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Article

The Effect of Digital Service Innovation on Strengthening Supply Chain Networks Against Disruptions: A Network Embedding Approach

by
Yanjie Gou
1,2,*,
Maozeng Xu
1,
Lucille Aba Abruquah
3,4 and
Xudong Li
1
1
School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
2
School of Innovation and Entrepreneurship, Sichuan University of Arts and Science, Dazhou 635000, China
3
Research Global, Accra P.O. Box AN 18923, Ghana
4
Heritage and Cultural Foundation of Africa Foundation, Accra P.O. Box LG 586, Ghana
*
Author to whom correspondence should be addressed.
J. Theor. Appl. Electron. Commer. Res. 2025, 20(3), 164; https://doi.org/10.3390/jtaer20030164
Submission received: 7 May 2025 / Revised: 20 June 2025 / Accepted: 23 June 2025 / Published: 1 July 2025
(This article belongs to the Special Issue Digitalization and Sustainable Supply Chain)
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Abstract

The advent of digital technology has transformed traditional service management approaches and offers new opportunities for the supply chain network to resist these interruptions. However, current research on how digital service innovation directly affects supply chain resilience is limited. This study constructs a theoretical research framework to explore how digital service innovation promotes supply chain resilience in the manufacturing industry, using a network embeddedness perspective. Through extensive survey data, the research demonstrates that digital service innovation enhances resilience by fostering two types of network embeddedness, namely relational and structural embedding, which in turn enhances resistance to interruption. IT support capabilities further reinforce the relationship between digital service innovation and relational embedding and structural embedding, and enhance the overall impact on resilience. This paper is among the first to integrate digital service innovation into supply chain resilience research, unveiling a network embedding approach for enhancing the ability to respond to supply chain network disruptions.

1. Introduction

As an important player in the global supply chain, China’s manufacturing industry has developed rapidly and has maintained its position as the world’s number one for 15 consecutive years. According to data from the National Bureau of Statistics of China, China’s industrial added value increased by 5.8% year-on-year in 2024, an increase of 1.2 percentage points from 2023. The number of industrial enterprises reached 512,000, an increase of 6.1% from the end of 2023, and manufacturing investment increased by 9.2% year-on-year. These data demonstrate the strong vitality of China’s manufacturing industry. However, behind these seemingly beautiful data, due to global infectious diseases, regional conflicts, and trade protectionism in recent years, the current market environment for the manufacturing industry has become increasingly unpredictable, and the supply chain of Chinese manufacturing enterprises is actually facing a serious crisis of interruption. In order to effectively respond to fluctuating market demand, manufacturing enterprises need to integrate network resources, strengthen connections with upstream and downstream suppliers, and build a solid ability to resist supply chain disruptions.
Current studies have shown that servitization and digitalization in manufacturing firms are possible solutions that play an important role in reducing supply chain concentration and maintaining supply chain stability [1]. Digital Service Innovation (DSI), as a deep integration of digital transformation and service-oriented strategy, will provide important assistance to manufacturing enterprises in enhancing their supply chain resilience. Especially with the empowerment of digital technologies such as the Internet of Things and cloud computing, digital service innovation (DSI) has become a critical driver for enterprises aiming to deliver high-quality, high-performance services [2], it not only changed the traditional service model, but also reshaped the operation mechanism of the supply chain network. However, there are still three key gaps in current research on how DSI affects supply chain network resilience to resist interruption (RRI).
First, the relationship between DSI and RRI is not well-defined. As the line between services and commodities blurs, service capabilities have become fundamental for profitability and differentiation in competitive markets [3]. Previous studies confirm the value of service innovation in supply chain management: as a defensive strategy, service innovation can increase customer loyalty, reducing competitive risks by enhancing supply chain value. Additionally, diversified service products open pathways to new business opportunities, strengthening the adaptability of supply chain networks [4]. In today’s digital economy, service innovation is increasingly digitalized. Enterprises have begun to leverage digital platforms, big data monitoring, and other technologies to optimize the quality of their services, significantly improving the responsiveness and dynamic capacities of supply chain networks [5]. However, limited research exists on how DSI impacts RRI.
Second, with the rise of DSI, the interactions within supply chains have become more complex and dynamic, impacting the embeddedness of networks among enterprises. Network embeddedness (NE) refers to the informal cooperative relationship established between different business entities, and strong network connections are crucial for enterprises to access external resources [6]. While existing research often emphasizes NE’s role in enhancing collaboration efficiency and innovation performance [7,8], there is limited focus on the influence of DSI on NE itself. Moreover, studies have confirmed that NE positively impacts a supply chain’s responsiveness to market demands [9]. This raises a key question: How can enterprises leverage DSI to strengthen NE in a way that enhances RRI? This question is significant both theoretically and practically, as it invites a deeper exploration of the underlying mechanisms and relationships between DSI, NE, and RRI.
Third, the effect of DSI on NE is likely moderated by an enterprise’s IT support capabilities (ITSC). Effective DSI relies on robust digital infrastructure and advanced digital management capabilities to drive success [10]. Furthermore, networked relationships and IT capabilities are essential for breaking down information barriers between customers and suppliers, strengthening process integration and collaboration across the supply chain [11]. As a boundary condition, ITSC has the potential to amplify the effect of DSI on NE. However, empirical evidence on this moderating role remains limited.
To address these issues, this paper establishes a research framework examining the relationship between DSI and RRI from an NE perspective, using Chinese manufacturing enterprises as the study sample. This approach enables a deeper exploration of the underlying mechanism through which DSI impacts RRI and identifies relevant boundary conditions. By expanding the application of NE theory within supply chain research, this study offers both theoretical foundations and practical insights for advancing knowledge and practice in this field.

2. Theory and Hypothesis

2.1. Network Embeddedness

According to Granovetter [12], network embeddedness comprises two key dimensions: relational embedding (RE) and structural embedding (SE). RE emphasizes the role of interaction and trust among network members. SE reflects how the structural configuration, or the centrality and density of an entity’s network position, impacts its cooperative relationships.
Network embeddedness theory posits that every enterprise is integrated within a cooperative network comprising various business entities [13]. Such network relationships facilitate not only knowledge and information sharing but also influence the decision-making behaviors of network members [14]. Through the network embedding relationship, network members achieve cooperative consensus through effective communication, minimize unnecessary conflicts [6], and curb opportunistic behaviors of partners to some extent [15].
In the field of supply chain management, Peng, et al. [16] explored the impact of multi network embeddings on green supply chain performance; Zhao, Wang and Gu [14] states that network embeddedness can weak a partner’s weak form opportunism; Yan, et al. [17] found that knowledge-based network embedding is beneficial for promoting supply chain learning; Feng, et al. [18] found that relationship embedding have a significant impact on promoting supply chain transparency. The above research has preliminarily explored the application of network embedding theory in supply chain management, but lacks evidence to support the promotion of supply chain resilience.
Therefore, this study will explore the impact mechanism of DSI on RRI using network embedding theory. Meanwhile, according to the definition, network embedding is divided into relational embedding and structural embedding, and this study will explore these two path mechanisms.

2.2. The Relationship Between DSI and RRI

Currently, there is no standardized definition for RRI. Much of the research in this area emphasizes supply chain resilience (SCR) within the field of supply chain management [19]. For example, Ponomarov and Holcomb [20] conceptualized SCR as a supply network’s capability to resist and adapt to disruptions and return to its usual state swiftly. Kamalahmadi and Parast [21] further defined SCR as the adaptability of the supply chain to quickly respond to external disruptions through the emergency mechanism, thus restoring operational stability. Wieland [22] approached SCR from an ecological resilience perspective, incorporating sustainability, adaptability, and transformation as key attributes. Drawing on these definitions, this paper defines RRI as the stability and defensive capacity of the supply chain network in the face of uncertain interruption risk.
Digital technology promotes the monetization of service data and drives innovations in business models, playing a key role in influencing user decision-making, satisfaction, and optimizing user experiences [23]. As a result, DSI has transformed how digital service products are designed, developed, and transacted [24]. Building on Rabetino, et al. [25], this paper defines DSI as the use of digital technology to develop new service products and establish a service ecosystem involving various stakeholders. This ecosystem fosters the integration of diverse resources and supports value co-creation across the network.
DSI impacts RRI in two significant ways. On the one hand, DSI enables logistics enterprises to digitize warehousing and transportation processes, strengthening information processing capabilities and enhancing the self-regulation and responsiveness of the supply chain network [26]. This digitization ensures that service levels are maintained as much as possible during disruptions, supporting stability and continuity across the network. On the other hand, DSI allows logistics enterprises to quickly adjust service strategies and improve emergency decision-making capabilities [27]. In data-rich environments, DSI facilitates efficient knowledge and information integration from both suppliers and customers, helping enterprises explore innovation opportunities [28]. This integration strengthens the supply chain network’s resilience and capacity to manage complex, unforeseen situations.
Based on this, we propose the following assumptions:
H1:
DSI has a positive effect on the RRI.

2.3. The Mediating Effect of RE

Digital technology has transformed the service innovation environment in several ways. Enterprises can leverage big data and artificial intelligence to acquire valuable market information, allowing them to anticipate partner behaviors [29]. This predictive capability enables more effective management of network relationships, fostering proactive collaboration and response across the supply chain.
DSI also supports an agile, collaborative approach to value creation [30]. Through DSI, enterprises can form cross-functional teams with supply chain members to co-develop products or customize services [31]. This process promotes robust relationship governance and positions enterprises as integral members of value co-creation, evolving from traditional service providers [32].
Furthermore, DSI enables enterprises to refine service processes through advanced digital tools (Soto Setzke et al. [33]), such as metaverse and virtual reality technologies and other technologies, simulating different service scenarios and reshaping interactions between enterprises and users [34]. At the same time, digital technologies support dynamic service data management, giving enterprises a precise understanding of customer needs and enhancing the user experience [35]. High-quality services foster trust with partners, building the RE within enterprise networks.
Therefore, we propose the following assumptions:
H2a:
DSI has a positive effect on RE.
In an open ecosystem, changes in user preferences or economic systems can create unpredictable impacts on the supply chain network [36]. Establishing strong cooperative relationships enhances the network’s stability and adaptability in the face of such changes. A high level of RE can make it easier for supply chain partners to reach consensus on objectives and reduce uncertainty in organizational activities [37]. Trust and collaboration within the network facilitate knowledge sharing, which drives operational efficiency across the supply chain. Furthermore, when network members align through collective interests, they are more motivated to engage in collaborative problem solving [38]. RE enables supply chain network members to swiftly identify disruption risks, efficiently allocate resources, and jointly coordinate contingency plans, thus enhancing RRI.
Based on this, we propose the following assumptions:
H2b:
RE has a positive effect on RRI.
DSI not only accelerates and densifies the exchange of service resources but also reshapes collaborative dynamics within the service ecosystem [36]. Unlike traditional models that rely on a single service provider, digital service development emerges from a broader network of cooperative efforts [39]. The inherent characteristics of digitalization [40] enable DSI to enhance knowledge and technology sharing among partners, thereby improving service quality and fostering a stable collaborative environment. Within supply chain networks, a strong foundation of trust facilitates resource integration and supports rapid service recovery in the face of disruption. DSI strengthens collaborative ties among network members and improves the stability and resilience of the supply chain network in the event of disruption.
Based on this, we propose the following assumptions:
H2c:
RE plays a mediating role between DSI and RRI.

2.4. The Mediating Effect of Structural Embeddedness

DSI creates optimal conditions for enterprises to improve their SE within supply networks. According to Burt [41], structural holes refer to non-redundant connections between network members. Enterprises occupying structural holes have the advantage of obtaining information, resources, and influence. Through DSI, enterprises can strategically position themselves within structural holes, establishing extensive relational networks rich that facilitate access to diverse and high-value resources from external connections and augmenting information synthesis capabilities [42]. The modern supply chain, as a densely interconnected and interdependent network of suppliers and customers, requires more diversified and tailored services to address complex interdependencies (Sahebjamnia, et al.) [43]. DSI supports this need by enabling firms to develop digital service platforms that integrate resources across the network, fostering seamless collaboration with various participants [5]. As network size and activity reach critical thresholds, network effects activate, amplifying resource acquisition and absorption capabilities for central firms [44]. As a result, DSI empowers enterprises to leverage their network positions for greater resource control and active engagement in service innovation, transforming positional advantages into tangible resource advantages.
Based on this, we propose the following assumptions:
H3a:
DSI has a positive effect on SE.
Enhancing SE can significantly impact the resilience of supply chain networks against disruptions. The position of entities in the organizational network often holds more influence over access to resources than the strength of individual relationships [41]. Stronger SE allows firms to leverage a broad network of relationships, enabling them to command strategic resources from various partners. According to Luthra, et al. [45], supply chain alliances facilitate the transfer and integration of knowledge among different institutions such as suppliers, manufacturers, warehouses, and distributors, making the supply chain network more adaptive and robust. Structural embedding further enhances this integration of heterogeneous knowledge by enabling firms to establish interactive platforms, participate in industrial clusters, form alliances, and engage in network embedding across multiple layers of the supply chain [16]. On this basis, enhanced adaptability and resilience within the supply chain network directly contribute to improved RRI.
Based on this, we propose the following assumptions:
H3b:
SE has a positive effect on RRI.
In the context of DSI, the boundaries between service network entities are increasingly blurred. Digital technology’s attributes empower distributed innovation, and the open, fluid division of labor enables more seamless transfer and integration of service resources [46]. This shift allows enterprises to build expansive innovation networks centered around their operations, which enhances their positional and resource advantages. It also provides favorable conditions conducive to the combination of diverse resources among network partners, fueling the innovation and development of the entire supply chain network. Therefore, DSI strengthens the ability of enterprises to integrate resources from upstream and downstream partners, helps their central role within the network, and ultimately promotes the establishment of adaptive and recovery mechanisms in the supply chain network.
Based on this, we propose the following assumptions:
H3c:
SE plays a mediating role between DSI and RRI.

2.5. The Moderating Effect of ITSC

In the era of the digital economy, building a robust ITSC is crucial for enterprises aiming to effectively allocate data assets and optimize operational efficiency. ITSC reflects an organization’s capability to manage IT-related resources, strengthen communication and collaboration, analyze business data, and improve innovation performance [47]. IT has significantly transformed the product and service development process within enterprises [48]. Advanced technologies, such as cloud computing and big data analytics, enable firms to effectively collect and process relevant information on competitors and industry trends to respond to market opportunities. Additionally, by integrating IT into core business operations, organizations can efficiently share information with other partners to form a cohesive business system [49].
The moderating effect of ITSC manifests across three main areas:
First, ITSC plays a pivotal role in amplifying the effects of DSI on both RE and SE by providing technical support. Service innovation is increasingly focused on delivering unique experiences to customers with novel service experiences [50]. High-quality service requires communication and interaction, and digital technology is the key driving factor in improving the quality of user interaction and collaboration [51]. Service products now act as vessels for digital advancements, and the success of today’s service industries lies in the seamless integration of IT, operational functions, and ecosystem interactions [31]. By enhancing IT support for decision management, enterprises can form a high-performance technical environment, strengthening their competitive edge [52] and enhancing the advantages of network embedding derived from digital services.
Second, ITSC significantly strengthens the effect of DSI on RE and SE by improving the efficiency of network collaboration. ITSC can help enterprises establish a stable trust mechanism with partners in the process of providing digital services [49]. For instance, manufacturing enterprises can leverage IT to build databases, automate management processes, and fully integrate supplier and customer data, which greatly improves collaboration efficiency. At the same time, IT applications boost enterprises’ capabilities for identifying and reorganizing critical information [53], enabling real-time detection of collaborative issues by assessing supplier or potential customer satisfaction level [54]. This adaptability further expands the advantages of network embedding fostered by digital services.
Finally, enterprises positioned near the center of network alliances are more likely to acquire key resources from both upstream and downstream partners. ITSC strengthens enterprises’ ability to identify opportunities and retrieve relevant information efficiently. In dense enterprise networks, resource redundancy and information overload are common [55], which can hinder effective decision-making. ITSC enables enterprises to better perceive market trends and capitalize on innovation opportunities [56], which heightens market awareness and amplifies the effect of DSI on RE and SE.
Based on this, we propose the following assumptions:
H4a:
ITSC positively moderates the relationship between DSI and RE.
H4b:
ITSC positively moderates the relationship between DSI and SE.
To sum up, the research framework of this paper is shown in Figure 1.

3. Method

3.1. Measurement

3.1.1. Dependent Variable

At present, academia lacks a unified definition of RRI, yet several studies have explored strategies for enabling supply chains to respond flexibly to disruptions and recover swiftly. Research by Azadegan and Dooley [57] and Shi, et al. [58] suggests that strong cooperative relationships within supply networks enhance risk resistance, facilitating a more resilient response. Estay, et al. [59] highlighted that digital technology can enhance responsiveness and minimize losses during network interruptions. Additionally, Annarelli and Palombi [60] proposed that continuous learning from disruption events helps improve existing supply networks. Drawing on these insights, we measured RRI through three core dimensions: (1) cooperation, (2) digital technology, and (3) continuous learning. Regarding cooperation, when interruptions occur, established partnerships become essential, as partners can offer critical resources to help the organization mitigate potential losses within the supply network. Leveraging digital technology enables the rapid acquisition and integration of both internal and external information, which strengthens the supply network’s response capabilities during disruptions. Furthermore, by systematically evaluating and implementing optimizations, we aim to enhance each network’s resistance to future interruptions.

3.1.2. Independent Variable

Service innovation in the digital age requires a more extensive conceptualization of services and new frameworks that capture the potential impact of IT capabilities on service experience and innovation. In the DSI process, companies can adopt the business platform model to jointly explore service strategies with different suppliers [61]. Platforms such as B2B networks and product R&D ecosystems enable firms to broaden their innovation reach and expand service development opportunities [25]. Successful DSI requires companies to build digital business models with stakeholders to optimize the service experience and create value for customers through data collection and related technologies. Combining the above studies and the DSI framework defined by Rabetino, Kohtamäki and Huikkola [25], this paper mainly measures DSI from three dimensions: service process, innovation mode, and new products. Under the service process, digital technology is integrated within the service system to create new optimized service processes, enabling a fully digitized and intelligent service experience. For innovation mode, through digital technology, a cooperation network with stakeholders will be established. This will enable the collection, analysis of customer and supplier data and the extraction of valuable information to optimize service quality. Finally, concerning new products, digital intermediate components will be incorporated into existing products, creating personalized new products that align with customer service needs.

3.1.3. Mediating Variables

Relational Embedding
Building on the research of Zhao, Wang and Gu [14], this paper evaluates RE within a manufacturing context through three critical dimensions: (1) behavior information is maintained openly and transparently with all partners to ensure trust and clarity in interactions, (2) power is equitably distributed across the cooperation networks, and (3) trust is established not only with direct partners but also with the extended network of partners’ partners.
Structural Embedding
According to the definition of SE by Granovetter [12], this paper assesses SE using three key factors: (1) interactions with partners are maintained over a long period and are relatively stable, (2) we maintain a high frequency of interaction with other partners, and (3) the behavior of our partners is significantly affected by the broader network structure of the supply chain.

3.1.4. Moderating Variable

Based on the research of Chen, Wang, Nevo, Benitez-Amado and Kou [47], and tailored to the study’s specific context, this paper evaluates ITSC from four dimensions: IT collaboration, IT infrastructure, IT management, and IT business support. (1) IT Collaboration: The enterprise uses IT to effectively communicate and collaborate with partners. (2) IT Infrastructure: The enterprises’ IT systems are highly compatible with the organizational structures and effectively meet the requirements of relevant business applications. (3) IT Management: Relative to similar enterprises, the enterprise possesses certain advantages in IT-related activities, such as product development, evaluation, and improvement; (4) The IT resources of the enterprise are well aligned with business objectives with a deep integrated IT and organizational operations.

3.1.5. Control Variable

Enterprise resilience to supply chain disruption can vary significantly based on basic characteristics. Drawing on Tang, Yao, Boadu and Xie [49], this paper incorporates the following control variables: enterprise size (ES), ownership, enterprise age (EA), government subsidies (GS), R&D investment, net profit (NP), and other factors as control variables. Among them, the size of the enterprise is measured by the number of employees. Ownership is defined as a binary 0/1 variable, with private enterprises represented by 1 and state-owned enterprises represented by 0.

3.2. Data Collection

Over the past 30 years, the manufacturing industry has been pivotal in driving China’s economic development. This study focuses on manufacturing enterprises, gathering data through executive-completed questionnaires. Prior to distribution, the survey’s purpose was clearly explained to respondents, consent was obtained, and assurances were provided to safeguard personal privacy throughout the data collection and application processes.
First, a pre-survey was conducted by inviting 10 senior executives from manufacturing enterprises to assist in completing the questionnaire and provide feedback for improvement. Their suggestions focused on ensuring clarity in the questions, aligning the question design with real-world enterprise practices, and assessing the reasonableness of variable measurements. Based on this feedback, the questionnaire was refined to enhance its accuracy and relevance.
Secondly, the data collection for the questionnaire was conducted over a month, from March 2025 to April 2025. A total of 443 questionnaires were distributed to manufacturing executives via the alumni platform. Out of these, 348 responses were received, and after discarding incomplete or invalid responses, 336 valid samples were retained, resulting in an effective rate of 75.85%.

3.3. Data Analysis

3.3.1. CMV and Dimensional Consistency Test

First, this study uses principal component analysis to test for the presence of common method deviation in the data. The factor analysis results revealed that five factors emerged from the data, with the first factor explaining 31.56% of the variance. Since this value is less than the critical threshold of 40%, it indicates that common method bias is not a significant issue in the data.
Secondly, principal component analysis was performed to calculate the dimensions, and the maximum variance method was used to rotate the factors. The results demonstrate that the combination of latent variables in the questionnaire data is consistent with the combination of latent variables in the theoretical model, as shown in Table 1. This indicates that the data has strong dimensional consistency.

3.3.2. Reliability and Validity

Reliability and validity testing are crucial to ensure the consistency, stability, reliability, and effectiveness of empirical research results. This paper uses SPSS and AMOS software to measure reliability and validity.
Table 2 shows the reliability and validity, such as factor loading, KMO, Cronbach’s α, CR and AVE.
The reliability tests conducted in this study demonstrate that the data is highly consistent and stable. The Cronbach’s α coefficients for each variable—ranging from 0.885 to 0.924—are all well above the recommended value of 0.7. The combination of reliability results further confirms the robustness of the data used in this study. With values ranging from 0.928 to 0.952, all measures exceed the recommended threshold of 0.7. This suggests that the questionnaire and data are reliable.
The results of the validity test also support the quality of data and constructs used in this study. Firstly, this study uses Kaiser–Meyer–Olkin (KMO) and Bartlett’s spherical test for factor analysis. The results show that the KMO values for all variables (ranging from 0.746 to 0.835) exceed the recommended threshold of 0.7. Secondly, this study uses the average variance extracted (AVE) to test the discriminant validity. The results show that the AVE values for all variables (ranging from 0.764 to 0.868) are higher than the recommended threshold of 0.5. This shows that each construct explains a sufficient amount of variance, and the indicators for each latent variable are reliable measures of that variable.
In assessing the model fit and structural validity, this study used AMOS software to conduct a confirmatory factor analysis. The results revealed that the benchmark model is the best fit, as all key model fit indices fall within acceptable ranges. The results are as follows: χ2/DF is 1.058, RMSEA is 0.013, SRMR is 0.0439, CFI is 0.998, GFI is 0.964, TLI is 0.998, IFI is 0.998, NFI is 0.972, which are all in a good range. These results further confirm that the model fits the actual data well and demonstrates good structural validity.

3.3.3. Variable Description and Correlation

Table 3 shows the descriptive statistics and correlation analysis of this study. The results show that the two variables with the greatest correlation are DSI and RRI, with a correlation coefficient of 0.498, which is statistically significant (p < 0.01). The correlation coefficients of all variables are less than 0.7, which means there is no strong collinearity interference among the variables.

4. Results

In this study, SPSS version 26, AMOS version 23, and PROCESS version 4.1 software are employed to conduct regression analysis on the model to verify the research hypothesis.

4.1. Main Effects and Mediation Effects Regression Analysis

Table 4 presents the regression analysis results for both the main and mediated effects models utilized in this study. This analysis was conducted using the SPSS version 26 software.
The dependent variable for M1 to M4 is the RRI.
In Model M1, the regression analysis evaluates only the control variables—size, ownership, age, financial stability (FS), R&D investment (RD), and net profit (NP)—in relation to the dependent variable RRI. The results revealed the following significant relationships. Ownership has a negative effect on RRI (β = −0.115, p < 0.05), FS also shows a negative effect on RRI (β = −0.253, p < 0.001), and RD has a positive effect on RRI (β = 0.446, p < 0.001). These results align with typical enterprise behaviors.
In Model M2, the regression analysis incorporates both the control variables and the independent variable DSI in relation to the dependent variable RRI. The results indicate that DSI has a significant positive effect on RRI with a regression coefficient of β = 0.367, p < 0.001.
In Model M3, the regression analysis includes the control variables, the independent variable DSI, and the mediator variable RE with respect to the dependent variable RRI. The results demonstrate that RE has a significant positive effect on RRI, with a regression coefficient of β = 0.151, p < 0.001. This supports Hypothesis H2b.
In Mode M4, the regression analysis incorporates control variables, the independent variable DSI, and the mediating variable SE in relation to the dependent variable RRI. The results show that SE has a significant positive effect on RRI, with a regression coefficient of β = 0.249, p < 0.001. This result supports H3b.
In Model M5, the regression analysis examines the effect of control variables—size, ownership, age, FS, RD, and NP—on the mediator variable RE. The results indicate that size and RD have significant positive effects on RE.
Model M6 considers the regression analysis of the control variables and the independent variable DSI to assess their effect on RE. The results show a regression coefficient of DSI on RE β = 0.204, p < 0.001, indicating a significant positive effect of DSI on RE.
In Model M7, the regression analysis includes the control variables—size, ownership, age, FS, RD, and NP—to assess their impact on SE. The results show that size and RD have significant positive effects on RE.
In Model M8, the regression analysis considers the control variables and the independent variable DSI on SE. The results show that the regression coefficient of DSI on SE is β = 0.213, p < 0.001. This indicates that DSI has a significant positive effect on SE, and H3a is supported.
The results from M1-M8 indicate that DSI has a positive effect on RRI. Additionally, DSI has positive effects on SE and RE SE, while both SE and RE individually have positive effects on RRI. These findings suggest SE and RE are likely to play a mediating role between DSI and RRI. To verify the robustness of these mediating effects, bootstrap analysis is performed using Model 4 in PROCESS, with a confidence interval set to 95%. The results after conducting the analysis are presented in Table 5.
The results from Table 5 show that the mediating effect of RE is significant, with the following key findings. The direct effect of RE on RRI is 0.301 with a p-value < 0.001. The indirect effect of DSI on RRI through RE is 0.028 with a p-value < 0.001. The confidence intervals are [0.223, 0.380] for direct effects and [0.011, 0.054] for indirect effects. Since neither of the confidence intervals includes 0, it confirms the significance of both the direct and indirect effects. Therefore, the mediating effect of RE is statistically validated, providing support for Hypothesis H2c.
The results from Table 5 show that the mediating effect of SE is significant. The direct effect of SE on RRI is 0.281 (p < 0.001), and the indirect effect is 0.048 (p < 0.001). The confidence intervals are [0.205, 0.358] and [0.024, 0.083] for direct and indirect effects, respectively. Since neither of the confidence intervals contains 0, it confirms the significance of both the direct and indirect effects. This confirms that SE has a significant mediating role between DSI and RRI, and supports H3C.

4.2. Regression Analysis of Moderating Effects

The paper decentralized DSI and ITSC before performing the regression analysis. The results are summarized in Table 6.
The results from M10 in Table 6 demonstrate that ITSC on DSI significantly moderates the relationship between DSI and RE, with a moderating effect of 0.214 (p < 0.001). This finding indicates that ITSC positively enhances the impact of DSI on RE. In other words, the higher the level of ITSC, the stronger the positive effect of DSI on RE. Therefore, Hypothesis H4a is supported.
The results from Model M12 in Table 6 show that ITSC has a significant moderating effect on the relationship between DSI and SE, with a moderating effect of 0.184 (p < 0.001). This result suggests that this ITSC positively moderates the relationship between DSI and SE, meaning that a higher level of ITSC strengthens the positive effect of DSI on SE. Thus, H4b is supported.
To further illuminate the moderating effect, this study includes plots in Figure 2 and Figure 3. These figures show that as ITSC increases, the slopes of DSI to RE and SE also increase. This indicates that, at the same level of DSI, both RE and SE increase with the higher ITSC levels. The visualization provides further support for the study’s framework, highlighting the moderating role of ITSC, and reinforces the conclusions that H4a and H4b are supported.

4.3. Analysis of Moderated–Mediation Effects

Using PROCESS with Model 7, this study analyzed the moderated mediation effect. Table 7 shows that the conditional indirect effect of RE is not significant at low levels of ITSC. However, as the level of ITSC increases, the mediating effect of RE becomes significant. The value of the moderated mediation effect is 0.017, with a confidence interval of [0.006, 0.037], which does not include 0. This indicates that ITSC has a significant moderated mediation effect within the DSI-RE-RRI pathway. In addition, the conditional indirect effect of SE is also not significant when the ITSC level is low. This pattern suggests that the impact of both RE and SE as mediators between DSI and RRI strengthens with higher ITSC levels. The value of the moderated mediation effect is 0.024, with a confidence interval of [0.008, 0.044], does not contain 0. This indicates that ITSC has a significant moderated mediation effect in the DSI–SE–RRI pathway.

5. Discussion and Conclusion

5.1. Theoretical Implications

The impact mechanism of DSI on RRI in manufacturing is an important topic warranting academic exploration. The main research contributions of this paper are as follows:
This study makes a notable contribution by creatively constructing variable measurement indicators for DSI and RRI, which brings important reference value for future research on supply chain network resilience. Following the definition of DSI by Rabetino, Kohtamäki and Huikkola [25], this paper measures DSI across three dimensions: service process, innovation mode, and new products. For RRI, drawing on the work of Azadegan and Dooley [57], Shi, Yuan and Deng [58], Estay, Sahay, Barfod and Jensen [59], and Annarelli and Palombi [60], we constructed measures based on cooperation, digital technology, and continuous learning. The concepts and measurement used in this study combined existing theories, practical applications, and enterprise-level research. These metrics have undergone rigorous reliability and validity tests, demonstrating strong reliability and providing a solid foundation for future research in related fields.
Additionally, from the perspective of NE, this study uncovers the intrinsic influence mechanism through which DSI influences corporate RRI. While existing research has extensively explored DSI’s core components and essence [46], diagnostic frameworks for innovation in digital products and services [35], and the dynamic of uncertain environmental supply chain networks [62], the internal logic by which ITSC impacts RRI remains unexplored. In the context of manufacturing enterprises, understanding the dynamics of supply chain networks is crucial to operational and economic advancement, which highlights the need to delve into their underlying mechanisms. Based on the NE perspective, this paper reveals the intrinsic process mechanism of “DSI–RE and SE–RRI”, pathways, significantly contributing to the ongoing research on RRI.
Finally, this study reveals the moderating effect of ITSC from the NE perspective, identifying critical boundary conditions for the relationships between DSI and RE and SE, as well as between RE/SE and RRI. DSI promotes RE and SE by enabling the design and delivery of service products to be intelligent and digitalized. However, this process also brings risks and potential errors, which can lead to instability or negative outcomes. To address these challenges, this study introduces ITSC as a moderating variable, asserting that ITSC enhances the ability of enterprises to embed and integrate resource information through digital technology and service innovation. From the NE perspective, this paper explores how ITSC strengthens the relationship between DSI, RE, and SE, and examines its moderating–mediating effects. This research extends the applicability of ITSC and NE theories in the field of service innovation and supply chain management.

5.2. Management Implications

This paper has management implications in two key areas.
First, the empirical results reveal that DSI positively drives RRI by integrating information and resources, deepening network embedding, and enhancing supply chain memory and selection through technologies like digital storage and traceability tools. This suggests that promoting digital technology applications within supply chain networks can have significant benefits. Therefore, policy makers should encourage the adoption of digital technologies in supply chain networks. This would not only improve RRI but also enhance NE, enabling companies to improve their competitive advantage. Further, this push for digitalization aligns with broader goals for the supply chain field to evolve toward green, sustainable, and multi-network models.
Second, ITSC has a moderating–mediating effect on DSI–RE and SE–RRI. This suggests that ITSC plays an important moderating–mediating role in enhancing RRI in manufacturing firms, particularly in the context of DSI. Therefore, ITSC can be regarded as a critical factor in the future development and application of supply chain management. To fully leverage its potential, it is essential to provide enterprises and managers at all levels with comprehensive, more adaptable knowledge and skills training. Additionally, organizing digital and informational knowledge exchange sessions and experience-sharing meetings will help enhance the benefits derived from digital service innovations. This approach will not only support enterprises in improving their supply chain capabilities but also contribute to their long-term development and competitiveness in the evolving field of supply chain management.

5.3. Limitations

This study has made significant contributions in terms of variable design, exploring the intrinsic mechanism, and applying theoretical frameworks. However, some limitations remain.
First, the limitations of variable design. The variables and measurement indicators for ITSC and RRI introduced in this paper are innovative. Although the design process considered potential outcomes from both academic research and enterprise practices, it remains challenging to eliminate all subject biases. Additionally, practical testing of these variables requires more in-depth scientific validation. Subsequent research should focus on refining and optimizing the measurement tools through repeated testing, ensuring that the objects and objectives of measurements are more accurate and comprehensive. This will help achieve more precise and reliable results in future studies.
Secondly, the limitations of the research sample. This study is limited to investigating the activities at the enterprise level, only collecting research data of relevant enterprises, which results in a relatively simplified scope. However, factors such as the operating environment, changes in customer demand, and the flow of information among stakeholders also significantly influence RRI and play a key role. These factors were not considered in the survey and research samples. Future research should expand to include these additional variables, enabling a more objective, accurate, and comprehensive analysis.
Finally, the limitations of theoretical exploration: In the context of enterprise survey research, the impact of digital service innovation on RRI is more complex than what has been explored in this study. DSI may influence RRI through multiple pathways that extend beyond the scope of the current research framework and the NE perspective. Future research should explore these additional dimensions, considering different aspects and variables, in order to get more extensive, multi-layered conclusions from a variety of perspectives.

5.4. Conclusions

To address the risks of global supply chain disruptions in the manufacturing industry, this study proposes a DSI-RRI research framework based on the perspective of network embedding. This paper conducts empirical analysis using questionnaire data from Chinese manufacturing enterprises, the results demonstrates that: (1) digital service innovation in manufacturing has a positive impact on supply chain resilience against disruptions; (2) digital service innovation enhances resilience by fostering two types of network embeddedness namely relational and structural embedding, which in turn enhances resist interruption; (3) IT support capabilities further reinforce the relationship between digital service innovation and relational embedding, structural embedding, enhancing the overall impact on resilience. The findings offer both theoretical insights and practical guidance for academics and practitioners focused on improving supply chain robustness in the face of frequent and severe disruptions.

Author Contributions

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

Funding

This work was supported by the Chongqing Social Science Planning Key Project “Research on Enhancing the Service Functions of Inland Comprehensive Open Hub Nodes” (2025CXZD12).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Sichuan University of Arts and Science (protocol code is SCWLLL20250002 and date of approval is 5 March 2025).

Informed Consent Statement

Informed consent has been obtained from all subjects to publish this paper.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

The authors would like to thank the Editor and the anonymous reviewers for their efforts to help us improve this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
DSIDigital Service Innovation
NENetwork embeddedness
RERelational embedding
SEStructural embedding
RRIResilience to Resist Interruption
ITSCIT Support Capabilities

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Jtaer 20 00164 g001
Figure 1. Research framework.
Figure 1. Research framework.
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Figure 2. Moderating effect of ITSC on DSI and RE.
Figure 2. Moderating effect of ITSC on DSI and RE.
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Figure 3. Moderating effect of ITSC on DSI and SE.
Figure 3. Moderating effect of ITSC on DSI and SE.
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Table 1. Principal component analysis.
Table 1. Principal component analysis.
Item12345
Q10.0230.8900.2030.1240.120
Q2−0.0040.8800.2320.0980.097
Q30.0000.9100.2180.1060.088
Q4−0.0820.0730.1200.1070.873
Q5−0.0830.1100.1070.1030.885
Q6−0.0160.0990.1210.0760.887
Q70.0280.1540.1320.8850.106
Q80.0130.0400.1560.8900.087
Q9−0.0360.1210.1790.8670.099
Q100.8910.0420.0050.012−0.031
Q110.846−0.0090.058−0.031−0.049
Q120.8830.0120.013−0.025−0.047
Q130.869−0.027−0.0390.049−0.057
Q14−0.0220.2630.8600.1780.113
Q150.0180.2400.8690.2010.118
Q160.0470.1910.8600.1450.165
Table 2. Reliability and validity of the scale.
Table 2. Reliability and validity of the scale.
Variables Factor LoadingKMOCronbach’s αCRAVE
DSIDSI 10.9290.7540.9240.9520.868
DSI 20.920
DSI 30.946
RERE 10.8940.7460.8850.9280.812
RE 20.908
RE 30.901
SESE 10.9140.7480.8910.9320.821
SE 20.904
SE 30.900
ITSCITSC 10.8920.8350.8970.9280.764
ITSC 20.849
ITSC 30.885
ITSC 40.870
RRIRRI 10.9240.7510.9100.9430.847
RRI 20.932
RRI 30.905
Table 3. Means, standard deviations, and correlations.
Table 3. Means, standard deviations, and correlations.
1234567891011
1. Size1
2. Ownership0.211 **1
3. Age0.258 **0.187 **1
4. FS0.219 **0.252 **0.278 **1
5. RD0.062−0.154 **0.0300.0161
6. NP−0.068−0.047−0.064−0.0230.0891
7. DSI0.144 **−0.109 *−0.024−0.206 **0.191 **0.113 *1
8. RE0.316 **0.1010.163 **0.0970.241 **−0.0320.249 **1
9. SE0.290 **0.0890.154 **−0.0040.182 **0.0680.278 **0.242 **1
10. ITSC−0.015−0.064−0.117 *−0.075−0.095−0.0360.009−0.115 *0.0001
11. RRI0.112 *−0.219 **−0.080−0.249 **0.471 **0.0550.498 **0.306 **0.390 **0.0191
12. Mean3.160.612.732.682.903.023.943.873.944.163.79
13. SD1.470.491.451.491.491.421.911.811.851.661.71
N = 336, ** p < 0.01, * p < 0.05.
Table 4. Regression analysis of DSI, RE, SE, and RRI.
Table 4. Regression analysis of DSI, RE, SE, and RRI.
DV: RRI M1–M4DV: RE M5–M6DV: SE M7–M8
M1M2M3M4M5M6M7M8
Size0.177 (0.056)0.102 (0.052) *0.068 (0.053)0.046 (0.051)0.267 (0.066) ***0.225 (0.066) ***0.268 (0.069) ***0.224 (0.068) ***
Ownership−0.115 (0.170) *−0.091 (0.155) *−0.103 (0.153) *−0.112 (0.148) **0.067 (0.2)0.079 (0.196)0.071 (0.207)0.084 (0.203)
Age−0.047 (0.057)−0.047 (0.052)−0.058 (0.051)−0.073 (0.05)0.074 (0.067)0.074 (0.066)0.103 (0.07)0.102 (0.068)
FS−0.253 (0.056) ***−0.166 (0.052) ***−0.173 (0.052) ***−0.151 (0.05) ***−0.003 (0.066)0.045 (0.066)−0.11 (0.068) *−0.059 (0.068)
RD0.446 (0.053) ***0.387 (0.049) ***0.356 (0.05) ***0.353 (0.048) ***0.235 (0.063) ***0.202 (0.063) ***0.168 (0.065) **0.133 (0.065) *
NP0.013 (0.055)−0.025 (0.051)−0.018 (0.05)−0.039 (0.048)−0.027 (0.065)−0.048 (0.064)0.078 (0.067)0.056 (0.066)
DSI 0.367 (0.04) ***0.336 (0.04) ***0.314 (0.039) *** 0.204 (0.050) *** 0.213 (0.052) ***
RE 0.151 (0.043) ***
SE 0.249 (0.04) ***
R20.3100.4280.4450.4790.1450.1790.1200.158
F26.103 ***36.818 ***34.598 ***39.424 ***10.469 ***11.456 ***8.631 ***9.987 ***
N = 336, *** p < 0.001, ** p < 0.01, * p < 0.05. (Standard error).
Table 5. Bootstrap analysis.
Table 5. Bootstrap analysis.
TypesEffectStandard ErrorpLLCIULCI%
MediatorMain Effects0.3290.039<0.0010.2510.407100%
REDirect effect0.3010.040<0.0010.2230.38091.49%
Indirect effect0.0280.011<0.0010.0110.0548.51%
SEDirect effect0.2810.039<0.0010.2050.35885.41%
Indirect effect0.0480.015<0.0010.0240.08314.59%
Table 6. Moderating effects of ITSC.
Table 6. Moderating effects of ITSC.
VariablesDV: RE M9–M10DV: SE M11–M12
M9M10M11M12
Size0.228 (0.066) ***0.225 (0.064) ***0.223 (0.069) ***0.221 (0.067) ***
Ownership0.075 (0.196)0.056 (0.192)0.086 (0.203)0.07 (0.2)
Age0.065 (0.066)0.071 (0.064)0.105 (0.069)0.11 (0.067) *
FS0.042 (0.066)0.068 (0.065)−0.058 (0.069)−0.036 (0.068)
RD0.193 (0.063) ***0.142 (0.063) **0.136 (0.065) **0.092 (0.066)
NP−0.051 (0.064)−0.047 (0.062)0.057 (0.066)0.061 (0.065)
DSI0.204 (0.05) ***0.204 (0.049) ***0.213 (0.052) ***0.213 (0.051) ***
ITSC−0.082 (0.055)−0.103 (0.054) *0.03 (0.057)0.012 (0.056)
DSI × ITSC 0.214 (0.028) *** 0.184 (0.029) ***
R20.1830.2250.1560.186
F10.406 ***11.782 ***8.765 ***9.504 ***
N = 336, *** p < 0.001, ** p < 0.01, * p < 0.05. (Standard error).
Table 7. Moderated mediation effects.
Table 7. Moderated mediation effects.
MediatorITSCEffectSELLCIULCI
RE2.503−0.0010.011−0.0270.020
4.1590.0280.0110.0110.054
5.8160.0560.0210.0230.104
Moderated mediation0.0170.0070.0060.037
SE2.5020.0070.018−0.0250.046
4.1590.0470.0150.0240.083
5.8160.0880.0240.0480.145
Moderated mediation0.0240.0090.0080.044
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MDPI and ACS Style

Gou, Y.; Xu, M.; Abruquah, L.A.; Li, X. The Effect of Digital Service Innovation on Strengthening Supply Chain Networks Against Disruptions: A Network Embedding Approach. J. Theor. Appl. Electron. Commer. Res. 2025, 20, 164. https://doi.org/10.3390/jtaer20030164

AMA Style

Gou Y, Xu M, Abruquah LA, Li X. The Effect of Digital Service Innovation on Strengthening Supply Chain Networks Against Disruptions: A Network Embedding Approach. Journal of Theoretical and Applied Electronic Commerce Research. 2025; 20(3):164. https://doi.org/10.3390/jtaer20030164

Chicago/Turabian Style

Gou, Yanjie, Maozeng Xu, Lucille Aba Abruquah, and Xudong Li. 2025. "The Effect of Digital Service Innovation on Strengthening Supply Chain Networks Against Disruptions: A Network Embedding Approach" Journal of Theoretical and Applied Electronic Commerce Research 20, no. 3: 164. https://doi.org/10.3390/jtaer20030164

APA Style

Gou, Y., Xu, M., Abruquah, L. A., & Li, X. (2025). The Effect of Digital Service Innovation on Strengthening Supply Chain Networks Against Disruptions: A Network Embedding Approach. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 164. https://doi.org/10.3390/jtaer20030164

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