Content area
Purpose
This study aims to examine the impact of digital transformation (DT) on supply chain resilience (SCR) in Chinese manufacturing enterprises by analyzing its technological and managerial pathways in the digital era. To achieve this, the study extends the understanding of DT through the lens of organization information processing theory, clarifying its role in strengthening SCR.
Design/methodology/approach
A two-stage methodology was used, commencing with a qualitative study that led to the development of a research framework formed through the content examination of semi-structured interviews conducted with 20 industry specialists. Subsequently, a quantitative investigation was carried out using questionnaire responses from 366 middle and senior management personnel in the Chinese manufacturing enterprises to corroborate the research framework.
Findings
Both the technological and managerial dimensions of DT make a significant contribution to improving SCR, with supply chain collaboration (SCC) serving as an important mediator in this process. In addition, government financial and non-financial support is essential in moderating the influence of DT on SCR.
Originality/value
First, the authors extend the current literature by considering managerial transformation as another pivotal facet of DT within manufacturing enterprises. Second, by clarifying the mediating role of SCC and the moderating influence of two forms of government support, the authors illuminate the complex mechanisms by which DT enhances SCR.
1. Introduction
The reversal or deceleration of globalization, driven by factors such as natural disasters, trade disputes, political tensions, regional instability, pandemics and global economic crises, has led to increased uncertainty and risks in international trade. This, in turn, significantly increases the likelihood of supply chain disruptions, thereby challenging the security and stability of supply chains (Rodríguez-Espíndola et al., 2022; Gong et al., 2020). Supply chain resilience (SCR) enables enterprises to respond to uncertainties and unforeseen events, maintain business continuity, recover quickly and adjust operational strategies when confronted with external shocks (Qader et al., 2022; Shi et al., 2023), thereby ensuring the stability and reliability of the supply chain. Manufacturing, as a critical industry for enhancing a nation’s overall strength, not only drives economic growth and technological innovation but also plays a pivotal role in the stability of the national industrial chain (Cui et al., 2023). Therefore, strengthening the resilience of manufacturing supply chains is crucial for promoting the sustainable development of the real economy, enhancing overall competitiveness and enabling enterprises to better withstand external shocks.
The emergence of the Internet has compelled senior executives to recognize the transformative potential of digital technology in gaining a competitive advantage (Dubey et al., 2023). Digital transformation (DT) represents a new trend in the integration and development of traditional manufacturing enterprises within the digital domain. Companies that implement DT strategies can achieve sustained benefits (Hamdy, 2024). Therefore, DT is not merely a strategic option, but an imperative for many enterprises. Although DT brings substantial benefits to manufacturing, its implementation still faces several challenges, including required investments (Fang and Liu, 2024), workforce adaptation issues (Nicolás-Agustín et al., 2022), organizational flexibility (Dubey et al., 2021) and concerns about data management and security. Consequently, the potential of DT to enhance SCR remains underrecognized. Faruquee et al. (2021) emphasized the importance of effectively applying technology in DT efforts to strengthen SCR. Although many organizations are engaged in advanced technological updates and management practices (Niu et al., 2023), and have adopted appropriate technologies for transformation, these companies have failed to fully leverage the potential of DT due to inefficient management capabilities and a lack of focus on effective execution, which has led to unmet expectations. Accordingly, the first research question was formulated:
Given the overwhelming number of players in the supply chain, the need for more interplay and coexistence among these participants becomes crucial in bridging or reducing existing barriers (Uddin and Akhter, 2022). Supply chain collaboration (SCC) involves the synchronized and effective functioning of all stakeholders through information exchange, resource integration and collaborative decision-making (Li et al., 2024a). This includes strategic objectives, benefit allocation and risk management (Acquah et al., 2021). Numerous studies have demonstrated the vital role of SCC, a key aspect highlighted by Uddin (2022), who identified its positive impact on operational and innovation performance. Furthermore, integrating digital technology into SCC could substantially enhance supply chain management performance (Zhou et al., 2024). By leveraging digital tools and restructuring resources, enterprises can effectively mitigate supply chain risks and challenges. Accordingly, the second research question was formulated:
Moreover, considering the significant capital investment required for DT and its associated uncertainties (Guo et al., 2020), companies face substantial challenges in the absence of external support. Given the importance of DT in enhancing global competitiveness and promoting economic growth, governments around the world have been accelerating the process of DT in enterprises (Bao et al., 2021). Governments in many countries actively encourage enterprises to invest in new product development by providing financial support, including various forms of development subsidies and tax credits (Wang et al., 2023). In addition, government has been promoting enterprise DT through supportive regulations, workforce development and other initiatives. This not only fosters industrial innovation but also contributes to the expansion of the local digital economy (Ning and Yuan, 2023). Despite substantial evidence on the impact of government support (Baah et al., 2023), the nuanced relationship between DT, government support and SCR has yet to be fully explored. Accordingly, the third research question was posed:
This article presents several innovative perspectives on the subject. First, we aim to enrich the literature on DT by proposing strategies for advancing enterprise DT from both technological and managerial standpoints. Second, integrating both qualitative and quantitative methodologies provides a more comprehensive and appropriate exploration of the precise mechanisms through which DT enhances SCR. Finally, scrutinizing the role of government financial and non-financial support as a moderator highlights the crucial significance of such support in DT and strengthening SCR.
2. Literature review
Organizational information processing theory
Organizational information processing theory (OIPT) emphasizes the mechanism through which organizations use information processing capabilities to respond to information processing needs for achieving robust organizational performance (Galbraith, 1974). SCR is used to overcome disruptions caused by uncertainty (Faruquee et al., 2021) and achieving resilience requires processing large amounts of information (Dubey et al., 2021). Therefore, SCR research can apply OIPT. For example, Manikas et al. (2023) emphasized that big data utilization can enhance SCR to withstand extreme disruptions. According to Qader et al. (2022), from the perspective of information processing theory, Industry 4.0 can improve SCR and thus enhance supply chain performance. Although scholars have extensively studied this theory, it remains unclear how DT empowers SCR. This study attempts to bridge this gap and reveal how companies can foster SCC through DT, thereby enhancing SCR.
Supply chain resilience
SCR is considered as a dynamic capability that absorbs shocks, reduces vulnerability and maintains business continuity (Qader et al., 2022; Shi et al., 2023), resilience is critical for mitigating risks and enhancing performance for supply chains (Tortorella et al., 2024). Contemporary literature on resilience has focused primarily on examining the factors, antecedents and capacities necessary for establishing a resilient supply chain. Chowdhury and Quaddus (2016) emphasized proactivity by categorizing SCR as proactive, adaptive and resilient capabilities. Pu et al. (2024) identified sensing capability, seizing capability and reconfiguring capability as the three dimensions of resilience. Owing to the increasing globalization and evolving risk landscape, researchers studying the SCR have shifted their focus from internal capabilities to encompass a broader array of influencing factors. An analytical review of the existing literature shows that studies have primarily focused on the technical and organizational factors. From a technological perspective, big data, blockchain and artificial intelligence have garnered considerable attention. These technologies optimize resource allocation and enhance operational efficiency, thus strengthening resilience (Manikas et al., 2023; Modgil et al., 2022; Dubey et al., 2023; Zhao et al., 2023a; Tiwari et al., 2024). From an organizational perspective, studies on capabilities such as organizational flexibility, agility, risk management and top management commitment have examined how organizations integrate resources and deploy capabilities to address the risks and enhance performance of supply chains (Dubey et al., 2023; Uddin and Akhter, 2022). In conclusion, there is a pressing need for up-to-date research on the effects of antecedents on SCR, considering current technological advances and increased supply chain dynamics (Belhadi et al., 2024). Table 1 lists relevant research on SCR.
Digital transformation
DT is a continuous process of adapting to new business models, innovative products, evolving business processes and enhancing customer experience (Niu et al., 2023). It empowers businesses to enhance their strategic agility, making them more responsive and effective in adapting to rapidly changing market conditions (Peng and Tao, 2022). Consequently, DT has emerged as an essential element for entrepreneurs to effectively cope with external pressures while maintaining competitiveness and fostering business growth (Zhang et al., 2023). The application of digital technology is the linchpin of enterprise DT (Verhoef et al., 2021). Vial (2021) defined DT as the use of digital technology to transform the processes involved in creating and maintaining a competitive edge. Digital technology innovation involves the pursuit of creating new technologies and integrating them into existing products, production processes and business models (Fang and Liu, 2024). Over time, academic research has broadened its perspective, recognizing that DT requires not merely the application of digital technology within companies but also represents a process of organizational innovation (Li et al., 2024b). Successful DT depends on streamlining business operations (Nicolás-Agustín et al., 2022) and involves optimizing organizational structures (Chanias et al., 2019). Moreover, investing in IT talent development is crucial (Kee et al., 2023). DT encompasses not only the rapid advancement of technology but also the profound impact of digitalization on the organization, which includes, but not limited to, product transformation, processes, organizational structure and management principles. In this study, DT is categorized into technological transformation and managerial transformation. The technological transformation refers to the use of digital and innovative technologies to enhance existing business practices and products. The managerial transformation involves the organizational structure redesign and talent development, coupled with leadership development to foster innovation and digital market adaptability.
Supply chain collaboration
SCC can be viewed as both an internal and external resource that helps overcome traditional barriers of information and functional silos, promoting coordination and integration across the entire supply chain, thereby enabling companies to create value (Zhou et al., 2024; Uddin et al., 2024). SCC entails seamless cooperation among various links of the supply chain, working together synergistically to achieve comprehensive operational efficiency and shared business objectives (Acquah et al., 2021; Seow et al., 2024). Hamann-Lohmer et al. (2023) suggested that collaboration could enhance cooperation among partners by providing reliable real-time data and prompt responses to changes. Sharing facilities, equipment and technological assets with supply chain partners can reduce costs while optimizing resource utilization. Li et al. (2024a) proposed that businesses could jointly share benefits and risks to mitigate supply chain disruptions. Uddin and Akhter (2022) posit that SCC revolves around two key concepts: process collaboration and relationship collaboration. These elements work in tandem to achieve shared goals and equitably distribute returns. Expertise and joint strategies can help companies enhance the innovative potential of their partners. Overall, this approach not only enhances synergies but also increases the flexibility and adaptability of firms to market fluctuations and uncertainties (Cui et al.; 2023). In addition, sharing of knowledge, resources and technologies allows partners to collectively address external challenges, which contributes to greater overall resilience and competitiveness of supply chains (Jia and Li, 2024).
3. Research design
A mixed-method approach, including both qualitative and quantitative methodologies, is appropriate for three reasons: First, qualitative methods offer a profound understanding of a research topic, whereas quantitative methods provide rigorous measurement and hypothesis validation. Using surveys and interviews together offers a comprehensive overview of supply chain management practices (Dubey et al., 2023). Second, qualitative research can contextualize quantitative data, aiding researchers better understand phenomena and trends in the DT process. Finally, given the diverse and intricate nature of manufacturing DT across industries, firm sizes and production processes, existing theories may be insufficient to fully explain these phenomena, necessitating a hybrid approach for more comprehensive insights.
In the first stage, we used in-depth qualitative interviews, using semi-structured methods, with subject matter experts to explore the vast scope of DT and its potential to boost SCR. In the second stage, a questionnaire was distributed to validate our theoretical framework and research hypotheses. The research method is shown in Figure 1.
Stage 1 – the qualitative study
Semi-structured interviews
From July to December 2023, we conducted a total of 20 semi-structured interviews, ranging from 30 to 60 min, in partnership with school-enterprise cooperation programs, advanced business seminars and alumni association members. Each interview was carefully recorded and transcribed verbatim, ensuring that every detail was captured. The interviewees were all manufacturing experts from a wide range of fields, including automotive industries, pharmaceutical industries, chemical industries, food industries and all met our preselected criteria. The rich experience and deep expertise of these professionals in their fields have provided valuable support for our research work. Information on the researchers can be found in Table A1 (Supplementary Information). The interview process is semi-structured and offers a flexible and open format, ensuring thorough coverage of relevant data collection topics. The interview guide systematically examined the pertinent literature and it into two primary sections (Appendix). The first section collects fundamental information from each respondent, whereas the second part investigates the ongoing DT within the manufacturing industry and its myriad benefits.
Data analysis
The study used both a priori and inductive data analysis strategies (Miles, 1994), which included a “start list” to identify new and related findings. The three team members individually pinpointed key sentences on our start list by extensively reviewing and cross-referencing each transcript to compile a list of codes aligned with our framework. The team conducted an in-depth literature review to identify relevant theories and associated constructs that could be used to group and categorize the codes. In addition, we conducted a comprehensive analysis of the potential associations between SCC, GS and SCR. Tables A2 (Supplementary Information) and A3 (Supplementary Information) provide partial examples of respondents presenting key variables and their relationships.
Important findings
The data indicates that most managers are acutely aware that DT significantly influences the resilience of supply chains. Managers knew they were steering the company toward DT through technological and managerial transformation. For example, 13 respondents highlighted the importance of technological transformation and six emphasized managerial transformation. In addition, some respondents highlighted the need for collaboration and government support. Government support was specifically addressed by 16 respondents, with nine citing financial support and seven citing policy support.
Hypotheses development
3.2.1 DT and SCR
DT, including technological and managerial shifts, have a significant influence on overall SCR. Technological transformation involves deploying hardware and software systems, integrating advanced technologies such as QR codes and radio frequency identification into manufacturing resources and achieving high data integration across the supply chain (Lin and Mao, 2024). Furthermore, technological advancements such as big data and cloud computing enable continuous, real-time analysis of supply chain processes, aiding in demand prediction and inventory management (Peng and Tao, 2022). Managerial transformation entails optimizing organizational structure and enhancing employee digital capabilities. Streamlining organizational structures facilitates quicker response times, reduces decision transmission time, fosters cross-departmental innovation and knowledge sharing and improves organizational resilience (Dubey et al., 2021). Digital employees typically possess expertise in automation and intelligent technologies, facilitating the automation of supply chain business processes and mitigating the likelihood of disruptions (Kee et al., 2023). These outcomes collectively contribute to enhancing the resilience of companies to risk, thereby improving SCR. Therefore, we hypothesize that:
Digital transformation and supply chain collaboration
In a swiftly evolving and intensely competitive business environment, standardizing information exchange interfaces across the supply chain is crucial for maintaining stability, enhancing efficiency and fostering adaptability (Hamann-Lohmer et al., 2023). Presently, integrating IoT and blockchain technology with production processes facilitates real-time product tracking, ensures transparency within the supply chain and cultivates trust and cooperation among stakeholders (Ning and Yuan, 2023). Optimizing organizational structure improves interdepartmental relationships, enhances the collective understanding of the supply chain, reduces information barriers and promotes overall efficiency (Cui et al.; 2023; Dubey et al., 2021). Digital talent not only possesses superior analytical and decision-making skills but also excels in collaborative communication and teamwork, enabling effective coordination and communication with all stakeholders in the supply chain. By enhancing information sharing and data analysis capabilities with collaboration tools and digital monitoring systems, thereby enhancing overall SCC capability. Therefore, we hypothesize that:
Supply chain collaboration and supply chain resilience
SCC has emerged as a pivotal factor in improving SCR during disruptive events (Zhou et al., 2024). Jia and Li (2024) found that the strength of collaborative capabilities significantly influences SCR. By sharing information and resources, supply chain partners can strengthen their relationships and create more collaboration opportunities (Uddin, 2024). Practices like benefit and risk sharing help reduce the costs of supply chain, thus lowering the frequency of disruptions (Parast, 2020). Together, these elements enhance SCC’s effectiveness in maintaining supply chain stability and sustainability, improving risk management and mitigating the impact of risks, ultimately boosting SCR (Li, et al., 2024a). Therefore, the research hypothesis is formulated as follows:
Mediating effect of supply chain collaboration
SCC enhances resilience by enhancing agility and adaptability through the sharing and integration of resources and risks and is widely recognized as a key factor in effective management (Li et al., 2024a; Zhou et al., 2024; Seow et al., 2024). In cases of disruptions or risks, DT facilitates seamless communication and boosts information sharing, resulting in flexibility and responsiveness across the supply chain (Hamann-Lohmer et al., 2023). Technological advances drive collaboration between upstream and downstream partners, fostering transparency and problem-solving in a timely manner. Simultaneously, managerial transformation, involving structural and operational adjustments to aligned with digital strategies, strengthens supply chain integration (Li et al., 2024b). Technology enhances operational agility, managerial transformation enhances adaptability and long-term resilience, thereby increasing competitiveness. Based on this understanding, it can reasonably be assumed that SCC acts as a mediator in the relationship between DT and SCR. Therefore, we hypothesize the following:
Moderating effect of government support
The expected result of government support is SCR (Gong et al., 2020), which offers enterprises with critical resources to overcome constraints that impede their business operations (Guo et al., 2020). In addition, by playing direct and indirect roles in the adoption, promotion and successful implementation of DT, government support can be either financial (Dubey et al., 2023) or non-financial (Nguyen et al., 2023).
Government financial support (GFS) functions as a financial incentive for enterprises (Bao et al., 2021). There is evidence that governments, through measures such as subsidies, tax exemptions and loans, encourage businesses to increase investment in research and development for launching new products, thus, alleviating infrastructure and sunken cost pressures related to enterprise DT investments (Prodi et al., 2022; Wang et al., 2023). Furthermore, the widespread belief and dedication of governments to digitalization contributes to increased investment in digital technologies. Enterprises also benefit from government non-financial support (GNFS), such as talent development programs and policy assistance (Nguyen et al., 2023). Studies indicate that when government departments provide businesses with legal and regulatory support, it encourages them to adopt emerging technologies and foster continuous innovation (Baah et al., 2023). In addition, government support for employee training equips enterprises with digitally skilled workers and fosters innovative thinking among management. Support for technological improvements enhances employees’ skills, thereby promoting creativity and the ability to manage risks associated with uncertainty (Anwar and Li, 2021). Nevertheless, the relationship between government support and the integration of digital technologies with SCR remains underexplored. Figure 2 illustrates the research model, consolidating hypotheses and their interrelationships:
Control variables
To distinguish between industries, we use company size, age and market position as control variables. Larger companies typically have more resources than smaller ones, enabling them to better withstand external risks associated with investing in digital technology. Furthermore, larger companies are more likely to achieve high-level supply chain capabilities and resilience (Dubey et al., 2023). This study uses the number of employees as an indicator of company size, number of years since the company was founded as its age and company sales revenue as an indicator of the market position.
Stage 2 – quantitative analysis
Questionnaire development
The questionnaire underwent a three-step process. Initially, an extensive literature review was conducted, using established scales from reputable scholars. These scales were then translated by faculty members from the School of Foreign Languages and Management to ensure quality and content validity. Second, considering the profound digitalization in China and the unique characteristics of its manufacturing enterprises, the questionnaire was refined through consultation with researchers. Finally, to ensure comprehensibility and logical structure, the questionnaire was simplified, avoiding complex terminology.
After the preliminary design was completed, small-scale pretesting was conducted across 25 manufacturing enterprises. The results of the pretest underwent rigorous reliability and validity tests. Consequently, the developed measurement tool was proven to be suitable. The final questionnaire includes basic company information and measurement scales for various variables. Each latent variable is assessed by —three to six items using a five-point Likert scale, covering technological transformation, managerial transformation, SCC, government financial and non-financial support and SCR, as illustrated in Table 2.
Sample selection and data collection
The primary reasons behind conducting the survey in China are twofold: First, China has achieved remarkable economic development, emerging as the second-largest economy globally and the largest manufacturing nation, contributing nearly 30% of the world’s value added to manufacturing (Gu and Liu, 2022). Consequently, the Chinese manufacturing industry has received significant attention. Second, the Chinese Government has been committed to DT and has implemented progressive policies to foster the growth of the digital economy (Bao et al., 2021). Researchers can gain insights into the government’s policy trajectory and support measures by examining DT companies in China. This unique context provides an invaluable platform for exploratory research within the manufacturing industry.
Through random sampling, the questionnaire data was collected from manufacturing enterprises across various industries in China. The data was gathered through two channels to ensure data adequacy and diversity. First, online questionnaires were distributed to senior executives from companies that participated in semi-structured interviews, allowing us to engage directly with key decision-makers with deep insights into DT and resilience, thereby enhancing data quality. Second, we collaborated with SoJump.com, a widely used survey platform, to reach a wider range of manufacturing enterprises. Although most responses from SoJump.com also came from senior managers, anonymity of the platform encouraged more candid responses, improved data representativeness and reduced potential biases from single firms or subindustries. A total of 405 survey questionnaires were collected; after excluding questionnaires with incomplete or invalid responses, 366 questionnaires were considered valid, yielding a valid response rate of 90.37%. Table 3 shows the demographic information of the participants, including company type, enterprise age, annual revenue, number of employees and other related details.
Data analyses and results
Measurement validation and reliability. Reliability and validity were assessed using SPSS 25.0 and AMOS 24.0. Drawing from the method by Hair et al. (2011), we calculated Cronbach’s α and composite reliability (CR) as 0.881–0.924 and 0.838–0.874, respectively, all exceeding the threshold of 0.70, indicating strong reliability. Detailed results are presented in Table 4. Convergent validity was confirmed using average variance extracted (AVE) and factor loadings (Fornell and Larcker, 1981). All AVE values were greater than 0.5, and factor loadings were greater than 0.7, confirming sufficient convergent validity. Discriminant validity was also verified, as the square root of AVE for each variable exceeded the correlation coefficients in the corresponding rows and columns (Table 5).
Common method bias (CMB) and no response bias testing. Checking for CMB and no response bias is crucial in research. This study controlled for CMB through both procedural and statistical remedies (Kock et al., 2021). Respondents were informed about the academic purpose of the study and assured of anonymity to encourage candid responses (Saris and Gallhofer, 2020). The question item arrangement was optimized to minimize bias, including a clear introduction and strategic placement of variables to reduce subjective associations. After obtaining the data, Harman’s single-factor test was used to test any significant issues of CMB (Podsakoff et al., 2003), which involved conducting an exploratory factor analysis on all items, followed by an examination of the percentage of variance explained by the first principal component factor. This percentage was 15.594%, which is in line with Centobelli et al. (2019) recommendations, indicating that the CMB had no significant impact on the accuracy of the study results. To examine potential nonresponse bias, Armstrong and Overton (1977) t-test independent samples were used. The sample was divided into two groups: 254 respondents and 112 respondents in chronological order. None of the measured variables were significantly different between the two groups according to the t-test, so the accuracy of the study findings was not compromised by nonresponse bias.
Robustness checks. To minimize potential biases and ensure the objectivity of the model and empirical results, we used several measures. First, following the approach of Uddin and Akhter (2022), we randomly selected 40% of the sample for structural equation modeling (SEM) analysis. The results were consistent with the original findings. Second, we applied the repeated index method, reestimating the model with DT as a second-order construct. The results confirmed that overall, DT has a significant effect on resilience.
Main effect test. SEM analyses the covariance matrix to assess associations between variables. It expands regression models, allowing researchers to explore multilevel relationships involving both observed and latent variables within a comprehensive framework, providing advantages over traditional regression techniques. Therefore, SEM is used to assess and validate the earlier hypotheses.
Table 6 summarizes the beta coefficients (β) and their corresponding p-values. The results reveal a significant direct influence of technological transformation on SCR (β = 0.324, p < 0.001) and managerial transformation on SCR (β = 0.190, p = 0.003); thus, supporting H1a and H1b. Notably, significant positive effects on SCC are observed due to technological (β = 0.234, p 0.001) and managerial transformations (β = 0.246, p < 0.001). In addition, the positive and significant impact of SCC on SCR (β = 0.198, p 0.001) is evident, supporting H2a, H2b and H3.
Mediating effect test. To explore the mediating effect of SCC, we used the bootstrap confidence interval method (Preacher and Hayes, 2008) to construct a structural equation model. This experiment was executed with a sample size of 5,000, and the resulting 95% confidence interval excluding zero indicates that an indirect effect is present; revealing the existence of a mediating effect.
Table 7 shows indirect effects in the paths “Technological transformation → Supply chain collaboration → Supply chain resilience” (indirect effect = 0.046, LLCI = 0.015, ULCI = 0.08) and “Managerial transformation → Supply chain collaboration → Supply chain resilience” (indirect effect = 0.049, LLCI = 0.015, ULCI = 0.088), indicating partial mediation. Thus, H4a and H4b are supported.
Moderating effect test. To examine the moderating effect of government support, a hierarchical regression analysis approach was used. This method involves initially establishing a baseline model comprising control variables and independent variables, followed by the addition of moderating factors and their corresponding interaction terms to the baseline model.
The interaction between GFS and technological transformation on SCR is significant ( , p 0.05), indicating a positive moderating effect of GFS on technological transformation and SCR, thus, supporting H5a. Similarly, the interaction effect of GFS and managerial transformation on SCR is significant (β = 0.257, p 0.01), also demonstrating a significantly positive influence and supporting H5b. Figure 3(a) and (b), illustrate that the relationship between technological and managerial transformation and SCR is more pronounced at higher levels of GFS compared to lower levels of support.
The interaction between GNFS and technological transformation on SCR is statistically significant (β = 0.279, p 0.01), revealing a notably positive moderating effect of GNFS on technological transformation and SCR; thus, corroborating H6a. Similarly, the interaction effect of GNFS and managerial transformation on SCR (β = 0.207, p 0.01) is also significantly positive, supporting H6b.
As depicted in Figure 4(a) and 4(b), the relationship between technological and managerial transformation and SCR is notably stronger at higher levels of GNFS compared at lower levels. Table 8 offers a summary of the moderating effects of GFS and GNFS.
4. Discussion
Our study empirically examines how DT contributes to manufacturing industry resilience and the findings indicate that DT significantly enhances resilience, consistent with prior research (Yuan et al., 2024). Studies by Hamdy (2024) and Tortorella et al. (2024) on Egypt’s industrial sector and Australia’s food sector, respectively, have also documented the positive effects of DT, which is primarily driven by the application and innovation of digital technologies (Peng and Tao, 2022; Shi et al., 2023). The results also indicate that managerial transformation plays a crucial role in enhancing SCR. Organizational restructuring optimizes resource coordination and ensures a quick response to market changes. Leadership development improves decision-making flexibility and responsiveness to unexpected events (Chanias et al., 2019). Employees’ digital literacy is seen as a core capability driving innovation and operational efficiency (Zhao et al., 2023b). Together, both technological and managerial transformation guide DT and contribute to enhancing resilience.
Second, SCC is a key mediator in the effect of DT on SCR. The ability to swiftly gather business data amid risks and engage in timely communication with both upstream and downstream companies is paramount for the rapid recovery of supply chains (Hamann-Lohmer et al., 2023). Integrating digital technologies with collaboration can enhance efficiency, reduce the costs of collaboration among supply chains, foster long-term survival and growth in turbulent environments and improve resilience. By updating management concepts and processes, organizations can more flexibly adapt to fluctuating market trends, which can contribute to enhancing their overall transparency and responsiveness (Kee et al., 2023). This synergy enhances the supply chain’s ability to swiftly recover from disruptions and minimizes risks related to uncertainty by optimizing decision-making and resource allocation, which improves resilience (Li et al., 2024b). Consequently, studies have explored the role of SCC in resilience development, lending support to the mediating effect identified in Section 3.2.4 (Cui et al., 2023; Zhou et al., 2024).
Finally, government support emerges as a critical external factor that amplifies the effect of DT on resilience. Governments possess significant resources and can greatly benefit enterprises by fostering strong partnerships between businesses and public entities (Anwar and Li, 2021). As government investments in both financial and non-financial support increase, the DT process of enterprises is accelerated, thereby enhancing the capacity to respond to external shocks. Different countries adopt varying approaches and levels of support for DT. Germany promotes business self-investment through financial subsidies and low-interest loans (Prodi et al., 2022), whereas China focuses on large-scale direct financial investments (Wang et al., 2023). In terms of non-financial support, Latin American and Caribbean countries use open technology platforms and intellectual property protection to promote digital innovation (Viglioni et al., 2020), the Vietnamese Government focuses on staff training as well as quality and technical assistance (Nguyen et al., 2023). Although the support mechanisms are not the same across countries, these policies all play a crucial role in enhancing the effectiveness of DT and improving resilience.
Theoretical implications
Our research adopts a combination of qualitative and quantitative methods to uncover the influence of DT on the SCR of manufacturing enterprises. By analyzing the impact of DT across multiple dimensions, we offer a nuanced understanding of how its various facets enhance resilience. DT involves more than the adoption and integration of new technologies; it represents a profound transformation in business operations, organizational culture and decision-making. The convergence of these elements enables enterprises to thrive in the digital era, enriching both the theoretical and practical understanding of DT and SCR.
Furthermore, by analyzing the specific pathways of DT and SCR, this study verifies the mediating role of SCC, deepening our understanding of its mechanism of action. Collaboration emerges as a pivotal factor, facilitating seamless information sharing, trust-building and joint problem-solving across the supply chain. Importantly, the successful implementation of DT within a company is highly contingent on the digital maturity and collaborative readiness of its business partners. As a result, fostering robust digital collaborations across all stakeholders in the value chain has become an indispensable strategic priority for companies seeking to enhance their resilience.
Finally, by examining the moderating effect of government support, this research highlights the scenarios where DT and SCC improve SCR. Government support plays a dual role: such as financial incentives and infrastructure investments, while also creating an enabling environment that encourages innovation and collaboration. Successful DT requires a balance between internal commitment and external support from governments and industry alliances, offering valuable insights for building resilient, digitally empowered supply chains.
Managerial implications
This study provides valuable insights for business managers and governments to enhance SCR. Managers should recognize the key role of DT and drive the integration of digital technologies with supply chains, ensuring digital empowerment across all stages. Investments in AI, IoT and cloud computing should be prioritized to enhance production efficiency and product intelligence. In addition, enterprises should foster digital innovation and expand its application areas. Regarding management, companies should enhance governance structures, leadership and organizational frameworks to support DT. Manufacturers should recruit digital talents as needed and establish training systems to improve employees’ digital literacy. Entrepreneurs should seize market opportunities, embrace innovation and view DT as a key to enhancing competitiveness.
Moreover, it is critical for managers to proactively engage with supply chain partners, leverage digital platforms for collaboration and substantially enhance existing collaboration models to enhance the overall efficiency of supply chains. For example, Temu, an overseas e-commerce platform, provides sales channels and customer traffic, whereas merchants handle manufacturing. This SCC model enhances SCR by allowing merchants to focus on production, whereas Temu ensures steady customer flow. This division of responsibilities fosters flexibility, enabling quick adaptation to demand changes, and helps mitigate risks by reallocating demand to other suppliers in case of disruptions, boosting overall resilience. Companies should learn from Temu’s collaborative innovation model and use differentiated cooperation to create a more resilient supply chain.
Finally, the government should implement various support policies to motivate enterprises for embracing DT, increase R&D investment, promote technical training and offer tax incentives; Enterprises should also be motivated to invest in the development of transportation, communication, logistics and other infrastructure, actively use digital technology to enhance supply chain efficiency, connectivity, transparency and response speed and prioritize talent training. Furthermore, enterprises’ innovation, management and supply chain optimization capabilities must be enhanced, and an information exchange platform must be established to enhance information communication and decision-making efficiency, so as to assist enterprises in enhancing security and stability in supply chains.
5. Conclusion and limitations
This paper explores the relationships and underlying mechanisms that link DT to SCR. Several hypotheses are proposed and the mediator variable “supply chain collaboration” and the moderator variable “government support” are introduced into the research model. After literature review, research design and hypothesis testing, the proposed theoretical model was empirically tested on 366 samples. The results support the model and provide guidance for DT strategies in enterprises.
Certain important limitations warrant recognition. First, this study relies on cross-sectional data, which may not fully capture the dynamic and complex nature of DT. To gain deeper insights into how DT evolves over time and its long-term effects on SCR, as well as to enhance the generalizability of the findings, future research could expand the sample to include diverse industries and regions and adopt methodologies such as panel data analysis or longitudinal case studies.
The impact of DT on SCR was explored in this study. Future research could investigate how DT at different levels affects SCR or examine its impact at various stages of the supply chain. In addition, the application of digital technologies such as machine learning, big data and blockchain in the supply chain should be further explored to analyze their individual and combined effects, thus enriching contemporary research.
Finally, the findings highlight the crucial role of SCC in linking DT to resilience. However, this relationship may also be influenced by technological, organizational and environmental factors, among other variables. Future studies should explore these factors to more comprehensively reveal the mechanism of the effects of DT on resilience and provide more actionable insights for improving supply chain adaptability and competitiveness.
Funding: Chongqing Social Science Planning Project no. 2021ZDZK14; Chongqing Graduate Research Innovation Project no. CYS23463.
Figure 1Proposed research method
Figure 2Theoretical framework
Figure 3(a, b) GFS moderation effect
Figure 4(a, b) GNFS moderation effect
Table 1
Previous empirical studies on resilience
| Reference | Independent variable | Dependent variable | Mediating variables | Moderating variable | Research |
|---|---|---|---|---|---|
| Dubey et al. (2021) | Data analytics capability | Competitive advantage, SCR | SCR | Organizational flexibility | Survey |
| Qader et al. (2022) | Industry 4.0 | Supply chain performance | SCR | Supply chain visibility | Survey |
| Manikas et al. (2023) | Business personnel expertise, BDA management capabilities | SCR | – | – | Survey |
| Zhao et al. (2023a) | Supply chain digitalization | SCR | Supply chain performance | – | Survey |
| Dubey et al. (2023) | Digital adaptability, digital agility | SCR | – | Government policy effectiveness | Mixed-method |
| Belhadi et al. (2024) | Artificial intelligence | SCR, supply chain |
Supply chain collaboration, adaptive capabilities | Supply chain dynamism | Survey |
| Pu et al. (2024) | Dynamic management capability | SCR | Dynamic capability | – | Survey |
| Tiwari et al. (2024) | Supply chain visibility | Healthcare SCR | Digital capabilities | Environmental dynamism | Survey |
| Yuan et al. (2024) | Digital transformation | SCR | Supply chain process integration | Environmental uncertainty | Survey |
| Zhou et al. (2024) | IT capability | Firm performance, |
Supply chain collaboration | – | Survey |
Source(s): Authors’ own work
Table 2
Key structure measurement scale
| Construct | Item | Statement | Source |
|---|---|---|---|
| Supply chain resilience (SCR) | SCR1 | My company uses advanced predictive analytics and real-time data monitoring to identify potential disruptions in the supply chain | Belhadi et al. (2024) |
| SCR2 | My company uses agile supply chain practices and real-time communication systems to respond quickly to disruptions | ||
| SCR3 | My company has the ability to promptly create and execute contingency plans in the event of supply chain disruptions | ||
| SCR4 | My company can swiftly resume supply chain operations if there is a supply chain interruption caused by natural disasters, human factors, technical failures, etc | ||
| SCR5 | My company has the capability to promptly resume material flow following a supply chain disruption | ||
| Supply chain collaboration (SCC) | SCC1 | Most software applications on my platform work seamlessly with suppliers | Cui et al. (2023) and Zhou et al. (2024) |
| SCC2 | My company shares resources to assist suppliers in enhancing their capabilities | ||
| SCC3 | My company conducts transactions with suppliers using digital systems | ||
| SCC4 | My company engages in cooperative efforts with our supply chain partners to maximize benefits while managing risks collaboratively | ||
| SCC5 | My company shares resources such as data, information, knowledge, and infrastructure with its partners | ||
| Technological transformation (TT) | TT 1 | My company possesses data management services and architecture, including databases, data warehouses, etc | Lin and Mao (2024) |
| TT 2 | The operational and service equipment for the information technology infrastructure, such as servers and large-scale processors, is owned by my company | ||
| TT 3 | My company integrates digital technologies into its products to expedite research and development, manage design expenses, enhance product quality and promote business innovation | ||
| TT 4 | My company harnesses the power of digital technology to innovate and create new and valuable products and services | ||
| TT 5 | My company possesses the technological resources required to support intelligent information processing systems, such as automated operations and intelligent decision-making in each business segment of the supply chain, using artificial intelligence and automation technologies | Rodríguez-Espíndola et al. (2022) | |
| TT 6 | My company uses digital technologies to enhance various business processes, such as research and development, design, manufacturing, warehousing and transportation, to achieve intelligent operations | ||
| Managerial transformation (MT) | MT 1 | My company has long recognized the benefits of implementing other digital management systems, such as digital resource management systems based on digital platforms (e.g. ERP systems, SaaS systems, etc.), for business development | Lu and Ramamurthy (2011) |
| MT 2 | The leaders in our company serve as mentors and coaches, actively driving the digital transformation process | ||
| MT 3 | The organizational structure of my company is very flexible, making it adaptive to the rapidly changing digital landscape | ||
| MT 4 | The optimization of the organizational structure is more conducive to communication between the internal and external parts of the company (e.g. the gradual shift from a vertical to a flat organizational structure) | Nicolás-Agustín et al. (2022) and Dubey et al. (2021) | |
| MT 5 | The technical staff of the company can use digital technology to effectively manage business operations | ||
| MT 6 | The company is committed to enhancing digital skills and management knowledge by promoting the development of digital talent | ||
| Government financial support (GFS) | GFS1 | My company’s technological innovation has received government support, such as subsidies and financial subsidies | Dubey et al. (2023) |
| GFS2 | The government provided financial support and facilitation to our company, such as loans, based on our financial needs | ||
| GFS3 | My company has received tax incentives from the government | ||
| Government non-financial support (GNFS) | GNFS1 | The government provided the company with the necessary technical information and support | Nguyen et al. (2023) |
| GNFS2 | My company has received policy support and facilitation for introducing and cultivating innovative talent | ||
| GNFS3 | My company benefits from government-provided intellectual property protection, which safeguards its innovations |
Source(s): Authors’ own work
Table 3
Profile of enterprises and respondents
| Variables | Frequency | % |
|---|---|---|
| Gender | ||
| Male | 193 | 50.27 |
| Female | 173 | 49.73 |
| Education background | ||
| College degree and below | 106 | 28.96 |
| Bachelor degree | 213 | 58.20 |
| Master degree or above | 47 | 12.84 |
| Number of employees | ||
| ≤500 | 119 | 32.52 |
| 501–5,000 | 118 | 32.24 |
| ≥5,001 | 129 | 35.25 |
| Enterprise age | ||
| 5–10 years | 55 | 15.02 |
| 11–20 years | 103 | 28.14 |
| ≥20 years | 208 | 56.83 |
| Annual revenue（million dollar） | ||
| <10 | 15 | 4.10 |
| 10–100 | 61 | 16.67 |
| 100–5,000 | 178 | 48.63 |
| >5,000 | 112 | 30.60 |
| Sector | ||
| Textile and clothing | 15 | 4.09 |
| Publishing and printing | 4 | 1.09 |
| Metal and machinery | 74 | 20.22 |
| Chemistry and petrochemical | 18 | 4.92 |
| Wood and furniture | 6 | 1.64 |
| Food, beverages and alcohol | 23 | 6.28 |
| Building materials | 18 | 4.92 |
| Biomedicine | 17 | 4.64 |
| Electronic and electrical equipment | 93 | 25.40 |
| Rubber and plastic | 14 | 3.83 |
| Equipment manufacturing | 73 | 19.95 |
| others | 11 | 3.01 |
Note(s):n = 366
Source(s): Authors’ own work
Table 4
Validity and reliability
| Constructs | Item | Factor loading | Cronbach’s α | CR | AVE |
|---|---|---|---|---|---|
| Supply chain resilience (SCR) | SCR1 | 0.748 | 0.885 | 0.838 | 0.509 |
| SCR2 | 0.636 | ||||
| SCR3 | 0.679 | ||||
| SCR4 | 0.736 | ||||
| SCR5 | 0.761 | ||||
| Supply chain collaboration (SCC) | SCC1 | 0.702 | 0.900 | 0.843 | 0.518 |
| SCC2 | 0.732 | ||||
| SCC3 | 0.688 | ||||
| SCC4 | 0.715 | ||||
| SCC5 | 0.761 | ||||
| Technological transformation (TT) | TT1 | 0.709 | 0.924 | 0.874 | 0.536 |
| TT2 | 0.713 | ||||
| TT3 | 0.713 | ||||
| TT4 | 0.744 | ||||
| TT5 | 0.744 | ||||
| TT6 | 0.768 | ||||
| Managerial transformation (MT) | MT1 | 0.708 | 0.917 | 0.861 | 0.508 |
| MT2 | 0.672 | ||||
| MT3 | 0.720 | ||||
| MT4 | 0.679 | ||||
| MT5 | 0.757 | ||||
| MT6 | 0.735 | ||||
| Government financial support (GFS) | GFS1 | 0.793 | 0.870 | 0.841 | 0.637 |
| GFS2 | 0.802 | ||||
| GFS3 | 0.800 | ||||
| Government non-financial support (GNFS) | GNFS1 | 0.812 | 0.881 | 0.850 | 0.654 |
| GNFS2 | 0.833 | ||||
| GNFS3 | 0.781 |
Source(s): Authors’ own work
Table 5
Correlation coefficient and the square root of the AVE
| Variables | SCR | SCC | TT | MT | GFS | GNFS |
|---|---|---|---|---|---|---|
| SCR | 0.713 | |||||
| SCC | 0.313 | 0.720 | ||||
| TT | 0.381 | 0.323 | 0.732 | |||
| MT | 0.323 | 0.327 | 0.455 | 0.713 | ||
| GFS | 0.340 | 0.289 | 0.374 | 0.415 | 0.798 | |
| GNFS | 0.262 | 0.352 | 0.295 | 0.381 | 0.309 | 0.809 |
Note(s):Square roots of AVE shown on diagonal
Source(s): Authors’ own work
Table 6
Direct effect hypothesis testing results
| Path | Standardized path coefficient | S.E. | C.R. | P | Decision |
|---|---|---|---|---|---|
| TT → SCR | 0.324 | 0.055 | 5.075 | *** | H1a supported |
| MT → SCR | 0.190 | 0.057 | 3.010 | 0.003 | H1b supported |
| TT → SCC | 0.217 | 0.059 | 3.696 | *** | H2a supported |
| MT → SCC | 0.241 | 0.062 | 3.865 | *** | H2b supported |
| SCC → SCR | 0.183 | 0.055 | 3.347 | *** | H3 supported |
Source(s): Authors’ own work
Table 7
Results of mediation analysis
| Bia-corrected 95% CI | ||||||
|---|---|---|---|---|---|---|
| Path | Indirect effect coefficient | lower | upper | p | Type | Decision |
| TT → SCC → SCR | 0.046 | 0.015 | 0.080 | *** | Partial mediation | H4a supported |
| MT → SCC → SCR | 0.049 | 0.015 | 0.088 | *** | Partial mediation | H4b supported |
Source(s): Authors’ own work
Table 8
Moderation effect of government financial/nonfinancial support
| Construct | Variables | Supply chain resilience | ||
|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | ||
| Technological transformation and government financial support | Number of employees | 0.027 | 0.044 | 0.051 |
| Enterprise age | −0.036 | −0.026 | −0.032 | |
| TT | 0.297*** | 0.281*** | ||
| GFS | 0.229*** | 0.228*** | ||
| TT × GFS | 0.199*** | |||
| R2 | 0.002 | 0.193 | 0.232 | |
| Change in R2 | −0.004 | 0.184 | 0.221 | |
| F-statistics | 0.305 | 21.527*** | 21.745*** | |
| Changes in F-statistics | 0.305 | 42.678*** | 18.457*** | |
| Technological transformation and government non-financial support | Number of employees | 0.027 | 0.034 | 0.023 |
| Enterprise age | −0.036 | −0.016 | −0.027 | |
| TT | 0.335*** | 0.230*** | ||
| GNFS | 0.160** | 0.158** | ||
| TT × GNFS | 0.207*** | |||
| R2 | 0.002 | 0.171 | 0.245 | |
| Change in R2 | −0.004 | 0.161 | 0.235 | |
| F-statistics | 0.305 | 18.569*** | 23.399*** | |
| Changes in F-statistics | 0.305 | 36.772*** | 35.601*** | |
| Managerial transformation and government financial support | Number of employees | 0.027 | 0.048 | 0.046 |
| Enterprise age | −0.036 | −0.031 | −0.051 | |
| MT | 0.224*** | 0.198*** | ||
| GFS | 0.247*** | 0.221*** | ||
| MT × GFS | 0.257*** | |||
| R2 | 0.002 | 0.158 | 0.222 | |
| Change in R2 | −0.004 | 0.149 | 0.211 | |
| F-statistics | 0.305 | 16.958*** | 20.559*** | |
| Changes in F-statistics | 0.305 | 33.557*** | 29.589*** | |
| Managerial transformation and government non-financial support | Number of employees | 0.027 | 0.04 | 0.023 |
| Enterprise age | −0.036 | −0.022 | −0.027 | |
| MT | 0.266*** | 0.230*** | ||
| GNFS | 0.157** | 0.158** | ||
| MT × GNFS | 0.207*** | |||
| R2 | 0.002 | 0.128 | 0.17 | |
| Change in R2 | −0.004 | 0.119 | 0.158 | |
| F-statistics | 0.305 | 13.289*** | 14.716*** | |
| Changes in F-statistics | 0.305 | 26.230*** | 17.934*** | |
Note(s):*p < 0.05; **p < 0.01; ***p < 0.001
Source(s): Authors’ own work
Table A1
Basic information of interviewees
| Interviewer | Gender | Work experience | Industry | Position |
|---|---|---|---|---|
| 1 | Male | 25 | Automotive industry | Vice President |
| 2 | Female | 13 | Textile and other light industries | Production Manager |
| 3 | Male | 11 | Electronic manufacturing industry | Supply Chain Manager |
| 4 | Female | 8 | Machinery manufacturing industry | Purchasing Manager |
| 5 | Female | 7 | Medical instrument and equipment industry | Logistics Manager |
| 6 | Male | 12 | Automotive industry | Commercial Department Manager |
| 7 | Male | 10 | Food manufacturing industry | Transportation Supervisor |
| 8 | Female | 9 | Food manufacturing industry | Operation Manager |
| 9 | Male | 23 | Chemical manufacturing industry | President |
| 10 | Male | 15 | Building materials manufacturing industry | Supply chain Manager |
| 11 | Male | 17 | Machinery manufacturing industry | Vice President |
| 12 | Male | 22 | Medical instrument and equipment manufacturing industry | Purchasing Manager |
| 13 | Male | 41 | Automotive industry | President |
| 14 | Female | 7 | Machinery manufacturing industry | Purchasing Manager |
| 15 | Male | 9 | Medical devices and pharmaceutical industry | Risk Management Manager |
| 16 | Male | 12 | Food and beverage manufacturing industry | Global Purchasing Manager |
| 17 | Female | 10 | Chemical manufacturing industry | Inventory Control Manager |
| 18 | Male | 21 | Printing industry | Logistics Manager |
| 19 | Female | 6 | Automotive industry | Production Planner |
| 20 | Male | 17 | Chemical manufacturing industry | Supply Chain Manager |
Source(s): Authors’ own work
Table A2
Selected statements from interviewees (dimensions of digital transformation)
| Dimensions | Definition | Partial statement |
|---|---|---|
| Technological transformation (TT) | The process of fundamentally changing and upgrading existing infrastructure and technologies within an enterprise or society | “We integrate procurement, production, sales and other operations through the CPS system, while also promoting internal resource integration” (interviewee 6) |
| Managerial transformation (MT) | The process of changing internal business structures, including IT functions and business models, to improve the organization’s ability to adapt to unforeseen changes in the business environment | “In order to promote process improvement, our company has recruited many excellent IT talents from universities and actively provided regular training for employees” (interviewee J) |
Source(s): Authors’ own work
Table A3
Selected statements from interviewees (relationships)
| Relationships | Description | Selected quotes |
|---|---|---|
| TT → SCR | The ability of a supply chain to quickly adapt, restore operation, and maintain a stable state in the face of external or internal changes, impacts, or pressures. |
"Our company has been using a bunch of advanced technologies to make our supply chain run better. This makes it more flexible and efficient than ever before” (interviewee 9) |
| MT → SCR | Managerial transformation will have a positive influence on supply chain resilience | "To accommodate the ever-evolving world of supply chains, our team continuously strives to stay informed and proficient in the latest tools and systems. We prioritize a substantial investment of resources to ensure that our employees are well trained and equipped with the skills needed to keep pace with the changing trends” (interviewee 14) |
| TT → SCC | Supply chain collaboration refers to the close cooperation and coordination between various parts of the supply chain, working collectively to achieve overall operational efficiency and business objectives. |
"Our remote monitoring system ensures a seamless production process, manages production, logistics, and warehouses, matches production capacity requirements, rapidly expands production, and adapts to changes in customized order production requirements…” (interviewee 6) |
| MT → SCC | Enhancing managerial transformation can lead to an improved level of collaboration within the supply chain | "We have undergone a complete transformation over the past few years, and in the process … we have also focused on collaboration and communication within the organization. By building closer partnerships, we are better able to respond to the changing environment” (interviewee 15) |
| SCC → SCR | Effective collaboration between supply chains can significantly improve their resilience | "Our firm cultivates enduring supplier relationships, ensuring the availability of high-quality raw materials and parts. We regularly adjust supply plans to meet market demand, communicating and coordinating with our suppliers to ensure optimal supply and demand matching” (interviewee 9) |
| GFS → SCR | Government financial support will positively influence the supply chain resilience | “The government provides money to companies to help them with research and development (R&D) and innovation projects such as making new products or improving technology. This helps companies become more competitive and gain more market share” (interviewee 17) |
| GNFS → SCR | Government non-financial support will positively influence the supply chain resilience | “Thanks to government policy support, we have a more secure and stable business environment, which helps protect our legitimate rights and interests and reduces the risks of doing business” (interviewee 13) |
Source(s): Authors’ own work
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