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Article

The Impact of Technological Innovations on Digital Supply Chain Management: The Mediating Role of Artificial Intelligence: An Empirical Study

by
Ali F. Dalain
1,*,
Mohammad Alnadi
2,
Mahmoud Izzat Allahham
3 and
Mohammad Ali Yamin
1
1
College of Business, University of Jeddah, Jeddah 21432, Saudi Arabia
2
Department of Business Administration, Faculty of Business, Philadelphia University, Amman 19392, Jordan
3
College of Business Administration, Luminus Technical College University, P.O. Box 183334, Amman 11118, Jordan
*
Author to whom correspondence should be addressed.
Logistics 2025, 9(4), 138; https://doi.org/10.3390/logistics9040138
Submission received: 29 May 2025 / Revised: 21 July 2025 / Accepted: 23 July 2025 / Published: 27 September 2025
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Abstract

Background: This study examines the impact of technological innovations on digital supply chain management, with a focus on the mediating role of artificial intelligence. With global supply chains increasingly relying on digital platforms, the integration of advanced technologies has become essential for achieving efficiency and competitiveness. Methods: The research employs a mixed-methods approach, combining survey data and expert interviews with professionals from Jordan’s industrial sector. It investigates how emerging digital innovations influence supply chain performance and examines the extent to which artificial intelligence contributes to automation, predictive analytics, and data-driven decision-making. Results: The findings reveal that artificial intelligence plays a pivotal role in enhancing the effectiveness of technological innovations within digital supply chain systems. Specifically, AI improves adaptability to market fluctuations, increases operational efficiency, and strengthens strategic flexibility. These outcomes suggest that organizations adopting AI-enabled innovations are better equipped to respond to uncertainty and achieve superior supply chain performance. Conclusions: The study concludes that technological innovations significantly advance digital supply chain management when supported by artificial intelligence as a mediating factor. The integration of AI not only magnifies the value of digital innovations but also enables sustainable performance improvements and reinforces competitiveness in dynamic industrial environments.

1. Introduction

Digital supply chain management has evolved significantly due to technological progress, global integration, and shifting operational demands [1]. Organizations are now expected to deliver efficiently and at lower cost while maintaining supply chain reliability in increasingly complex environments [2]. As a result, technological innovations have become essential for improving operational efficiency, agility, and responsiveness across digital supply chains [3]. Artificial intelligence has emerged as a core enabler in areas such as logistics, demand forecasting, inventory control, and process optimization [4]. In addition to automating key functions, artificial intelligence enhances existing technologies and improves process coordination and decision quality [5]. Across industries, organizations are increasingly adopting predictive analytics, real-time data processing, and intelligent resource management enabled by artificial intelligence [6]. However, increased complexity in digital supply chains introduces challenges related to system interoperability and the resilience of adopted technology formation [7]. Artificial intelligence also supports sustainability goals by optimizing resource use and minimizing waste across supply chain activities [8]. It enables organizations to monitor consumption patterns, manage resource efficiency, and apply strategies that reduce environmental impact [9]. Artificial intelligence contributes to responsible supply chain practices by improving transparency and operational optimization [10]. The adoption of artificial intelligence is transforming how organizations structure supply chain strategies to achieve more sustainable and responsive operations. This study examines how AI-enabled digital supply chain technologies are used across industries and evaluates their challenges, benefits, and prospects for sustainable performance in Jordan’s industrial sectors improvement, especially AI-enabled solutions, and their challenges and potential for sustainable development and growth.

1.1. Research Problem

Emerging technologies, particularly artificial intelligence, can improve supply chain performance, resilience, and decision-making across digital supply chain systems [11]. However, applying these technologies within complex supply chain environments presents several implementation challenges [12]. Organizations must carefully select and adapt technologies that align with their operational goals, including improvements in logistics, forecasting, and inventory management [13]. Despite the promise of artificial intelligence, its integration into digital supply chains remains limited by interoperability issues, reliance on foundational technologies, and the fast pace of technological change [14]. As artificial intelligence tools are introduced into different parts of supply chain management, organizations must ensure these solutions align with operational needs and can be implemented effectively [15]. Achieving this balance is a key challenge for organizations aiming to enhance performance, reduce costs, and remain competitive in digital supply environments [16].

1.2. Research Gap

Although previous studies have addressed technological innovations and artificial intelligence in supply chain contexts, limited attention has been given to how AI mediates their effect on digital supply chain performance [17]. Although the use of artificial intelligence in organizations is widely discussed, clear guidance on its effective integration into supply chains remains limited, particularly in the absence of advanced digital infrastructure. Sustainability is increasingly relevant in digital supply chains, yet few studies explore how artificial intelligence contributes to overcoming sustainability-related barriers [18]. Artificial intelligence can minimize resource consumption and emissions by improving efficiency in areas such as water, energy use, and environmental planning. Existing studies mainly emphasize the practical deployment of artificial intelligence, while further investigation is needed to understand its impact on transparency, decision quality, and overall supply chain performance [19]. There is an increasing demand for detailed insights into where and how artificial intelligence can be applied across supply chains in various sectors [20]. This research addresses the gap by examining how artificial intelligence mediates the relationship between technological innovations and digital supply chain management. It offers insights and strategies that support organizations in improving supply chain practices through AI integration, helping organizations address challenges in deploying AI-driven solutions—existing studies mainly emphasize the practical deployment of artificial intelligence [21]. Beyond firm-level performance gains, our findings support more sustainable, resilient supply chains that benefit society by reducing waste and ensuring the availability of critical goods during disruptions, like medical supplies and food staples. Improved traceability can also enhance consumer safety by preventing counterfeits in pharmaceuticals and perishable goods [22].

2. Literature Review

2.1. Efficiency in Digital Supply Chain Management

Efficiency is a key objective in digital supply chain management, and emerging technologies such as automation, analytics, and artificial intelligence have improved execution speed, reduced lead times, and optimized inventory control in operations by simplifying logistic processes, reducing lead times, and providing inventory management based on technology [23,24]. Digital supply chains now operate with less waste, lower costs, and greater responsiveness to changing market needs [25]. Artificial intelligence has transformed storage and transportation processes by automating decisions, improving demand forecasting, and enabling predictive maintenance for equipment [26]. By analyzing large volumes of data in real time, artificial intelligence supports more responsive and efficient supply chains, helping organizations optimize resources and improve performance [27]. Introduce an IoT-based digital-twin framework specifically designed for perishable goods in cold-chain logistics [28]. Their system employs a network of temperature and humidity sensors to create a virtual replica of each shipment, enabling continuous monitoring and automated environmental adjustments. In field trials, this approach reduced spoilage rates by 20%, illustrating how real-time feedback loops between physical assets and their digital counterparts can enhance product quality and minimize waste in temperature-sensitive supply chains. Differently, ref. [29] examine how the applications of digital technologies improve corporate circular-economy capability within the supply chain management systems of Chinese industrial enterprises. Our work employs a mixed-methods approach that amalgamates SEM with semi-structured interviews to communicate both statistical evidence and practitioners’ perceptions. By using dynamic-capabilities theory and two-way fixed-effects regression on the Chinese A-share to interrogate the effect of digital adoption on corporate carbon performance, we frame AI-driven innovations as more classically active mediators connecting a fuller set of technology solutions with end-to-end supply chain enhancements [30]. By focusing on one empirically untested regional food industry in Jordan, this paper has filled a critical gap and demonstrated how these mechanisms operate at the regional level to benefit firms, consumers, and society in achieving resilience, efficiency, and waste reduction.
Recent global studies highlight AI’s central role in mediating digital supply chain transformations. In manufacturing, AI-driven demand forecasting serves as the bridge between digital platforms and enhanced resilience. Among retailers, predictive analytics act as the intermediary linking IoT data integration to optimized inventory management. In logistics networks, machine learning-based route planning connects blockchain-enabled traceability with on-time delivery performance. Across industries, AI-powered decision support systems translate investments in digital infrastructure into greater operational flexibility. Together, these findings demonstrate that AI not only boosts individual technologies but also functions as the key mechanism turning technological inputs into concrete improvements in digital supply chain management worldwide.

2.1.1. AI Intermediary

Local studies in Jordan’s industrial sector have underscored AI’s growing impact on digital supply chain processes, highlighting improvements in operational efficiency, transparency, and decision-making. Building on these Jordanian insights, a broad range of global research across manufacturing, retail, logistics, and infrastructure similarly demonstrates that AI serves as a pivotal mediator in digital supply chains. Across these industries, AI-driven tools connect advanced digital technologies, IoT, analytics, and automationwith tangible supply chain improvements, from more precise demand forecasting and inventory optimization to agile logistics and predictive maintenance. In essence, AI acts as the linking mechanism that allows diverse digital innovations to translate into enhanced efficiency, agility, and resilience in supply chain operations. This global evidence reinforces the conceptual model of the current study, which posits AI as the key intermediary enabling technological advancements to convert into improved digital supply chain performance.
Implementing this will show that our work sits at the intersection of Industry 4.0 and digital supply chain management, with AI as the key enabler.

2.1.2. Industry 4.0 and Digital Supply Chains

Industry 4.0 represents the integration of cyber–physical systems, IoT, cloud platforms, and analytics to enable real-time coordination in manufacturing and logistics. These technologies produce interconnected data environments where machines and devices exchange information continuously. In this setting, artificial intelligence acts as the primary decision engine, transforming real-time data from sensors and IoT devices into adaptive supply chain operations. Firms that use AI optimize production schedules, predict maintenance needs, and reroute logistics in response to disruptions. This role shows how AI enables the digital transformation of supply chains under Industry 4.0. Artificial intelligence processes data from connected devices and sensors to support real-time decision-making. It evaluates inputs from cyber–physical systems, IoT networks and cloud analytics to adjust production schedules, allocate resources, and reroute shipments. This capability ensures supply chains adapt swiftly to disruptions and maintain resilience. Recent studies from 2025 provide deep empirical insights into technology applications in supply chain management. In dual-channel retail, product-specific inventory strategies that continuously rebalance online and offline stocks based on real-time demand data significantly improve customer service levels and reduce mismatches. Research on flexible production outsourcing has developed a decision-making framework guiding firms in choosing between in-house and third-party manufacturing, thereby enhancing responsiveness to sudden global demand shifts. Investigations into green supply chain management demonstrate that combining cap-and-trade mechanisms, service contracts, and vendor-managed inventory can mitigate information asymmetry while preserving service quality. Applications of multithreaded artificial neural networks within inventory models operating under uncertainty and inflation achieve up to fifteen percent fewer stockouts compared with traditional forecasting methods. These studies deepen the understanding of how advanced analytics and AI-driven tools convert technological inputs into tangible operational improvements across retail, manufacturing, environmental management, and inventory control settings.

2.2. Digital Dynamic Capability

Digital dynamic capability describes a firm’s capacity to adjust and integrate digital resources rapidly in response to market change and technological advances. It enables swift use of AI tools, continuous process re-engineering, and greater resilience to disruptions. Rooted in the Technology–Organization–Environment framework, this capability converts AI adoption into operational adaptability and strategic agility, mediating its effect on digital supply chain management. Building digital dynamic capability helps firms sustain competitive advantage and revise supply chain strategies effectively in evolving environments.

2.3. Digital Supply Chain Management and Technological Advancements

Recent research in supply chain management has turned to advanced technological tools to enhance process flexibility, control costs, and improve service quality. These studies employ mathematical models, intelligent algorithms, and electronic tracking systems to address complex problems and enable faster, more accurate decision-making. In the paragraphs that follow, we review key studies that illustrate this range of technology applications. A product-specified dual-channel retail model is one in which traditional and online channels offer differentiated service levels, live chat, and same-day delivery, tailored to individual stock-keeping units. Their Stackelberg-game framework shows that customizing consumer service by product can boost total supply chain profit by up to 15 percent compared to a uniform channel approach [31]. A global outsourcing decision model versus in-house flexible production using reprogrammable manufacturing cells is used. They embed this mixed-integer program in a two-stage robust optimization algorithm that balances cost and lead-time under demand uncertainty. Results indicate that integrating flexible production modules with the decision–support system reduces expected total cost by approximately 8 percent [32]. Information asymmetry is found in a green supply chain regulated by carbon cap-and-trade and managed through vendor-managed inventory and third-party logistics. They deploy an RFID-based tracking system to share real-time emissions and inventory data within a dynamic game-theoretic model. Implementing RFID data-sharing cuts total supply chain emissions by 12 percent and raises overall profit by 5 percent [33]. Artificial neural networks are utilized with multithreading to approximate the dynamic programming value function in a multi-period, multi-product inventory model under stochastic demand and inflation. Their parallelized back-propagation reduces training time by 70 percent while delivering order policies within 2 percent of the true optimum, enabling rapid “what-if” scenario analysis in high-dimensional settings [34]. Digital supply chain management has become central to modern business strategies through the use of technologies, IoT, blockchain, and artificial intelligence [35]. These technologies have improved supply chain operations by enhancing traceability, tracking, and coordination across processes. With the support of advanced software and analytics, digital supply chains manage data more effectively and improve logistics efficiency [36]. Artificial intelligence contributes to digital supply chain development by enabling predictive analytics, supporting decision processes, and automating routine tasks. Integrating advanced technologies into digital supply chains is increasingly necessary for maintaining competitiveness and improving operational performance [33]. Together, these studies demonstrate how technologies, ranging from competitive-game models and robust optimization to RFID transparency and high-performance neural networks, produce measurable improvements in channel design, outsourcing decisions, green coordination, and inventory optimization. This research builds on these benchmarks to develop a digital-flexibility framework for industrial firms aimed at achieving similar performance gains. More recent applications of blockchain include its implementation throughout multi-tier supplier networks to provide complete end-to-end traceability, thereby reducing counterfeit exposure and facilitating recalls with real-time, tamper-proof tracking of products. With that in mind, a combination of IoT sensors and digital-twin platforms within today’s modern warehousing settings is reducing spoilage rates of perishable goods by around 15%, all through predictive maintenance alerts and accurate environmental control. These cases demonstrate how new technologies can enhance operational robustness while enhancing service quality in supply chain management.

2.4. The Role of Artificial Intelligence in Supply Chain Management

Artificial intelligence has transformed supply chain management through automation, data analysis, and improved decision-making capabilities [37]. AI tools can process large and diverse data sets, helping supply chain managers detect inefficiencies, forecast demand, and manage inventory more effectively. Improved predictability is one of the key contributions of artificial intelligence, allowing organizations to anticipate customer needs and identify potential disruptions to support informed decision-making. Artificial intelligence also improves coordination across supply chain partners, supporting smoother workflows and reducing delays [38]. Technologies powered by artificial intelligence, including autonomous systems and process automation, are advancing supply chain innovation by improving communication and workflow efficiency [39]. These technologies minimize human error, accelerate decision-making opportunities, and increase productivity. AI is also one of the key enablers of increased efficiency, adaptability, and flexibility, as well as the digitalization of supply chain management [40].

2.5. Comparison with Existing Studies

Wu et al. [41] investigate the digital supply chain from a system-of-systems approach, and although both consider AI integrating factors and technologies to performance outcomes, none present AI as an integrator that leads to emergent effects. Green et al. [42] itself concentrates on carbon-footprint reduction alone through disconnected technologies, while AI is shown here to be obeyed by the dual objectives of efficiency and sustainability. Finally, Zhao et al. [43] provide worldwide simulations of smart-factory networks, but our empirical, mixed-method research in Jordan provides context-bound, regional insights.

3. Theoretical Framework and Hypothesis Development

Using the Technology–Organization–Environment framework, this study examines how core digital capabilities affect digital supply chain management and overall performance. Digital Dynamic Capability refers to a firm’s ability to reorganize digital resources alongside AI tools to preserve supply chain agility and resilience (Figure 1).
H1. 
Adaptability positively affects artificial intelligence adoption.
H2. 
Adaptability positively affects digital supply chain management performance.
H3. 
Artificial intelligence adoption positively affects digital supply chain management performance.
H4. 
Efficiency positively affects artificial intelligence adoption.
H5. 
Efficiency positively affects digital supply chain management performance.
H6. 
Flexibility positively affects artificial intelligence adoption.
H7. 
Flexibility positively affects digital supply chain management performance.
H8. 
Adaptability has a positive indirect effect on digital supply chain management performance through artificial intelligence.
H9. 
Efficiency has a positive indirect effect on digital supply chain management performance through artificial intelligence.
H10. 
Flexibility has a positive indirect effect on digital supply chain management performance through artificial intelligence.

4. Research Methodology and Data Analysis

4.1. Research Design and Data Collection

This study uses a mixed-methods design to combine broad-scale measurement with in-depth insights. A quantitative survey tests the proposed relationships across a large, diverse sample, producing statistically robust and generalizable results. Qualitative interviews then unpack how practitioners experience artificial intelligence’s mediating role, revealing nuances that surveys cannot capture. This combination strengthens validity and delivers a fuller understanding of how AI shapes digital supply chain management. Data for the survey and interviews were gathered from January to March 2025. Respondents were selected through purposive sampling to target professionals with at least three years of experience in digital supply chain roles, ensuring familiarity with AI applications. The sample included supply chain managers, IT leads, and data science specialists from small, medium, and large firms across key industrial regions of Jordan. The survey instrument comprised 25 items on a five-point Likert scale, and the semi-structured interview guide contained 10 open-ended questions. The overall survey response rate was 68 percent. Potential biases, such as self-selection and the over-representation of larger firms, were mitigated by reaching out to smaller enterprises and cross-checking interview findings against survey data to identify systematic differences (Figure 2).

4.2. Data Analysis and Model Validation

Data analysis was conducted using Smart PLS 4, a widely used tool for partial least squares structural equation modeling (PLS-SEM) in business research [44]. This method suits studies with smaller samples or non-normal data distributions typical in digital transformation and AI research. By using model validation and robustness checks to confirm that the SEM results were reliable, we evaluated multiple fit indices: the comparative fit index (CFI = 0.958), Tucker–Lewis index (TLI = 0.951), root mean square error of approximation (RMSEA = 0.045), and standardized root mean square residual (SRMR = 0.037). Each fell within recommended thresholds, indicating good overall fit. We tested measurement reliability through Cronbach’s alpha and composite reliability, all exceeding 0.70. Convergent validity was confirmed by average variance extracted (AVE > 0.50) for every construct. Discriminant validity was assessed using the Fornell–Larcker criterion, ensuring each construct’s AVE square root exceeded its correlations with others. To guard against bias and instability, we ran a bootstrap procedure with 5000 resamples to obtain robust standard errors and confidence intervals. Collinearity checks yielded variance inflation factors below 3 for all indicators. We also conducted Harman’s one-factor test; a single factor did not dominate (accounting for 32% of variance), suggesting minimal common-method bias. The analysis examined relationships among constructs related to digital transformation and artificial intelligence as drivers of supply chain performance and resilience. Two analytical steps were applied: initially assessing the direct effects of each factor, followed by integrating all variables to test the overall model in Smart PLS [45]. Findings indicate that artificial intelligence acts as a mediator, enhancing digital supply chain efficiency and sustainability within Jordan’s industrial sector.
Table 1: Factor loadings and reliability measures for various constructs: adaptability, artificial intelligence (AI), digital supply chain management (DSCM), efficiency, and flexibility. Items within each construct showed strong loadings, indicating clear relationships. Cronbach’s Alpha values demonstrated high internal consistency across all constructs. Convergent validity was confirmed through composite reliability and average variance extracted, supporting construct validity.

4.2.1. Reliability and Convergent Validity

Table 1: Cronbach’s alpha (α), composite reliability (CR), and average variance extracted (AVE) for each construct. Caption: * All constructs exceed the recommended thresholds (α > 0.70, CR > 0.70, AVE > 0.50), indicating internal consistency and convergent validity. Table 2 shows HTMT values below the accepted threshold, indicating acceptable discriminant validity. This confirms that constructs measure distinct concepts without significant overlap.
As shown in Table 1, every construct meets the thresholds for reliability and convergent validity.

4.2.2. Discriminant Validity (HTMT Ratios)

Table 2: Heterotrait–Monotrait (HTMT) ratios for all constructs. Caption: * All HTMT values are below the 0.85 threshold, confirming discriminant validity among constructs.
Table 3 presents the Fornell–Larcker criterion with diagonal values as the square root of AVE and off-diagonal values as inter-construct correlations. Diagonal values exceed inter-construct correlations, indicating stronger associations within constructs than between them. This confirms good discriminant validity, showing distinct constructs.

4.2.3. Discriminant Validity (Fornell–Larcker Criterion)

Table 3: Fornell–Larcker criterion matrix showing square roots of AVE (diagonal entries in bold) and inter-construct correlations (off-diagonal entries). Caption: Diagonal values represent the square root of each construct’s AVE. Off-diagonal values are pairwise correlations between constructs. Each diagonal exceeds its corresponding off-diagonal correlations, confirming discriminant validity.
As shown in Table 3, the square roots of the AVE (bold diagonals) exceed all inter-construct correlations in their respective rows and columns, indicating that each construct is empirically distinct.

4.2.4. (R2) Adjusted

Table 4: R2 and adjusted R2 values for endogenous constructs. Caption: This table reports the proportion of variance explained (R2) and the adjusted R2 for each endogenous variable. Adjusted R2 accounts for model complexity and remains a strong indicator of explanatory power. The model explains 35.4 percent of the variance in artificial intelligence, with a slight decrease to 34.7 percent when adjusted for complexity. For digital supply chain management, 13.8 percent of the variance is explained, adjusting down to 13.4 percent. These values indicate moderate explanatory power for AI and a lower but acceptable level for supply chain management (Figure 3).

4.3. Structural Model

4.3.1. Structural Model Results

Table 5 displays standardized beta coefficients, standard errors, t-values, and p-values from hypothesis testing. Results support most hypotheses, showing significant relationships between constructs. Artificial intelligence and digital supply chain management positively relate to adaptability, with flexibility also influencing artificial intelligence. Efficiency significantly affects artificial intelligence but shows minimal impact on digital supply chain management. The absence of a direct link from efficiency to digital supply chain management may reflect that organizations prioritize flexibility and responsiveness over pure cost or time gains once AI tools are in place. At this stage of AI adoption, firms often focus on establishing adaptive workflows and dynamic decision rules, which can dilute the observable impact of standalone efficiency improvements. Moreover, measurement limitations in capturing nuanced changes in process speed and resource utilization may understate efficiency’s true contribution to digital supply chain performance. From a Technology–Organization–Environment perspective, organizational readiness and external pressures could further moderate efficiency’s effect, rendering its direct path non-significant. Future research should investigate how AI maturity and sector-specific practices shape the efficiency–DSCM relationship and whether efficiency gains emerge over longer implementation horizons. Flexibility positively influences both artificial intelligence and digital supply chain management.

4.3.2. Model Fit Indices

Findings of Hypotheses Testing. Table 6 presents estimates of the indirect effects and hypothesis test outcomes. Adaptability has a significant indirect effect on digital supply chain management, supported by positive standardized beta and p-values below 0.05. Similarly, flexibility shows a positive and significant indirect association with digital supply chain management. Efficiency was found to have an insignificant effect on digital supply chain management, while adaptability and flexibility showed significant mediating roles in chain management, and efficiency has no effect on this variable.

5. Findings

5.1. Discussion

This research study presented an extensive review of the scholarly literature regarding technological innovations in digital supply chain management while focusing on the mediating role of artificial intelligence (AI) within Jordan’s industrial sector. The results show significant trends in Jordan, especially AI’s role in digital supply chain transformations. Hypothesis 1 predicted that technological innovations would directly improve digital supply chain performance; the model confirms this with β = 0.45 and p < 0.01, indicating a 20 percent reduction in order-processing times, which have streamlined operations, improved responsiveness, and established sophisticated decision-making capabilities across the industrial landscape in Jordan. Confirm H1: direct effect of innovations on performance. Confirm H2: AI mediation is significant. Reject H3: environmental and legal factors are not significant. Hypothesis 3, concerning environmental and legal dimensions, was not supported (β = 0.12, p = 0.15); this points to the need for clearer regulatory frameworks and interoperability standards.
This study highlights the multifactorial nature of contemporary supply chain management, where technical, financial, environmental, and legal dimensions converge to shape industry practice. Nevertheless, areas such as interoperability, complexity, standardization, and compliance remain underexplored and represent opportunities for future research on AI adoption. As blockchain, AI, and machine learning are emerging technologies in the industrial supply chain, there are ample opportunities to investigate their applications for optimizing operations. The proposed implications in this study offer researchers and supply chain practitioners’ insights to identify areas for innovation and maintain adaptive, efficient, and resilient supply chain strategies in a volatile global environment.

5.2. Theoretical Implications

This includes the theoretical implications guiding the integration of AI into Jordan’s industrial digital supply chain management. This paper extends current understanding of how AI fosters supply chain digitalization and operational performance by demonstrating and explaining AI’s mediating role. Supporting the Technology-Organization-Environment (TOE) framework, these findings suggest that AI adoption in the industrial sector could be correlated with competitive benefits due to enhanced supply chain agility and reduced operational expenses. Moreover, the study highlights that alongside machine learning and blockchain, AI can foster a resilient digital supply chain ecosystem across industrial sectors, adapt to change, and promote long-term sustainability.

5.3. Managerial Implications

The results offer practical recommendations for operations managers interested in adopting AI technologies to optimize supply chain functions and facilitate operational efficiency. To meet evolving customer needs and market trends, firms should implement AI-driven tools, predictive analytics, and smart inventory systems to stay competitive. Moreover, as AI has emerged as the new developer of mitigating environmental damage by resource optimization, sustainability should also become an indispensable aspect of supply chain decisions. Managers need to see AI as a strategic asset, rather than simply technological, to optimize operations and drive sustainability across the supply chain.

5.4. Strategic Recommendations

For Industrial Firms: Industrial firms should establish AI competency centers that bring together supply chain, IT, and data science teams to pilot and scale AI applications in predictive maintenance and demand forecasting [41]. They should invest in modular digital platforms with open APIs to support seamless integration of AI modules and enable rapid feature updates. Firms need to implement continuous learning programs that provide AI tool training and data literacy workshops for staff to ensure effective adoption; they should create process-re-engineering squads empowered to redesign workflows around AI capabilities and enhance agility when disruptions occur [42]. For Government Policymakers: Government policymakers can accelerate AI adoption in supply chains by introducing targeted incentives such as tax credits or grants to lower financial barriers for firms. Establishing national data-sharing standards and investing in secure digital infrastructure will support interoperability among IoT devices and AI platforms. Funding public–private pilot projects can showcase AI’s value in logistics, giving smaller enterprises the chance to learn from best practices before scaling [43]. Finally, subsidized training programs in AI and data analytics for supply chain professionals will cultivate a skilled workforce equipped to drive and sustain digital transformation. For Technology Developers: Technology developers should prioritize open APIs and interoperability standards to ensure their AI solutions integrate smoothly with a variety of supply chain platforms. They must also create intuitive user interfaces and include embedded training modules so that end users can adopt new tools quickly and with minimal delay [46]. By employing a modular architecture, developers can allow firms to scale individual AI functions, like demand forecasting or predictive maintenance, independently. Finally, involving supply chain professionals in co-creation workshops will help align solution features with actual operational needs and everyday workflows.

5.5. Limitations and Further Research

This study provides important findings on the role of artificial intelligence as a mediating factor in enhancing digital supply chain management in Jordan’s industrial sector; however, several limitations need to be noted. Results show that adaptability and flexibility positively and significantly influence artificial intelligence and digital supply chain management [47]. Nonetheless, this study primarily centers on the electronics manufacturing industry; it may not apply to other industries, especially those with different technological landscapes or operational needs. The correlations seen in this study might differ when applied to another sector, such as health care or automotive services. Hence, future research should investigate whether these relationships are consistent across different industries or regions, particularly those with varying technological infrastructure and cybersecurity readiness levels [44]. Furthermore, even with the focus on sustainable innovation in this study, factors that could impact the interactions between cybersecurity measures and supply chain resilience were not considered in this research. Organizational factors like technological readiness, external pressures, and regulatory requirements may also affect how cybersecurity measures impact supply chain resilience [45]. Though adaptability and flexibility positively impact both digital supply chain management and artificial intelligence, efficiency was not found to relate to digital supply chain management significantly [48]. We think efficiency does not affect digital supply chain management; we need to do more research to have significant data on how different organizational characteristics affect digital supply chain management. Our study findings are specific to electronics manufacturing and thus may be less applicable to other industries. Flexibility was found to positively affect both artificial intelligence and digital supply chain management, which may not be found in other categories [49]. Further lines of inquiry should include examining flexibility and other organizational factors across industries to assess whether these relationships generalize beyond the knowledge sector. Furthermore, longitudinal studies may provide more profound understandings of the mediating role of sustainable innovation over time, particularly in dynamic environmental contexts, where market conditions and technological progress are constantly shifting. Lastly, future studies should also consider the maturing role of external stakeholders like suppliers, regulators, and customers in shaping the relationship among cybersecurity, sustainable innovation, and supply chain resilience [50]. Investigating the influence of organizational strategies for cybersecurity and innovation adoption on a resilient response of the supply chain to interruptions may have an additional meaningful impact. This study’s unique result relates to digital supply chain management having a limited impact from efficiency lowering costs, which indicates that other mechanisms of organizational efficiency could also have varying degrees of effects in other industries that require investigation [51].

5.6. Research Implications

This study paves the path for future research on the impact of AI on optimizing supply chain management across industries. Future studies could focus on the broader implications of AI tools for the sustainability of the supply chain, especially regarding their ability to reduce environmental footprints. This study also advocates for further exploration into the degree of interchangeability and standardization of AI technologies across various industrial sectors. This means that researchers need to examine other possible mediating factors like regulatory frameworks or consumer trust to capture the entire reach of AI in the domain of supply chain management.

6. Conclusions

This study examined the mediating role of artificial intelligence in linking technological innovations with digital supply chain management. The findings reveal that adaptability, efficiency, and flexibility are significantly improved when AI is embedded into supply chain systems, enabling predictive analytics, automation, and enhanced decision-making. Theoretically, the study advances the Technology–Organization–Environment framework by establishing AI as a key driver of digital transformation in supply chains. Practically, the results provide guidance for managers, policymakers, and technology developers on using AI to strengthen decision quality, streamline operations, and respond effectively to dynamic market conditions. Future research should extend these insights beyond the electronics sector to assess whether AI’s mediating effect on efficiency, adaptability, and flexibility holds across other industries and operational settings.

Author Contributions

Conceptualization, A.F.D. and M.I.A.; methodology, M.I.A.; software, M.A.; validation, M.A., A.F.D. and M.I.A.; formal analysis, M.A. investigation, M.A.Y.; resources, M.A.Y.; data curation, A.F.D.; writing—original draft preparation, M.I.A.; writing—review and editing, M.A.; visualization, M.A.Y.; supervision, A.F.D.; project administration, M.A.; funding acquisition, M.A.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia grant number MoE-IF-UJ-R2-22-20360-1.

Institutional Review Board Statement

The study was conducted in accordance with the ethical standards of Luminus Technical University College. Ethical approval was obtained from the Interim Ethical Committee of the Doctoral School of Business at its meeting on 28 November 2024 (Case No. 4/2024).

Informed Consent Statement

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

Data Availability Statement

The original data presented in this study are openly available in the paper.

Conflicts of Interest

The authors declare no conflicts of interest.

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Logistics 09 00138 g001
Figure 1. Conceptual framework of the mediating role of artificial intelligence in digital supply chain management [1].
Figure 1. Conceptual framework of the mediating role of artificial intelligence in digital supply chain management [1].
Logistics 09 00138 g001
Logistics 09 00138 g002
Figure 2. Research Process Flowchart.
Figure 2. Research Process Flowchart.
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Logistics 09 00138 g003
Figure 3. Measurement Model [1].
Figure 3. Measurement Model [1].
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Table 1. Reliability.
Table 1. Reliability.
ConstructsCronbach’s AlphaC.R.(AVE)
Adaptability0.8940.8960.922
Artificial Intelligence0.8690.870.906
Efficiency0.8940.8980.922
Flexibility0.870.8730.911
Table 2. HTMT Ratios.
Table 2. HTMT Ratios.
Construct PairHTMT Ratio
Adaptability—Artificial Intelligence0.42
Adaptability—Digital Supply Chain Mgmt.0.35
Adaptability—Efficiency0.28
Adaptability—Flexibility0.30
Artificial Intelligence—Digital SCM0.47
Artificial Intelligence—Efficiency0.40
Artificial Intelligence—Flexibility0.36
Digital SCM—Efficiency0.33
Digital SCM—Flexibility0.29
Efficiency—Flexibility0.31
Adaptability—Artificial Intelligence0.42
Adaptability—Digital Supply Chain Mgmt.0.35
Table 3. Fornell–Larcker Criterion.
Table 3. Fornell–Larcker Criterion.
AdaptabilityArtificial
Intelligence		Digital Supply
Chain Management		EfficiencyFlexibility
Adaptability0.838
Artificial Intelligence0.4830.811
Digital Supply Chain Management0.3880.3710.789
Efficiency0.5850.5110.5410.838
Flexibility0.5160.5320.5870.7070.848
Table 4. R2 Adjusted.
Table 4. R2 Adjusted.
VariableR2R2 Adjusted
Artificial Intelligence0.3540.347
Digital Supply Chain Management0.1380.134
Table 5. Hypotheses testing estimates (total effect).
Table 5. Hypotheses testing estimates (total effect).
HypoRelationshipsBetaStandard
Error		T
Statistics		p ValuesDecision
H1Adaptability -> Artificial Intelligence0.2340.0693.3730.001Supported
H2Adaptability -> Digital Supply Chain Management0.0870.0322.750.006Supported
H3Artificial Intelligence -> Digital Supply Chain Management0.3710.0864.2910Supported
H4Efficiency -> Artificial Intelligence0.1670.0812.0580.04Supported
H5Efficiency -> Digital Supply Chain Management0.0620.0361.710.087Unsupported
H6Flexibility -> Artificial Intelligence0.2930.0833.5290Supported
H7Flexibility -> Digital Supply Chain Management0.1090.0442.4560.014Supported
Table 6. Hypothesis testing estimates (indirect effect).
Table 6. Hypothesis testing estimates (indirect effect).
HypoRelationshipsStandardized
Beta		Standard
Error		T
Statistics		p ValuesDecision
H8Adaptability -> Digital Supply Chain Management0.0870.0322.750.006Supported
H9Efficiency -> Digital Supply Chain Management0.0620.0361.710.087Unsupported
H10Flexibility -> Digital Supply Chain Management0.1090.0442.4560.014Supported
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MDPI and ACS Style

Dalain, A.F.; Alnadi, M.; Allahham, M.I.; Yamin, M.A. The Impact of Technological Innovations on Digital Supply Chain Management: The Mediating Role of Artificial Intelligence: An Empirical Study. Logistics 2025, 9, 138. https://doi.org/10.3390/logistics9040138

AMA Style

Dalain AF, Alnadi M, Allahham MI, Yamin MA. The Impact of Technological Innovations on Digital Supply Chain Management: The Mediating Role of Artificial Intelligence: An Empirical Study. Logistics. 2025; 9(4):138. https://doi.org/10.3390/logistics9040138

Chicago/Turabian Style

Dalain, Ali F., Mohammad Alnadi, Mahmoud Izzat Allahham, and Mohammad Ali Yamin. 2025. "The Impact of Technological Innovations on Digital Supply Chain Management: The Mediating Role of Artificial Intelligence: An Empirical Study" Logistics 9, no. 4: 138. https://doi.org/10.3390/logistics9040138

APA Style

Dalain, A. F., Alnadi, M., Allahham, M. I., & Yamin, M. A. (2025). The Impact of Technological Innovations on Digital Supply Chain Management: The Mediating Role of Artificial Intelligence: An Empirical Study. Logistics, 9(4), 138. https://doi.org/10.3390/logistics9040138

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