	Not Supported
Moderating effects
I4DT_X_CI → SCAG	H8	0.149 ***	0.023	3.300	0.055	0.245	0.001	Supported
I4DT_X_CI → SCR	H9	0.095 **	0.014	2.568	0.041	0.145	0.007	Supported
I4DT_X_CI → SCAD	H10	0.139 **	0.045	2.577	0.018	0.253	0.010	Supported

Note(s): I4DT_X_CI: interaction term between Industry 4.0 digital technologies and customer integration; *** Statistically significant at p < 0.001; ** Statistically significant at p < 0.010.

, which details the standardized path coefficients and significance levels for all the hypothesized relationships.

5.3. Moderating Effect Analysis

To test the moderating effect of CI on the relationship between I4DT and supply chain capabilities and resilience, we applied the product indicator method [93]. The findings in Table 4 indicate that CI significantly enhanced the positive effect of I4DT on SCAG, with a standardized path coefficient of (β = 0.149, p < 0.001). This suggests that, when customer integration is high, firms are more capable of leveraging digital technologies to improve their agility in responding to dynamic market demands. Thus, Hypothesis 8 was supported, as illustrated by the interaction effect shown in Figure 3, which presents the simple slopes for high and low levels of CI, indicating a steeper slope for high-CI firms.

Additionally, CI positively moderated the relationship between I4DT and SCR with a significant path coefficient (β = 0.095, p < 0.01), further supporting Hypothesis 9. This result highlights the critical role of customer collaboration in fortifying a firm’s ability to recover from and adapt to supply chain disruptions through the implementation of advanced digital technology. The moderating effect is visually represented in Figure 4, which shows that firms with higher CI demonstrate a stronger positive slope, suggesting a more pronounced resilience improvement when CI is high.

Finally, the analysis revealed that CI significantly moderated the impact of I4DT on SCAD with a path coefficient of (β = 0.139, p < 0.01), confirming Hypothesis 10. This indicates that firms with stronger customer integration are better equipped to adapt their supply chain operations to fluctuating market conditions when deploying digital technology. The interaction effect, displayed in Figure 5, clearly depicts that the adaptability gain from I4DT adoption is more substantial for firms with higher CI levels. The slope differences suggest the potential for threshold effects, where very high CI may lead to disproportionately greater adaptability improvements. Together, these results underscore the pivotal role of customer integration in maximizing the benefits of digital technologies across the multiple dimensions of supply chain performance.

6. Discussion and Implications

6.1. Discussion of Major Findings

This study delves into the intricate relationships between I4.0 digital technology implementation, supply chain agility, adaptability, resilience, and customer integration, providing significant insights through the lens of the RBV and the DCV. The findings reveal how I4.0 acts as both a static resource and a dynamic capability to enhance SCR within the manufacturing sector. The focus on implementation reflects that value creation occurs when these technologies—such as IoT, big data analytics, and automation—are effectively integrated into processes and decision-making, rather than simply being available as unused resources [73]. Hypothesis testing sheds light on the nuanced interactions and underscores how firms can effectively leverage I4.0 technologies to build more resilient supply chains in response to unpredictable market dynamics.

H1 posits a direct positive relationship between I4DT and SCR. The results align with the existing literature that shows how digital technologies, such as IoT, big data, and automation, help firms reduce lead times and improve operational efficiency, thereby strengthening resilience [50,68]. In line with DCV, this finding highlights that merely adopting digital technologies is insufficient for sustaining resilience. Instead, continuously adapting and reconfiguring these technological resources is essential to maintain resilience in fluctuating environments [7]. This dynamic aspect emphasizes that I4.0’s role goes beyond that of a static enabler and is pivotal in facilitating continuous adjustment.

H2 demonstrates a positive relationship between I4DT and supply chain agility. This corroborates research indicating the positive impact of digital technologies on enhancing agility [14,76]. According to the RBV, I4.0 represents a rare and valuable resource that improves a firm’s ability to respond swiftly to disruptions [7]. By enhancing decision-making capabilities, providing real-time visibility, and facilitating rapid response times, firms are better equipped to adapt to market fluctuations, contributing to increased agility [33,40]. Furthermore, H3 illustrates that supply chain agility significantly enhances resilience. As noted by Karmaker and Ahmed [103] and Tarigan et al. [81], agility is a critical capability that enables firms to operate continuously during disruptions, thereby reducing vulnerability and fortifying resilience [79]. DCV further asserts that agility, a vital aspect of dynamic capabilities, strengthens long-term resilience by facilitating quicker adaptation to external changes [12].

H4, which explores the relationship between I4.0, digital technologies, and supply chain adaptability, highlights the strategic importance of adaptability. Adaptability, as indicated by the RBV, is a valuable capability that allows firms to pivot and realign their supply chains swiftly in the face of challenges [12,15]. For firms adopting I4.0, technologies gain a significant advantage by enabling rapid adjustments, thus enhancing their overall resilience [58]. However, the results of H5 indicate that supply chain adaptability negatively affects SCR. This counterintuitive finding suggests that while adaptability is conceptually linked to resilience in some studies [35], excessive adaptability can lead to capability trade-offs, resource dissipation, or path dependency, where constant adjustments undermine the stability and efficiency needed during disruptions [58]. From an RBV perspective, overemphasis on adaptability may divert resources away from resilience-focused capabilities, while DCV implies that repeated reconfigurations without strategic alignment can weaken the firm’s long-term viability [12,13,14]. Moreover, in manufacturing contexts—characterized by high capital intensity, complex logistics, and long production cycles—frequent realignments may disrupt established processes and reduce operational continuity, ultimately harming resilience [47,48]. This aligns with recent insights by Ismail et al. [65], who emphasize that I4.0-driven supply chain strategies must balance robustness and resilience rather than relying excessively on adaptability. Their work highlights how firms face inherent capability trade-offs, where pursuing constant adjustments to remain flexible can inadvertently weaken structural robustness and undermine resilience. By linking our negative SCAD finding to this debate, we extend the emerging literature on how digital transformation influences the delicate balance between flexibility, robustness, and resilience.

However, H6 introduces a nuance in understanding the role of supply chain agility in mediating the relationship between I4DT and SCR. While supply chain adaptability is hypothesized to mediate this relationship, the findings show a negative indirect effect, leading to the rejection of H7. Although this path was statistically significant, the direction contradicted the hypothesized positive mediation, suggesting that adaptability may not serve as an effective conduit for translating digital technology implementation into resilience gains. This suggests that, while adaptability is crucial for long-term shifts in external environments, its role in mediating the effects of digital technologies on resilience may not be as strong as anticipated. This highlights the complexity of how digital technologies influence resilience, with adaptability not necessarily being a direct bridge in the relationship but potentially a contributor to more gradual resilience-building processes over time [35,58].

Finally, the results emphasize the moderating role of customer integration in enhancing the relationship between I4DT, supply chain agility, resilience, and adaptability H8–H10. Firms that actively engage customers in decision-making processes are better equipped to align their operations with market demands [21,22]. This engagement fosters collaboration, ensures that supply chain activities are better aligned with customer expectations, and significantly improves operational outcomes [29]. By enhancing customer integration, firms not only strengthen their supply chains’ agility but also enhance their resilience and adaptability, facilitating quicker and more effective responses to market disruptions [38]. Therefore, customer integration has emerged as a key moderating factor in improving supply chain outcomes, ensuring more sustainable and adaptive responses to changing business environments.

6.2. Theoretical Implications

This study provides substantial theoretical contributions by integrating the RBV and the DCV to explain how I4DT enhances SCR in manufacturing contexts. One of the key theoretical advancements lies in framing I4.0 as a strategic digital resource that is both valuable and rare, aligning with RBV, and as a platform for continuous reconfiguration and innovation, aligning with DCV. This dual-lens approach advances the literature by clarifying that technology adoption alone does not guarantee resilience; rather, resilience emerges when firms possess the dynamic capabilities—such as sensing, seizing, and transforming—that enable the strategic deployment of these resources to manage disruption [5,13,14,48]. The findings underscore how digital technologies enhance operational effectiveness across warehouses, distribution centers, suppliers, and manufacturing units, strengthening resilience against uncertainties and geopolitical risks. By highlighting the transformative role of I4.0 in expanding global connectivity and proactive disruption management, this study contributes to the growing body of work on digitally enabled resilience [64,75].

Moreover, the research advances theory by positioning SCAG and SCAD as essential dynamic capabilities mediating the I4.0–SCR relationship. These capabilities allow firms to continuously adjust, optimize, and fine-tune operations in response to both expected and unexpected disruptions [12,53]. By embedding these constructs within DCV, the study offers a more precise theoretical framing in which agility primarily addresses short-term market turbulence, while adaptability facilitates strategic, long-term structural changes [35,36,58]. This distinction adds conceptual clarity and aligns with calls to disaggregate dynamic capabilities to better understand their performance implications. Notably, the unexpected finding that SCAD can negatively influence SCR provides a novel theoretical insight. From an RBV perspective, this suggests that over-adaptability may dilute scarce strategic resources by dispersing them across low-value or misaligned initiatives. From a DCV lens, excessive or poorly timed reconfiguration—particularly in rigid manufacturing environments or during early phases of technology implementation—may erode stability, leading to inefficiencies and reduced resilience. This nuance challenges the prevailing assumption that “more adaptability is always better” and extends resilience theory by identifying boundary conditions under which adaptability may be detrimental.

Furthermore, this study extends RBV and DCV by conceptualizing CI as a boundary-spanning dynamic capability that strengthens the pathways between I4.0 technologies, agility, adaptability, and resilience. By integrating customers into supply chain processes, firms can align operational responses with evolving market needs, improve real-time demand sensing, and co-create adaptive solutions [21,22]. The exclusive focus on CI, rather than also including supplier or internal integration, is theoretically grounded in the downstream pressures faced by manufacturing firms in this study’s context, where rapid customization demands, volatile consumer trends, and sustainability expectations require fast and accurate market feedback. In such environments, CI offers more immediate resilience benefits because it enables firms to rapidly reconfigure production priorities using I4.0 tools such as AI-driven demand forecasting and blockchain-enabled transparency [29,38]. This focus demonstrates how customer collaboration can enhance the value extracted from internal resources, complementing both RBV and DCV.

Overall, this research contributes to theory by: (1) integrating RBV and DCV to establish a clearer theoretical logic connecting digital resource possession with dynamic capability deployment; (2) identifying agility and adaptability as distinct mediating capabilities with differentiated temporal scopes; (3) revealing a counterintuitive negative adaptability–resilience link and theorizing its boundary conditions; and (4) justifying CI as a contextually optimal integration mechanism in manufacturing supply chains. These contributions deepen understanding of how digital transformation reshapes the capability–resilience nexus and offer a refined lens for future studies to explore both the enabling and constraining effects of dynamic capabilities in volatile, digitally connected environments.

6.3. Managerial Implications

The findings offer several crucial managerial implications for manufacturing firms seeking to enhance their SCR in the digital age. First, manufacturing managers should prioritize investments in I4.0 technologies, such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). These technologies not only optimize current supply chain operations but also enable firms to enhance their agility and adaptability—capabilities that are essential for navigating both sudden disruptions and structural market shifts [13,14]. Managers should view such investments not merely as operational upgrades but as strategic initiatives that embed digital resources into the firm’s long-term competitive advantage, in line with RBV and DCV. The ability to respond quickly to short-term fluctuations while maintaining long-term alignment with evolving market needs is critical for building a resilient and sustainable supply chain. As highlighted by Raji et al. [68], digital technologies can significantly reduce lead times, increase visibility, and improve efficiency, directly strengthening an organization’s capacity to adapt and respond to changing conditions.

Furthermore, managers should place strategic emphasis on deliberately developing and balancing SCAG and SCAD as distinct yet complementary capabilities. Agility is essential for making rapid, data-driven decisions in the face of immediate disruptions, whereas adaptability enables a firm to reconfigure operations, technologies, and strategies to address long-term shifts in market structure or customer behavior [53,58,78]. The results of this study reinforce that these two capabilities play differentiated roles in mediating the relationship between I4.0 technologies and SCR. Importantly, the finding that SCAD can have an adverse effect on resilience under certain conditions signals a managerial caution: excessive or poorly targeted adaptability efforts—especially in resource-constrained or highly automated manufacturing settings—may disrupt operational stability and dilute strategic focus. Managers should, therefore, adopt a selective adaptability approach, ensuring that long-term changes are aligned with core resources, market signals, and technological readiness [35]. By consciously nurturing SCAG and SCAD in alignment with firm strategy, managers can ensure their supply chain is equipped to handle both transient shocks and enduring transformation [7,48].

This study also emphasizes the importance of relationship-based strategies, particularly strong CI, as a critical enabler of resilience in manufacturing supply chains. Managers should foster CI as a strategic tool for amplifying the benefits of I4.0 technologies. Customer integration enables firms to synchronize production priorities with real-time market demands, improve demand forecasting accuracy, and accelerate innovation through co-creation [21]. In contexts such as the manufacturing sector examined here, where downstream volatility and customization pressures are high, CI offers faster resilience payoffs than supplier or internal integration, making it a priority focus for resource allocation [38]. This research indicates that customer collaboration is a key link between digital technology adoption and enhanced supply chain performance, suggesting that firms should invest in digital collaboration platforms, shared analytics tools, and joint planning processes with customers to gain a competitive edge [22]. Firms that actively involve customers in decision-making are better positioned to anticipate demand shifts, identify emerging risks, and proactively address potential disruptions, thereby reinforcing their overall resilience.

In summary, managers should: (1) treat I4.0 adoption as a strategic investment that builds dynamic capabilities; (2) balance agility and adaptability to optimize both short-term responsiveness and long-term transformation while avoiding the pitfalls of excessive adaptability; and (3) leverage customer integration as a high-impact mechanism for translating digital capability into resilience gains. These actionable insights provide manufacturing leaders with a roadmap for designing digitally enabled, capability-driven, and market-aligned supply chains in an increasingly turbulent global environment.

6.4. Limitations and Future Studies

While this study offers valuable insights into the impact of I4DT on SCR within Turkish manufacturing firms, its scope presents certain limitations. The sample was restricted to manufacturing firms, which may limit the generalizability of the findings; future studies could extend the analysis to service-oriented sectors and other industries with distinct supply chain structures, such as healthcare, retail, or logistics, to assess whether the mechanisms linking I4.0 and SCR operate consistently across contexts. Although a broad range of digital technologies was considered, the study did not explicitly incorporate emerging solutions such as blockchain, Machine-to-Machine (M2M) communication, and autonomous robotics, which enhance transparency, automation, and responsiveness [36,73]. Future research could also examine advanced technologies like digital twins, interoperability platforms, and AI-driven decision-support systems to capture the compounding benefits of integrated digital ecosystems for SCR.

From a theoretical perspective, the study applied the RBV and DCV to explain the mechanisms linking I4.0 to SCR; future research could enrich this foundation by incorporating complementary perspectives, such as the Management of Technology (MoT) theory, to better understand how firms align technology adoption with strategic objectives [31]. Additionally, the unexpected finding of a negative relationship between SCAD and SCR calls for deeper investigation. Future studies could explore whether this effect is shaped by contextual factors such as industry maturity, resource constraints, process rigidity, or technology implementation stages, thereby clarifying the boundary conditions under which adaptability supports or hinders resilience [35]. Addressing these areas will contribute to a more comprehensive and nuanced understanding of how I4.0 innovations can be harnessed to design robust, adaptable, and future-ready supply chains.

7. Conclusions

This study examined how I4.0 technologies enhance SCR in manufacturing firms, with SCAG and SCAD as mediators, and CI as a moderator. Drawing on the RBV and DCV, the findings demonstrate that I4.0 serves as a critical strategic resource, and its value is realized through the development of dynamic capabilities that allow firms to sense, seize, and transform in response to disruptions. The results confirm the positive mediating role of SCAG, highlighting agility as an essential mechanism through which digital technologies translate into resilience. In contrast, SCAD displayed a significant but negative mediating effect, suggesting that excessive adaptability may lead to resource misallocation or operational instability under certain conditions. CI was found to strengthen the relationship between I4.0-driven capabilities and resilience, reinforcing the importance of external collaboration in leveraging internal resources.

These findings offer both theoretical and practical contributions. Theoretically, the research advances the integration of RBV and DCV by showing how digital technologies function as strategic resources that require dynamic capabilities to generate resilience. Practically, the results underscore the necessity for manufacturing managers to invest not only in advanced digital technologies but also in the complementary capabilities and collaborative relationships that ensure these technologies deliver resilience benefits. While agility emerges as a universally positive enabler, adaptability’s mixed effects highlight the need for strategic balance when adjusting supply chain processes. Taken together, the study contributes to the growing body of knowledge on the role of I4.0 in future-proofing supply chains and offers a foundation for further research exploring industry-specific dynamics, emerging technologies, and context-dependent factors that shape the I4.0–resilience relationship.