Introduction
Over the past decade, global supply chains have undergone profound transformations driven by the Fourth Industrial Revolution and accelerated digitalization. Digital technologies have become pivotal in strengthening supply chain resilience, enhancing operational efficiency, and supporting sustainability-oriented practices. Tools such as big data analytics, block chain, artificial intelligence (AI), digital twins, and the Internet of Things (IoT) have redefined the management of information and physical flows, offering firms new capabilities for prediction, integration, and real-time control.
Early studies highlight the role of big data analytics in enabling predictive insights into demand and supply variations, thereby improving decision-making accuracy and efficiency (Wang et al., 2016). Subsequent research has shown that digital analytics capabilities serve as essential mediators that enhance agility and resilience and translate digital adoption into operational performance gains (Bahrami et al., 2022; Gopal et al., 2024). Similarly, Zhao et al. (2023) emphasize that digitalization goes beyond efficiency improvements to reinforce adaptability and responsiveness in volatile environments—a trend reinforced by the operational challenges observed during the COVID-19 pandemic (Spieske & Birkel, 2021; Ning et al., 2023).
Block chain technologies contribute to improved transparency and traceability, particularly in sensitive industries such as food and healthcare (Agi & Shahzad, 2022; Sahoo et al., 2024). Digital twins further expand predictive capabilities and real-time simulation, offering firms a competitive advantage in navigating uncertainty (). Despite the broad literature base, research gaps remain, particularly regarding the integration of multiple digital technologies within unified data-governance frameworks and the assessment of long-term sustainable returns on digital investment (Wu et al., 2025).
With the proliferation of modern technologies—RFID, IoT, cloud computing, AI, and 3D printing—traditional supply chain approaches have become insufficient for meeting the rapidly evolving requirements of contemporary industries. Consequently, digital supply chain management (DSCM) has emerged as a critical field within both academic and industrial research (Agrawal & Narain, 2018; Büyüközkan & Göçer, 2018; Garay-Rondero et al., 2020). Recent studies have explored multiple dimensions of DSCM, including dynamic capabilities (Queiroz et al., 2021), data-driven innovation (Nica, 2019), security and trust (Zhang et al., 2019), and resilience during global disruptions (Ivanov, 2021) .
Research Problem
Despite the global shift toward digitalized supply chains, delivery companies in developing cities—such as Mosul—continue to face operational obstacles, including limited information integration, high operational costs, weak resilience to disruptions, and suboptimal resource utilization. Moreover, empirical research exploring the relationship between DSCM adoption and operational efficiency in the delivery sector remains scarce.
Thus, the research problem can expressed through the following central question:
"To what extent do the dimensions of digital supply chain management (DSCM) contribute to enhancing the operational efficiency of delivery companies in Mosul and their economic growth?"
Research Importance
The growing demand for delivery services in Mosul, combined with rising operational costs and heightened expectations for service quality, underscores the need for digital transformation in the delivery sector. Implementing DSCM practices can strengthen transparency, improve resource allocation, enhance productivity, and increase a firm’s ability to respond to disruptions.
The value of this research lies in its attempt to empirically, link digital supply chain adoption with operational efficiency, focusing on its future benefits within a context that has received limited scholarly attention. The study further contributes by offering insights relevant to competitive advantage and sustainable performance in delivery companies.
Research Objectives
1. Identify the measurable dimensions of the digital supply chain applicable to delivery companies.
2. Analyze the impact of adopting DSCM dimensions on the operational efficiency of delivery companies in Mosul.
3. Evaluate the levels of operational efficiency across dimensions such as time, resource utilization, quality, productivity, flexibility, and financial performance.
4. Eventually, the role of those dimensions and effects, as well as levels of operational efficiency, in achieving the expansion and growth of supply chains, and achieving economic growth for delivery companies.
5. Propose recommendations to enhance operational efficiency through digital integration.
Beyond its operational and technological importance, Digital Supply Chain Management (DSCM) carries broader economic implications that are particularly relevant for emerging and post-conflict urban environments such as Mosul. As global economies increasingly rely on digital infrastructure to enhance productivity, reduce transaction costs, and support market integration, DSCM becomes a strategic mechanism through which firms contribute not only to their own performance but also to wider economic revitalization. The transformation of supply chains through digitalization fosters innovation, strengthens local value creation, expands service-sector activities, and enhances economic resilience—factors that are fundamental to sustainable growth. Therefore, examining DSCM within delivery companies in Mosul provides an opportunity to understand not only its operational effects but also its potential to stimulate economic development in the city.
1. Theoretical Framework
Here we start with the research model and hypotheses development. Based on the theoretical framework, a conceptual model developed to illustrate the logical relationships among the study variables (Figure 1). The model examines the association between DSCM dimensions and operational efficiency, as well as the direct effects of these dimensions on performance outcomes.
Hypotheses of Correlation (H1)
H1: There is a significant correlation between the digital supply chain (DSC) and operational efficiency.
H1-1: Information and System Integration correlates with operational efficiency.
H1-2: Traceability and Transparency correlate with operational efficiency.
H1-3: Big Data Analytics and AI correlate with operational efficiency.
H1-4: IoT and Automation correlate with operational efficiency.
H1-5: Cloud Platforms and Collaboration correlate with operational efficiency.
H1-6: Resilience and Responsiveness correlate with operational efficiency and growth.
Hypotheses of Effect (H2)
• H2: Digital supply chain dimensions have a statistically significant effect on operational efficiency.
H2-1 through H2-6 correspond to the effect of each DSCM dimension on operational efficiency.
Figure 1.the logical relationships among the study variables
Data Collection Methods
The theoretical component of the research relied on scholarly books, theses, and peer-reviewed articles. The empirical component based on a structured questionnaire using a five-point Likert scale, administered to managers and employees across three delivery companies (Al-Barq, Al-Fayhaa Express, and Al-Salihin Group for International Transportation). The questionnaire method enabled the researchers to capture detailed insights regarding operational mechanisms and the systemic characteristics of the studied environment.
After data collection, the Statistical Package for the Social Sciences (SPSS) used to perform the necessary analyses. The following statistical tools employed:
With the rapid acceleration of technological advancements, organizations increasingly rely on digitalization to address operational challenges and enhance supply chain performance. Over the past two decades, digital supply chain management (DSCM) has emerged as a central theme in academic literature. Christopher (2000) argued that competition no longer takes place between individual firms but rather among entire supply chains, emphasizing the strategic importance of effective supply chain integration.
Early contributions, such as Butner (2010), underscored the necessity of investing in advanced technologies to transform traditional supply chains into intelligent and adaptive systems capable of meeting emerging market requirements. Wu et al. (2016) further characterized DSCM by six core attributes: interconnectivity, intelligence, automation, integration, innovation, and physical-digital alignment.
Agrawal and Narain (2018) provided an extensive review of the evolution of DSCM, highlighting its role in integrating cross-functional processes and enhancing overall supply chain effectiveness. Complementary perspectives by Büyüközkan and Göçer (2018) emphasized the contribution of DSCM to sustainability, recommending future research linking digitalization with the circular economy.
Industry 4.0 developments have significantly influenced supply chain dynamics. Hofmann and Rüsch (2017) pointed out that digitization represents a transformative force in logistics and supply chain operations. Similarly, Ivanov et al. (2018) demonstrated that digital tools play a vital role in risk management by enhancing flexibility and mitigating volatility.
Blockchain applications also gained increasing attention. Saberi et al. (2019) showed that blockchain contributes to strengthening trust and transparency across supply chains. Likewise, Nica (2019) highlighted the role of data-driven innovation in enhancing operational effectiveness within digital supply chains.
More recent studies, including (Wang et al. 2023), confirmed that digital transformation has become a strategic priority for organizations—particularly in light of the disruptions associated with the COVID-19 pandemic, which revealed an urgent need for resilience, agility, and rapid reconfiguration within supply chains.
Digital supply chain management builds on technologies such as e-procurement, automation systems, digital distribution, and advanced after-sales services. These technologies significantly influence efficiency, competitiveness, and overall performance (Nica, 2019; Wamba et al., 2020). Because traditional supply chains often lack the agility and adaptability required in dynamic environments, digital transformation increasingly adopted to create fully integrated, data-driven supply networks (Ivanov, 2021; Zhang et al., 2024). Initiatives such as Industry 4.0 and Made in China 2025 have accelerated the global diffusion of DSCM practices (Lasi et al., 2014; Zhong et al., 2017).
Although DSCM is a relatively new concept, literature frequently uses overlapping terms such as:
Drawing from Butner (2010) and Wu et al. (2016), DSCM can described as a technologically enhanced supply chain characterized by instrumentation, interconnectivity, intelligence, automation, integration, and innovation.
Concept of Digital Supply Chain Management (DSCM)
DSCM refers to the integration of advanced digital technologies, including IoT, Big Data Analytics (BDA), Artificial Intelligence (AI), cloud computing, and blockchain, into conventional supply chain operations to enhance visibility, integration, and real-time decision-making. According to Büyüközkan and Göçer (2018), DSCM provides a framework that transforms traditional linear supply chains into intelligent, interconnected networks driven by real-time data.
Garay-Rondero et al. (2020) describe DSCM as an extension of traditional supply chain systems, adding a technological layer that supports collaboration, real-time tracking, and adaptation. (Queiroz et al. 2021) further argue that DSCM is not merely a set of digital tools but a dynamic capability that enables firms to respond to rapidly changing environments.
Importance of the Digital Supply Chain
3. Enhancing Transparency and Traceability:
Blockchain and similar technologies strengthen trust and ensure secure information exchange across the supply chain (Saberi et al., 2019).
4. Increasing Flexibility and Resilience:
5. Enabling Innovation in Service Delivery:
6. Supporting Sustainability and Competitiveness:
Dimensions of the Digital Supply Chain (DSC)
Recent literature classifies DSCM into multiple dimensions that influence performance:
1. Information and System Integration (ISI)
Refers to the digital integration of internal and external systems, enabling real-time information flows across sales, warehousing, logistics, and distribution ().
2. Traceability and Transparency (Blockchain-Based Systems)
Enhance trust through secure, auditable, and tamper-proof information records (Saberi et al., 2019).
3. Big Data Analytics and Artificial Intelligence
Enable predictive modeling, demand forecasting, and data-driven decision-making, thus reducing waste and improving operational outcomes ().
4. Internet of Things (IoT) and Automation
Facilitate real-time tracking of vehicles and shipments, improving monitoring accuracy and process automation (Ivanov et al., 2018).
5. Cloud Platforms and Collaboration
Support scalable, integrated infrastructures that enhance cooperation among supply chain partners (Garay-Rondero et al., 2020).
6. Resilience and Responsiveness
Strengthen the supply chain’s ability to handle crises, disruptions, and unexpected fluctuations (Zhao et al., 2023).
2. Methodology
2.1Concept and Dimensions of Operational Efficiency
Operational efficiency refers to an organization’s ability to maximize productivity while minimizing time, cost, effort, and waste in transforming inputs into high-quality outputs. Empirical studies emphasize that digital transformation significantly enhances operational and logistics efficiency by improving information flow and reducing cost and time (He et al., 2024).
Operational efficiency commonly assessed across six dimensions:
2.2Hypothesis Testing
This section examines the strength and significance of correlation and effect relationships between the dimensions of DSCM and operational efficiency, in line with the study’s hypotheses.
Study Sample Description:
The work distributed 80 questionnaires to the research sample individuals as shown in tables (1,2).
| Descriptive Information | Category | Count | Percentage |
|---|---|---|---|
| Sex | Male | 66 | 82.5 |
| Female | 14 | 17.5 | |
| Total | 80 | ||
| Age Group | 25 and below | 28 | 35.0 |
| 26–34 | 24 | 30.0 | |
| 35–44 | 7 | 8.8 | |
| 45 and above | 21 | 26.3 | |
| Total | 80 | ||
| Job Title | Logistics Manager | 4 | 5.0 |
| IT Department | 8 | 10.0 | |
| Administrative Employee | 34 | 42.5 | |
| Operations Manager | 14 | 17.5 | |
| Driver | 20 | 25.0 | |
| Total | 80 | ||
| Experience years (Tenure) | Less than ( 1 ) year | 18 | 22.5 |
| 1–3 years | 53 | 66.3 | |
| 4–6 years | 9 | 11.3 | |
| Total | 80 |
| Item | arithmetic mean | standard deviation | % deal (4-5) | % neutral ( 3 ) | % difference (1-2) |
|---|---|---|---|---|---|
| DSCM_1 | 2.91 | 0.97 | 27.5% | 40.0% | 32.5% |
| DSCM_10 | 2.99 | 1.08 | 33.8% | 36.2% | 30.0% |
| DSCM_11 | 3.00 | 1.06 | 35.0% | 31.2% | 33.8% |
| DSCM_12 | 2.94 | 1.02 | 30.0% | 30.0% | 40.0% |
| DSCM_13 | 2.95 | 1.10 | 28.7% | 36.2% | 35.0% |
| DSCM_14 | 2.95 | 0.97 | 23.8% | 45.0% | 31.2% |
| DSCM_15 | 2.99 | 1.01 | 33.8% | 32.5% | 33.8% |
| DSCM_16 | 3.02 | 1.02 | 30.0% | 38.8% | 31.2% |
| DSCM_17 | 2.98 | 1.14 | 28.7% | 36.2% | 35.0% |
| DSCM_18 | 3.00 | 1.04 | 30.0% | 33.8% | 36.2% |
| DSCM_19 | 2.94 | 1.04 | 27.5% | 38.8% | 33.8% |
| DSCM_2 | 2.85 | 0.94 | 28.7% | 32.5% | 38.8% |
| DSCM_20 | 3.02 | 1.06 | 32.5% | 31.2% | 36.2% |
| DSCM_21 | 3.04 | 0.97 | 33.8% | 36.2% | 30.0% |
| DSCM_22 | 3.14 | 1.03 | 37.5% | 31.2% | 31.2% |
| DSCM_23 | 3.05 | 1.10 | 33.8% | 32.5% | 33.8% |
| DSCM_3 | 2.83 | 1.02 | 26.2% | 36.2% | 37.5% |
| DSCM_4 | 2.85 | 0.99 | 28.7% | 30.0% | 41.2% |
| DSCM_5 | 2.89 | 0.87 | 23.8% | 38.8% | 37.5% |
| DSCM_6 | 2.81 | 0.93 | 23.8% | 37.5% | 38.8% |
| DSCM_7 | 2.84 | 0.92 | 26.2% | 36.2% | 37.5% |
| DSCM_8 | 3.01 | 1.02 | 33.8% | 37.5% | 28.7% |
| DSCM_9 | 2.85 | 1.02 | 28.7% | 32.5% | 38.8% |
| DSCM_Cloud | 2.98 | 0.97 | 17.5% | 12.5% | 22.5% |
| DSCM_InfoSys | 2.86 | 0.90 | 17.5% | 6.2% | 23.8% |
| DSCM_IoT | 2.96 | 0.94 | 17.5% | 10.0% | 16.2% |
| DSCM_Resilience | 3.06 | 0.95 | 23.8% | 10.0% | 17.5% |
| DSCM_Trace | 2.85 | 0.82 | 16.2% | 12.5% | 26.2% |
| DSCM_total | 2.95 | 0.77 | 10.0% | 2.5% | 12.5% |
| EFF_1 | 2.85 | 1.14 | 25.0% | 37.5% | 37.5% |
| EFF_10 | 2.99 | 1.12 | 36.2% | 28.7% | 35.0% |
| EFF_11 | 3.00 | 1.09 | 35.0% | 35.0% | 30.0% |
| EFF_12 | 3.09 | 1.12 | 37.5% | 32.5% | 30.0% |
| EFF_13 | 2.98 | 1.07 | 31.2% | 40.0% | 28.7% |
| EFF_14 | 2.98 | 1.15 | 33.8% | 33.8% | 32.5% |
| EFF_15 | 3.01 | 1.16 | 41.2% | 30.0% | 28.7% |
| EFF_16 | 3.05 | 1.12 | 36.2% | 31.2% | 32.5% |
| EFF_17 | 3.06 | 1.16 | 36.2% | 33.8% | 30.0% |
| EFF_18 | 3.12 | 1.18 | 38.8% | 31.2% | 30.0% |
| EFF_2 | 2.98 | 1.16 | 35.0% | 31.2% | 33.8% |
| EFF_3 | 2.91 | 1.14 | 35.0% | 27.5% | 37.5% |
| EFF_4 | 2.83 | 1.10 | 27.5% | 33.8% | 38.8% |
| EFF_5 | 2.94 | 1.16 | 31.2% | 30.0% | 38.8% |
| EFF_6 | 2.92 | 1.19 | 32.5% | 27.5% | 40.0% |
| EFF_7 | 2.89 | 1.11 | 31.2% | 32.5% | 36.2% |
| EFF_8 | 3.08 | 1.10 | 36.2% | 32.5% | 31.2% |
| EFF_9 | 3.08 | 1.12 | 38.8% | 27.5% | 33.8% |
| EFF_Financial | 3.08 | 1.08 | 28.7% | 11.2% | 17.5% |
| EFF_Flex | 3.02 | 1.03 | 25.0% | 6.2% | 17.5% |
| EFF_Productivity | 2.99 | 1.06 | 26.2% | 18.8% | 21.2% |
| EFF_Resource | 2.90 | 1.09 | 22.5% | 13.8% | 30.0% |
| EFF_ServiceQ | 3.01 | 1.05 | 25.0% | 10.0% | 22.5% |
| EFF_TimePerf | 2.91 | 1.07 | 23.8% | 16.2% | 27.5% |
| EFF_total | 2.99 | 0.93 | 12.5% | 2.5% | 16.2% |
2.3 Description of the Study Sample
80 questionnaires were distributed among respondents from different functional units across the participating delivery companies. table 1 presents demographic and occupational characteristics, including gender, age groups, job titles, and years of experience.
2.4 Correlation Analysis between DSCM Dimensions and Operational Efficiency
Pearson correlation coefficients calculated to examine the relationships between the six dimensions of Digital Supply Chain Management (DSCM) and overall operational efficiency (EFF_total). As shown in table 3, all dimensions exhibit statistically significant positive correlations with operational efficiency at (α ≤ 0.05). Correlation Coefficients between DSCM
Dimensions and Operational Efficiency table 4. Stratkle after DSCM intensity and (6) efficiency intensity in table 5.
| Dimension | r | p-value | Interpretation |
|---|---|---|---|
| InfoSys | 0.613 | 0.000 | Significant positive correlation |
| Traceability | 0.529 | 0.000 | Significant positive correlation |
| BDA & AI | 0.565 | 0.000 | Significant positive correlation |
| IoT | 0.529 | 0.000 | Significant positive correlation |
| Cloud | 0.510 | 0.000 | Significant positive correlation |
| Resilience | 0.557 | 0.000 | Significant positive correlation |
These findings indicate that increased adoption of DSCM practices is associated with higher levels of operational efficiency among delivery companies in Mosul.
| Item | arithmetic mean | standard deviation | % Agreement (average > = 4) | CV (%) | Cronbach's Alpha (subscale) |
|---|---|---|---|---|---|
| DSCM – InfoSys | 2.86 | 0.90 | 17.5% | 31.35 | 0.934 |
| DSCM – Trace | 2.85 | 0.82 | 16.2% | 28.81 | 0.888 |
| DSCM - BDA_AI | 2.96 | 0.97 | 21.2% | 32.73 | 0.945 |
| DSCM – IoT | 2.96 | 0.94 | 17.5% | 31.75 | 0.934 |
| DSCM – Cloud | 2.98 | 0.97 | 17.5% | 32.58 | 0.938 |
| DSCM – Resilience | 3.06 | 0.95 | 23.8% | 30.86 | 0.929 |
| EFF – TimePerf | 2.91 | 1.07 | 23.8% | 36.64 | 0.925 |
| EFF – Resource | 2.90 | 1.09 | 22.5% | 37.74 | 0.946 |
| EFF – ServiceQ | 3.01 | 1.05 | 25.0% | 34.74 | 0.936 |
| EFF – Flex | 3.02 | 1.03 | 25.0% | 33.99 | 0.918 |
| EFF – Productivity | 2.99 | 1.06 | 26.2% | 35.57 | 0.938 |
| EFF – Financial | 3.08 | 1.08 | 28.7% | 35.10 | 0.927 |
| Item | R | p-value | sincere |
|---|---|---|---|
| InfoSys | 0.613 | 0.000 | moral |
| Trace | 0.529 | 0.000 | moral |
| BDA_AI | 0.565 | 0.000 | moral |
| IoT | 0.529 | 0.000 | moral |
| Cloud | 0.510 | 0.000 | moral |
| Resilience | 0.557 | 0.000 | moral |
2.5 Multiple Regression Analysis
To test the effect of the six DSCM dimensions on operational efficiency, a multiple linear regression model estimated. The model yielded statistically significant results, as shown below:
This confirms that the DSCM dimensions collectively explain approximately 41.4% of the variance in operational efficiency.
| Variable | B | Std. Error | t | p-value | 95% CI |
|---|---|---|---|---|---|
| Constant | 0.642 | 0.320 | 2.007 | 0.048 | [0.005, 1.280] |
| Infosys | 0.314 | 0.141 | 2.232 | 0.029 | [0.034, 0.594] |
| Traceability | 0.036 | 0.154 | 0.231 | 0.818 | [-0.271, 0.342] |
| BDA & AI | 0.168 | 0.135 | 1.243 | 0.218 | [-0.102, 0.438] |
| IoT | 0.080 | 0.128 | 0.630 | 0.530 | [-0.174, 0.335] |
| Cloud | 0.003 | 0.130 | 0.026 | 0.979 | [-0.255, 0.262] |
| Resilience | 0.195 | 0.123 | 1.590 | 0.116 | [-0.049, 0.440] |
Interpretation
Only Information and System Integration (InfoSys) has a statistically significant unique effect on operational efficiency (p = 0.029).
The remaining five dimensions, while important in the correlation analysis, do not show significant individual effects when included together in the regression model.
This discrepancy explained through multicollinearity.
Cronbach’s alpha values for both scales demonstrate excellent internal consistency:
All sub-dimensions also exceeded accepted reliability thresholds (α > 0.88), confirming the robustness of the measurement instruments.
3. Results and Analysis
The analysis confirmed strong, statistically significant correlations between the overall DSCM construct and operational efficiency. Hence:
This indicates that each DSCM dimension is positively associated with operational efficiency.
H2-1 (Infosys): Accepted
H2-2 to H2-6: Rejected (No significant unique effect after accounting for multicollinearity)
This means Information and System Integration is the only dimension with a distinct contribution in the presence of other predictors.
High VIF values (ranging from 3.91 to 5.41) indicate substantial multicollinearity among DSCM dimensions. This means the dimensions are strongly interrelated and often implemented jointly, complicating the isolation of individual effects.
Practical implication:
DSCM dimensions work as an integrated system rather than independent variables.
To handle multicollinearity, PCA generated two uncorrelated components explaining 82.9% of DSCM variance.
Regression results using PC1 and PC2:
R² = 0.432, F = 29.45, p = 0.000
Both components were statistically significant predictors.
Interaction terms revealed additional insights:
These results suggest that DSCM dimensions reinforce each other and perform better when jointly implemented.
4. Economic Implications and Contributions to Growth
Digital Supply Chain Management (DSCM) extends its value far beyond operational optimization and represents a strategic foundation for stimulating economic growth—particularly in emerging or recovering urban economies such as Mosul. The empirical findings of this study, which show a strong positive relationship between DSCM adoption and operational efficiency, are consistent with theoretical perspectives in growth economics that link productivity improvement, technological progress, and market efficiency with macroeconomic expansion (Solow, 1956; Romer, 1990).
4.1 Productivity and Total Factor Productivity (TFP) Enhancement
Economic growth models emphasize productivity as a fundamental driver of long-term expansion. Digital integration—such as real-time information systems, automation, and data analytics—raises Total Factor Productivity by improving the efficiency of capital, labor, and logistical assets.
Within delivery companies, improved routing algorithms, integrated information systems, and reduced idle time lead to higher output per worker and per vehicle, contributing to firm-level and sector-level productivity gains. According to Porter and Heppelmann (2015), digitally enabled operations create a “smart productivity frontier” that expands the economic value added.
4.2 Market Efficiency and Reduced Transaction Costs
DSCM reduces transaction costs associated with information asymmetry, delays, and coordination barriers. Coase (1937) and Williamson (1985) argued that lower transaction costs lead to more efficient markets and broader participation.
In the case of Mosul, digital delivery networks enhance market functioning by:
This contributes to a more efficient urban economy, stimulating consumer spending and business activity.
4.3 Enabling E-commerce Expansion and Service-Sector Growth
Modern economic growth increasingly depends on service-sector dynamism. Reports by UNCTAD (2022) and McKinsey (2021) show that e-commerce growth is strongly tied to the maturity of digital logistics infrastructure.
By adopting DSCM, delivery companies become enablers of:
The growth of e-commerce adds directly to GDP through increased trade volume, job creation, and higher tax revenues.
4.4 Employment Creation and Workforce Upgrading
Although automation raises concerns globally, empirical studies in developing economies show that digitization often reshapes rather than reduces employment (Baldwin, 2019).
DSCM contributes to job creation in:
Moreover، technology adoption increases demand for skilled labor, supporting human capital development—one of the strongest predictors of long-term growth (Lucas, 1988).
4.5Improving Economic Resilience and Reducing Shock Vulnerability
Economic resilience has become a key determinant of sustainable growth. Digital supply chains enhance resilience by:
Resilient supply chains stabilize broader economic activity and reduce systemic risks, particularly in post-conflict cities like Mosul.
4.6 Strengthening Local Value Chains and Multiplier Effects
By lowering costs and improving reliability, DSCM strengthens the integration of local suppliers, retailers, and service providers into value chains. Baldwin and Evenett (2020) highlight the importance of integrating domestic firms into digitally enabled supply networks to maximize national economic multipliers.
Potential multiplier effects in Mosul include:
Collectively, these mechanisms align DSCM with both short-term economic activation and long-term sustainable growth.
This work suggests considering the possibility of applying the results of this study by companies in Mosul and elsewhere, and benefiting from them, especially the recommendations in the fifth paragraph of the conclusion, to enhance operational efficiency through digital integration.
Comprehensive Conclusion
The empirical analysis provides strong evidence supporting the pivotal role of Digital Supply Chain Management in enhancing operational efficiency among delivery companies in Mosul. The findings can be summarized as follows:
1. Strong Overall Relationship:
All DSCM dimensions exhibit significant positive correlations with operational efficiency.
2. Collective Impact is Substantial:
DSCM dimensions jointly explain more than 41% of the variance in operational efficiency.
3. Integration is Crucial:
Multicollinearity and significant interaction effects confirm that DSCM components operate synergistically, not independently.
4. Information and System Integration is the Key Driver:
It is the only dimension with a statistically significant standalone effect, underscoring its foundational role in enabling other technologies.
5. Digital Synergy Enhances Efficiency:
Combinations such as (InfoSys × Resilience) and (BDA/AI × IoT) create a cumulative positive effect greater than individual contributions.
The findings of this study demonstrate that the benefits of DSCM extend beyond measurable improvements in operational efficiency. When viewed through the lens of economic development, digital supply chains act as catalysts for productivity enhancement, workforce upgrading, market expansion, and resilience building—particularly in rapidly transforming urban economies. In Mosul, where economic recovery and modernization are central priorities, the adoption of DSCM practices by delivery companies can accelerate the city’s reintegration into regional markets and stimulate broader economic activity. By strengthening value chains, reducing inefficiencies, and supporting the growth of digital commerce, DSCM becomes not merely a managerial tool but a driver of long-term economic revival. As such, the study underscores the strategic importance of fostering digital capabilities within logistics and service firms as part of a holistic approach to sustainable economic growth.
References
- Agi, M. A. N., & Shahzad, A. (2022). Digital supply chain management and performance: The role of blockchain and big data analytics. International Journal of Production Economics, 243, 108327. https://doi.org/10.1016/j.ijpe.2021.108327.DOI
- Alicke, K. (2017). Supply Chain 4.0 in consumer goods. McKinsey & Company.
- https://www.mckinsey.com/.
- Baldwin, R. (2019). The globotics upheaval: Globalization, robotics, and the future of work. Oxford University Press.
- Baldwin, R., & Evenett, S. (2020). COVID-19 and trade policy: Why turning inward won’t work. CEPR Press.
- Bahrami, M., Abbaszadeh, M., & Shokouhyar, S. (2022). Big data analytics capabilities and supply chain performance: The mediating role of agility and resilience. Journal of Business Research, 146, 420–432.
- Büyüközkan, G., & Göçer, F. (2018). Digital supply chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157–177. https://doi.org/10.1016/j.compind.2018.02.010.DOI
- Butner, K. (2010). The smarter supply chain of the future. Strategy & Leadership, 38(1), 22–31.
- Christopher, M. (2000). The agile supply chain: Competing in volatile markets. Industrial Marketing Management, 29(1), 37–44.
- Coase, R. (1937). The nature of the firm. Economica, 4(16), 386–405.
- Garay-Rondero, C. L., García-Alcaraz, J. L., & Maldonado-Macías, A. (2020). Digital supply chain: Literature review and proposed structure for decision-making. Journal of Industrial Engineering and Management, 13(1), 57–79.
- Gopal, P. R. C., Thakkar, J., & Kumar, R. (2024). Digital transformation and productivity in logistics: A supply chain analytics perspective. Transportation Research Part E, 174, 103125. https://doi.org/10.1016/j.tre.2023.103125.DOI
- He, P., Liu, F., & Wang, L. (2024). Digitalization, logistics performance, and operational efficiency: Evidence from emerging markets. International Journal of Logistics Management, 35(2), 450–472.
- Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23–34.
- Ivanov, D. (2021). Supply chain viability and the COVID-19 pandemic: A conceptual and formal generalisation of four major adaptation strategies. International Journal of Production Research, 59(12), 3535–3552.
- Ivanov, D., Dolgui, A., & Sokolov, B. (2018). The impact of Industry 4.0 on supply chain resilience. International Journal of Production Research, 56(1–2), 3935–3950.
- Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International Journal of Operations & Production Management, 37(1), 10–36.
- Lasi, H., Fettke, P., Kemper, H. G., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242.
- Lee, S., Park, K., & Lim, J. (2023). Delivery efficiency and digital integration: A multilevel analysis. Operations Management Research, 16(1), 88–102.
- Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42.
- McKinsey & Company. (2021). The future of e-commerce logistics.
- https://www.mckinsey.com/.
- Nica, E. (2019). Smart supply chains and digital innovation. Economics, Management, and Financial Markets, 14(1), 9–14.
- Ning, X., Li, M., & Fan, T. (2023). Supply chain disruption, resilience, and digital technology adoption: Post-pandemic evidence. Journal of Cleaner Production, 412, 137013.
- Porter, M. E., & Heppelmann, J. E. (2015). How smart, connected products are transforming companies. Harvard Business Review, 93(10), 53–71.
- Queiroz, M. M., Ivanov, D., Dolgui, A., & Fosso Wamba, S. (2021). Impacts of digitalization on supply chain resilience: A framework for the emerging economy context. Annals of Operations Research, 303(1), 2527–2546.
- Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5), S71–S102.
- Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117–2135.
- Solow, R. M. (1956). A contribution to the theory of economic growth. Quarterly Journal of Economics, 70(1), 65–94.
- Spieske, A., & Birkel, H. (2021). Improving supply chain resilience through digital transformation: A conceptual and empirical analysis. Journal of Business Economics, 91(5), 1–28.
- Subramanian, N., Gunasekaran, A., & Abdulrahman, M. D. (2020). Logistics performance and sustainable supply chains: The role of digital technologies. Journal of Supply Chain Management, 56(2), 15–33.
- UNCTAD. (2022). Digital Economy Report 2022. United Nations Publications.
- Wamba, S. F., Gunasekaran, A., & Akter, S. (2020). Big data analytics in supply chain management: State-of-the-art and future research directions. International Journal of Production Economics, 221, 107450.
- Wang, J., Wu, S., & Li, Y. (2022). Digital twins and predictive supply chain management. Computers & Industrial Engineering, 173, 108665.
- Wang, Y., Lin, Z., & Li, F. (2023). Digital transformation, supply chain integration, and firm performance: Insights from emerging economies. Technovation, 123, 102628.
- Williamson, O. E. (1985). The economic institutions of capitalism. Free Press.
- Wu, L., Yue, X., Jin, A., & Yen, D. C. (2016). Smart supply chain management: A review and implications for the future. International Journal of Logistics Management, 27(2), 395–417.
- Zhang, X., Luo, Y., & Fan, T. (2024). Digital ecosystems and smart logistics: A systemic perspective. Information Systems Frontiers, 26(1), 181–198.
- Zhao, L., Liu, F., & Xu, H. (2023). Digital resilience and supply chain response capability: Evidence from the pandemic period. Journal of Business Research, 158, 113665.
- Zhong, R. Y., Xu, X., & Chen, C. (2017). Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 3(5), 616–630.
