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Article

How Does Digital Capability Shape Resilient Supply Chains?—Evidence from China’s Electric Vehicle Manufacturing Industry

by
Yanxuan Li
1,2,* and
Vatcharapol Sukhotu
2,*
1
College of Traffic and Transportation, Nanning University, Nanning 530200, China
2
Faculty of Logistics and Digital Supply Chain, Naresuan University, Phitsanulok 65000, Thailand
*
Authors to whom correspondence should be addressed.
Future Internet 2025, 17(3), 123; https://doi.org/10.3390/fi17030123
Submission received: 22 January 2025 / Revised: 7 March 2025 / Accepted: 10 March 2025 / Published: 11 March 2025
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Abstract

:
In recent years, the rapid advancement of digital technologies and the growing demand for sustainability have driven unprecedented transformations in the automotive industry, particularly toward electric vehicles (EVs) and renewable energy. The EV supply chain, a complex global network, has become increasingly vulnerable to globalization and frequent “black swan” events. The purpose of this study, grounded in organizational information processing theory, aims to systematically examine the role of digital capability in strengthening supply chain resilience (SCR) through improved risk management effectiveness. Specifically, it explores the multidimensional nature of digital capability, clarifies its distinct impact on SCR, and addresses existing research gaps in this domain. To achieve this, this study develops a theoretical framework and validates it using survey data collected from 249 EV supply chain enterprises in China. Partial Least Squares Structural Equation Modeling (PLS-SEM) is employed to empirically test the proposed relationships. The findings provide valuable theoretical insights and actionable guidance for EV manufacturers seeking to leverage digital transformation to mitigate risks effectively and enhance supply chain resilience. However, as the study focuses on Chinese EV supply chain enterprises, caution is needed when generalizing the findings to other regions. Future research could extend this investigation to different markets, such as to Europe and the United States, to explore potential variations.

1. Introduction

In recent years, the rapid development of digital technology has been reshaping the automotive industry [1]. With the growing global demand for sustainability and reducing environmental impact, the automotive sector has been undergoing unprecedented changes. At the same time, advancements in digital technology and environmental needs are driving the global shift toward electric vehicles (EVs) and renewable energy [2]. The transition from traditional cars to EVs is experiencing rapid growth.
The automotive supply chain is an extremely complex global network involving tens of thousands of components and numerous suppliers. According to statistics from the China Association of Automobile Manufacturers (CAAM), a single car consists of about 20,000 parts assembled or supplied by suppliers across the upstream supply chain, with approximately 2000 parts directly assembled by automakers [3]. Guided by lean production principles, inventory management in the automotive industry is highly stringent [4], often calculated by the hour for domestic components. Even for long-lead-time overseas parts, automakers typically maintain inventory sufficient for only two months of production. As globalization advances and “black swan” events become more frequent, the vulnerability of automotive supply chains has become increasingly evident [5]. According to Resilinc’s supply chain disruption monitoring report, 10,629 disruptions were reported in the first half of 2024, a 30% increase compared to the same period in 2023. The automotive industry has ranked among the top five most affected sectors for four consecutive years. The COVID-19 pandemic in 2020 is a notable example, where the disruption of a single component could halt an entire production line. In the EV sector, the high complexity and cost of components pose additional risks during the industry’s transition phase.
The Chinese State Council’s New Energy Vehicle Industry Development Plan (2021–2035) emphasizes leveraging digital information technology to unlock the potential of enterprise data assets and accelerate digital transformation. Aligning with this policy direction, this study adopts the perspective of supply chain risk management and leverages organizational information processing theory to explore the mechanisms through which digital capability enhances SCR. The findings provide innovative strategies for EV manufacturers to mitigate supply chain risks.
Current research on SCR shows that as global supply chains face more disruptions and uncertainties, scholars have increasingly recognized the importance of supply chain resilience for maintaining business operations. Many studies focus on the key elements of building supply chain resilience, such as risk management [6], supply chain collaboration [7,8], and information sharing [9]. However, despite extensive research on supply chain resilience, there is still a significant gap in understanding the role of digital capabilities in shaping supply chain resilience.
The current understanding of supply chain digital capabilities is gradually deepening. Some scholars believe that digital transformation is an effective way to enhance supply chain resilience. In particular, the use of digital technologies to improve supply chain agility and strengthen risk prediction and management has been shown to have significant positive effects [10,11,12]. However, while some theoretical and empirical studies have highlighted the positive impact of digital technologies on SCR [13,14,15], there is still a lack of comprehensive exploration on how digital capabilities influence supply chain resilience, especially regarding the role of different dimensions of digital capabilities.
The research gap regarding the moderating role of digital capability lies in several areas. First, although studies have shown that digital capability can enhance supply chain resilience, many treat it as a single variable [16]. Since companies adopt different technologies and strategies in their digital transformation, evaluating digital capability as a single dimension fails to fully capture its impact on supply chain resilience. Additionally, while some scholars have recognized the role of technologies such as big data analytics, cloud computing, and artificial intelligence in improving supply chain resilience [17,18], there is a lack of in-depth research on the different aspects of digital capability (e.g., digital infrastructure capability, digital analytics capability, and strategic support capability). Specifically, the relationships among these dimensions and their differentiated impacts on supply chain resilience remain underexplored. Therefore, more empirical studies are needed to clarify the mechanisms through which different dimensions contribute to enhancing SCR.
The purpose of this research is to fill the gap in supply chain management literature regarding the impact of digital capability on supply chain risk management and resilience. It focuses on how digital capability improves SCR through effective risk management [19]. The three specific objectives are to (1) develop a research framework based on organizational information processing theory; (2) analyze the pathways through which digital capability enhances SCR in EV supply chains; and (3) offer practical guidance for EV manufacturers in designing effective supply chain risk management strategies.
The structure of this paper is as follows: Section 2 outlines the theoretical foundations of this study; Section 3 reviews relevant literature and presents the research hypotheses and model framework; Section 4 provides a detailed description of the research methods and data collection process; Section 5 presents the data analysis results; and Section 6 discusses the study’s significance, limitations, and future research directions.

2. Theoretical Foundation

Organizational Information Processing Theory

As global supply chains face increasingly complex and uncertain environments, supply chain resilience (SCR) has become a critical capability for enterprises to maintain competitiveness and achieve sustainable development. SCR refers to an enterprise’s ability to rapidly adjust, recover, and optimize supply chain operations when confronted with external disruptions. In recent years, the rapid advancement of digital technologies has made the role of digital capabilities in enhancing SCR a key research focus [13,20].
Organizational Information Processing Theory (OIPT), first proposed by John (1973) [21], emphasizes that firms respond to environmental uncertainty and complexity through information processing capability. When faced with increasing task uncertainty, enhancing real-time information processing capability enables firms to achieve optimal performance outcomes [22]. Information processing needs may arise from uncertainties within the supply chain or from external environmental factors. When a firm’s information processing capability matches its information needs, it can attain optimal performance [23]. In the field of supply chain management, information processing capability is reflected in a firm’s ability to utilize digital technologies—such as big data, the Internet of Things (IoT), and artificial intelligence (AI)—to collect, analyze, and transmit information to support decision making and operations [24]. OIPT posits that when information processing capability aligns with information needs arising from supply chain uncertainties, firms can achieve optimal operational performance.
Supply chain operations are inherently uncertain, with sources of uncertainty stemming from social, natural, and human factors [25]. These uncertainties mainly arise from internal and external factors, such as fluctuating customer demand, inter-organizational complexity, natural disasters, and “black swan” events [26,27]. Neglecting such events exacerbates supply chain vulnerabilities, particularly as weak links often become primary sources of risk [10]. Therefore, identifying and mitigating these critical vulnerabilities, along with enhancing supply chain risk management capability, is essential for shaping supply chain resilience.
According to OIPT, enterprises require robust information processing capabilities to support risk management [28]. Risk management enhances firms’ adaptability by dynamically monitoring risks, analyzing uncertainties, and facilitating emergency decision making [29]. Efficient risk information processing helps firms collect essential data, ensure transparency among supply chain partners, and translate data analytics into actionable insights [6]. Additionally, collaborative mechanisms among supply chain enterprises foster risk information sharing, thereby improving overall response capabilities. These practices strengthen supply chain stability, enabling firms to swiftly recover and optimize operations in the face of external disruptions.
Digital capability refers to a firm’s ability to leverage digital technologies—such as AI, blockchain, and big data analytics—to enhance information processing capacity [17]. Digital capability not only improves the efficiency of risk management but also amplifies its positive impact on SCR [29,30]. By increasing information transparency and traceability, digital capability enhances firms’ ability to identify and respond to supply chain risks. For example, AI and big data analytics can analyze supply chain data in real time, predict potential risks, and provide decision-making support [18]. Meanwhile, digital technologies facilitate information sharing and collaboration across enterprises, strengthening the adaptability of various supply chain segments [17,31]. The application of blockchain technology ensures data authenticity and immutability [32], thereby reinforcing trust mechanisms and enhancing risk management effectiveness. Moreover, digital capability supports the development of agile response mechanisms, such as intelligent scheduling systems and real-time supply chain visualization tools [22]. These tools enable firms to rapidly adjust supply chain operations during disruptions, mitigating the impact of external shocks.
OIPT provides a robust theoretical foundation for understanding the moderating role of digital capability in the relationship between risk management and SCR. Based on this theory, this study examines how digital capability acts as a key moderating factor influencing the mechanism through which risk management impacts SCR. The findings not only offer theoretical support for enhancing SCR but also provide practical insights for optimizing supply chain management in the context of digital transformation. The theoretical framework is illustrated in Figure 1.

3. Literature Review

3.1. Understanding of Supply Chain Resilience

The concept of resilience, originating from ecology, was defined by Holling (1973) [33] as an ecosystem’s ability to return to stability after disturbances. This idea expanded to urban systems [34] and later to logistics and supply chains, where resilience is associated with recovery speed and disruption absorption.
SCR has emerged as a vital research topic in supply chain management, defined as the ability to prepare for, respond to, and recover from disruptions. Ponomarov and Holcomb (2009) [35] introduced SCR as the adaptive capacity of supply chains to handle disruptions, while subsequent studies linked SCR to relational capabilities and practices such as supply chain redesign, collaboration, agility, and risk management [36]. These elements enhance stability and mitigate risks during disruptions.
The COVID-19 pandemic brought new perspectives to SCR research. Stephanie and Daniel (2020) [37] proposed a strategic mitigation model comprising leadership, preparedness, digitization, resilience, and pivot dimensions, offering practical guidance for handling global pandemics and large-scale disruptions. Jessica and Alejandro (2021) [38] introduced a scenario-planning and system-analysis-based roadmap for SCR, enabling organizations to plan for unexpected events with a comprehensive framework from exploration to monitoring. Stephens et al. (2022) [39] using the Stimulus–Organism–Response model, studied the relationship between SCR and market performance, emphasizing the critical role of organizational culture. A culture of vigilance prepares firms for disruptions, enhancing resilience and improving market outcomes.
In this study, we propose that SCR is the ability of a supply chain to adapt, recover, and transform when facing disruptions or uncertainties.

3.2. Understanding of Supply Chain Risk Management

Supply chain risk arises from external factors, internal vulnerabilities, and network design issues, leading to adverse events with significant consequences [4]. External risks include low-probability, high-impact events like natural disasters and pandemics, while internal risks stem from operational disruptions such as material shortages and equipment failures [40]. These risks, amplified by vertical dependencies, can trigger a “ripple effect” across supply chain partners. Traditional cost-efficient designs like JIT systems often lack resilience during crises [41], underscoring the need for resilience-focused design to enhance supply chain robustness.
Supply chain risk management aims to improve supply chain continuity and profitability while reducing vulnerabilities through collaborative risk management processes [42]. By enhancing risk identification, assessment, mitigation, and monitoring, supply chain risk management optimizes resilience [43]. Recent research highlights shifting focus from individual firms to managing risks across supply chain networks, embedding risk identification and response into strategic decision making to create systemic frameworks that minimize disruptions and their impacts [44].
Supply chain risk management studies encompass risk identification, assessment, and mitigation. SCR is categorized into supply–demand mismatches and operational disruptions (e.g., natural disasters, strikes, political instability). Parast and Subramanian (2021) [45] identified four drivers: demand, supply, process (internal), and environmental (external) disruptions. Increasingly, researchers are exploring the role of digital and intelligent technologies in supply chain risk management. Rauniyar et al. (2023) [46] highlighted blockchain’s role in improving transparency and resilience, while fostering collaboration and information sharing. Khan and Keramati (2023) [47] noted that smart technologies like barcodes and social media effectively address complexity and cost-related risks. Yang et al. (2023) [48] demonstrated that machine learning enhances risk prediction accuracy and responsiveness, reducing overall supply chain risk.
This study defines supply chain risk management as the process of identifying, assessing, and addressing risks that may affect the operation of the supply chain to minimize losses, ensure stability, and enhance competitiveness.

3.3. Understanding of Digital Capability

3.3.1. Definition of Digital Capability

In recent years, digital transformation research has brought digital capability to the forefront of academic and industry discussions. Scholars have expanded the understanding of digital capability by examining how organizations perform in digital practices. However, its definition remains inconsistent, with research primarily focusing on three perspectives: resource value realization, digital technology application, and dynamic adaptability [49].
From the resource perspective, digital capability involves identifying and utilizing complementary resources effectively, though it overlooks drivers like organizational structures and processes [50]. From the technology perspective, digital capability highlights the skills and innovation needed to develop and manage scalable, flexible digital technologies, enabling value creation and collaborative innovation [51]. From the adaptability perspective, digital capability allows organizations to integrate and reconfigure resources to address uncertainty and seize opportunities in volatile environments, ensuring sustainable competitive advantages [52].
This study defines digital capability as the ability to use digital technologies and tools to collect, process, and analyze data in order to support decision making, optimize processes, and drive innovation.

3.3.2. Dimension of Digital Capability

A review of the literature reveals ongoing debate among scholars regarding the definition of digital capability, leading to a lack of consensus on its structural dimensions. Some researchers conceptualize digital capability as a unidimensional construct, but the majority view it as a multidimensional concept. Lenka et al. (2017) [53], using qualitative data from four case studies, were pioneers in classifying digital capability into three key dimensions: analytical, connectivity, and intelligence capabilities. Tumbas; et al. (2020) [54], through interviews with chief digital officers across various industries, identified three dimensions: digital innovation, data analytics, and customer engagement. Levallet and Chan (2018) [55] proposed a two-dimensional structure comprising IT infrastructure capability and information management capability, emphasizing both technical and managerial aspects.
Warner and Wäger (2019) [56] extended the traditional dynamic capability framework by introducing three dimensions of digital capability: digital sensing, digital seizing, and digital transformation, offering a systematic perspective on sensing and responding processes. Building on this, we propose that digital capability relies on robust digital infrastructure, advanced data analytics, and the application of analytical insights to strategic decision making, aligning with Gong et al. (2022) [57], who categorize digital capability into three dimensions: digital infrastructure capability, digital analysis capability, and strategic support capability.

3.4. The Relationship Between Supply Chain Risk Management and Supply Chain Resilience

The importance of supply chain risk management in detecting disruptions, minimizing their effects, and strengthening SCR has been widely explored. Studies by Manuj and Mentzer (2008) [43] and Fan and Stevenson (2018) [58] identified supply chain risk management as a vital tool for addressing risks effectively. By mitigating risk impacts and enhancing knowledge reserves related to risks, supply chain risk management plays a direct role in improving SCR. El Baz and Ruel (2021) [6] underscored the significance of supply chain risk management practices in building resilience and robustness, particularly during the COVID-19 pandemic, further validating the Resource-Based View (RBV) and OIPT.
From an information processing perspective, Rashid et al. (2024) [29] explored how information processing capabilities (e.g., disruption orientation and visibility) and digital supply chains enhance SCR through supply chain risk management. Their findings indicate that information processing capabilities significantly influence supply chain risk management and SCR, with a stronger effect on supply chain risk management. Similarly, Al-Ayed and Al-Tit (2023) [59] demonstrated through empirical research that the Internet of Things (IoT), as a mediating variable, enables supply chain risk management to directly and indirectly enhance SCR.
Other studies have examined the specific mechanisms through which different supply chain risk management practices contribute to SCR. Um and Han (2020) [60], using the dynamic capability framework, identified resilience capability as a mediator between supply chain risks and SCR with mitigation strategies serving as a moderator. Harju et al. (2023) [10] showed that procurement digitalization enhances SCR by improving information sharing and risk management capabilities, thereby reducing uncertainty. Gupta et al. (2022) [50], using a contingency RBV approach, confirmed the significance of fourth industrial revolution technologies, such as big data analytics and additive manufacturing, in enhancing risk control capabilities and improving SCR.
These studies collectively suggest a close interdependence between supply chain risk management and SCR. For instance, fostering a risk management-oriented culture is considered essential for building resilient supply chains. Thus, this study proposes the following hypothesis:
H1: 
Supply chain risk management positively influences SCR.

3.5. Moderating Effect of Digital Capability

Recent academic research has extensively explored the moderating effects of digital capability in organizational management. Existing literature highlights the significant impact of digital capability across various fields and contexts, primarily in the following areas:
(1)
Organizational Performance and Digital Transformation
Research demonstrates that digital capability significantly influences organizational performance. Chatterjee et al. (2023) [61] identified that dynamic capabilities improve employee and organizational performance via workplace digital transformation. Martins (2022) [62] confirmed that digitalization enhances the positive effects of dynamic capabilities on SME performance. Saleem et al. (2023) [63] highlighted that digitalization boosts employee resilience, strengthening the link between task challenges and performance, especially during COVID-19. Aghazadeh et al. (2023) [64] stressed the moderating role of digital business model maturity in transforming digital platform capability into SME growth. Lin and Huang (2023) [65] observed that digitalization’s moderating effect on renewable energy integration intensifies beyond a critical threshold. Zhou et al. (2023) [66] validated rural digitalization’s positive moderating role in shaping the environmental impact of green mergers and acquisitions.
(2)
Innovation and Institutional Environment
Jiao et al. (2022) [67] examined how the institutional environment influences operations in a digital context, finding that government digitalization significantly moderates the technological entrepreneurship ecosystem, with digital networks and innovation culture exerting a notable influence on entrepreneurial environments. Lee et al. (2024) [68] used SEM to show that digital capability strengthens the relationship between institutional pressures and triple-bottom-line performance while indirectly enhancing this effect through environmental, social, and governance strategies.
(3)
Supply Chain Management and Performance
Liu and Chiu (2021) [69] showed that supply chain digitalization positively moderates the link between supply chain integration and firm performance. Similarly, Ali et al. (2018) [70] found that trade digitalization moderates the relationship between supply chain finance and firm performance. Chatterjee et al. (2022) [71] found that digitalization levels in family firms moderate the impact of antecedents on internationalization intent. Rodríguez and Barbier (2024) [72] highlighted that human factor management’s impact on productivity is amplified in digitalized environments, especially in areas beyond performance evaluation. Feng et al. (2024) [73] highlighted the impact of relational embeddedness on supply chain transparency and confirmed that digitalization enhances the positive effects of supplier concentration. Wang et al. (2024) [74] investigated how digital capability positively contributes to mitigating pandemic disruptions, thereby boosting firm resilience. Finally, Harju et al. (2023) [10] noted that data analytics significantly reduce supply chain uncertainty by improving information processing capabilities, thereby enhancing SCR.
These studies collectively validate the moderating role of digital capability across various fields. However, its subdimensions (digital infrastructure capability, digital analysis capability, and strategic support capability) require further investigation in the context of SCR. Based on the reviewed literature, we propose the following hypotheses:
H2: 
Digital capability positively moderates the relationship between supply chain risk management and SCR.
H3: 
(a) Digital infrastructure capability, (b) digital analysis capability, and (c) strategic support capability exert varying degrees of positive moderating effects on the relationship between supply chain risk management and SCR.
In summary, the theoretical framework proposed in this paper is shown in Figure 2.

4. Research Method

4.1. Measurement Development

Based on a literature review of digital capability definitions and its sub-dimensions, we define digital capability as comprising 3 sub-dimensions: digital infrastructure capability, digital analysis capability, and strategic support capability. Measurement items were adopted from Gong et al. (2022) [57], as their scale aligns well with the conceptualization of digital capability in the context of supply chains. This include 5 items each for digital infrastructure capability and digital analysis capability, and 4 items for strategic support capability, totaling 14 items. digital capability was measured holistically across these dimensions. SCR was assessed using an 8-item scale from Madhavika et al. (2023) [8], which has been used to assess the resilience of the tea supply chain during the COVID-19 pandemic and is highly relevant to the context of this study. On the other hand, supply chain risk management relied on a 4-item scale from Manal Munir et al. (2020) [75], which evaluates the level of supply chain risk management across four aspects: detection, prevention, response, and recovery from disruptions.
To enhance the applicability of the measurement scale to the electric vehicle industry, we made adjustments to certain items. For example, the original items from Madhavika et al. (2023) [8] were designed for the tea supply chain; therefore, we replaced references to “tea supply chain” with “supply chain” to ensure contextual relevance. To ensure cross-cultural applicability, the scale development process involved translating the original English items into Chinese while refining the language for clarity and contextual alignment. A professional translator then back-translated the questionnaire into English to verify semantic equivalence and maintain consistency.
Studies have shown that a 5-point Likert scale generally provides higher reliability. Compared to fewer options (e.g., a 3-point scale), it helps avoid oversimplification of respondents’ attitudes and opinions, ensuring a more accurate reflection of subtle differences. On the other hand, a larger number of options (e.g., a 7-point scale) may increase cognitive load, making it harder for respondents to clearly distinguish between adjacent choices, which could affect data reliability and consistency [76]. Therefore, we adopted a 5-point Likert scale for measurement, where 1 represents “strongly disagree” and 5 represents “strongly agree” [77].
To validate the questionnaire’s relevance in EV supply chain context, 5 industry experts rated the items’ relevance to their respective dimensions. The Item-Content Validity Index (I-CVI) and Scale-Content Validity Index (S-CVI) values were calculated. While most items achieved an I-CVI of 1 [78], two items from digital infrastructure capability, one from digital analysis capability, and two from SCR scored 0.8, below the critical threshold [79]. The scale’s overall S-CVI/Ave was 0.97, exceeding Polit and Beck’s (2006) [79] recommended threshold of 0.9, indicating strong content validity. Items with low I-CVI values were removed based on expert feedback, resulting in the final questionnaire items presented in Table 1.

4.2. Measurement Validation and Reliability

To further validate the scale’s applicability in the EV industry, we conducted a pilot analysis using a random sampling technique. A total of 59 samples were randomly selected from our dataset (detailed in Section 4.3). The analysis involved item analysis and reliability testing to assess scale validity and reliability. First, we divided responses into high-score and low-score groups and conducted independent-sample t-tests to compare mean values and check for significant differences (p-values). Second, reliability tests were conducted for each construct.
As shown in Table 1, the absolute values of the critical ratio for all items exceeded 1.96 and were statistically significant. The reliability coefficients for all constructs were above 0.7. However, the Corrected Item-Total Correlation (CITC) for the first item of DAC1 was below 0.3 (0.237), while all other items had CITC values above 0.5. After removing DAC1, the Cronbach’s α for the DAC construct increased from 0.741 to 0.817. Based on these results, DAC1 was excluded, and all other items were retained [80].

4.3. Sample and Data Collection

In this study, research volunteers were systematically recruited through a targeted questionnaire on the “Credamo” platform, focusing on professionals in EV supply chain industry. The recruitment process implemented stringent selection criteria: participants were required to provide a brief work histories, demonstrating industry-specific experience. To ensure comprehensive geographic representation across China, a minimum 10 km distance between volunteer locations was established. A total of 713 initial applicants underwent rigorous screening. Exclusion criteria included less than three years of industry experience, lack of supply chain-related work experience, or employment outside the EV supply chain sector. Where multiple candidates from the same company applied, only the highest-position individual was selected. Ultimately, 593 qualified volunteers comprised the research sample pool.
The determination of sample size follows the “10-times rule”, which states that the minimum sample size should be at least 10 times the number of measurement items for each construct [80]. In this study, the construct with the most items is Supply Chain Resilience (6 items), so based on the “10-times rule”, the minimum sample size should be 60. Additionally, statistical power analysis using G*Power was conducted, considering a medium effect size (f2 = 0.15) and 80% statistical power. The calculation results indicate that for the basic path model, the minimum sample size should be 85 to effectively detect significant effects [81]. Given that this study involves moderation effects, which increase the complexity of the model, it is recommended that the sample size should range between 150 and 300 to ensure the robustness and effectiveness of the moderation effects.
Participants for the formal survey were randomly selected from the sample pool. To avoid duplication, those who participated in the pilot study were excluded. A total of 300 questionnaires were distributed, with invalid responses (e.g., incomplete or quickly completed) excluded. This resulted in 249 valid responses, achieving an 83% response rate. The valid sample size falls within the recommended range of 150–300. The sample’s demographic characteristics are shown in Table 2.

5. Results

5.1. Measure Validation and Reliability

This study designed two SEM models. Model 1 (Figure 3a) focuses on the three key sub-dimensions (digital infrastructure capability, digital analysis capability, and strategic support capability) and how they moderate the relationship with supply chain risk management and SCR. Model 2 (Figure 3b) investigates the overall moderating effect of digital capability on this relationship. These models aim to demonstrate how different aspects of digital capability significantly influence SCR, especially in an era marked by growing uncertainty and complexity.
To assess the internal consistency and reliability of each construct, Cronbach’s α, rho_A, and composite reliability were used. As shown in Table 3, all constructs had Cronbach’s α coefficients well above 0.7, and rho_A and composite reliability values were above 0.8, indicating satisfactory reliability [82]. Convergent validity was confirmed through significant factor loadings and AVE [80]. Table 4 reveals that factor loadings ranged from 0.723 to 0.901 in Model 1 and 0.634 to 0.862 in Model 2, indicating effective measurement of constructs and supporting convergent validity. Additionally, the AVE for each construct exceeded 0.5, further confirming this validity. Multicollinearity was checked using VIF values, which were all below the 3 threshold [80], confirming no multicollinearity in the study.
This study evaluated discriminant validity using factor loadings, the Fornell–Larcker criterion, and HTMT values. Results showed that factor loadings were significantly higher on their respective constructs, the square root of AVE exceeded inter-construct correlations (Table 5), and all HTMT values were below 0.85. These findings confirm strong discriminant validity of the measurement model.

5.2. Hypothesis Testing

SmartPLS 3.0 was chosen for Structural Equation Modeling (SEM) analysis due to its suitability for handling complex models with latent variables, small-to-moderate sample sizes, and non-normal data distributions. Unlike Covariance-based SEM (CB-SEM), which requires large sample sizes and strict assumptions about data normality, Partial Least Squares SEM (PLS-SEM) is more appropriate for exploratory research and theory development [83].
The evaluation of model fit in the PLS-SEM analysis indicates that both Model 1 and Model 2 exhibit strong explanatory power and acceptable overall fit, the data as shown in Table 6. According to recommended thresholds, an R2 value above 0.26 suggests substantial explanatory power [81], while an SRMR value below 0.08 indicates a good model fit [84]. Additionally, an NFI value above 0.80 is generally considered acceptable [85]. Specifically, Model 1 achieves an R2 of 0.642, demonstrating strong explanatory capability, with an SRMR of 0.06, indicating a good fit, and an NFI of 0.841, reflecting an acceptable fit. Similarly, Model 2 attains an R2 of 0.603, also showing strong explanatory power, with an SRMR of 0.07 and an NFI of 0.825, both meeting acceptable thresholds. These results confirm the robustness of the proposed models in explaining supply chain resilience within the research framework.
Model 1 results show a positive correlation between supply chain risk management and SCR (ß = 0.561, t = 9.187) with a substantial impact (f2 = 0.436), supporting H1. Digital analytical capability (ß = 0.2, t = 3.829) significantly moderates this relationship, though the effect is small (f2 = 0.061), supporting H3b (Figure 4a). However, the moderating effects of digital analysis capability (ß = −0.013, t= 0.232) and strategic support capability (ß = −0.024, t = 0.446) are weak and negative, suggesting a slight weakening of the supply chain risk management-SCR relationship with increases in digital analysis capability and strategic support capability, but these effects are not significant due to their small size. Model 2 results also show a positive supply chain risk management–SCR correlation (ß = 0.554, t = 9.125) with a substantial impact (f2 = 0.415), supporting H1. Overall digital capability (ß = 0.150, t = 3.479) significantly moderates this relationship with a small effect size (f2 = 0.058), supporting H2 (Figure 4b).

6. Conclusions

6.1. Discussion on Results

6.1.1. The Positive Impact of Supply Chain Risk Management on SCR Is Significant

The data analysis shows that supply chain risk management positively impacts SCR, indicating that effective risk identification, assessment, mitigation, and monitoring measures can greatly enhance a company’s resilience to uncertain risk factors. In EV supply chain, this positive relationship may be particularly critical, given the high-risk scenarios the industry faces, such as policy changes, technological advancements, and market demand fluctuations. Strengthening risk management can help the supply chain better navigate potential crises, thereby maintaining continuity and efficiency in operations.

6.1.2. The Moderating Effect of Digital Capability and Digital Analysis Capability Is Significant

Digital capability significantly moderates the relationship between supply chain risk management and resilience. This indicates that when a company possesses a certain level of digital capability, the effectiveness of risk management measures is further amplified, thereby enhancing SCR [57]. Digital capability enhances risk management efficiency and effectiveness through data monitoring, analysis, and swift decision support.
Digital analytics, as a key aspect of digital capability, significantly moderates the relationship between supply chain risk management and resilience. This indicates that by leveraging efficient data analytics tools (e.g., predictive analytics and machine learning models), companies can enhance the accuracy of risk identification and the specificity of corresponding strategies, thereby significantly improving SCR [86]. In the EV supply chain, digital analytics enables precise demand forecasting [87], logistics optimization, and inventory management, minimizing the risk of disruptions from market fluctuations. In practice, supply chain managers could prioritize investing resources in data analytics tools and talent development to more effectively support risk management decisions, thereby significantly strengthening SCR.

6.1.3. The Moderating Effects of Digital Analysis Capability and Strategic Support Capability Are Not Significant

  • Digital Infrastructure Capability
The deployment of digital infrastructure, such as cloud computing platforms and IoT devices, serves as the foundation for digital transformation. However, its “supportive” nature may limit its direct contribution to enhancing the effectiveness of supply chain risk management. Digital infrastructure primarily provides technical support for data collection, storage, and transmission, rather than directly participating in risk management decision-making processes [57], leading to certain challenges. Its passive functionality limits its role to data collection and transmission, preventing active identification and management of supply chain risks. If significant investments are made in digital infrastructure without a corresponding enhancement of data analysis capabilities or managerial decision-making skills, its contribution to improving SCR may be limited. Furthermore, the lack of alignment between the use of infrastructure and the specific needs of supply chain risk management could result in resource inefficiencies or wastage.
  • Strategy Support Capability
The insignificant moderating effect of strategic support capability on the supply chain risk management–resilience relationship may stem from its “macro-level” nature. Specifically, strategic support capability primarily focuses on long-term planning and prioritization of resource allocation, whereas supply chain risk management often requires rapid responses and real-time adjustments. This mismatch in time dimensions may weaken its moderating effect. Additionally, strategic support capability is more oriented toward enterprise-wide planning, while risk management relies heavily on support at the operational execution level [6], limiting its direct influence at a micro level. In the context of highly dynamic and complex supply chain environments, strategic support capability alone may struggle to address diverse risk scenarios effectively, further constraining its impact on the outcomes of risk management.

6.2. Practical Inspiration

Based on research findings, EV enterprises should prioritize investment in key factors that significantly enhance supply chain resilience (such as supply chain risk management and digital capabilities) to improve their ability to cope with uncertainties. Meanwhile, for factors with insignificant moderating effects (such as digital infrastructure capability and strategic support capability), enterprises should treat them as long-term optimization directions, focusing on how to better integrate these capabilities into risk management practices to overcome bottlenecks and enhance overall supply chain resilience.

6.2.1. Priority Investment Targets: Factors Positive Affecting SCR

(1)
Supply Chain Risk Management
Research findings indicate that supply chain risk management has a positive impact on supply chain resilience. This suggests that enterprises can significantly enhance their ability to manage uncertainties through systematic risk identification, assessment, mitigation, and monitoring measures. Therefore, companies should prioritize the establishment of a comprehensive risk management framework [44], develop standardized risk management processes, and embed them into daily operations. For example, enterprises can implement supply chain risk mapping tools to systematically identify potential risk sources [88]. Additionally, companies should regularly conduct risk drills, simulating emergencies (such as natural disasters or policy changes) to test their response capabilities and optimize contingency plans. Furthermore, enhancing risk information-sharing mechanisms with suppliers and logistics partners ensures a quick and coordinated response in the event of risks [10], thereby improving overall supply chain responsiveness and risk resistance.
(2)
Digital Capability and Digital Analysis Capability
Research findings reveal that digital capability and digital analytics capability play a significant moderating role between supply chain risk management and resilience. This implies that digital capabilities can enhance the effectiveness of risk management measures, thereby further strengthening supply chain resilience. Consequently, enterprises should prioritize investment in data analytics tools, such as AI-driven predictive analytics platforms and real-time monitoring systems [89], to improve the accuracy and speed of risk identification and response. Simultaneously, companies should focus on cultivating digital talent and forming cross-functional data analytics teams to ensure that data-driven insights effectively support risk management decision making. For instance, in the EV supply chain, machine learning models can be used to predict price fluctuations of battery raw materials [3], enabling proactive procurement strategy adjustments. Additionally, enterprises should promote digital collaboration within the supply chain by leveraging blockchain technology or cloud-based collaboration platforms [90] to achieve data sharing across different supply chain segments, thereby enhancing transparency and coordination.

6.2.2. Long-Term Development Strategies: Factors with Unsupported Moderating Effects

Although some factors did not exhibit significant moderating effects, they still play a fundamental or supportive role in the long-term development of supply chain management and hold considerable potential for future growth. Enterprises should adopt long-term development strategies to integrate these capabilities effectively, forming a comprehensive pathway for enhancing supply chain resilience.
(1)
Digital Infrastructure Capability
Although the present study did not verify a significant moderating effect of digital infrastructure capability, it remains a critical foundation for digital transformation and holds substantial growth potential in the future. Enterprises should treat it as a long-term development goal, ensuring that infrastructure investments align with supply chain resilience needs. For example, companies can deploy IoT and cloud computing platforms to enable real-time collection, storage, and analysis of supply chain data [91], thereby improving risk prediction and response capabilities. Furthermore, enterprises must ensure the effectiveness of digital infrastructure investments, focusing on how digital technologies can directly empower risk management and enhance overall supply chain visibility and intelligence. Additionally, strengthening data security and privacy protection is essential to ensure the reliability and confidentiality of critical supply chain information.
(2)
Strategic Support Capability
EV companies should integrate strategic planning with short-term operations and rapid response mechanisms [92]. While strategic support can provide resource allocation and long-term development direction, addressing dynamic risks in the supply chain requires aligning strategic levels with the coordination and support at the execution level. This integration helps enhance the ability to manage diverse risk scenarios effectively.

6.3. Theoretical Significance

This study highlights the multidimensional nature of digital capability and its differentiated role in SCR, emphasizing the need to analyze complex variables at the dimensional level. It provides a case study for interpreting moderating variables by dimensions, offering new analytical perspectives for future research in related fields. Additionally, this research enriches the theoretical framework of SCR, explaining the positive impact of supply chain risk management on resilience and revealing the critical role of digital capability as a moderating variable. By breaking down the components of digital capability, this study clarifies its specific contributions to supply chain management, providing valuable insights for understanding the theoretical mechanisms of supply chain digitization.

6.4. Limitations and Future Research

This study is based on data from China’s EV industry, and its generalizability may be limited by industry and region-specific characteristics. Due to differences in global market environments, policy support, and technological maturity, the findings of this study may not be directly applicable to Western markets such as Europe and the United States. Future research could further validate the applicability of these conclusions in different market contexts.
In terms of measuring digital capability, the focus was solely on three dimensions: digital infrastructure capability, data analytics capability, and strategic support capability, without including other crucial areas such as organizational culture in digital transformation. Future research could further supplement and expand the measurement dimensions of digital capability, including organizational culture and technology application capability, and explore their impacts on supply chain risk management and resilience in different contexts. For example, future studies could examine the role of corporate culture in digital transformation or investigate how technology application capability influences supply chain resilience in various market environments, thereby establishing a more comprehensive evaluation framework.
Additionally, the use of cross-sectional data analysis may not fully capture the causal relationships between supply chain risk management, digital capability, and resilience. Future research could adopt longitudinal data or experimental methods to explore the dynamic mechanisms at play. Expanding the scope of research to include cross-industry comparisons between the EV industry and other industries (such as manufacturing, pharmaceuticals, consumer goods, etc.) could reveal different levels of reliance on digital capabilities and their varying roles in risk management and resilience building, providing theoretical support for industry-specific strategic recommendations.

Author Contributions

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

Funding

This research received no external funding.

Data Availability Statement

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Organizational information processing theory framework.
Figure 1. Organizational information processing theory framework.
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Figure 2. Proposed conceptual framework.
Figure 2. Proposed conceptual framework.
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Figure 3. (a) Framework and testing result of Model 1; (b) Framework and testing result of Model 2. Note: *** p < 0.001, ** p < 0.01.
Figure 3. (a) Framework and testing result of Model 1; (b) Framework and testing result of Model 2. Note: *** p < 0.001, ** p < 0.01.
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Figure 4. (a) Simple slope plot of DAC’s moderating effect, (b) Simple slope plot of DC’s moderating effect. Note: DAC: Digital analysis capability, DC: Digital capability.
Figure 4. (a) Simple slope plot of DAC’s moderating effect, (b) Simple slope plot of DC’s moderating effect. Note: DAC: Digital analysis capability, DC: Digital capability.
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Table 1. Pilot study.
Table 1. Pilot study.
ConstructDimensionItemSourceCritical RatioCITCCIIDαResult
Digital capability (DC)Digital infrastructure capability (DIC)DIC1 We can access large amounts of unstructured (e.g., text, images, audio) and real-time data.Gong et al. (2022) [57]3.264 **0.5770.6070.728Keep
DIC2 We can integrate data from multiple sources into databases.3.845 ***0.5070.69Keep
DIC3 We use digital technologies (e.g., big data, cloud computing, mobile) to process and analyze data.3.839 ***0.5650.621Keep
Digital analysis capability (DAC)DAC1 We collect customer feedback through digital channels (e.g., official website, e-commerce platforms, customer management systems).3.399 ***0.2370.8170.741Delete
DAC2 We use digital technologies (e.g., OA systems) for internal connectivity.8.053 ***0.5940.647Keep
DAC3 We effectively predict customer demand through data analysis.7.151 ***0.5780.658Keep
DAC4 We support decision making through data visualization (e.g., product analysis charts, profit growth graphs).12.583 ***0.770.539Keep
Strategic support capability (SSC)SSC1 Our executives clearly understand where to use digital analytics results.7.458 ***0.6670.7020.789Keep
SSC2 Our executives are aware of the digital transformation goals and needs of each department.6.314 ***0.5930.741Keep
SSC3 Our executives can use digital analytics to support management decisions.6.717 ***0.5330.777Keep
SSC4 Our executives can use digital analytics to support management decisions.6.571 ***0.6140.729Keep
Supply Chain Resilience (SCR)SCR1 The supply chain can quickly respond to disruptions during interruptions.Madhavika et al. (2023) [8]5.706 ***0.6690.8470.869Keep
SCR2 The supply chain can provide appropriate responses to crisis scenarios during interruptions.8.175 ***0.6330.853Keep
SCR3 The supply chain can promptly address critical situations during interruptions.6.658 ***0.6830.847Keep
SCR4 The supply chain can prevent disruptions before they occur.7.405 ***0.7040.842Keep
SCR5 The supply chain has the potential to recover from disruptions in a short time.7.183 ***0.6760.845Keep
SCR6 The supply chain can recover from disruptions with minimal investment.7.939 ***0.6670.847Keep
Supply chain risk management (SCRM)RM1 Our company can identify potential risks and disruptions in the supply chain.Manal Munir et al. (2020) [75]7.14 ***0.7490.8070.862Keep
RM2 Our company can accurately assess the severity of risks.8.727 ***0.6840.836Keep
RM3 Our company has comprehensive strategies for supply chain risk prevention and response.6.686 ***0.6830.834Keep
RM4 Our company can flexibly implement risk response strategies to effectively mitigate supply chain risks.7.332 ***0.7250.818Keep
Note: *** p < 0.001, ** p < 0.01; CITC: Corrected Item-Total Correlation; CIID: Cronbach’s α if item deleted.
Table 2. Sample demographic information.
Table 2. Sample demographic information.
Demographic CharacteristicsFrequencyPercentage
Enterprise TypeState-Owned10040.2
Privately Owned14959.8
Employees<10062.4
100–2998132.5
300–99912951.8
1000–1999239.2
≥2000104
History1–5 years135.2
6–10 years16867.5
11–25 years5923.7
26–50years83.2
>50 years10.4
Role in EV supply chainRaw Material Supplier3313.3
Component Manufacturer9739
Vehicle Manufacturer197.6
Logistics Provider228.8
Distributor3413.7%
Technical Service Provider4417.7%
Table 3. Cronbach’s α coefficient.
Table 3. Cronbach’s α coefficient.
ConstructItem NumbersCronbach’s αrho_ACRAVE
Digital infrastructure (DIC)30.780.780.870.7
Digital analysis capability (DAC)30.790.820.880.7
Strategic support capability (SSC)40.830.840.890.67
Digital capability (DC)10 (contains items of DIC, DAC, and SSC)0.890.90.910.5
Supply chain resilience (SCR)60.870.880.90.61
Supply chain risk management (SCRM)40.840.840.890.67
Note: Cronbach’s α (criterion: ≥0.7); Composite Reliability (CR) (criterion: ≥0.7); rho_A (criterion: ≥0.7); Average Variance Extracted (AVE) (criterion: ≥0.5).
Table 4. Results of confirmatory factor analysis.
Table 4. Results of confirmatory factor analysis.
ConstructsItemsModel 1Model 2
Loading (>0.6)VIF (<3)Loading (>0.6)VIF (<3)
Digital infrastructure capability (DIC)DIC10.9022.5140.752.856
DIC20.7611.3370.6411.554
DIC30.8322.1920.6342.26
Digital analysis capability (DAC)DAC10.8211.7550.6461.824
DAC20.8211.6320.6721.848
DAC30.8671.6150.6841.791
Strategic support capability (SSC)SSC10.7861.6610.7261.758
SSC20.7991.8460.7522.044
SSC30.852.080.792.207
SSC40.8251.7410.7641.908
Supply chain resilience (SCR)SCR10.7231.6160.721.616
SCR20.8632.7740.8622.774
SCR 30.741.7180.7391.718
SCR 40.7461.7490.7481.749
SCR50.8162.0030.8182.003
SCR 60.7952.1270.7952.127
Supply chain risk management (SCRM)SCRM10.8271.9410.8271.941
SCRM20.8231.8970.8231.897
SCRM30.8051.6640.8051.664
SCRm40.8291.860.8291.86
Table 5. Assessment of discriminant validity.
Table 5. Assessment of discriminant validity.
Model 1Model 2
ConstructDACDICSCRSCRMSSCConstructDCSCRSCRM
DAC0.837 DC0.708
DIC0.5450.834 SCR0.660.782
SCR0.4680.4530.782 SCRM0.6520.7180.821
SCRM0.5510.4530.7180.821
SSC0.6020.6390.6880.630.815
Note: Square root of AVE is on the diagonal, DAC: Digital analysis capability, DIC: Digital infrastructure capability, SCR: Supply chain resilience, SCRM: Supply chain risk management, SSC: Strategic support capability, DC: Digital capability.
Table 6. Results for hierarchical regression analysis.
Table 6. Results for hierarchical regression analysis.
HypothesisPathßMSTDEVt-Valuep-ValueCIf2Decision
Model 1: R2 = 0.642 (strong explanatory power); SRMR = 0.06 (good fit); NFI = 0.841(acceptable fit)
H1SCRM->SCR0.5610.5630.0619.1870.000[0.442, 0.684]0.436accepted
H3aModerating of DIC−0.013−0.0090.0570.2320.817[−0.115, 0.109]0.000rejected
H3bModerating of DAC0.2000.1960.0523.8290.000[0.091, 0.299]0.061accepted
H3cModerating of SSC−0.024−0.0270.0540.4460.655[−0.131, 0.081]0.001rejected
Model 2: R2 = 0.603(strong explanatory power); SRMR = 0.07(good fit), NFI = 0.825 (acceptable fit)
H1SCRM->SCR0.5540.5560.0619.1250.000[0.437, 0.676]0.415accepted
H2Moderating of DC0.1500.1470.0433.4790.001[0.061, 0.228]0.058accepted
Note: ß: path coefficients (original sample), M: sample mean, STDEV: standard deviation, CI: confidence interval, DAC: Digital analysis capability, DIC: Digital infrastructure capability, SCR: Supply chain resilience, SCRM: Supply chain risk management, SSC: Strategic support capability, DC: Digital capability.
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Li, Y.; Sukhotu, V. How Does Digital Capability Shape Resilient Supply Chains?—Evidence from China’s Electric Vehicle Manufacturing Industry. Future Internet 2025, 17, 123. https://doi.org/10.3390/fi17030123

AMA Style

Li Y, Sukhotu V. How Does Digital Capability Shape Resilient Supply Chains?—Evidence from China’s Electric Vehicle Manufacturing Industry. Future Internet. 2025; 17(3):123. https://doi.org/10.3390/fi17030123

Chicago/Turabian Style

Li, Yanxuan, and Vatcharapol Sukhotu. 2025. "How Does Digital Capability Shape Resilient Supply Chains?—Evidence from China’s Electric Vehicle Manufacturing Industry" Future Internet 17, no. 3: 123. https://doi.org/10.3390/fi17030123

APA Style

Li, Y., & Sukhotu, V. (2025). How Does Digital Capability Shape Resilient Supply Chains?—Evidence from China’s Electric Vehicle Manufacturing Industry. Future Internet, 17(3), 123. https://doi.org/10.3390/fi17030123

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