Industry 4.0-Enabled Supply Chain Performance: Do Supply Chain Capabilities and Innovation Matter?

Abstract

Background: The present study investigates Industry 4.0’s (I4.0) impact on supply chain capabilities (SCCs), supply chain innovation (SCI), and supply chain performance (SCP). The influence of SCCs and SCI on SCP is also explored. Additionally, the mediating impacts of SCCs and SCI on the I4.0-SCP relationship are analyzed. Methods: The study’s population consisted of manufacturing companies located in Amman, Jordan. A purposive sample of 211 companies was selected. Self-administered questionnaires were completed by targeted managers in the participating companies. Results: The outcomes indicated that the total impact of I4.0 on SCP was significant and positive. I4.0 positively affected both SCCs and SCI. Additionally, SCCs and SCI were found to positively affect SCP. Finally, the results demonstrated a full mediating impact of SCCs and SCI on the I4.0-SCP relationship, with over two-thirds of the mediation impact attributed to SCCs. Conclusions: This research is among the earliest to examine I4.0’s impact on SCP. It also fills a research gap by exploring I4.0’s influence on both SCCs and SCI. To the best of our knowledge, the present study is the first to investigate the mediation effect of SCCs and SCI on the I4.0-SCP relationship, thus providing a valuable contribution to the existing literature.

1. Introduction

Advancements and technological transformations driving industries forward are collectively referred to as the fourth industrial revolution, often summarized as Industry 4.0 (I4.0) [1,2]. This concept originated in 2011 through a German government initiative, in collaboration with the private sector, to promote manufacturing and its long-term competitiveness by implementing digital technologies [3]. The I4.0 revolution is not about a single breakthrough invention but a combination of several technological elements that work together to achieve greater synergy and performance [4]. The anticipated benefits of these advancements aim to enhance manufacturing companies’ performance and competitiveness [3,5].

While the existing literature includes numerous articles on digitalization and various aspects of I4.0, there is still a lack of clarity on how I4.0 is comprehensively implemented to manage supply chains (SCs) in manufacturing companies [6,7,8,9,10]. Specifically, I4.0’s impact on supply chain performance (SCP) has received limited scholarly focus in the literature [6,11]. In this context, the extant literature has explored the influence of I4.0 on a multitude of performance metrics, encompassing market and environmental performance [12,13], industrial performance [14], organizational performance [3,5], operational performance [15], business performance [9], relational performance [10], innovation performance [4], SC process performance [8], and SCP [16,17]. Although the two latter studies have attempted to explore I4.0’s impact on SCP, research into this area remains scarce, thus underscoring the necessity for future studies to further explore this relationship. Additionally, these two studies were conducted in Europe, indicating a notable gap in the literature regarding the performance consequences of implementing I4.0 in developing countries overall, and specifically in the Middle East and Jordan.

Furthermore, prior studies have explored a limited number of mediating variables to clarify I4.0’s impact on performance. For example, Frederico et al. [8] examined the mediation of SC process performance on the I4.0–profitability relationship. Li et al. [13] examined the mediation of digital SC platforms on the relationship between I4.0 and environmental and economic performance. Additionally, Erboz et al. [16] explored the mediation of SC integration on the I4.0-SCP relationship. In this regard, SC capabilities (SCCs) and SC innovation (SCI) are recognized as major outcomes of adopting I4.0 that contribute to enhanced performance [18,19,20,21,22]. Nevertheless, to the best of the researchers’ knowledge, no past research has analyzed the mediation of SCCs and SCI in the I4.0-SCP relationship. SCCs enable manufacturing companies to leverage both external and internal resources to enhance SCP [23]. Moreover, SCCs bolster a company’s ability to efficiently share information with SC members, coordinate diverse processes throughout the SC, and promptly respond to various requests from SC partners [24]. SCI encourages SC partners to explore novel capabilities and opportunities, rather than relying solely on their existing strengths [25]. This entails a high level of resource, skill, and expertise exchange among SC partners, as individual companies may lack the requisite capabilities and resources for innovation endeavors [26]. Both SCCs and SCI are anticipated to empower industrial firms to address unforeseen circumstances effectively, navigate uncertain conditions, and additionally enhance SCP [27]. To address the identified gaps in the literature, the present study examines I4.0’s influence on SCCs, SCI, and SCP within manufacturing firms in Jordan. Additionally, it analyzes the mediation effects of SCCs and SCI on the influence of I4.0 on SCP. Therefore, the subsequent research questions are introduced as follows:

RQ1. What impact does I4.0 have on SCCs, SCI, and SCP?

RQ2. What mediating role do SCCs and SCI play in the I4.0–SCP relationship?

The manufacturing sector is a primary driver of Jordan’s economy, directly contributing over 25% to the GDP and another 40% indirectly [28]. It also constitutes around 20% of the labor force and has attracted 80% of all investments over the past ten years [28]. However, despite its significance, Jordan’s manufacturing sector faces numerous challenges, such as restricted access to funding, rising production costs, limited marketing potential, inexperience in international markets, regional political instability, bureaucratic procedures, and significant tax burdens [28,29]. Notably, around 95% of all Jordanian industrial firms are identified as small and medium-sized enterprises (SMEs) [28,30]. Therefore, this research additionally contributes by being among the first to examine the suggested relationships with a specific focus on SMEs in a developing economy.

2. Literature Review

2.1. Industry 4.0 (I4.0)

I4.0 has recently undergone significant evolution, driven by the rapid advancements in information and telecommunication technologies and their seamless integration with SCs [31]. The first industrial revolution (1760–1840) was marked by the mechanization of production primarily utilizing steam-operated engines. The second revolution (1840–1870) ushered in technological advancements and mass production capability through the invention and industrial application of electrically operated machines. The third revolution (1960s–2000) marked the onset of the digital era with breakthroughs in computers, semiconductors, personal computing, and the Internet [10]. The fourth industrial revolution (I4.0) is centered around the comprehensive digitalization of business processes through information and communication technologies [31]. I4.0 integrates information technology with operational technology, thereby facilitating interactions between humans and machines, enabling intelligent machine-to-machine communication, ultimately enhancing product manufacturing and meeting customized demands [20]. Acioli et al. [1] highlighted that increased implementation of I4.0 results in better delivery and flexibility, customer satisfaction, logistical efficiency, waste reduction, and SC coordination.

In the present study, we conceptualize I4.0 using five widely cited practices in the literature. These selected practices include “internet of things (IoT), cyber physical systems (CPSs), big data analytics (BDA), cloud computing (CC), and horizontal and vertical integration (HVI)” [7,9,32,33,34,35]. These I4.0 technologies were selected because of their wide implementation by Jordanian manufacturing companies and appropriateness to the context of SMEs. Given that most manufacturing companies in Jordan are SMEs and that Jordan is a developing country, the selected technologies are fundamental and represent the basic infrastructure for SCs to undergo digital transformation. Other advanced I4.0 technologies, such as augmented reality, virtual reality, simulation, additive manufacturing, robotics, and blockchains, were excluded from the operationalization of I4.0 in this study due to their limited adoption by Jordanian manufacturing SMEs. The excluded technologies usually require substantial investments and high technical expertise, which are often not possessed by SMEs in a developing country like Jordan. While the exclusion of these advanced technologies may limit the study’s generalizability to contexts where such technologies are more prevalent, the selected technologies still offer valuable insights into the impact of I4.0 on SCP. Drawing upon the extant literature,

Table 1. Industry 4.0 practices.

I4.0 Practices	Scope and Definition
Internet of things (IoT)	IoT is an emerging global network where information, manufacturing resources, assets, and individuals are digitally linked [36]. This connectivity across internal process networks and subsystems enables intelligent control, real-time coordination, and dynamic management of the physical world—including goods, machines, factories, and industrial infrastructure. Such an ecosystem profoundly impacts the value chain of businesses [37] and facilitates the production of high-quality products with minimal human intervention [17].
Cyber physical systems (CPSs)	CPSs are integrated manufacturing systems composed of hardware components, such as sensors and processors, along with software communication technologies [38]. These components are able to share information autonomously, initiating actions, and regulating each other in a smart and independent manner [39]. CPSs networks create a virtual world (cyberspace) that merges with the physical world, enabling communication between machines, products, and humans through human–machine interface (HMI) systems [17].
Big data analytics (BDA)	BDA refers to technological solutions designed to analyze massive datasets that exceed the capabilities of traditional tools [2]. These analytics are used to process and support real-time decision making, ultimately enhancing firms’ competitive advantage [37]. BDA’s value lies in its ability to collect and comprehensively evaluate diverse data from numerous sources, swiftly processing them regardless of volume [2].
Cloud computing (CC)	CC refers to the aggregation of software, information, and data on a virtual server, enabling devices connected to the Internet to access these resources seamlessly and utilize the full spectrum of their associated functionalities and services [39]. CC facilitates the sharing and storage of information over the Internet, allowing various stakeholders to easily access data from multiple locations [2].
Horizontal and vertical integration (HVI)	Horizontal integration refers to the extent of technology-enabled collaboration and communication between different organizations, including cyber–physical interactions [37]. Conversely, vertical integration involves the unification of various hierarchical subsystems within a single organization, leading to a flexible, dynamic, and efficient manufacturing system that enhances the entire spectrum of value chain activities, such as inventory, supply chain, and customer service management [40]. HVI ensures a seamless connection among diverse functional processes—spanning design, analysis, scheduling, manufacturing, quality, and maintenance—leveraging digital tools to foster efficient operations [37].

provides a comprehensive overview of each selected I4.0 practice, including its definition and key characteristics.

2.2. Supply Chain Capabilities (SCCs)

SCCs encompass a blend of strategic and management capabilities, development skills, and technologies that facilitate information exchange among SC partners, as well as inherent abilities and collaboration within an organization [41]. SCCs also refer to companies’ ability to interact with the members of their SC to enhance network visibility, improve productivity, and facilitate real-time information sharing and feedback throughout the different phases of the SC [20]. Effective SCCs necessitate the utilization of comprehensive information and resources, encompassing both internal and external sources, to optimize all SC activities [23,42]. These capabilities facilitate responsiveness to partner requests and end-consumer needs [23], enable adaptation to environmental changes [24], and contribute to a superior performance and sustained competitive advantage [43].

Building on prior research, this study conceptualizes SCCs using three key dimensions: “information sharing (IS), supply chain coordination (SCCo), and supply chain responsiveness (SCR)” [23,24,42,44,45]. IS is a vital capability that enables sharing of the accurate information with the appropriate SC partners at the appropriate time [44]. SCCo is a critical capability for firms, enabling effective coordination of various SC activities and transactions with their partners [43,44]. SCR is the capability to adapt and respond swiftly to customer needs and environmental changes [27].

2.3. Supply Chain Innovation (SCI)

SCI embodies “a change (incremental or radical) within the supply chain (SC) network, SC technology, or SC processes (or combination of those) that can take place in a company function, within a company, in an industry or in a SC in order to enhance new value creation for the stakeholder” [46] (p. 8). Additionally, it is “a complex process which deals with uncertainty in the environment, so as to provide solutions for customer needs and find new ways to better organizational processes using new technologies” [47] (p. 1194). It explores new opportunities and capabilities and applies new techniques and methods, instead of depending on existing capabilities and available strengths [25].

2.4. Supply Chain Performance (SCP)

SCP is a firm’s capacity to promptly fulfill customer requirements reliably and cost-effectively [48]. It can also be viewed as how effectively an SC fulfills its functional objectives [44]. Queiroz et al. [20] emphasized the significance of digitalization capabilities for achieving significant SCP. They argued that organizations must develop the ability to leverage technologies that integrate seamlessly with workers, customers, and suppliers across the SC. Building on prior research, this study conceptualizes SCP considering indicators of inventory turnover, cost, demand adaptability, delivery, responsiveness, flexibility, and SC process speed [25,49,50,51,52].

3. Theoretical Foundations and Hypotheses Formulation

3.1. The Resource-Based View (RBV)

This research draws its theoretical foundation from the resource-based view (RBV) theory [53]. This theory indicates that companies can attain superior performance and competitive advantage via their resources and capabilities [41,54]. It explains that the resources driving sustainable performance should be valuable, rare, non-substitutable, and hard to imitate [53]. The resources companies possess include both tangible and intangible types. Tangible resources can encompass plant, equipment, and infrastructure, whereas intangible resources may include human capital and knowledge [16]. Capabilities can be viewed as “firm-specific formal or informal processes developed over time through the complex allocation and use of resources and are embedded in organisational routines” [55] (p. 665). They include the company’s skills, competencies, knowledge, and routines that enable it to utilize, coordinate, and combine the available resources in an effective manner to attain superior performance and competitive advantage [53]. In this vein, SCCs and SCI are regarded as major capabilities generated due to the effective deployment and integration of I4.0 resources [16,41,54,56]. Furthermore, existing research points that SCCs and SCI represent essential mediators in realizing superior SCP [41,54,56,57]. Hence, we propose, drawing from the RBV, that the technologies of I4.0 represent manufacturing firms’ resources that lead to the establishment of SC and innovation capabilities. These resources and capabilities will result in noticeably enhanced SCP. Figure 1 presents the research model driving this study. The proposed direct and indirect hypotheses of the present study are discussed and developed in the following sections.

3.2. I4.0 and SCP

Implementing I4.0 technologies boosts SCP by improving planning through increased accuracy of forecasting, cultivating logistics networks, and enhancing supplier performance [31]. Erboz et al. [16] demonstrated that I4.0 directly enhances SCP. They attributed this positive impact to the close relation of I4.0 to intelligent manufacturing. Companies adopting I4.0 technologies are expected to outperform their competitors by having lower costs, providing more innovative services, and meeting customer requirements, which ultimately enhance SCP [56]. Furthermore, companies implementing I4.0 will have higher ability to gather and share real-time market and operational information with SC partners, better responsiveness to changing customer needs, and more real-time decisions that reflect current demand patterns, leading to enhanced SCP [20]. In addition, implementing I4.0 technologies notably increases automation levels in production and SC processes, leading to optimized processes along the SC. This will minimize the cost of unnecessary workforce and waste of resources along the SC, resulting in improved SCP [7].

Extant research suggests a positive relationship between I4.0 and SCP [16,34]. Sharma et al. [58] found that I4.0 positively mediates SCM-SCP relationship. Other researchers have demonstrated that implementing I4.0 positively affects various performance dimensions (e.g., [3,7,12,14,15]). Consequently, we posit the following hypothesis:

H1: 

I4.0 positively and significantly affects SCP.

3.3. I4.0 and SCCs

To our knowledge, no prior studies have linked I4.0 to SCCs. However, research suggests that I4.0 technologies impact traditional SCs, accelerating the transition towards digitalization, which leads to improved digital SCCs [20]. I4.0 practices increase the levels of integration, coordination, synchronization, and information sharing with SC partners, thereby improving SCCs [31]. I4.0 technologies enable seamless information exchange among SC partners, fostering higher inter-organizational cooperation, real-time process interconnection, and enhanced responsiveness to SC demands [16]. Moreover, I4.0 technologies remarkably improve visibility into the entire SC operations and processes from different perspectives, enabling rapid decision making across the whole SC and leading to enhanced SCCs [59]. The fast-paced evolution of cloud computing and other I4.0 technologies enables timely and instantaneous sharing and integration of information, promoting operational collaboration within the company and along the SC [6]. Generally, the literature points to I4.0 technologies as major drivers of SC cooperation, information sharing, and visibility [16,17,20,34,60]. Accordingly, our hypothesis is as follows:

H2: 

I4.0 positively and significantly affects SCCs.

3.4. I4.0 and SCI

Benitez et al. [6] asserted that I4.0 technologies are essential to achieving smart production systems that promote innovation along the SC. In this vein, Jaouadi [61] found that BDA (an I4.0 technology) is vital in achieving SCI and sustainable SCP. Similarly, Hopkins [62] has stated that digital technologies are essential components for driving operational and SC innovation. I4.0 technologies further drive SCI by enabling the digital transformation of the SC and overcoming the challenges related to limited information sharing and disconnected processes in the traditional SC [21]. Furthermore, I4.0 technologies like IoT and BDA facilitate the collection of real-time data across the SC. These data enable gaining deeper insights into operational and SC problems, bottlenecks across the SC, and customer requirements. SC members can benefit from this information to develop innovative solutions across the SC, leading to enhanced SCI [62]. In addition, I4.0 technologies such as CC and VHI promote a more collaborative and transparent environment. Accordingly, different types of information will be available to various SC members, resulting in improved communication, integration, and collaboration. This will encourage the generation of new innovative ideas, leading to high levels of innovation across the SC [21]. Based on this, we advance the next hypothesis:

H3: 

I4.0 positively and significantly affects SCI.

3.5. SCCs and SCP

Cooperation and information sharing initiatives across SC members foster a reliable and flexible supply of various components with reduced lead times and costs. These initiatives also promote team spirit and reinforce trust, all of which contribute to improved SCP [29]. Timely sharing of information across the SC allows each firm’s information concerning demand forecasts, inventory levels, production schedules, order tracking, and delivery status to be shared with all SC members. This leads to remarkable improvements in quality, cost, flexibility, and delivery performances, boosting, therefore, SCP [48]. SC coordination boosts integration levels among SC partners, smooths the flow of information along the SC, improves overall SC responsiveness, and promotes shared decision making, leading to enhanced SCP [29]. SCCs enable companies to attain higher product availability, on-time reliable delivery, and decreased inventory levels required to have an efficient, flexible, and reliable SC with higher performance levels [44]. Asamoah et al. [44] concluded that SCCs positively impact SCP. Rajaguru and Matanda [24] also concluded a positive influence of SCCs on operational and competitive performance. As a result, we assert the subsequent hypothesis:

H4: 

SCCs positively and significantly affect SCP.

3.6. SCI and SCP

SCI leads to an improved overall SCP by boosting SC effectiveness, enhancing on-time delivery, promoting efficient manufacturing processes, encouraging a problem-solving culture, and increasing the deployment of improved methods and standards in various operations across the SC [63]. SCI allows SC members to explore new capabilities and opportunities, instead of relying on current capabilities and strengths [64]. This will result in improved customer value, increased SC efficiency, decreased errors across the SC, and effectively managed data, contributing to improved SCP [25]. SCI contributes to lead time reduction, minimizes waste via the efficient processes, and enables the delivery of the required quantities of products at the right time, enhancing, thus, SCP [25]. In addition, it boosts the integration levels of information systems across the SC, enhances tracking systems, and streamlines inbound and outbound logistics, resulting in better SCP [65]. In light of this, we offer the subsequent hypothesis:

H5: 

SCI positively and significantly affects SCP.

3.7. Mediation Impact of SCCs on the I4.0-SCP Relationship

The previous discussion underscores the direct influence of I4.0 on SCP. However, does I4.0 exclusively influence SCP or could additional variables provide further insights into this relationship? The extant literature emphasizes that I4.0 leads to higher performance by fostering SCCs that improve performance [16,20,59,66]. Even though I4.0 and SC digitalization help create and mold the information infrastructure within SCs and facilitate access to external information, companies may encounter difficulties in realizing benefits if they rely only on digital technologies to attain high SCP [66]. They further indicated that to maximize the benefits of I4.0 and digital technologies in boosting SCP, SCCs are required to effectively use and manage these technologies. We argue that manufacturing companies adopting I4.0 can remarkably improve their SCP by successfully leveraging these technologies to develop and enhance SCCs. Prior research has established that SC coordination, responsiveness, and information sharing are essential SCCs that lead to higher performance [20,23,24,25,27,44]. I4.0 technologies enhance SCCs by increasing the quantity and quality of shared information, boosting cooperation levels, improving SC responsiveness, and promoting visibility across the SC. SCCs will result in reduced uncertainty across the SC, and will in turn further improve SCP [16]. Queiroz et al. [20], drawing from a review of the literature, concluded that the deployment of I4.0 technologies positively affects SCP via digital SCCs. Based on this, we advance the subsequent hypothesis:

H6: 

SCCs positively mediate the impact of I4.0 on SCP.

3.8. Mediation Impact of SCI on the I4.0-SCP Relationship

Digital technologies alone are not sufficient to enhance performance; according to the RBV, they may affect performance via organizational capabilities [67]. In this regard, SCI is regarded as a key organizational capability that leverages the advantages of I4.0 technologies into higher SCP [68]. I4.0 technologies are widely recognized for promoting SCI (e.g., [6,61,62]). This stems from their ability to facilitate high-level knowledge management platforms, leading to innovation in terms of improved processes, increased flexibility, enhanced decision making, and reduced lead times, which ultimately boost SCP [62]. SCI services as an intervening mechanism that converts the progress and advantages driven from digitalizing the SC through the deployment of I4.0 technologies into tangible improvements in the company’s SCP [67]. Digital technologies result in innovations across the SC by fundamentally changing and improving processes via process reconfiguration, enhancing operational efficiency, boosting technological capabilities, and strengthening inventory management effectiveness, which in turn ultimately enhance SCP [63]. Additionally, SCI facilitated by I4.0 technologies fosters the adoption of novel transportation methods and systems, improved distribution approaches, and enables the effective redesign of SC processes, ultimately leading to enhanced SCP [61]. Successful companies leverage the benefits of adopting I4.0 technologies to develop effective SCI, thereby optimizing various SC processes, refining existing products, or innovating new ones, enhancing the customer experience, leading to enhanced SCP [67]. In light of this discussion, we present the ensuing hypothesis:

H7: 

SCI positively mediates the impact of I4.0 on SCP.

4. Methodology

4.1. Sample and Data Collection

This study employed a deductive research approach and a quantitative survey methodology to analyze the posited relationships. The current research’s population comprised firms belonging to the manufacturing sector located in Amman, Jordan’s capital city, totaling approximately 1690 [28]. The authors contacted around 1200 of these companies to invite them to participate in the study if they have implemented I4.0 practices. Accordingly, the purposive sampling method was employed to ensure that all participating companies had adopted I4.0 technologies. Several companies were excluded from the study because they clarified that they do not implement I4.0 practices. The present study relied on individual manufacturing firm as the unit of analysis. From each firm, one representative in a managerial role with adequate understanding of both I4.0 practices and SCM was targeted to participate. Given that most Jordanian industrial firms are SMEs, not all have a formal SC manager position. In such cases, managers with other titles often handle SC responsibilities. Therefore, we targeted managers whose duties most closely aligned with SCM, including managers of the SC, production, and operations, as well as general managers, vice general managers, and similar positions. Ultimately, 219 returned questionnaires were received, of which 8 were deemed invalid because they contained several missing responses. Hence, the total number of acceptable responses amounted to 211, demonstrating an effective response rate of 17.5%.

Table 2. Profiles of respondents and surveyed companies.

Category	Frequency	Percentage (100%)
Gender
Male	189	89.6
Female	22	10.4
Total	211	100.0
Job Position
General manager	48	22.7
Operations manager	40	18.9
Supply chain manager	38	18.1
Vice general manager	35	16.6
Production manager	32	15.2
Others	18	8.5
Total	211	100.0
Experience
Less than 5	24	11.4
5–less than 10	39	18.5
10–less than 15	46	21.8
15 and above	102	48.3
Total	211	100
Industry Type
Food, agricultural, and livestock	39	18.5
Chemical and cosmetic	28	13.3
Therapeutics and medical supplies	27	12.8
Engineering (machinery)	23	10.9
Packaging and paper	21	9.9
Plastic and rubber	16	7.6
Leather and garment	14	6.6
Construction industries	13	6.2
Electrical and information technology	13	6.2
Mining	9	4.2
Wood and furniture	8	3.8
Total	211	100.0
Number of employees
Fewer than 50	89	42.2
50–fewer than 100	43	20.4
100–fewer than 200	34	16.1
200–fewer than 300	27	12.8
300 and above	18	8.5
Total	211	100.0

provides an overview of the study participants’ demographics and a profile of the participating firms.

4.2. Research Design and Analytical Approach

The present study employs a quantitative survey-based approach combined with structural equation modeling (SEM) to examine I4.0’s impact on SCCs, SCI, and SCP. The novelty of this methodological approach is highlighted by focusing on the mediation of SCCs and SCI in the I4.0-SCP relationship, specifically in a developing country context. During the hypotheses testing, bootstrapping techniques and multiple parallel mediator models are used. These techniques are expected to capture the dynamic and interconnected nature of SCs, ensuring relevance to SCP. This approach addresses a critical gap in the literature by providing a robust framework for analyzing how I4.0 technologies boost SCP through enhanced SCCs and SCI. This offers actionable insights for both researchers and practitioners.

4.3. Questionnaire and Measures

To fulfill the research’s purpose and acquire essential data for the study, the researchers developed a self-administered survey questionnaire. The questionnaire, originally crafted in English, underwent translation into Arabic by the authors. The measurement scales employed in this study were developed from previous research conducted on manufacturing companies and published in the English language [5,24,48,52,64,69]. In addition, we ensured that all the adopted constructs exhibited acceptable validity and reliability in prior studies, supporting the content validity for our research. To establish face validity, four academics in SC and operations management, along with five managers in companies that implement I4.0 practices, were requested to assess the questionnaire. We ensured that these academics and managers possessed appropriate knowledge and experience related to the study variables. Following the feedback gathered, appropriate adjustments were implemented. To assess their level of endorsement for the statements, participants were instructed to employ a five-point Likert scale, with 1 denoting “strongly disagree” and 5 “strongly agree”.

5. Data Analysis and Results

5.1. Measurement Model Assessment

To ensure the accuracy and internal consistency of our measurements, we evaluated the validity and unidimensionality of the constructs using appropriate techniques. Additionally, we tested the reliability of the scales by means of composite reliability (CR) and Cronbach’s alpha coefficient. Confirmatory factor analysis (CFA) was employed with Amos 24.0 to verify the unidimensionality of the study’s variables and estimate the fit of the presented measurement model. To guarantee unidimensionality of the research variables, only items exhibiting standardized loadings exceeding 0.50 were included [70]. The number of items that did not meet this criterion was four and they were deleted. Next, model fit statistics using the first-order scales of I4.0 and SCCs, SCI construct, and SCP construct were computed. These indices revealed adequate levels (χ² = 954.735, df = 568, χ²/df = 1.680, CFI = 0.921, TLI = 0.913, IFI = 0.923, RMR = 0.044, and RMSEA = 0.045). The obtained values of the model fit statistics specified a strong fit between the suggested model and the data, providing evidence for its credibility. In addition, all items demonstrated statistical significance (p < 0.01), affirming the criteria for convergent validity [70]. Also, the average variance extracted (AVE) for the first-order constructs was verified to further affirm convergent validity. This condition was also satisfied, as all the AVE scores exceeded 0.50, the criterion advocated by Fornell and Larcker [71].

Furthermore, to ensure the reliability of the measures, we calculated Cronbach’s alpha and CR for the first-order constructs, evaluating their internal consistency. The resultant values for each test and construct surpassed the proposed cut-off of 0.70 [70,71], establishing that the measures employed in our study exhibited adequate levels of reliability and credibility.

In the subsequent section, when testing the study hypotheses, the overall scales of I4.0 and SCCs (second-order constructs) are utilized, necessitating the retesting of validity and reliability analyses, along with model fit statistics, using these overall scales. The fit statistics for our model with the overall scales (second-order constructs) indicated suitable values (χ² = 1062.275, df = 589, χ²/df = 1.803, CFI = 0.912, TLI = 0.904, IFI = 0.914, RMR = 0.049, and RMSEA = 0.047). Moreover, the factor loadings for the overall constructs of I4.0 and SCC surpassed 0.50 and exhibited statistical significance (p < 0.01). In addition, our analysis revealed strong AVE values for the second-order constructs. The AVE value for I4.0 was 0.686, and the AVE for SCCs was 0.743. In terms of reliability tests, both overall constructs achieved values for CR and Cronbach’s alpha tests above the recommended threshold of 0.70. Subsequently, validity and reliability were confirmed by employing the second-order constructs.

Table 3. Measurement items and CFA results.

Item Code	Measurement Item	Mean	Std.	Factor Loading a	Cronbach’s Alpha	Composite Reliability
Cyber physical systems [5]		3.59	0.714		0.771	0.799
CPSs1	Machines in our company have radio frequency identification labels			0.649
CPSs3	Real-time (instant) data can be obtained in our company			0.842
SCPs4	A fast return system has been established for customer requests			0.768
Internet of things [5]		3.19	1.032		0.821	0.839
IOT1	A network system has been established among smart devices in our company			0.925
IOT2	In our business, security has been provided in advanced production processes			0.715
IOT4	The internet of things is used in logistics activities in our business			0.741
Big data analytics [5]		3.36	0.829		0.868	0.859
BDA1	Our company has a database management system			0.729
BDA2	Problems arising with big data are detected in our business			0.673
BDA3	Big data is used in decision making methods			0.775
BDA4	With big data, estimates are made about the quality and timely delivery of the products			0.918
Cloud computing [5]		3.17	1.026		0.897	0.911
CC1	Fast data transfer and backup are provided with cloud computing			0.716
CC2	Our company has one of the cloud computing infrastructure, software or platforms			0.867
CC3	Our company benefits from cloud services from outside			0.892
CC4	Employees can easily access the desired information from anywhere with cloud computing			0.906
Vertical and horizontal integration [69]		3.12	0.867		0.883	0.892
VHI1	Our company has a high degree of digitalization in its vertical value chain (from product development to production).			0.673
VHI2	Our company has an end-to-end IT-enabled planning and control process from sales forecasting, over production to warehouse planning and logistics			0.846
VHI3	Our company has a high degree of digitalization in its horizontal value chain (from customer order over supplier, production, and logistics to service)			0.839
VHI4	Our company’s IT integration with customers, suppliers, and fulfillment partners is advanced			0.913
Information sharing [24]		3.28	0.773		0.874	0.897
IS1	Our company and supply chain partners share information frequently			0.809
IS2	Our company and supply chain partners share information accurately			0.886
IS3	Our company and supply chain partners share detailed information on business activities			0.861
IS4	Timely information sharing is achieved between our company and our supply chain partners			0.751
Supply chain coordination [24]		3.59	0.746		0.847	0.858
SCCo1	We are very satisfied with the collaborative relationships that we have with our supply chain partners			0.659
SCCo2	We have good collaborative relationships with supply chain partners			0.905
SCCo3	We have achieved efficiency in coordinating relationships with supply chain partners			0.873
Supply chain responsiveness [24]		3.67	0.816		0.902	0.897
SCR2	Our supply chain responds effectively to changing customer needs			0.656
SCR3	Our supply chain responds quickly to changing competitors’ strategies			0.769
SCR4	Our supply chain develops new products quickly			0.927
SCR5	Our supply chain responds effectively to changing competitors’ strategies			0.935
Supply chain innovation [64]		3.46	0.735		0.916	0.928
SCI1	We frequently try out new ideas in the supply chain context			0.814
SCI2	We seek out new ways to do things in our supply chain			0.823
SCI3	We are creative in the methods of operation in the supply chain			0.816
SCI4	We often introduce new ways of servicing the supply chain			0.832
SCI5	We motivate supply chain members to suggest new ideas			0.762
SCI6	We pursue continuous innovation in core processes			0.847
SCI7	We pursue new technological innovation			0.736
Supply chain performance [48,52]		3.53	0.676		0.906	0.897
SCP1	Our supply chain reduces total product cost to the final customer			0.678
SCP2	Our supply chain increases our inventory turns			0.664
SCP3	Our supply chain has fast customer response time			0.758
SCP4	Our supply chain is able to respond to changes in market demand without overstocks or lost sales			0.782
SCP5	Our supply chain has the ability to quickly modify products to meet customer’s requirements			0.643
SCP6	We are satisfied with the speediness of the supply chain process			0.815
SCP7	Our supply chain has an outstanding on-time delivery record			0.858
Industry 4.0 (second-order construct)		3.28	0.724		0.896	0.916
I4.0 1	CPSs			0.836
I4.0 2	IOT			0.814
I4.0 3	BDA			0.868
I4.0 4	CC			0.842
I4.0 5	VHI			0.778
Supply chain capabilities (second-order construct)		3.51	0.681		0.877	0.896
SCC 1	IS			0.812
SCC 2	SCCo			0.902
SCC 3	SCR			0.869

^a Standardized factor loadings in the CFA model are shown.

presents the measurement items for the study’s constructs and the corresponding validity and reliability assessments for both first-order and second-order levels.

Finally, a discriminant validity assessment was executed. As per Fornell and Larcker’s [71] criteria, the square root of each variable’s AVE was calculated. Subsequently, it was verified that each variable’s calculated square root was greater than the correlation coefficients between that variable and other variables, thereby affirming discriminant validity as shown in

Table 4. Correlation matrix and the analysis of discriminant validity.

Construct	AVE	1	2	3	4	5	6	7	8	9	10
1. CPSs	0.573	0.757
2. IoT	0.639	0.418	0.799
3. DBA	0.607	0.513	0.584	0.779
4. CC	0.721	0.345	0.468	0.419	0.849
5. VHI	0.677	0.516	0.679	0.680	0.530	0.823
6. IS	0.686	0.341	0.420	0.419	0.342	0.533	0.828
7. SCCo	0.672	0.385	0.478	0.509	0.427	0.555	0.586	0.819
8. SCR	0.689	0.369	0.456	0.444	0.331	0.537	0.556	0.765	0.830
9. SCI	0.648	0.259	0.296	0.470	0.219	0.444	0.437	0.414	0.535	0.805
10. SCP	0.557	0.314	0.402	0.479	0.307	0.530	0.489	0.690	0.696	0.637	0.746

Note: Square root of AVE is on the diagonal.

.

5.2. Results

To evaluate the study’s hypotheses, the multiple parallel mediator model was applied utilizing the PROCESS macro (model 4) in SPSS (version 25). In accordance with Hayes’ [72] guidelines, we performed the analysis by selecting 5000 bootstrapping samples and established confidence intervals (CIs) at a 95% level. Both limits of the confidence intervals (CIs), specifically the lower limit (LL) and the upper limit (UL), were utilized to assess the significance of the mediation effects.

Hypothesis testing began by examining the total influence of I4.0 on SCP in the absence of the two mediators. The analysis demonstrated a significant positive impact of I4.0 on SCP (β = 0.503, p ≤ 0.01), validating hypothesis H1. Subsequently, the two mediators were introduced to test the full model. Consequently, the direct influence of I4.0 on SCP (with the presence of both mediators) turned insignificant and approached zero (β = 0.014, p > 0.014), suggesting a full mediation effect [73]. I4.0 exhibited a positive significant impact on SCCs (β = 0.626, p ≤ 0.01) and SCI (β = 0.437, p ≤ 0.01), confirming hypotheses H2 and H3. Furthermore, the analysis uncovered a positive significant impact of SCCs on SCP (β = 0.554, p ≤ 0.01), lending credence to hypothesis H4. Additionally, SCI had a significant positive impact on SCP (β = 0.325, p ≤ 0.01), thus providing evidence in favor of hypothesis H5.

With respect to the mediation impacts, the analysis exhibited that the confidence intervals (CIs) for the mediation impact of SCCs on the I4.0-SCP relationship (CI_LL = 0.188, CI_UL = 0.519) did not comprise zero, with a standardized indirect impact of 0.347, thereby bolstering hypothesis H6. Similarly, the CIs for the mediation impact of SCI on the I4.0-SCP relationship (CI_LL = 0.047, CI_UL = 0.250) also excluded zero, with a standardized indirect impact of 0.142, thus substantiating hypothesis H7.

Aggregating both the direct and indirect effects allows us to calculate the overall effect of I4.0 on SCP. The direct effect of I4.0 on SCP (with the presence of the two mediators) was found to be 0.014. Additionally, the indirect effect of I4.0 on SCP via SCCs was 0.347 and via SCI was 0.142. Thus, the total effect of I4.0 on SCP is calculated as 0.014 + 0.347 + 0.142 = 0.503.

Table 5. Results of hypotheses testing.

Hypothesis	Path	Direct Impact	Indirect Impact	Total Impact	Bias-corrected Bootstrap 95% CI for Indirect Impact		Result
					Lower	Upper
H1	I4.0 → SCP	0.014	0.489 **	0.503 **			Supported
H2	I4.0 → SCC	0.626 **	NE	NE			Supported
H3	I4.0 → SCI	0.437 **	NE	NE			Supported
H4	SCC → SCP	0.554 **	NE	NE			Supported
H5	SCI → SCP	0.325 **	NE	NE			Supported
H6	I4.0 → SCC→SCP	0.014	0.347 **	0.361 **	0.188	0.519	Supported
H7	I4.0 → SCI → SCP	0.014	0.142 **	0.156 **	0.047	0.250	Supported

Notes: ** p < 0.01; NE: not estimated.

and Figure 2 summarize the hypothesis testing results.

5.3. Mathematica Justification of Comparative Analysis

To enhance the credibility of this study’s findings and conclusions, we offer a comprehensive mathematical justification of the comparative analysis of decision making in the numerical analysis section. The steps listed below describe the specific mathematical techniques and arguments used in our study.

• Confirmatory Factor Analysis (CFA): To ensure that the measurement scales of this study were unidimensional, we carried out CFA using Amos 24.0. The model fit indices (χ², df, χ²/df, CFI, TLI, IFI, RMR, and RMSEA) were computed to confirm the adequacy of the model. To make sure that the requirements of validity regarding the study’s constructs were met, we examined the standardized factor loadings of all the items. Only items with factor loadings greater than 0.50 were retained.
• Hypotheses Testing Using PROCESS Macro: The multiple parallel mediator model was applied using the PROCESS macro (Model 4) in SPSS (version 25) to test the study’s hypotheses. Each effect was assessed regarding its strength and significance through the β-value (path coefficient) along with its p-values (significance levels). For example, the results revealed an insignificant direct impact of I4.0 on SCP (β = 0.014, p > 0.05), indicating that full mediation exists. This methodological approach allowed us to simultaneously examine the direct and indirect impacts, providing a comprehensive understanding of the overall effects.
• Bootstrapping Techniques: We selected 5000 bootstrapping samples to test the two hypotheses regarding the indirect effects. By calculating the confidence intervals (CIs) for the indirect effects, the significance of the mediation effect was assessed. Both the CI’s lower limit (LL) and upper limit (UL) were examined to make sure zero was not contained in order to validate the mediation effect.

The significant mediation effects found in the previous analysis provide strong mathematical justification for the indirect impact of I4.0 on SCP through SCCs and SCI. For instance, the standardized indirect effect of SCCs is 0.347, which indicates that a one-unit increase in I4.0 adoption will result in a 0.347-unit increase in SCP via enhanced SCCs. This improvement enhances decision making by allowing for faster and more accurate responses to changes in demand, and more effective inventory control.

6. Discussion

Our findings demonstrate I4.0’s crucial role in enhancing manufacturing firms’ SCP. In today’s globalized, competitive, and swiftly evolving environment, which heavily relies on technological advancements, it will be difficult for manufacturing companies to enhance their SCP without fostering SC digitalization. I4.0 technologies remarkably boost the levels of SC digitalization and enhance the digital transformation of manufacturing companies, resulting in improved SCP. They provide the necessary capabilities to digitalize various aspects of the SC including production processes, inventory management, procurement, distribution and logistics, information sharing and communication, and decision-making processes, as well as data collection and analysis. Additionally, as shown in Table 2, 91.8% of the surveyed companies were SMEs employing fewer than 300 employees. Therefore, our results enrich the current body of knowledge by presenting empirical evidence that SMEs have the potential to boost their SCP and gain valuable benefits from deploying I4.0 technologies in their operations. This implies I4.0 benefits firms of all sizes. Generally, the present study’s results align with those of some previous studies [16,17,34]. Nevertheless, there are certain differences between our study and those studies. Our study examined I4.0’s impact on SCP, whereas Chauhan et al. [34] focused on I4.0’s effect on operational performance in India. Similarly, the work of Erboz et al. [16] was undertaken in Turkey, with 65.5% of the participating companies large companies employing over 250 employees, and about 50% of the surveyed companies employed more than 500 employees. In contrast, the majority of the participating companies in our study were SMEs. Additionally, Fatorachian and Kazemi [17] employed a systematic literature review, while our study utilized a survey questionnaire approach.

Further, our analysis provided strong evidence of a positive impact of I4.0 on SCCs. To our knowledge, no prior research has explicitly explored the I4.0-SCC relationship; therefore, our study is among the first to investigate these linkages. The findings revealed that I4.0 is a crucial under-examined antecedent of SCCs, contributing to information sharing, coordination, and responsiveness among SC participants. To some extent, this result corroborates those of some prior studies (e.g., [16,17,20,34,60]). Nonetheless, our study diverges from prior research in some ways. For example, Erboz et al. [16] concluded a positive impact of I4.0 on SC integration. Queiroz et al. [20] applied a narrative literature approach and proposed a framework linking I4.0 technologies to digital SC capabilities. Our study extends prior research by offering empirical findings and new insights on I4.0’s impact on SCCs of industrial firms, particularly SMEs, in the specific case of a developing economy.

Moreover, the implementation of I4.0 was found to positively affect SCI. This result aligns with past studies underscoring the essential contribution of I4.0 technologies in fostering innovation [6,61,63,74]. For instance, Benitez et al. [6] highlighted the positive effect of I4.0 technologies on technology innovation in Brazilian SMEs. Similarly, Feng et al. [74], through a review of the literature, highlighted the important contribution of I4.0 technologies in advancing green SCI. Jaouadi [61] highlighted the positive influence of BDA capability on SCI in manufacturing companies in Saudi Arabia. Our study differs from those studies by focusing on examining I4.0’s impact on SCI, whereas those studies either explored I4.0’s impact on technology innovation or BDA’s impact on SCI.

Additionally, our results demonstrated that SCCs positively affect SCP. This finding highlights the major role of SCCs in boosting the levels of SCP. Our results align with the conclusions of past research [24,44]. It is not enough to integrate internal processes and technical systems to attain high levels of SCP, but integrating and synchronizing business practices, systems, and processes across the SC can ensure superior levels of SCP [44]. Manufacturing companies that focus on increasing information sharing, coordination, and responsiveness across their SCs are expected to outperform other companies in their SCP levels. SCCs enhance SC partners’ ability to respond swiftly and effectively to varying customers’ and SC partners’ needs [24], leading to improved cost, delivery, flexibility, service levels, and overall SCP.

The analysis also confirmed that SCI positively affected SCP. This corroborates prior research demonstrating that SCP is positively and significantly affected by SCI [25,27,63,65,67]. This finding suggests that manufacturing SMEs in developing countries can improve SCP through innovations within their SCs. Interestingly, our finding contradicts some prior arguments that raised doubts on the capacity of SMEs to derive advantages from and improve their performance through innovation (e.g., [75,76]). SCI facilitates the introduction of more effective and efficient methods of managing SC processes, ultimately leading to improved SCP.

The results further indicated that SCCs and SCI fully mediate the I4.0-SCP relationship. Furthermore, about 72% of the mediation effect was attributed to SCCs. This highlights the vital role of SCCs as an essential outcome of I4.0 that further enhances SCP. This is a major finding of our research, which is the first, to our knowledge, to offer explicit empirical support regarding the mediation of SCCs on the I4.0-SCP relationship. Generally, our result regarding the mediation of SCCs aligns with some prior studies [16,20,59,66]. However, our research departs from previous works in several respects. Specifically, Erboz et al. [16] found that SC integration serves as a partial mediator in the relationship between I4.0 and SCP. Queiroz et al. [20] concluded, using a narrative review of the literature, that the application of I4.0 positively influences SCP via digital SCCs. Our research differs from those studies by empirically examining the mediating effect of SCCs on the I4.0-SCP relationship. Another notable outcome of our research was the revealed mediating effect of SCI on the I4.0-SCP relationship. The present study is one of the first to offer empirical findings regarding this mediation effect within manufacturing companies. This result is aligned with some prior studies [63,67,68]. Nonetheless, our work is distinguished from those studies. For instance, Bahrami et al. [63] demonstrated that SCI mediates the BDA-SCP relationship, whereas our study examined the mediation of SCI on the I4.0-SCP relationship. Chatterjee et al. [68] concluded that the deployment of I4.0 technologies in healthcare positively affected healthcare SCI, which in turn positively affected healthcare SCP. Our study investigated the proposed impacts in the context of industrial firms. All in all, this research is the first, to our knowledge, that explores the concurrent mediation of SCCs and SCI in the I4.0-SCP relationship. It expands the current knowledge base by demonstrating full mediation, indicating that the influence of I4.0 on SCP is entirely explained by SCCs and SCI.

The outcomes of the present study highlight the pivotal contribution of I4.0 to augment SCCs and SCI, thereby enhancing SCP. Such augmentations have major impacts on manufacturing competitiveness, especially in constrained environments such as Jordan. For example, the facilitated information exchange and coordination by SCCs can contribute to accelerated decision making and better responsiveness to changes in the market, which are necessary to ensure competitiveness in uncertain and changing environments. Similarly, SCI-fostered innovation can drive process innovation and product differentiation to enable manufacturing companies to provide better services to their customers and mitigate market fluctuations.

Although SCP is the primary focus of our research as a holistic measure of SC effectiveness, the improved SCCs and SCI demonstrated in this study confirm that companies can achieve tangible financial and operational gains. For instance, better coordination and higher levels of information sharing can reduce inventory holding costs and, at the same time, improve capacity utilization. Also, capital efficiency and material flow can be improved due to innovations in SC processes. Even though explicit financial metrics were not included, these outcomes point to the essential role of I4.0 adoption in yielding measurable improvements in manufacturing competitiveness. To provide a more thorough understanding of the benefits of I4.0 adoption, future research could extend these findings by investigating how improved SCCs and SCI can boost specific financial and operational gains.

7. Conclusions, Implications, and Limitations

7.1. Conclusions

The present study sought to fill gaps in the existing literature regarding I4.0’s impact on SCCs, SCI, and SCP. Moreover, to our knowledge, no prior studies have addressed the mediation of SCCs and SCI on the I4.0-SCP relationship. Furthermore, this study augments the literature by presenting empirical support relating to the influence of SCCs and SCI on SCP within manufacturing SMEs in a developing economy. The theoretical underpinning of this research relies on the RBV theory of the firm. Thus, the present research expands upon prior studies on I4.0 and advances the extant knowledge by tackling the highlighted gaps in the literature. The results disclosed that the total effect of I4.0 on SCP is positive and significant. Moreover, the findings revealed that I4.0’s impact on both SCCs and SCI is significant and positive. In addition, the findings of the present study corroborated that SCCs and SCI positively and significantly affect SCP. Furthermore, the study’s outcomes showed that SCCs and SCI have a full mediation effect on the I4.0-SCP relationship. More than two-thirds of this mediation effect was attributed to SCCs, highlighting their crucial intervening role between I4.0 and SCP. This implies that the effect of I4.0 on SCP is entirely explained by SCCs and SCI. Companies attempting to increase the levels of their SCP should direct the benefits of implementing I4.0 technologies towards enhancing SCCs and SCI, which in turn will further improve SCP.

7.2. Theoretical Contribution

The present work offers numerous theoretical implications and significantly extends the existing research and theory on I4.0. While the literature adequately investigated I4.0’s influence on numerous performance dimensions (e.g., environmental, organizational, operational, and business performance), limited prior studies have examined its impact on SCP. This study extends previous research (e.g., [16,17]) and adds to the literature by examining I4.0’s impact on SCP. In addition, our study fills an apparent research gap by underscoring the fundamental role of I4.0 in boosting SCCs and SCI in manufacturing companies. This is shown by the substantial positive influence of I4.0 on SCCs and SCI, as indicated by standardized beta coefficients. Furthermore, a remarkable contribution of this study is demonstrated by revealing a full mediation effect of both SCCs and SCI on the relationship between I4.0 and SCP. To our knowledge, this research pioneers in examining the joint mediation of SCCs and SCI on the I4.0-SCP relationship. Likewise, our results revealed that more than two-thirds of the mediation impact is attributed to SCCs, highlighting their crucial role as outcomes of implementing I4.0 technologies and as antecedents of SCP. These findings are significant as they expand on prior research that explored the mediation of some variables on the relationship between I4.0 and some of its practices on various performance measures (e.g., [16,54,59,63,66,67,68]), offering valuable insights for future research. Overall, our study advances theoretical understanding by offering empirical support pertaining to the contribution of I4.0 on SCCs, SCI, and SCP.

7.3. Managerial Implications

The outcomes of our research present some useful implications and perspectives for practitioners in industrial firms. Managers in these firms need to emphasize the pivotal contribution of I4.0 technologies in enhancing the levels of SCCs, SCI, and SCP. Managers must recognize that in an era characterized by rapid developments and advancements in information technologies, manufacturing companies cannot achieve high levels of SCP without adopting I4.0 technologies. Relying solely on one or two of these technologies may not achieve the desired levels of SCP. Therefore, managers are recommended to leverage SC digitalization through the implementation of various I4.0 technologies, including CPSs, IoT, BDA, and CC, as well as HVI, among others. The full mediation effect of SCCs and SCI on the I4.0-SCP relationship revealed in this study strongly suggests that managers should not anticipate direct improvements in SCP solely from implementing I4.0 technologies; rather, improvements in SCP can be achieved if the advantages of implementing I4.0 technologies are directed by managers to foster and enhance SCCs and SCI. This requires managers to invest significant resources and efforts to promote SCCs, which include information sharing, SC coordination, and SC responsiveness with upstream and downstream SC partners by involving them in the SC-wide implementation process of I4.0. Moreover, the deployment of I4.0 technologies with SC partners should aim to facilitate the integration of knowledge with suppliers and customers, promote continuous improvement initiatives across the SC, and foster a culture of innovation throughout the SC in order to improve SCI and subsequently enhance SCP.

7.4. Limitations and Directions for Future Research

Although the present study provides practical and theoretical insights, it includes some limitations that future research can tackle. Firstly, I4.0 has been operationalized in this study using five widely adopted practices commonly employed by Jordanian manufacturing companies, suitable for the context of SMEs. However, other technologies within I4.0, such as blockchain, additive manufacturing, and artificial intelligence, are discussed extensively in the literature. Future studies are encouraged to broaden the operationalization of I4.0 by including additional technologies and validating the findings of this research. Secondly, this research relied on purposive sampling to ensure that all participating companies have implemented I4.0 practices. The purposive sampling method is non-probabilistic, limiting the generalizability of the results to the specific population from which the sample was obtained. Therefore, future studies are recommended to apply the simple random method to enhance the representativeness of the sample and provide broader insights. Thirdly, our study did not focus on a specific industry; rather, our sample included companies from various industries. This was due to the challenge of finding an adequate sample size in Jordan consisting of firms that both implement I4.0 practices and belong to a single industry type. This resulted in a relatively small sample size and uneven sectoral distribution, which prevented a detailed sector-specific analysis. Therefore, the potential differences in I4.0 adoption and its effects across sectors, such as food/agriculture versus engineering, remain unexplored. Future research with larger, more balanced samples could provide a deeper understanding of these sectoral variations. Additionally, different industry types exhibit distinctions and SC-related differences, encompassing the structure and configuration of their SCs. Thus, future research could target a specific industry to consider its unique context and attributes, thereby gaining more in-depth insights into that particular industry. Fourthly, while this study provides significant results concerning the impact of I4.0 on SCCs, SCI, and SCP, it did not explicitly examine some financial and operational metrics such as cost ratios, value-add, material flow, capital efficiency, purchase-to-sales ratio, inventory holding costs, transport costs, financing costs, and capacity utilization. Such metrics are necessary in order to ascertain how the implementation of I4.0 translates into tangible benefits for manufacturing competitiveness. Our primary focus in the research was on highlighting how I4.0 enhances SCCs and SCI and, subsequently, SCP rather than direct financial and operational performance. Future research is encouraged to include such performance metrics to investigate how SCC and SCI enhancements can be translated into actual financial and operational gains. Future studies can also employ case studies to investigate these relationships in specific industrial contexts. Finally, the cross-sectional design of this study captures a snapshot of I4.0’s impact. Longitudinal studies, tracking the effects of I4.0 over time, would offer a more comprehensive understanding of its dynamic influence on SCCs, SCI, and SCP.