Unveiling the Impact of Digitalization on Supply Chain Performance in the Post-COVID-19 Era: The Mediating Role of Supply Chain Integration and Efficiency

Abstract

In the context of the rapidly evolving landscape shaped by the increased prevalence of digital technologies and the transformative dynamics of supply chains in the post-COVID-19 era, this research seeks to address a crucial gap by examining the mediating role played by supply chain integration and efficiency. The primary aim is to provide a more comprehensive and nuanced understanding of how digitalization influences networks of supply chain performance. Moreover, we delve into the moderating impact of supply chain dynamism on shaping this association. Through a simple random sampling technique, survey data were collected from 293 Turkish manufacturing firms via an online survey and analyzed using structural equation modeling. The findings underscore that digitalization significantly enhances supply chain integration and efficiency, thereby contributing to improved supply chain performance. Notably, supply chain integration and efficiency were identified as key mediators in the relationship between digitalization and supply chain performance. Furthermore, we investigate the moderating effect of supply chain dynamism, revealing its positive influence on the association between digitalization and supply chain integration. Rooted in key theories such as the resource-based view and dynamic capabilities, this study provides valuable insights by unraveling the intricate processes through which digitalization’s impact is channeled in the post-COVID-19 era. The research extends the current literature by considering the contextual role of supply chain dynamism, shedding light on the complex dynamics between digitalization and supply chain outcomes.

1. Introduction

Supply chains have long been recognized as critical components of business operations, playing a pivotal role in ensuring the efficient flow of goods and services from suppliers to end consumers. In recent years, the concept of supply chain digitalization has emerged as a focal point for businesses striving to adapt to the rapidly changing landscape of the global marketplace [1,2,3]. Defined as the extent to which firms adopt and deploy digital supply chain systems to transact with various players along the supply chain [4,5,6] supply chain digitalization has gained remarkable prominence, particularly in the post-COVID-19 pandemic [7,8,9].

The pandemic served as a catalyst, accelerating the adoption of digital technologies in supply chain management [10,11]. As highlighted by [12] a staggering 84% of supply chain executives have significantly increased their utilization of digital technology within supply chains. This surge in adoption is not only a response to the pandemic-induced disruptions but also an acknowledgment of the broader digital trend reshaping business practices [13]. Firms that fail to embrace and harness these digital advancements may find themselves at a considerable disadvantage, struggling to keep pace with their digitally adept counterparts, and in some cases, even facing the risk of going out of business [14,15]. However, amidst the rush towards digitalization, a significant challenge emerges. Many firms, despite recognizing the importance of supply chain digitalization, grapple with a lack of clarity on what precisely it entails [12,16]. This confusion is emblematic of the complex and multifaceted nature of digitalization in the supply chain context.

The motivation for this study is deeply rooted in addressing critical gaps and advancing our understanding of the dynamic landscape of digitalized supply chains. Many existing studies have primarily focused on isolated aspects of digitalization without considering the interdependencies and interactions that define the supply chain ecosystem. Furthermore, limited attention has been directed towards the mediating mechanisms that elucidate the pathways through which digitalization translates into enhanced supply chain performance [12,17]. As a result, the existing literature has yet to provide a comprehensive framework that integrates these variables within a dynamic and interconnected context. These divergent findings underscore the complexity of the relationship between supply chain digitalization and performance outcomes, leaving critical gaps in understanding the underlying mechanisms and conditions that determine the effectiveness of digitalization efforts [18]. While the existing literature has recognized the significance of supply chain digitalization, it has not conclusively resolved whether it primarily facilitates or potentially obstructs supply chain management [19]. Despite the growing recognition of digitalization’s potential impact on performance, previous studies often fall short in addressing the intricate relationships at play [12]. This research paper aims to address these gaps by taking a more holistic and integrated approach, thereby shedding light on the nuanced relationships that underlie the impact of supply chain digitalization on performance through the mediating roles of supply chain integration and efficiency. Additionally, we also explore the moderating role of supply chain dynamism in this association.

The theoretical foundation underpinning this study draws on established concepts within the realm of supply chain management and digital transformation. The adoption of digital technologies in supply chain operations has been driven by the necessity to overcome disruptions and uncertainties [20,21,22] which aligns with the contemporary challenges posed the ongoing digital revolution [23,24]. The theoretical framework of this study is rooted in the concepts of resource-based view [25] and dynamic capabilities [26] emphasizing the role of unique resources and the ability to adapt to changing environments [8,9,13,27]. These frameworks provide a lens through which we can analyze the intricate interplay of digitalization, supply chain integration, efficiency, and performance, while accounting for the moderating effect of supply chain dynamism, a crucial contextual factor in today’s ever-evolving business landscape [12,28,29]. By integrating these theoretical perspectives, this research aims to provide a comprehensive and nuanced understanding of the multifaceted relationships that drive supply chain performance in the digital age. The questions driving this study are as follows:

RQ1. Do supply chain integration and supply chain efficiency mediate the relationship between supply chain digitalization and supply chain performance?

RQ2. How does supply chain dynamism moderate the impact of digitalization on supply chain integration, supply chain efficiency, and supply chain performance?

To address these questions, a comprehensive survey was conducted among 293 manufacturing firms in Turkey, contributing significantly to the scholarly discourse. This study enriches the domains of the resource-based view and dynamic capabilities theories, providing a nuanced extension that elucidates the intricate mechanisms underlying the transformational impact of digital technologies within the supply chain. Additionally, the investigation sheds light on the relative significance of supply chain integration and efficiency as facilitators in amplifying the value of supply chain digitalization for achieving superior performance within manufacturing firms. Our study contributes to the existing body of knowledge by delving into the specifics of how companies can leverage digitalization to improve supply chain integration and supply chain efficiency in the post-COVID-19 era. By focusing on the aftermath of this global crisis, we aim to provide actionable insights and recommendations that are particularly relevant in today’s business landscape, where the need for agile and efficient supply chains has become more pronounced. Finally, by introducing the moderating factor of supply chain dynamism, this study contributes to the evolving discourse on the contingencies shaping the outcomes of digitalization efforts, recognizing the importance of adaptability and responsiveness in today’s rapidly changing business landscape.

2. Literature Review and Hypotheses Development

2.1. Underpinning Theory

In the domain of supply chain management (SCM) and the context of digital transformation, the underpinning theories form a crucial framework for understanding the intricate interplay between digitalization efforts and supply chain performance outcomes. The resource-based view (RBV) [25] is a cornerstone in this study, offering a lens through which we examine how firms’ unique digital resources and capabilities contribute to competitive advantages and, subsequently, improved supply chain performance [30,31,32]. In the age of digital transformation, firms must strategically deploy digital technologies, ensuring their alignment with the specific needs of the supply chain [33] RBV helps us analyze why some firms excel in this aspect, leveraging digitalization to enhance coordination, streamline processes, and create value across the supply chain, while others may struggle to harness these advantages [34]. Supplementing RBV, Transaction Cost Economics (TCE) [35] is pivotal in exploring the governance structures and cost dynamics associated with supply chain digitalization [36,37]. TCE illuminates how digitalization affects the transactional aspects of supply chain activities, revealing whether it leads to reduced information asymmetry, altered contractual relationships, or changes in coordination costs [6,38]. This is crucial in the era of digital transformation, as understanding the cost implications and the potential to mitigate transactional inefficiencies is essential for firms striving to optimize their supply chains. Ref. [39] by examining how digitalization influences the economics of transactions within the supply chain, TCE enhances our comprehension of the mediating variables, supply chain integration, and efficiency, which in turn influence the overall supply chain performance.

Furthermore, the Dynamic Capability Theory (DCT) introduces a forward-looking perspective to this study, particularly significant in the context of today’s rapidly evolving digital landscape. DCT is defined as “the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments” [26]. Thus, as supply chains face constant disruptions and changing market conditions, the ability to adapt, innovate, and learn becomes paramount. DCT provides a theoretical foundation for understanding how firms, armed with dynamic capabilities, respond to these changes, effectively leveraging digitalization to enhance supply chain performance [9,40,41]. In a dynamically changing environment, firms with strong dynamic capabilities are better positioned to exploit the benefits of digital transformation, especially when dealing with the inherent uncertainties and challenges posed by supply chain dynamism [42,43].

By integrating these theories, this study aims to provide a comprehensive framework that addresses critical gaps in the literature. It offers a holistic understanding of the mechanisms through which digitalization, mediated by supply chain integration and efficiency, impacts supply chain performance, while considering the moderating effects of supply chain dynamism (Figure 1). This multidimensional approach not only contributes to academic knowledge but also provides valuable insights for businesses striving to navigate the complex landscape of digital transformation and enhance their supply chain performance in an ever-changing business environment.

2.2. Supply Chain Digitalization

In the evolving landscape of supply chain management, the integration of digital technologies has become pivotal for companies seeking a competitive edge in the digital era [22]. The outbreak of COVID-19 further intensified the need for digitalization, prompting companies to adopt remote working, paperless operations, and reconstruct supply chain structures [23]. To address specific supply chain challenges, companies are embracing distinct digital technologies that offer targeted solutions. One notable example is the application of block chain technology in the food supply chain, enabling core companies and stakeholders to monitor and trace the entire food production process [44]. This innovation enhances transparency and integrity within the supply chain. Companies embarking on the journey of digitalization go beyond introducing digital technologies; they incorporate digital strategy, organizational structure, culture, and talent [10,20,45,46]. This comprehensive approach ensures a holistic integration of digitalization.

Supply chain digitization (SCD) is a driving force, integrating digital technologies like big data, cloud computing, blockchain, Internet of Things (IoT), and artificial intelligence into supply chain activities [1,47]. SCD focuses on “data-driven decision-making”, creating operational processes enriched by digital technologies. Ref. [12] emphasize the significance of digital technologies such as smart contracts, digital storage, and intelligent labels in achieving traceability throughout the product lifecycle. This not only offers a digital traceability service but also significantly improves the transparency and integrity of the supply chain.

Accordingly, digitalization can be categorized into internal and external digitalization [48]. Internal digitalization aims to enhance efficiency in operational processes through technologies like video conferencing and digital training [49]. External digitalization focuses on leveraging digital technology to strengthen interactions with stakeholders, reduce communication costs, and anticipate customer needs [17,50] analyze the steps to achieve digitalization, emphasizing the extraction of valuable knowledge from data analysis results to improve operational processes. Digitalization is reflected in two main areas: digital products/services and digital processes [51]. Highly digital enterprises provide complete digital products/services and operate with mature digital processes.

Digitalization contributes to the servitization of manufacturing companies, creating new digital business models and value-creation opportunities [52,53,54]. This transformative shift toward digital servitization is reshaping the landscape of manufacturing. A macro perspective on digitalization, considering digital connectivity, internet use, e-business, e-commerce, and e-government, showcases a positive impact on the development of financial markets and institutions [55,56] demonstrate that enhancing digital capabilities enables buyer companies to improve information sharing and relationship transparency with supplier firms, reducing opportunistic and unethical behavior and fostering a stronger partnership. In essence, supply chain digitization involves a nuanced adoption of specific digital technologies tailored to address diverse supply chain challenges, reflecting the dynamic nature of the digital era.

2.3. Digitalization and Supply Chain Performance

Digitalization, involving the incorporation of digital technologies into business processes, is recognized for its transformative potential in improving organizational performance [20,57,58]. In supply chain management, digitalization entails the implementation of digital systems for seamless transactions across the supply chain network [6,21,59]. This integration enables real-time information exchange, enhances communication, and fosters collaboration among supply chain partners. These advancements increase supply chain responsiveness and agility, helping businesses navigate market fluctuations and meet evolving customer demands [17,60].

The impact of digitalization on supply chain performance is a nuanced area of study, reflecting divergent findings and complex dynamics [61]. Some scholars argue that digitalization fosters greater efficiency and collaboration across the supply chain, leading to improved performance. Ref. [1] contend that digitalization enables real-time interaction and communication, enhancing overall productivity. Ref. [22] further support this perspective by highlighting how digitalization enhances supply chain agility and responsiveness. On the other hand, there are concerns about the potential downsides of digitalization. Ref. [62] points out that disparities in digital capabilities among supply chain partners can lead to imbalances and challenges in integration. Ref. [6] underlines the increased investments in IT infrastructure that digitalization might necessitate, potentially affecting the cost structure of supply chain operations [63].

A notable finding from recent studies underscores the positive influence of digitalization on supply chain performance outcomes [9,12,16,28,29,40]. The infusion of digital technologies, such as data analytics, Internet of Things (IoT) devices, and automation, empowers firms to optimize processes, minimize bottlenecks, and reduce lead times [13,32]. Consequently, supply chains become more agile and capable of swiftly adjusting to changes in consumer preferences or disruptions in the operational landscape. Furthermore [39] have highlighted how digitalization can enhance supply chain collaboration and coordination, leading to improved overall performance. By facilitating real-time data sharing and transparency, digitalization fosters stronger relationships among supply chain partners and reduces information asymmetry [34,37,64]. This not only enables more accurate demand forecasting but also allows for better inventory management and efficient resource allocation, contributing to enhanced supply chain efficiency and effectiveness. Firms that actively engage in digital collaboration with partners tend to experience growth, lower operating expenditures, and higher customer satisfaction [58] aligning with the holistic perspective presented by the integrated theoretical framework in this study. In light of these insights, we propose that digitalization is indeed positively associated with supply chain performance. Therefore, the following hypothesis is proposed:

Hypothesis (H1).

Digitalization is positively related to supply chain performance.

2.4. Digitalization and Supply Chain Integration

As scholars delve into the transformative impact of digitalization on supply chain dynamics, a particular focus emerges on its role in fortifying supply chain integration (SCI). The literature presents a multifaceted exploration of how digital technologies contribute to bolstering various dimensions of SCI, offering valuable insights for businesses navigating turbulent scenarios. SCI stands as a cornerstone in contemporary business strategy, epitomizing a firm’s collaborative alliance with major supply chain partners to orchestrate and streamline various supply chain activities [65,66]. SCI operates through two fundamental dimensions—supplier integration and customer integration—as a holistic approach that embodies cooperative relationships for effective supply chain management [55,65,67].

In the realm of supplier integration, the objective is to cultivate symbiotic collaborations with major suppliers, fostering mutual understanding and synchronized supply chain processes. This entails comprehensive processes such as information sharing, joint planning, and collaborative product development [67,68]. The benefits are multifold, encompassing timely production plan adjustments, swift material delivery, and reductions in supply cycle time, inventory levels, and potential conflicts [69,70]. High levels of supplier integration confer strategic advantages, allowing firms to curtail operational costs, bolster profitability, accelerate new product development, and create additional value for customers [44,71].

On the customer integration front, the emphasis is on streamlined information sharing and collaboration with major customers, with a focus on enhancing overall business performance. This collaborative effort includes sharing market insights, understanding shifts in buyer preferences, and co-developing market-oriented offerings [69,72]. The dividends of robust customer integration are evident in the efficient flow of information and products, leading to shortened lead times, reduced inventory obsolescence, and lowered operational costs, thereby improving business performance [73]. Moreover, customer integration facilitates agile responses to market changes, enhances customer satisfaction, stimulates sales growth, and augments market share [44,72].

In essence, SCI emerges as a strategic imperative for firms seeking to thrive in dynamic markets. The collaborative dimensions of both supplier and customer integration position companies to navigate the intricacies of the business landscape with agility and resilience. The strategic alignment with key supply chain partners, characterized by mutual understanding and effective collaboration, not only fosters efficient supply chain processes but also delivers superior value to customers. As such, SCI becomes not merely a logistical necessity but a transformative force that propels businesses toward enhanced operational efficiency, increased profitability, and sustained market competitiveness.

Thus, digitalization emerges as an intelligent and data-driven technology network, plays a pivotal role in fostering greater SCI by enabling seamless communication, real-time data sharing, and enhanced collaboration among supply chain partners [74,75,76] note that digital supply chain systems enable the integration of data from multiple sources, creating a transparent and interconnected information flow. This real-time data exchange is foundational for SCI as it enables partners to access and share critical information instantly, fostering a shared understanding of supply chain dynamics [37]. Ref. [77] emphasize that digitalization provides stakeholders with real-time visibility into supply chain operations, allowing them to monitor the status of goods, inventory levels, and production processes. This visibility minimizes delays, reduces the risk of disruptions, and fosters better coordination among supply chain partners, ultimately contributing to heightened integration. Furthermore, digitalization supports enhanced collaboration [76,78] highlight that digital tools enable partners to collaborate on joint projects, share demand forecasts, and synchronize their efforts. This collaborative environment fosters tighter supply chain integration as it promotes mutual understanding, trust, and a shared commitment to achieving common supply chain goals [79].

Numerous studies recognize the substantial advantages of digitalization in enhancing supply chain integration. For example, [16] emphasize digitalization’s pivotal role in cultivating internal supply chain integration. Empirical validation from previous research [37,80,81] demonstrates that IT integration capabilities lead to enduring enhancements in firm performance by fostering supply chain process integration. Ref. [82] further support this concept, indicating that digital technologies facilitate integration across functional domains within firms, aligning teams toward shared objectives such as material management, planning, and scheduling. While theoretical foundations support the positive association between digitalization and supply chain integration, additional empirical investigation is needed [37] With this in mind, we propose the following hypothesis:

Hypothesis (H2).

Digitalization is positively related to supply chain integration.

2.5. Digitalization and Supply Chain Efficiency

Digitalization’s profound impact on supply chain efficiency is evident through its transformative influence on various aspects of business processes [83]. Digitalization, defined as the incorporation of digital technologies into supply chain processes [1,9,21,59] plays a pivotal role in enhancing various dimensions of supply chain efficiency, contributing to improved performance and competitiveness. Ref. [84] emphasize how digital technologies such as advanced analytics, IoT devices, and automation contribute to streamlined processes, optimized resource allocation, and reduced waste, thereby increasing overall supply chain efficiency. For example, [47] emphasize how digital technologies enable the automation of routine and repetitive tasks within the supply chain, reducing manual labor and the potential for human error. Automation not only speeds up processes but also minimizes the risk of delays, ensuring smoother and more efficient supply chain operations [13]. As noted by [85] digital technologies, coupled with data utilization, minimize breakdowns and enhance the manufacturing process’s intelligence. Predictive analytics, for instance, can identify bottlenecks and inefficiencies, enabling firms to allocate resources where they are most needed [86,87]. This optimization leads to cost savings, reduced waste, and improved supply chain efficiency [88]. Moreover, digitalization contributes to cost reduction within the supply chain [19,66] emphasizes how digital technologies allow firms to identify cost-saving opportunities through data analysis. By optimizing transportation routes, managing inventory more efficiently, and reducing energy consumption, firms can significantly lower their operational costs, resulting in enhanced supply chain efficiency.

Refs. [89,90] corroborate this by highlighting how digitalization enhances operational efficiency through predictive analytics, enabling firms to preemptively address disruptions and minimize delays. The customer-centric benefits of digital transformation further contribute to supply chain efficiency. Supply chain digitalization enhance the customer journey by enabling personalized service delivery and efficient transactions [11,16]. Additionally, improved supply-side digitalization reduces the cash conversion cycle, directly impacting profitability, competitiveness, and value creation for firms [6]. This phenomenon reinforces the positive relationship between digitalization and supply chain efficiency. While the literature generally acknowledges the benefits of supply chain digitalization, there remains a limited academic exploration into the mechanisms driving these performance gains [9,12,17,80]. This research seeks to bridge this gap and proposes the following hypothesis:

Hypothesis (H3).

Digitalization is positively related to supply chain efficiency.

2.6. The Mediating Role of Supply Chain Integration

Supply chain integration, comprising both supplier integration and customer integration, defined as “a firm’s collaboration with major supply chain partners to manage various supply chain activities” [9,65]. Supplier integration involves collaboration with major suppliers to coordinate crucial supply chain activities, including product development, planning, and information sharing [91]. This collaboration yields numerous operational and strategic benefits. For instance, its faster material delivery, reduced supply cycle, and enable timely production plan [69]. Such efficiency enhancements lead to cost reductions through lower inventory levels and optimized supply chain operations [16]. Moreover, supplier integration catalyzes new product development and improves product delivery efficiencies. This value addition not only boosts customer satisfaction but also contributes to market share growth [9]. The alignment of products with customer needs, driven by supplier integration, translates into improved business performance. Customer integration further reinforces cost-cutting measures by reducing lead times and inventory obsolescence [73]. It streamlines information sharing and collaboration with customers, driving cost reduction while creating customer value [72].

Furthermore, customer integration allows firms to be responsive to shifts in buyer preferences and market trends, facilitating market-oriented offerings [69]. This responsiveness, combined with enhanced customer satisfaction, leads to increased sales growth and market [44,72]. The literature underscores the pursuit of the highest level of supply chain integration tailored to diverse markets [92]. While achieving high integration levels is desirable, it involves costs, persistence difficulties, risks, and context-specific considerations [88]. Therefore, achieving supply chain integration requires a strategic balancing act [93].

In essence, supply chain integration, encompassing both supplier and customer integration, serves as a crucial mediating mechanism between digitalization efforts and supply chain performance outcomes. By facilitating effective coordination, mutual understanding, and innovation within supply chains, supply chain integration bridges the gap between digitalization’s potential and tangible performance improvements [94]. This comprehensive perspective underscores the importance of considering supply chain integration as a mediating factor when exploring the impact of digitalization on supply chain performance, aligning with recent studies [37,58,95]. Recognizing that seamless collaboration with supply chain partners is fundamental for enhancing efficiency, reducing costs, and delivering value to customers, this mediation effect highlights the strategic significance of supply chain integration in the context of the evolving digital landscape. This aligns with the broader perspective of supply chain digitalization discussed earlier, where firms adopt and deploy digital technologies to transact with various supply chain players, thus transforming traditional business practices. Given the aforementioned context, the following hypotheses are proposed:

Hypothesis (H4).

Supply chain integration is positively related to supply chain performance.

Hypothesis (H5).

Supply chain integration mediates the positive relationship between digitalization and supply chain performance.

2.7. The Mediating Role of Supply Chain Efficiency

Efficiency, as defined by [83] underscores an organization’s ability to produce goods and services with minimal costs by minimizing waste and optimizing resource utilization. This definition extends to the supply chain context, where stakeholders engage in coordinated activities aimed at minimizing waste and optimizing resources across the entire chain [83,96]. Efficiency is a critical component for organizations seeking a competitive advantage, aligning with the RBV theory. Efficient operations result from timely and cost-effective production of products or services [97] ultimately contributing to a competitive edge [98]. In the supply chain context, the pursuit of efficiency is particularly pronounced, with the aim of achieving the lowest possible costs while meeting customer standards, including accuracy in delivery and lead time [99].

Efficient supply chain management requires a comprehensive understanding of relationships at various levels of the supply chain to mitigate costs and elevate service quality [100]. The growing focus on supply chain efficiency is evident as organizations recognize its pivotal role in supply chain performance improvement [88,101]. In the context of today’s rapidly evolving markets with short product life cycles and high demand variability, the importance of supply chain efficiency is underscored by the need for a “market-responsive supply chain strategy” [102]. Recent studies have extended [102] model, emphasizing the role of technology in enhancing supply chain efficiency [103,104]. For example, [105] found that technology interventions can enhance the effectiveness of supply chains, albeit with potential trade-offs in efficiency, highlighting the nuanced relationship between technology adoption and supply chain efficiency.

This background lays the foundation for understanding the role of supply chain efficiency as a mediator between digitalization and supply chain performance. The existing literature has established a positive relationship between digitalization and supply chain efficiency [88,100,105,106,107]. Efficiency, in turn, has been identified as a critical determinant of supply chain performance [29,56,83,96,98,108,109,110,111,112] with streamlined processes and reduced resource wastage contributing to enhanced overall supply chain outcomes. However, the intricate interplay between supply chain digitalization, efficiency, and their collective impact on performance remains relatively unexplored, forming the focal point of the present study. This perspective underscores the importance of considering supply chain efficiency as a critical mediator when exploring the impact of digitalization on supply chain performance. With this in mind, the following hypotheses are proposed:

Hypothesis (H6).

Supply chain efficiency is positively related to supply chain performance.

Hypothesis (H7).

Supply chain efficiency mediates the positive relationship between digitalization and supply chain performance.

2.8. The Moderating Role of Supply Chain Dynamism

Supply chain dynamism (SCD) is a crucial concept in supply chain management, representing the pace of changes in products and processes within the supply chain [43]. The constantly evolving nature of supply chains, driven by shifting business landscapes and technological advancements, has brought SCD to the forefront of research, especially in understanding how digitalization impacts supply chain outcomes [12]. The COVID-19 pandemic emphasized the critical role of SCD in contemporary operations [28] highlighting the need to explore the intricate interactions between SCD and digitalization.

In today’s dynamic business environment, characterized by the need for adaptability and rapid responses to changes, digital technologies play a pivotal role in enhancing SCI [29,91,113,114]. The literature also calls for further exploration of how digitalization can lead to supply chain performance improvement in crisis scenarios within SCI [115,116,117,118]. Researchers emphasize that achieving digitalization demands a holistic approach, requiring companies to gain market insight, adopt new technologies and management styles, transform core business processes, and leverage digital technologies to create value [119].

Digitalization acts as a catalyst for improved information sharing and collaboration throughout the supply chain, ultimately contributing to enhanced SCI [16,37,75,76,80,82,120,121] also support the idea that SCD positively influences information-sharing practices and inter-organizational relationships, resulting in improved supply chain performance. In this dynamic environment, where products and processes rapidly transform, digital tools enable heightened communication and collaboration among supply chain partners [43]. Consequently, this dynamic interplay between digitalization and SCD is hypothesized to have an amplified positive effect on supply chain integration, providing the basis for the following hypothesis.

Hypothesis (H8a).

Supply chain dynamism positively moderates the relationship between digitalization and supply chain integration.

In a supply chain marked by rapid changes in products and processes, agility and responsiveness become imperative. Digitalization is recognized as a facilitator for meeting these demands [12] Recent studies emphasize the need to understand the extent of Supply SCD to formulate resilient strategies and enhance supply chain performance [43,122,123]. Scholarly investigations consistently highlight SCD as a precursor to supply chain resilience and disruption orientation, with discernible impacts on financial performance [28,43,124,125]. These insights underscore the intricate connections between dynamism, resilience, and overall supply chain performance. In this context, the moderating role of SCD in the relationship between digitalization and supply chain performance becomes increasingly crucial. As the supply chain environment becomes more dynamic, the transformative influence of digital technologies on supply chain processes can potentially be amplified, resulting in heightened improvements in overall performance. Therefore, we propose that:

Hypothesis (H8b).

Supply chain dynamism positively moderates the relationship between digitalization and supply chain performance.

In a dynamic supply chain environment characterized by rapid and continuous transformations in products and processes [28], the interaction between digitalization and supply chain dynamism (SCD) holds the potential to yield amplified efficiency improvements. This proposition is rooted in the recognition that digitalization’s efficiency-enhancing capabilities can be especially potent in environments marked by constant change [126]. To comprehensively explore this interaction, insights from diverse industries where supply chain efficiency enhancement has been a focal point are crucial. Prior research has scrutinized how digitalization interfaces with SCD and its implications for efficiency and collaboration [29,120,127]. Additionally, empirical examinations of artificial intelligence’s (AI) impact on supply chain resilience and performance under SCD conditions underscore the significance of technological interventions on supply chain efficiency [28]. However, despite these valuable contributions to the field, the specific moderating role of SCD within the intricate relationship between digitalization and supply chain efficiency remains relatively unexplored territory [12]. This study aims to address this research gap by investigating how SCD moderates the relationship between digitalization and supply chain efficiency, enriching our understanding of the nuanced interplay between dynamic supply chains and the efficiency improvements driven by digitalization. Therefore, we propose that:

Hypothesis (H8c).

Supply chain dynamism positively moderates the relationship between digitalization and supply chain efficiency.

3. Methods

3.1. Data Collection Procedures

This paper employed a comprehensive data collection process to investigate the intricate relationships between digitalization, supply chain integration, efficiency, and performance. To achieve this objective, a questionnaire-based approach was adopted, offering a structured framework to quantitatively analyze the variables and extend support to relevant theories. This study targeted manufacturing firms across diverse industries located in Turkey, aiming to enhance the generalizability and external validity of the survey findings. The sample selection process focused on medium- and large-sized firms, excluding small firms with fewer than 50 employees due to resource limitations that may hinder investment in digital technologies [128]. The respondents of the survey were carefully chosen, with an emphasis on their roles within the organizations. Specifically, individuals serving as supply chain/operation managers or CEOs/senior executives were targeted. To ensure relevance and validity, respondents were required to have utilized at least one form of digital technology in supply chain transactions before participating in the survey [12]. The data for this study were collected through an e-mail survey using an online survey. To enhance the response rate, telephone and email reminders were utilized, contributing to the collection of data within a relatively short timeframe [129].

A cover letter accompanying the survey communicated the criteria for acceptable respondents, thereby streamlining the selection process. Respondents who did not meet the established criteria were excluded during the data evaluation phase. The sampling technique employed in this study is a simple random sampling technique, a method that ensures each member of the population has an equal chance of being included in the sample. The process began by constructing a sampling frame, or a list of all eligible units in the population. Such a technique has been adopted in numerous studies [130,131,132].

To construct the sampling frame, the researchers accessed the database of “TOBB (The Union of Chambers of Commerce, Industry, Maritime Trade and Commodity Exchanges of Turkey, available at http://www.tobb.org.tr, accessed on 15 September 2023)” [133], which encompasses a wide range of commodity exchange members, maritime commerce, and local chambers of commerce, totaling over one million firms [128]. After eliminating firms that did not align with this study’s criteria, a random sample of 1000 firms were drawn from this database. Following two waves of data collection and one reminder, a total of 306 questionnaires were returned. A rigorous data cleaning process was employed, eliminating 13 questionnaires due to reasons such as duplicate submissions, improper respondents/firms, and missing values. Therefore, 293 questionnaires were deemed usable, yielding an effective response rate of 29.3 percent. The summarized characteristics of the sample, including demographics and other pertinent details, are provided in

Table 1. Demographics of the research sample.

Measures	Item	Frequency	Percentage (%)
Gender	Male	162	55.3%
	Female	131	44.7%
Functional area	General management	85	29.0%
	Supply chain management	192	65.5%
	Operation management	16	5.5%
Respondent position	Executive/senior manager	166	56.7%
	Middle/first-level manager	105	35.8%
	Others	22	7.5%
Years of operation	Less than 5 years	17	5.8%
	5–10	70	23.9%
	11–30	86	29.4%
	31–50	54	18.4%
	More than 50	66	22.5%
Industry sector	Food, beverage and paper	88	30.0%
	Plastics, pharmaceutical, and Chemicals	42	14.3%
	Clothing and textile	32	10.9%
	Electrical equipment and machinery	56	19.1%
	Leather, wood, metal, and glass	48	16.4%
	Other manufacturing	27	9.2%
Number of employees	Less than 250	20	6.8%
	251–500	43	14.7%
	501–1000	95	32.4%
	1001–5000	89	30.4%
	More than 5000	46	15.7%
Total		293	100%

.

3.2. Power Analysis Check

In this study, an apriori power analysis was conducted using the G*Power 3.1.9.7 program [133] to ascertain the adequacy of the chosen sample size. The purpose of the power analysis was to ensure that this study had a sufficiently large sample to detect meaningful effects in the structural model [134].

The power analysis results indicated that, for the structural model, a minimum sample size of 119 participants is required to achieve a statistical power of 0.95 at a significance level of 0.05, assuming a medium effect size of 0.15 [135]. This effect size is considered appropriate for capturing meaningful relationships within the study context.

Accordingly, the chosen sample size for this study consisted of 293 participants, surpassing the minimum required size identified through the power analysis. This indicates that this study’s sample size is robust, providing a comfortable margin above the minimum needed for a statistically meaningful analysis. The decision to exceed the minimum sample size requirement enhances the reliability and generalizability of this study’s findings, ensuring that the sample adequately represents the population under investigation.

3.3. Measurement

The measurement of variables in this study was approached with a rigorous methodology to ensure the reliability and validity of the data collected. The design of the questionnaire was underpinned by existing scales that had established psychometric properties, providing a strong foundation for capturing the intended constructs accurately within the context of this study. To ensure cross-cultural applicability, the measures were translated from English to Turkish. The translated items were subjected to a back translation process conducted by two independent translators to confirm the integrity of conceptual alignment. Furthermore, the questionnaire underwent scrutiny from experts with expertise in both digitalization and supply chain management. Their insights were crucial in refining the questionnaire items to ensure their relevance and clarity within this study’s context. A critical pretesting phase was then conducted, involving 20 experienced practitioners in digital operations. Their feedback and observations played a pivotal role in identifying potential ambiguities and refining the questionnaire’s content for optimal comprehension and response accuracy. The constructs and items are shown in

Table A1. Measurement items.

Supply Chain Digitalization (DGT)
DGT1: The proportion of suppliers that our firm transacts with through the digital supply chain system.
DGT2: The proportion of transaction volume that our firm conducts with suppliers through the digital supply chain system.
DGT3: The proportion of transaction activities that our firm conducts with suppliers through the digital supply chain system.
DGT4: The frequency of suppliers that our firm transacts with through the digital supply chain system
Supplier Integration (SCIS)
SCIS1: We share information to our major suppliers through information technologies.
SCIS2: We have a high degree of strategic partnership with suppliers.
SCIS3: We have a high degree of joint planning to obtain rapid response ordering process (inbound) with suppliers.
SCIS4: Our suppliers provide information to us in the production and procurement processes.
SCIS5: Our suppliers are involved in our product development processes.
Customer Integration (SCIC)
SCIC1: We have a high level of information sharing with major customers about market information.
SCIC2: We share information to major customers through information technologies.
SCIC3: We have a high degree of joint planning and forecasting with major customers to anticipate demand visibility.
SCIC4: Our customers provide information to us in the procurement and production processes.
SCIC5: Our customers are involved in our product development processes.
Supply Chain Efficiency (SCE)
SCE1: Improve the efficiency of operation between our suppliers and us.
SCE2: Manage inventory between our suppliers and us.
SCE3: Manage material requirements planning of our facility.
SCE4: Manage production control between our suppliers and us.
SCE5: Coordinate (production and information) efficiently across suppliers and product lines.
Supply Chain Dynamism (SCD)
SCD1: New products account for a high fraction of total revenue.
SCD2: Products and services are innovated frequently.
SCD3: The innovation rate of operating processes is high.
Supply Chain Performance (SCP)
SCP1: Our supply chain has the ability to quickly modify products to meet customers’ requirements.
SCP2: Our supply chain allows us to quickly introduce new products into our markets.
SCP3: The length of the supply chain process is getting shorter.
SCP4: Our supply chain has an outstanding on-time delivery record.

in the Appendix A.

The measurement process utilized a seven-point Likert scale, ranging from “1” (strongly disagree) to “7” (strongly agree), enabling respondents to provide nuanced responses and express their perspectives effectively. To assess the digitalization construct, we selected four items from recent studies [12,16,61]. These items measured the extent to which firms embraced digital supply chain management systems for electronic transactions and interactions with internal stakeholders, suppliers, and customers across the supply chain.

We evaluated supplier and customer integration using five items each, adapted from [91,136]. These items assessed the depth of collaboration with major suppliers and customers, including aspects such as joint product development, information sharing, and collaborative planning. Recognizing the practical challenges of evaluating SCI with every major partner individually, we took a pragmatic approach. We aggregated data to represent a firm’s overall integration level with key suppliers and customers [129].

We assessed supply chain dynamism using a set of three items, drawing from [94] work and enriched with insights from [43,113]. Simultaneously, we quantified supply chain efficacy with five items adapted from [137], covering aspects of waste reduction and resource optimization [101]. Our measurement framework for supply chain performance was constructed based on established research, incorporating measures from studies conducted by [60,138,139,140]. These measures encompassed respondents’ assessments of their supply chain’s ability to meet customer requirements, introduce innovative products, expedite supply chain processes, and achieve exceptional delivery performance.

3.4. Common-Method Bias

A concerted effort was made to counteract the potential influence of common-method bias (CMB) in this research, as it could undermine the validity of the findings. Several recommended procedural remedies were systematically incorporated into the research design to mitigate the likelihood of CMB’s adverse effects [141]. To begin with, meticulous attention was given to item development to ensure clarity and precision. Additionally, respondent confidentiality was rigorously maintained, fostering an environment where respondents felt secure in sharing their authentic responses [141]. Subsequent to data collection, a comprehensive assessment of CMB was undertaken using multiple strategies. Ref. [142] one-factor test, a commonly utilized diagnostic tool for CMB evaluation, was executed to ascertain the extent of variance explained by a single underlying factor. The result demonstrated that the first factor accounted for only 34.95% of the total variance, considerably below the recommended threshold of 50%. This finding indicated that CMB was not a substantial concern in this study.

Moreover, insights from the recent literature underscored that moderation effects can serve as an additional means to gauge the potential influence of CMB [129,143]. The logic lies in the fact that informants are less likely to predict or guess the occurrence of moderation effects [144]. Notably, two out of three proposed moderation effects were substantiated by the data, implying that CMB was unlikely to have significantly distorted the results. This alignment between the data and the theoretical expectations bolstered the confidence that CMB did not cast a substantial shadow over the findings.

4. Analysis and Results

The analysis and results of this study were presented using a covariance-based structural equation modeling (SEM) approach. The analytical process unfolded in two distinct stages, beginning with the estimation of a measurement model to evaluate construct reliability. Subsequently, the hypothesized relationships were tested using SEM procedures [145].

4.1. Measurement Model Estimation

The initial step involved a meticulous examination of the measurement model’s development and validity. Confirmatory Factor Analysis (CFA) was conducted using AMOS 24 to rigorously assess the fitness of the model [146]. As per established criteria, the x²/df ratio was scrutinized, aiming for a value of less than 3, indicating a favorable model fit [147]. Guided by the recommendations of Hu and Bentler (1999), thresholds for various fit indices were set, including CFI, TLI, IFI, and NFI, where values exceeding 0.90 were indicative of a well-fitting model. Additionally, RMSEA and SRMR values below 0.08 were targeted. The results indicated that the measurement model demonstrated acceptable fit statistics: x² = 705.754, df = 284, x²/df = 2.485, CFI = 0.925, TLI = 0.913, IFI = 0.916, NFI = 0.907, SRMR = 0.058, and RMSEA = 0.061.

The validity of the measurement model was substantiated through a thorough analysis, as depicted in

Table 2. Scale measurement, reliability, and validity.

Construct/Indicators	Mean	Std	Factor Loadings	Cronbach’s Alpha Values	CR	AVE
Digitalization (DGT)				0.855	0.857	0.599
DGT1	5.04	1.171	0.803
DGT2	5.16	1.328	0.778
DGT3	5.21	1.327	0.760
DGT4	5.32	1.179	0.755
Supply Chain Integration (SCI)				0.915	0.920	0.853
Supplier Integration (SCIS)				0.860	0.843	0.520
SCIS1	4.87	1.329	0.643
SCIS2	4.60	1.506	0.638
SCIS3	4.83	1.542	0.676
SCIS4	4.77	1.313	0.774
SCIS5	4.85	1.332	0.852
Customer Integration (SCIC)				0.886	0.890	0.619
SCIC1	5.04	1.207	0.798
SCIC2	5.05	1.394	0.851
SCIC3	5.02	1.382	0.775
SCIC4	5.29	1.298	0.763
SCIC5	4.95	1.305	0.743
Supply Chain Dynamism (SCD)				0.825	0.828	0.618
SCD1	5.29	1.285	0.866
SCD2	5.30	1.288	0.810
SCD3	5.19	1.285	0.669
Supply Chain Performance (SCP)				0.893	0.894	0.679
SCP1	5.47	1.071	0.847
SCP2	5.37	1.153	0.792
SCP3	5.43	1.104	0.795
SCP4	5.37	1.098	0.859
Supply Chain Efficiency (SCE)				0.904	0.900	0.645
SCE1	5.26	1.214	0.703
SCE2	5.39	1.260	0.818
SCE3	5.53	1.283	0.863
SCE4	5.80	1.209	0.821
SCE5	5.43	1.219	0.802

Note(s): Composite reliability (CR); average variance extracted (AVE).

. The examination ensured that each item appropriately loaded into its corresponding factor while permitting correlations among the study constructs. Convergent validity was demonstrated through significant factor loadings for all items (p < 0.001), exceeding the recommended threshold of 0.5 [148]. In addition, Figure 2, captured from the statistical software Amos 24, visually represents the correlation and factor loading structure of the latent constructs. The figure provides a comprehensive overview of the relationships among observed variables and their respective latent factors. Each arrow in the diagram represents a factor loading, indicating the strength and direction of the relationship between an observed variable and its underlying latent construct. The correlations between latent constructs are depicted through double-headed arrows. This graphical representation aids in understanding the measurement model’s performance, demonstrating how well the observed variables align with their intended latent factors. The factor loadings and correlations shown in Figure 2 contribute to the assessment of the measurement model’s reliability and construct validity, supporting the robustness of the structural equation model.

Furthermore, the average variance extracted (AVE) values for each variable were also computed and found to surpass the threshold of 0.50, as stipulated by [149], strengthening the evidence of convergent validity. Assessment of internal construct consistency and validity was carried out through the examination of Cronbach’s alpha and composite reliability (CR) values, as outlined in Table 2. All Cronbach’s alpha values for the constructs surpassed the acceptable threshold of 0.70 [150]. Similarly, CR values exceeded the recommended threshold of 0.60 [151]. These findings indicated satisfactory internal consistency and reliability of the measurement items for each construct [148].

Discriminant validity was evaluated in accordance with [149] approach. The square root of each construct’s AVE was compared with the correlations among all other constructs in the model (

Table 3. Discriminant validity of measures.

Factors	1	2	3	4	5
1. Supply chain digitalization	0.774
2. Supply chain integration	0.463 ***	0.710
3. Supply chain dynamism	0.594 ***	0.275 ***	0.786
4. Supply chain performance	0.627 ***	0.533 ***	0.611 ***	0.824
5. Supply chain efficiency	0.555 ***	0.420 ***	0.498 ***	0.760 ***	0.803

Note(s): Square root of average variance extracted (AVE) is shown on the diagonal (in bolds) of the matrix; inter-construct correlations are shown off the diagonal; *** significant at level of 0.001.

). This analysis revealed that the square root of every AVE exceeded any correlation between pairs of latent constructs, solidifying the evidence of discriminant [149]. In conclusion, the measurement model’s estimations were solidly supported through the comprehensive assessment of CFA.

4.2. Structural Model Estimation

The analysis of the structural model revealed robust results, with all fit indices surpassing established standards [151,152], indicating an excellent fit of the model to the data. The fit indices demonstrated good model fit, including x²/df = 1.174, CFI = 0.997, TLI = 0.989, IFI = 0.996, NFI = 0.982, SRMR = 0.022, and RMSEA = 0.024. These outcomes affirmed the consistency of the structural model with the data and lent support to the theoretical framework. The outcomes of testing the structural model are detailed in

Table 4. Direct effect results.

Hypothesized Relationships	Standardized Coefficients	Standard Errors	t-Values	p-Values	Decision
Digitalization → supply chain performance	0.240 ***	0.039	6.048	0.001	H1: Supported
Digitalization → supply chain integration	0.513 ***	0.041	10.210	0.001	H2: Supported
Digitalization → supply chain efficiency	0.467 ***	0.045	9.492	0.001	H3: Supported
Supply chain integration → performance	0.185 ***	0.042	5.337	0.001	H4: Supported
Supply chain efficiency → performance	0.586 ***	0.041	15.446	0.001	H6: Supported
DGT * SCD → supply chain integration	0.144 **	0.027	2.673	0.008	H8a: Supported
DGT * SCD → supply chain performance	0.039	0.018	1.287	0.198	H8b: Not Supported
DGT * SCD → supply chain efficiency	−0.123 **	0.025	−2.759	0.006	H8c: Not Supported

Note(s): Digitalization (DGT), supply chain dynamism (SCD); ** statistically significant at p < 0.010; *** statistically significant at p < 0.001. * indicates a moderating effect of DGT on the SCD relationship with supply chain outcomes, supply chain performance, supply chain efficiency.

. As hypothesized, the study findings substantiate a significant positive impact of digitalization on supply chain performance (β = 0.240, t = 6.048, p < 0.001), thereby affirming the validity of H1. This study also uncovered a noteworthy direct effect of digitalization on supply chain integration (β = 0.513, t = 10.210, p < 0.001), thereby confirming H2. Furthermore, a significant positive relationship between digitalization and supply chain efficiency was evident (β = 0.467, t = 9.492, p < 0.001), providing robust support for H3. These findings collectively substantiate the considerable influence of digitalization on supply chain integration, efficiency, and performance. Lastly, this study’s results reveal that supply chain integration is positively and significantly associated with efficiency (β = 0.169, t = 3.538, p < 0.001).

Turning to the impact of supply chain integration on performance, this study’s hypotheses were vindicated as integration demonstrated a significant direct positive impact on supply chain performance (β = 0.185, t = 5.337, p < 0.001), thereby lending support to H4. In congruence with H6, the study findings indicate a notable positive and significant relationship between supply chain efficiency and supply chain performance (β = 0.586, t = 15.446, p < 0.001).

The explanatory power of the structural model was rigorously assessed using AMOS 24.0, encompassing the R² values. The results, as illustrated in Figure 3, showcased the substantial influence of digitalization, supply chain integration, and efficiency on supply chain performance. Notably, digitalization exhibited a significant explanatory power, accounting for 26.3% of the variance in supply chain integration, while also explaining an impressive 50.6% of the variance in supply chain efficiency. The culmination of these factors, along with supply chain integration and efficiency, collectively accounted for a substantial 75% of the variance in supply chain performance, underscoring the robustness of the model in capturing the intricate interplay between these constructs comprehensive analysis reaffirms the pivotal role of digitalization and its intricate connections in enhancing supply chain dynamics (Figure 3).

4.3. Mediation Analysis

The investigation of the mediation effect was a focal point in this study, specifically exploring the roles of supply chain integration and efficiency in mediating the influence of digitalization on supply chain performance. Utilizing the mediation technique proposed by the mediation pathways were thoroughly examined, with supply chain integration and efficiency as mediators. In doing so, a simple mediation analysis was employed using the bootstrapping percentile method available in AMOS [134,153,154,155]. In the initial model (Model 1), a direct connection between digitalization and supply chain performance was scrutinized, revealing a significant and robust association between digitalization and supply chain performance (β = 0.240, t = 6.048, p < 0.001). Moving to Model 2, a comprehensive analysis was conducted employing the bootstrapping percentile method available in AMOS, with resampling of 2000 and a 95% confidence interval. The outcomes presented in

Table 5. Indirect effect result.

Hypothesized Path	Indirect Effect	Lower Bound	Upper Bound	p-Values	Results
Digitalization → supply chain integration → performance	0.095 ***	0.066	0.129	0.001	Ind1: Supported
Digitalization → supply chain efficiency → performance	0.273 ***	0.221	0.333	0.001	Ind2: Supported
Digitalization → supply chain integration → efficiency → performance	0.087 ***	0.027	0.185	0.001	Ind3: Supported

Note(s): *** Statistically significant at p < 0.001.

unveiled the intricate mediation processes at play. The indirect effects of digitalization on supply chain performance through both supply chain integration (β = 0.095, p < 0.001) and efficiency (β = 0.273, p < 0.001) were found to be significant and positive, providing empirical support for H5 and H7, respectively. This in-depth analysis elucidates the mechanisms through which digitalization exerts its influence on supply chain performance, mediated by the vital constructs of supply chain integration and efficiency.

4.4. Moderation Analysis

This study conducted a moderation analysis to explore the potential moderating role of supply chain dynamism in the relationships between digitalization and key supply chain constructs. Following the guidelines proposed by [138], this analysis aimed to assess whether supply chain dynamism played a significant moderating role in the relationships involving (a) digitalization and supply chain integration, (b) digitalization and supply chain performance, and (c) digitalization and supply chain efficiency. Employing AMOS 24.0 for rigorous analysis, this study sought to uncover intricate interactions among these variables.

The results unveiled intriguing insights into the moderating effect of supply chain dynamism. In the context of the relationship between digitalization and supply chain integration, it became evident that supply chain dynamism exerted a positive moderating impact (a: β = 0.144, p = 0.008). This suggests that the influence of digitalization on supply chain integration was amplified when supply chain dynamism was more pronounced. However, when considering the association between digitalization and supply chain performance, the moderating role of supply chain dynamism did not reach statistical significance (b: β = 0.039, p = 0.198), indicating that changes in supply chain dynamism did not significantly alter the impact of digitalization on supply chain performance. Notably, in the context of the link between digitalization and supply chain efficiency, the analysis unveiled a significant negative moderating effect of supply chain dynamism (c: β = −0.123, p = 0.006). This implies that the positive influence of digitalization on supply chain efficiency was attenuated in more dynamic supply chain environments. These findings underscore the intricate interplay between digitalization, supply chain dynamism, and their collective impact on critical supply chain constructs. Consequently, these results supported H8a while rejecting H8b and H8c.

To gain a deeper understanding of the relationships, we employed the Sobel test method [29] to elucidate the role of supply chain dynamism in influencing the connections between digitalization and the examined supply chain constructs. Figure 4 visually represents the moderation relationships among the key variables. The presence of a moderation effect became evident as supply chain dynamism shaped the relationship between digitalization and supply chain integration, as indicated by the intersecting lines representing low and high supply chain dynamism intercepting each other (Figure 4a). However, this moderation influence was not observed when assessing the relationship between digitalization and supply chain performance, as illustrated by the parallel lines of low and high supply chain dynamism (Figure 4b). Remarkably, the moderation analysis highlighted that supply chain dynamism negatively moderated the relationship between digitalization and supply chain efficiency (Figure 4c), supported by the non-intersecting lines of low and high supply chain dynamism. This intricate exploration unveiled the complex interplay of supply chain dynamism in moderating the impact of digitalization on supply chain outcomes.

4.5. Serial Mediation Analysis

To enhance the robustness of the study’s findings, a serial mediation model was employed to delve into the intricate relationships between supply chain integration, efficiency, and digitalization. In this model, supply chain integration served as a predictor for supply chain efficiency, and the ensuing analysis provided valuable insights into the sequential effects of these variables on supply chain performance, as depicted in Figure 3.

The results of the analysis revealed a statistically significant positive association between supply chain integration and supply chain efficiency (β = 0.169, t = 3.538, p < 0.001). This signifies that higher levels of supply chain integration are associated with increased supply chain efficiency, indicating the importance of collaborative processes with major supply chain partners in optimizing operational performance.

To validate the presence of a serial mediation effect, a specific indirect effect analysis was conducted using AMOS 24.0 software, as outlined by [138]. The results, presented in Table 5, unequivocally support the existence of a significant serial mediation effect. The specific indirect effect, with a confidence interval of 95% bootstrap, ranged from 0.027 to 0.185 (β = 0.087, p < 0.001), and notably, it did not include 0. This outcome indicates a robust serial mediation effect, suggesting that the influence of digitalization on supply chain performance is channeled through the sequential mediation of supply chain integration and efficiency.

5. Discussion and Implications

5.1. Discussion of Major Findings

The results of this study shed light on the intricate dynamics of digitalization, supply chain integration, efficiency, and performance within the constantly evolving landscape of supply chain management. The analysis of the structural model firmly establishes the significant and positive impact of digitalization on supply chain integration, efficiency, and performance. These findings align with prior research that emphasizes the transformative potential of digital technologies in enhancing various aspects of supply chain operations [12,16,28]. The integration of digital tools, such as supply chain management systems, consistently proves its ability to foster collaboration, facilitate information sharing, and improve coordination among supply chain partners [13]. Consequently, this contributes to increased integration levels and, in turn, strengthens supply chain efficiency and performance [80].

Additionally, the mediation analysis underscores the central role played by supply chain integration and efficiency in mediating the relationship between digitalization and supply chain performance. This discovery suggests that digitalization’s impact on performance is channeled through the enhancement of integration and efficiency, aligning with the concept of digitalization as an enabler [10,78,84]. It serves as the foundation for seamless information flow, real-time visibility, and streamlined processes, all of which ultimately contribute to improved supply chain performance [32]. This study contributes to the existing body of research on the mediating mechanisms of supply chain digitalization on supply chain performance. While previous studies have explored mediation through factors such as internal integration [16,37,80], supply chain resilience [9,17,28,32,54], and supply chain traceability and agility our research delves deeper into the mediating roles of supply chain external integration (customer and supplier) and supply chain efficiency. This nuanced understanding offers valuable insights for businesses seeking to fully leverage digitalization for superior supply chain performance.

Interestingly, this study’s exploration of the moderating impact of supply chain dynamism revealed intricate dynamics. The positive moderating influence of supply chain dynamism on the relationships between digitalization and supply chain integration signifies that the effect of digitalization is amplified in supply chain environments characterized by greater volatility and rapid change. This implies that digitalization not only directly affects supply chain outcomes but also interacts with the level of dynamism in the supply chain environment, intensifying its influence. This aligns with the concept that digital technologies can imbue supply chains with agility and adaptability, enabling them to respond effectively to dynamic market conditions [3]. Contrary to our initial expectations, the analysis did not reveal a statistically significant moderating effect of supply chain dynamism on the relationship between digitalization and supply chain performance. This suggests that the impact of digitalization on supply chain performance remains relatively consistent regardless of the degree of supply chain dynamism [77,95]. This finding implies that the transformative potential of digitalization in enhancing supply chain performance may be somewhat independent of the level of dynamism in the supply chain environment. It suggests that digitalization initiatives have the capacity to yield performance improvements even in less dynamic or more stable supply chain contexts. Intriguingly, the examination of the connection between digitalization and supply chain efficiency revealed an unexpected significant negative moderating role played by supply chain dynamism. This suggests that while digitalization positively impacts supply chain efficiency, this effect is dampened in highly dynamic supply chain environments. This could be attributed to the challenges posed by increased dynamism, such as the need for rapid adaptation to changing market conditions and heightened uncertainty [8,13,24]. Consequently, while digitalization can enhance efficiency [7,100,105], its effectiveness in doing so might be hindered by the complexities introduced by a highly dynamic supply chain context [98,126].

5.2. Theoretical Implications

The present study makes a substantial contribution to the existing literature by addressing critical gaps in the field of supply chain digitalization. One significant gap that this study tackles relates to the limited understanding of the underlying mechanisms through which digitalization affects supply chain performance [1,9,12,77,80,123]. Our research enriches the field by elucidating the intricate relationships between digitalization, supply chain integration, efficiency, and performance. Previous studies have often examined these dimensions in isolation, overlooking the mechanisms that connect them [2,5,100]. Through the investigation of the mediation effect of supply chain integration and efficiency, our study uncovers the processes by which digitalization’s influence is channeled. This empirical evidence extends the RBV by demonstrating how digitalization, when integrated into supply chain processes, becomes a valuable resource that enhances coordination and collaboration with partners [32,34]. Furthermore, this mediation effect aligns with the DCT, revealing digitalization’s role in transforming dynamic capabilities into actionable practices, enabling firms to adapt and respond effectively to dynamic environments [13,41,153]. By demonstrating the mediating effects of supply chain integration and efficiency on the relationship between digitalization and supply chain performance, our study bridges this gap and offers a more comprehensive understanding of how these constructs interact to influence overall supply chain outcomes.

Beyond the direct and indirect relationships established in our theoretical framework, the unanticipated but intriguing discovery of the serial mediation effect in the analysis brings forth valuable theoretical implications. The sequential mediation involving supply chain integration, efficiency, and digitalization provides a nuanced understanding of the interplay within the digitalized supply chain landscape. This finding aligns with the call for a dynamic perspective in supply chain research, emphasizing the interconnected processes rather than treating constructs as static entities [88]. It underscores that the impact of digitalization on supply chain performance is not a singular event but a multifaceted process. Consequently, this study contributes to a more holistic conceptualization of the digitalized supply chain, framing it as an evolving mechanism where the collaborative integration of partners and efficient processes act as intermediate steps in translating digitalization into enhanced supply chain performance.

Another critical gap that this study addresses is the nuanced role of supply chain dynamism in moderating the relationships between digitalization and various supply chain dimensions. While prior research has emphasized the importance of supply chain dynamism [43,94,121], few studies have empirically examined the intricate interactions between digitalization and supply chain dynamism [12,28,29,124]. Our study, by uncovering the distinct moderating effects of supply chain dynamism, contributes to DCT, demonstrating how supply chain dynamism shapes the effectiveness of digitalization-driven capabilities and influences their impact on supply chain outcomes. We bridge a notable gap by exploring the moderating role of supply chain dynamism in the context of digitalization’s influence on supply chain outcomes. Our research provides valuable insights into the contextual conditions of supply chain digitalization, integration, and efficiency, emphasizing the relative importance of supply chain integration in highly dynamic supply chain environments. We reveal that in a dynamic supply chain context, characterized by volatility and rapid change, our findings suggest that supply chain integration plays a more significant role than supply chain efficiency. This challenges the predominant focus on supply chain integration, expanding our understanding of the relative importance of these two dimensions in specific boundary conditions. These insights contribute valuable knowledge to the existing literature, offering a deeper understanding of the dynamic relationship between supply chain digitalization, integration, and efficiency, particularly within the context of varying supply chain dynamism levels.

Furthermore, our study adds value to theoretical frameworks that emphasize the significance of digitalization strategies in supply chain management [2,3,4,13,77]. While these frameworks provide valuable insights, empirical validation has often been lacking. By empirically testing and validating these theoretical foundations, our research not only confirms their relevance in real-world contexts but also extends their applicability within the intricate dynamics of supply chain environments. This contribution bolsters the theoretical underpinnings of digitalization’s impact on supply chain integration, efficiency, and ultimately performance. Thus, this study bridges the gap between digitalization and TCE by empirically examining the relationship between digitalization, supply chain integration, efficiency, and transaction costs. The findings align with TCE by showcasing how digitalization mitigates information asymmetry and reduces transaction costs through enhanced supply chain integration and efficiency [6,36,37,38]. This empirical validation contributes to a better understanding of how digitalization can lead to improved efficiency in transactions and resource utilization.

5.3. Managerial Implications

The practical implications of this study carry significant relevance for supply chain practitioners and managers seeking to leverage digitalization to enhance their supply chain performance. Firstly, the confirmed positive relationship between digitalization and supply chain performance underscores the importance of adopting digital technologies to drive operational excellence. Supply chain managers should consider implementing a range of digital tools, such as advanced analytics, IoT devices, artificial intelligence, and blockchain, to streamline processes, improve visibility, and enhance decision-making. This emphasizes the need for a comprehensive digitalization strategy that aligns with the organization’s goals and supports seamless integration across the supply chain.

Secondly, the identified mediation effect of supply chain integration and efficiency emphasizes the pivotal role of collaboration and streamlined processes in achieving enhanced supply chain performance. Supply chain managers should prioritize creating an ecosystem where information and insights flow seamlessly between supply chain partners. Investing in technologies that facilitate real-time data sharing, collaborative planning, and synchronized operations can lead to improved efficiency and performance outcomes. Additionally, fostering relationships with reliable and technologically capable suppliers and customers can further reinforce supply chain integration and drive efficiency gains.

Finally, this study’s insights on the moderating effect of supply chain dynamism offer practical guidance for managing digitalization initiatives in dynamic environments. Supply chain managers should adopt a flexible and adaptive approach to digital transformation, taking into account the rapidly changing market conditions. This implies continuously assessing the organization’s digital capabilities and aligning them with the evolving needs of the supply chain. Proactively identifying emerging trends and technologies and adjusting digitalization strategies accordingly can enable organizations to stay competitive and resilient in the face of uncertainty.

5.4. Limitations and Future Research Directions

While this study has provided valuable insights into the relationship between digitalization, supply chain integration, efficiency, and performance, several limitations warrant consideration and offer opportunities for future research. One notable limitation is the potential for common-method bias. Despite efforts to mitigate this bias through procedural and statistical remedies, the reliance on self-reported data may introduce shared method variance. Future research could explore complementary data sources, such as objective performance metrics or archival data, to provide a more comprehensive and accurate assessment of the relationships under investigation. Another limitation relates to the generalizability of findings. This study focused on manufacturing enterprises in Turkey, which may limit the extent to which the results can be extrapolated to other industries and geographic locations. Future research could adopt a comparative approach across diverse sectors and regions to assess the extent of contextual variation in the observed relationships. The cross-sectional nature of this study presents a limitation in establishing causal relationships over time. Longitudinal research designs could offer insights into the temporal dynamics of the relationships, shedding light on how digitalization initiatives, supply chain integration, efficiency, and performance evolve and interact. Finally, considering the fast-paced nature of technological advancements and their effects on supply chain dynamics, future research could explore the evolving landscape of digitalization and its implications for supply chain management. Studying emerging technologies, such as blockchain, IoT, and artificial intelligence could provide a forward-looking perspective on the challenges and opportunities that lie ahead.

6. Conclusions

In conclusion, this study advances our understanding of the complex interplay between digitalization, supply chain integration, efficiency, and performance within the context of Turkish manufacturing firms. Employing a comprehensive methodology and a simple random sampling technique, our findings reveal that digitalization significantly enhances both supply chain integration and efficiency, acting as pivotal drivers for improved overall supply chain performance. Notably, supply chain integration and efficiency emerge as crucial mediating factors in the relationship between digitalization and performance, illuminating the mechanisms through which digital technologies exert their influence. Moreover, our exploration of the moderating effect of supply chain dynamism underscores the contextual relevance of dynamic environments in shaping the association between digitalization and supply chain integration. Rooted in established theories, this research not only contributes to the scholarly literature but also provides actionable insights for practitioners seeking to strategically integrate digital technologies into their supply chain processes. As businesses navigate the post-COVID-19 era, our study serves as a valuable resource for understanding the nuanced dynamics of digitalized supply chains and informs future research directions in the evolving landscape of supply chain management.