Abstract
Maintaining supply chain security(SCS) has become a strategic cornerstone of national economic security and industrial competitiveness. Using panel data of Chinese A-share listed manufacturing firms from 2012 to 2024, this study empirically examines the impact of digital intelligence technology adoption on SCS and explores the underlying mechanisms. The results show that digital intelligence technology adoption significantly enhances the SCS of manufacturing firms, with the effect being more pronounced among firms located in eastern China and those with lower supply chain concentration. Mechanism analyses indicate that this effect is primarily achieved by strengthening firms’ risk management capability and improving supply chain coordination capability. Moderation analysis further reveals that the positive effect of digital intelligence technology adoption on SCS is stronger in regions with higher levels of digital infrastructure development. Further analysis demonstrates that improvements in SCS of focal(chain-leading) firms generate significant network spillover effects, enhancing the SCS of both upstream suppliers and downstream customers. These findings provide robust empirical evidence to guide firms in deepening digital intelligence integration and optimizing supply chain structures, and to support governments in designing more targeted policies.
Keywords: Digital intelligence technology, Supply chain security, Risk management capability, Supply chain coordination, Supply chain spillover effects
Subject terms: Business and management, Business and management, Economics, Economics, Information systems and information technology, Operational research
Introduction
In recent years, the intensification of deglobalisation, trade protectionism, and geopolitical risks has fundamentally reshaped global production networks. Global manufacturing industrial and supply chains have become increasingly fragmented, regionalised, and exposed to heightened risks, leading to a marked decline in the stability and predictability of cross-border value chains1. On the one hand, major economies have promoted the reshoring of critical manufacturing industries and the expansion of “friend-shoring” through policy instruments, accelerating the geopolitical reconfiguration of the global manufacturing system2. For example, the United States enacted the CHIPS and Science Act and the Inflation Reduction Act in 2022, imposing multiple export restrictions on Chinese manufacturing firms, particularly targeting advanced semiconductor production processes and key manufacturing equipment such as lithography machines. At the same time, multinational corporations including Apple and Intel have been compelled to adjust their global supply networks, exerting substantial shocks on high-end manufacturing and semiconductor supply systems in regions such as Taiwan, South Korea, and Japan. On the other hand, the growing frequency of global public crises and regional conflicts has transformed supply chain uncertainty from isolated disruptions into systemic risks3. During the COVID-19 pandemic, global port congestion severely disrupted long manufacturing supply chains in industries such as automobiles and electronics, while the Red Sea shipping crisis triggered by the Israel–Palestine conflict has sharply increased global maritime transportation costs.
Against this backdrop, China has elevated supply chain security(SCS) to a core element of its national development strategy. Policy statements issued at the 20th National Congress of the Communist Party of China and subsequent plenary sessions of the 20th Central Committee have repeatedly emphasised the need to enhance the resilience and security of industrial and supply chains, with particular emphasis on accelerating the construction of a manufacturing powerhouse and a quality-driven manufacturing sector. As a result, SCS has evolved from a firm-level operational concern into a strategic issue central to China’s economic security and high-quality development, aimed at strengthening the capacity of manufacturing firms to withstand non-conventional and unforeseen shocks. It has become a critical foundation for safeguarding economic sovereignty and advancing China’s modernisation agenda. Accordingly, examining SCS at the firm level within China’s manufacturing sector is of substantial practical relevance for building a modern industrial system, mitigating external shocks, and strengthening the autonomy and controllability of manufacturing-related industrial supply chains.
With the deep integration and rapid iteration of new-generation information technologies—including big data, artificial intelligence, blockchain, and machine learning—digital intelligence technologies have emerged as a core driving force reshaping the operational logic of manufacturing industrial and supply chains and enhancing their security levels. Specifically, by enabling end-to-end visibility, sensing, and controllability, digital intelligence technologies facilitate a transition in manufacturing supply chain management from reactive responses to proactive risk prevention. Moreover, through cross-domain data integration and intelligent analytics, these technologies enhance the adaptability and stability of interconnected manufacturing supply chain stages at the firm level. Together, these capabilities provide a novel technological pathway for addressing structural bottlenecks in manufacturing industrial SCS. However, from a mechanistic perspective, SCS in manufacturing firms cannot be achieved through technological investment alone. Its formation depends jointly on firms’ internal risk management capabilities, the level of collaborative governance among supply chain actors, and the external digital environment. In this sense, firms’ risk management capabilities and supply chain coordination constitute critical internal foundations of SCS, while regional digital infrastructure plays a decisive role in shaping the efficiency and effectiveness with which digital intelligence technologies are embedded in supply chain governance systems.
At the same time, the strategic enabling role of digital intelligence technologies has received increasing attention from the Chinese government. Policy statements issued at the Third Plenary Session of the 20th Central Committee explicitly emphasise supporting firms in upgrading traditional manufacturing industries through the application of digital intelligence technologies, marking the first inclusion of digital intelligence in China’s top-level policy framework. In addition, the Outline of the 15th Five-Year Plan for National Economic and Social Development, adopted at the Fourth Plenary Session of the 20th Central Committee, repeatedly highlights digital intelligence-driven transformation of manufacturing firms and positions it as a key engine for manufacturing industrial upgrading and enhanced SCS. Against this policy and technological backdrop, this study investigates the impact of digital intelligence technology adoption on SCS among Chinese manufacturing firms and examines the underlying transmission pathways. Doing so not only helps clarify how digital intelligence technologies enhance risk identification capabilities and collaborative governance capacity within manufacturing firms, but also provides theoretical and practical insights for building more secure, efficient, and intelligent manufacturing SCS systems.
This study makes several potential marginal contributions. First, from a research perspective, it incorporates digital intelligence technology adoption into the analytical framework of SCS, thereby extending the technological governance perspective in SCS research. Moreover, by focusing on the micro-level of manufacturing firms, it enriches the theoretical and empirical evidence on how digital transformation affects firms’ SCS. Second, with respect to underlying mechanisms, this study reveals the internal logic through which digital intelligence technology adoption influences SCS in manufacturing firms from two dimensions: risk management capability and supply chain coordination capability, thus deepening the theoretical understanding of the formation mechanisms of SCS in manufacturing firms. At the same time, this study identifies regional digital infrastructure as a key external moderating factor, expanding research on the boundary conditions of the relationship between digital intelligence technology and SCS in manufacturing firms. Third, in terms of research deepening, this study finds that improvements in SCS among chain-leading manufacturing firms can significantly enhance the SCS of upstream and downstream firms. This finding goes beyond the limitations of prior studies that focus solely on firms’ own performance and provides new empirical evidence for understanding the network-based formation mechanisms of SCS.
Literature review
Digital intelligence technology represents an organic integration of digitalisation and intelligentisation and can be understood as an Industry 4.0 technological system characterised by “digitalisation plus intelligentisation”. It refers to the process of embedding and applying intelligent technologies such as machine learning and artificial intelligence on the basis of digitalisation4. Academic research on digital intelligence technology is still at an early stage, and studies most closely related to this concept have largely focused on digital-intelligent integration and digital-intelligent transformation. These streams of research are broadly consistent with digital intelligence technology in terms of conceptual definition, viewing digital-intelligent integration and digital-intelligent transformation as the integration, upgrading, or reconfiguration of digital and intelligent technologies5,6. The literature most closely related to the economic effects of digital intelligence technology can be broadly classified into three categories. First, studies on the economic effects of digital technologies primarily focus on the role of technologies such as cloud computing, big data, the Internet of Things(IoT), and mobile internet in improving operational efficiency, reducing transaction costs, and facilitating market expansion. For example, Tang et al. show that IoT technologies significantly enhance firms’ financial performance and market value7. Yang and Zhang find that mobile internet technologies help reduce transaction costs and strengthen firms’ competitive advantages8, while Coviello et al. emphasise that big data and cloud computing weaken the stage-based constraints of traditional internationalisation paths, enabling firms to enter foreign markets more rapidly and expand across borders9. Second, research on the economic effects of intelligent technologies highlights the transformative role of artificial intelligence, machine learning, deep learning, and knowledge graphs in reshaping firm-level decision-making and production processes, as well as in altering industry-level value creation patterns. A growing body of evidence suggests that artificial intelligence improves firms’ investment efficiency10, enhances ESG performance11, and strengthens supply chain resilience12. In addition, Costa-Climent et al. argue that machine learning technologies promote systematic optimisation of business models by enhancing firms’ value creation and value capture capabilities13. Third, an emerging stream of research focuses on the economic effects of digital intelligence transformation driven by the deep integration of digitalisation and intelligent technologies. These studies suggest that digital intelligence technologies reshape firms’ development paths and governance structures. Existing evidence shows that digital intelligence technologies facilitate green transformation14 and servitisation upgrading among manufacturing firms15. Other studies further indicate that digital intelligence transformation contributes to mitigating supply chain risks in manufacturing firms5.
SCS generally refers to the capability of a supply chain system to maintain the supply of critical resources, the continuity of core functions, and the controllability of chain operations when facing internal and external shocks, uncertainty risks, and potential threats. Its primary focus lies in risk prevention, loss avoidance, and the safeguarding of system stability16. Existing studies on the determinants of SCS mainly examine influencing factors from both internal and external firm-level perspectives. Internal factors primarily include firms’ integrated decision-making processes17, the level of digital transformation18, and corporate financialisation19. External factors mainly encompass government-related policies, such as transit trade facilitation20, export tax rebates21, and government subsidies22. In addition, structural characteristics of supply chains—such as supply chain digitalisation23 and supply chain complexity24—as well as empowerment from external digital platforms, including e-commerce platforms25, have also been shown to influence firms’ SCS. Overall, existing studies suggest that these internal and external factors generally contribute positively to firms’ SCS. However, emerging evidence indicates that certain factors may undermine SCS. For instance, excessive technological dependence26 and climate policy uncertainty27 have been found to reduce firms’ SCS, highlighting the complexity and nonlinearity of its determinants.
Based on the above literature review, several clear research gaps remain. First, in terms of research perspective, although some studies have examined firm risk or supply chain resilience from the angles of digital transformation or intelligent technologies, systematic investigations that directly analyse the impact of digital intelligence technology from the perspective of SCS remain limited. Second, at the research level, existing studies largely focus on macro-institutional analyses or firm performance outcomes, with a lack of empirical evidence based on micro-level firm behaviour, particularly with regard to SCS in manufacturing firms. Third, in terms of research extension, prior studies rarely examine spillover effects of SCS between upstream and downstream firms, thereby overlooking the network-based formation characteristics of SCS. Against this backdrop, this study uses a sample of Chinese A-share listed manufacturing firms from 2012 to 2024 to address the following research questions: Does digital intelligence technology adoption enhance SCS at the firm level? If so, through which mechanisms does this effect operate? Does the impact vary across regions and levels of supply chain concentration? Finally, do improvements in SCS generate spillover effects along supply chain relationships?
Theoretical analysis and research hypothesis
Digital intelligence technology adoption and SCS of manufacturing firms
Driven by the development of the digital economy, digital intelligence technologies provide a new technological foundation and governance paradigm for SCS management. From the perspective of technological attributes, digital intelligence technologies are characterised by real-time data acquisition, intelligent analytics, and cross-actor connectivity, with their governance effects mainly manifested in reducing the cost of identifying uncertainties and strengthening the foundations for coordination among supply chain participants28. Given that SCS essentially reflects the controllability of risks and the stability of inter-firm coordination, digital intelligence technologies are more likely to exert their influence through two primary mechanisms: risk management capability and supply chain coordination capability. Accordingly, this study investigates the mechanisms through which digital intelligence technology adoption enhances SCS among manufacturing firms via these two pathways.
Risk management capability-enhancing mechanism
Risk management theory suggests that if firms fail to identify and respond promptly and effectively to internal and external uncertainties, localized shocks may escalate into cross-stage and cross-actor systemic supply chain risks, thereby threatening firms’ normal operations and financial security. The introduction of digital intelligence technologies enables firms to transform risk management from traditional ex post remediation to ex ante early warning and process-oriented control, providing a technological foundation for building a proactive supply chain risk prevention system that integrates prediction, resistance, and adaptation.
First, by leveraging big data analytics and artificial intelligence algorithms, digital intelligence technologies allow manufacturing firms to conduct in-depth mining and intelligent assessment of massive internal and external risk-related data across supply chains, thereby expanding the scope of risk identification and improving the accuracy of risk prediction. For example, firms can use artificial intelligence–based natural language processing techniques to scan information related to macroeconomic policies, geopolitical developments, and public opinion, enabling early detection of potential risks such as supply disruptions and logistics blockages. By shifting risk identification forward, firms are able to dynamically adjust production plans, inventory structures, and sourcing strategies before shocks fully materialize, which reduces the probability of supply interruptions and enhances the stability of supply-demand relationships29. Second, digital intelligence technologies—particularly those based on the Internet of Things and blockchain—enhance the authenticity of supply chain transactions and the traceability of material flows. This reduces information asymmetry among supply chain participants and mitigates adverse selection and moral hazard problems30, thereby strengthening security controls in critical stages such as procurement, collateral management, warehousing, and logistics. Finally, supported by digital intelligence tools such as digital twins and simulation modeling, firms can construct panoramic mappings of supply chain operations and conduct low-cost, high-frequency stress tests on production disruptions, logistics delays, and demand fluctuations under different shock scenarios. By identifying vulnerable nodes within supply chains in advance and optimizing contingency and emergency response plans, firms can restore production and delivery order more rapidly after risk shocks occur, thereby shortening the duration of disruptions and suppressing the diffusion of localized shocks into systemic risks28. Taken together, digital intelligence technology adoption enhances firms’ risk management capabilities by reducing the likelihood of risk occurrence, buffering the intensity of external shocks, and accelerating system recovery and reconfiguration, which ultimately contributes to improvements in manufacturing firms’ SCS.
Supply chain coordination-enhancing mechanism
According to supply chain coordination theory, SCS is not determined by a single focal firm but is jointly shaped by supply chain members through coordinated planning, resource sharing, and emergency linkage mechanisms. The application of digital intelligence technologies fundamentally reshapes supply chain operating modes, providing the foundational conditions for manufacturing firms to establish collaborative systems characterised by efficient coordination, information consistency, and rapid responsiveness.
First, digital intelligence technologies promote coordination consistency at the planning level of the supply chain. By leveraging data-sharing platforms and intelligent forecasting tools, supply chain members are better able to integrate upstream and downstream demand forecasts, production rhythms, and delivery targets, thereby reducing problems such as inventory accumulation, raw material shortages, and capacity underutilisation caused by planning mismatches31. Improved planning coordination helps stabilise supply-demand matching by mitigating operational deviations related to excess inventory, supply disruptions, and idle capacity, ultimately enhancing firms’ SCS32. Second, digital intelligence technologies strengthen coordination in resource allocation across the supply chain. Through the dynamic orchestration of key resources related to supply, production, and transportation, supply chain members can achieve higher levels of capacity matching, order allocation, and inventory sharing, thereby avoiding process bottlenecks and node congestion arising from imbalanced resource allocation among firms33. Enhanced resource coordination increases the adjustment space and buffering capacity of the supply chain under external disturbances, effectively reducing the likelihood that local resource shocks propagate along the entire chain and thereby improving the operational security of manufacturing supply chains34. Finally, digital intelligence technologies enhance coordination in emergency response processes. When unexpected disruptions occur, supply chain members can rely on digital intelligence technologies to rapidly share abnormal information and jointly formulate response strategies within short time frames, including coordinated actions such as alternative production arrangements, emergency replenishment, and logistics route adjustments. Improved emergency coordination enables the supply chain to respond to disruptions more rapidly and at lower cost, reducing the negative impacts on individual firms’ operations and safeguarding supply chain continuity and stability35. Taken together, digital intelligence technology adoption enhances firms’ supply chain coordination capability by strengthening planning coordination, resource coordination, and emergency coordination, thereby contributing to higher levels of SCS among manufacturing firms.
Based on the above analysis, we propose the following hypotheses:
Hypothesis 1
Digital intelligence technology adoption enhances the SCS of manufacturing firms.
Hypothesis 2
Digital intelligence technology adoption enhances the SCS of manufacturing firms by strengthening risk management capability.
Hypothesis 3
Digital intelligence technology adoption enhances the SCS of manufacturing firms by improving supply chain coordination capability.
Moderating role of regional digital infrastructure
Regional digital infrastructure constitutes a critical external condition for the integration of digital intelligence technologies into firms’ operational activities. Its level of development directly affects the efficiency with which digital intelligence technology adoption is transformed into operational outcomes and the effectiveness of SCS governance. On the one hand, well-developed digital infrastructure—such as extensive 5G network coverage, greater accessibility to cloud computing resources, and the construction of public data platforms—significantly reduces the access and usage costs associated with digital intelligence technologies for manufacturing firms36, thereby accelerating technological spillovers and knowledge diffusion along the supply chain. Existing studies show that the expansion of regional digital infrastructure not only improves firms’ access to and utilisation of digital resources but also enhances supply chain risk early-warning and coordinated response capabilities by facilitating cross-domain data flows and sharing37.
On the other hand, higher levels of digital infrastructure improve transparency and visibility in supply chain operations, enabling real-time monitoring of logistics flows, information flows, and capital flows across a wider range of supply chain nodes. This, in turn, strengthens firms’ situational awareness, dynamic coordination, and risk control efficiency. Prior research indicates that digital infrastructure mitigates information delays and uncertainty within supply chains by increasing network connectivity and the efficiency of information interactions between firms and external networks, thereby enhancing firms’ recovery capacity and overall SCS under disruption scenarios38.
Based on the above analysis, we propose the following hypothesis:
Hypothesis 4
The positive relationship between digital intelligence technology adoption and the SCS of manufacturing firms is stronger in regions with higher levels of digital infrastructure.
Model specification
Baseline model
To examine the impact of digital intelligence technology adoption on SCS among manufacturing firms, this study specifies the following baseline model:
 |
1 |
Where 
denotes the supply chain security level of firm 
in year 
; 
represents the degree of digital intelligence technology adoption of firm 
in year 
; and 
is a vector of control variables. 
and 
represent industry and year fixed effects, respectively; and 
is the error term. In addition, standard errors are clustered at the firm level to ensure robustness.
Transmission channels and mechanism models
To further investigate the transmission channels through which digital intelligence technology adoption affects manufacturing firms’ SCS, this study extends the baseline model (1) by constructing Models (2) and (3) to test mediation effects, and Model (4) to examine moderation effects:
 |
2 |
 |
3 |
 |
4 |
Where 
denotes the mediating variables, including risk management capability (Risk) and supply chain coordination capability (SCC); and 
represents the moderating variable, namely the level of regional digital infrastructure.
Research design
Sample selection and data sources
We select Chinese A-share listed manufacturing firms from 2012 to 2024 as the research sample and process the data as follows. First, firms designated as ST or *ST are excluded. Second, firms with an asset-liability ratio greater than 1 or less than 0 are removed. Third, firms with substantial missing data are excluded. Fourth, all continuous variables are winsorised at the upper and lower 1% levels. After these procedures, the final sample consists of 1,287 manufacturing firms with 16,731 firm-year observations, forming a balanced panel dataset. Firm-level financial data are obtained from the China Stock Market & Accounting Research database(CSMAR) and Wind Information Financial Terminal database(Wind). Data on firm patents and annual report texts are sourced from the China Research Data Services Platform database(CNRDS), while region-level data are collected from the China Economic Information Network statistical database(CEIN).
Variable definitions
Dependent variable: Supply chain security (SCS)
This study constructs a composite index of SCS for manufacturing firms from three dimensions: stability of supply-demand relationships, supply-demand matching, and improvement in supply quality.
Stability of supply-demand relationships is proxied by firms’ capital occupation, which is measured as the natural logarithm of the ratio of the sum of notes receivable, accounts receivable, and prepayments to operating revenue. This indicator reflects the extent of capital tied up during procurement, production, and sales processes due to supply-demand mismatches. A lower value of this indicator implies that upstream firms rely more on cash-based sales, experience shorter cash recovery cycles, and face lower accounts receivable pressure, thereby indicating stronger supply-demand coordination and higher supply chain stability.
Supply-demand matching is captured using two indicators: inventory adjustment intensity and deviation of supply-demand fluctuations. Inventory adjustment intensity is measured as the natural logarithm of the absolute change in net inventory between two consecutive periods. Larger values indicate higher uncertainty in production organisation or demand forecasting and lower levels of supply-demand matching. The deviation of supply-demand fluctuations is measured as the ratio of the variance of firms’ production to the variance of demand. A lower deviation value implies stronger dynamic coordination between output and demand, indicating a higher degree of supply–demand matching.
Improvement in supply quality is proxied by four indicators: R&D investment intensity(measured as the ratio of firms’ R&D expenditure to operating revenue), skill structure(measured as the proportion of employees with a master’s degree or above), the number of patent applications(measured as the total number of invention, utility model, and design patent applications in the current year, transformed using the natural logarithm after adding one), and patent citations(measured as the number of invention patent citations after excluding self-citations, transformed using the natural logarithm after adding one). Higher values of these indicators indicate stronger innovative capabilities in product design, process optimisation, quality control, and technological iteration, thereby reflecting higher supply quality.
Based on the above three second-level dimensions and seven third-level indicators of SCS, we further employ the entropy weight method to construct a composite index, which serves as the proxy for the SCS of manufacturing firms.
Independent variable: Digital intelligence technology adoption (Digtech)
This study measures the extent of firms’ digital intelligence technology adoption using the frequency of keywords related to digital intelligence technologies disclosed in listed firms’ annual reports. The specific measurement procedure is as follows.
First, drawing on prior studies by Chen et al.39 and Yu et al.15, we identify an initial set of keywords capturing key dimensions of digital intelligence technology adoption. This keyword list is then further expanded through a systematic review of relevant policy documents issued by the Chinese government, including the Implementation Plan for Promoting the “Cloud Adoption, Data Utilisation, and Intelligence Empowerment” Initiative, the 2024 Digital China Development Report, the 2023 Digital Transformation Index Report, and the Special Action Plan for “Artificial Intelligence + Manufacturing”. Based on this process, we screen and retain 110 high-frequency and representative keywords related to digital intelligence technology adoption.
Second, based on the functional attributes and semantic meanings of the keywords, we categorise digital intelligence technology adoption into two dimensions from a functional perspective: (i) underlying technological infrastructure and (ii) digital intelligence-enabled business application technologies. Specifically, the underlying technological infrastructure dimension includes artificial intelligence, big data, cloud computing, and blockchain technologies. This procedure yields the final keyword database for measuring digital intelligence technology adoption (as illustrated in Fig. 1).
Fig. 1.
Feature word mapping of digital intelligence technology adoption.
Finally, we apply the Jieba word segmentation tool in Python to the “Management Discussion and Analysis” (MD&A) section of firms’ annual reports to conduct text segmentation and keyword frequency counts. To mitigate potential skewness in the distribution of keyword frequencies, we add one to the total keyword frequency count and then take the natural logarithm. The resulting value is used as a quantitative indicator of firms’ digital intelligence technology adoption intensity.
Control variables
Following prior studies18, firm-level control variables include: firm size(Size, the natural logarithm of total assets); firm age(Age, the natural logarithm of the number of years since establishment plus one); firm growth(Growth, the growth rate of operating revenue); financial leverage(Lev, the ratio of total liabilities to total assets); return on assets(Roa, the ratio of net profit to total assets); operating cash flow(Cash, the ratio of net cash flow from operating activities to total assets); government subsidies(Subsidy, the natural logarithm of government subsidies received by the firm); ownership structure(Soe, which equals one if the firm is state-owned and zero otherwise); ownership concentration(Top10, the sum of shareholding percentages of the top ten shareholders); and board size(Board, the natural logarithm of the number of board members). Region-level control variables include: the level of economic development(GDP, the natural logarithm of the gross domestic product of the city in which the firm is located in a given year); industrial structure(Structure, the ratio of value added in the secondary industry to that in the tertiary industry in the firm’s city); and financial development(Finlevel, the ratio of the total deposits and loans of financial institutions to GDP). this study includes industry (Industry) and year (Year) dummy variables as control factors.
Descriptive statistics of variables
Table 1 reports the descriptive statistics of the main variables. The dependent variable, SCS has a mean value of 0.1276, with a minimum of 0.0158 and a maximum of 0.3279, indicating substantial variation in SCS levels across manufacturing firms. The explanatory variable, Digtech has a mean of 1.0953 and a standard deviation of 1.1790, suggesting significant heterogeneity among manufacturing firms in terms of the stage and depth of digital intelligence adoption. While some firms have already achieved relatively systematic integration of digital intelligence technologies, others remain at an early exploratory stage or have not yet adopted such technologies. In addition, the variance inflation factor (VIF) test results show a maximum value of 2.63 and an average value of 1.53, both well below the conventional threshold of 5, indicating that multicollinearity is not a serious concern and that subsequent regression analyses can be reliably conducted.
Table 1.
Descriptive statistics.
| Variables | Obs | Mean | Std.Dev. | Min | Max |
|---|---|---|---|---|---|
| SCS | 16,731 | 0.1276 | 0.0694 | 0.0158 | 0.3279 |
| Digtech | 16,731 | 1.0953 | 1.1790 | 0.0000 | 4.3820 |
| Size | 16,731 | 22.3800 | 1.2332 | 20.0571 | 25.9568 |
| Age | 16,731 | 3.0102 | 0.3073 | 2.0794 | 3.6376 |
| Growth | 16,731 | 0.1146 | 0.3069 | -0.4885 | 1.6829 |
| Lev | 16,731 | 0.4169 | 0.1910 | 0.0570 | 0.8641 |
| Roa | 16,731 | 0.0310 | 0.0627 | -0.2346 | 0.1967 |
| Cash | 16,731 | 0.0497 | 0.0627 | -0.1221 | 0.2318 |
| Subsidy | 16,731 | 16.5675 | 1.6043 | 11.0821 | 20.5295 |
| Soe | 16,731 | 0.3570 | 0.4791 | 0.0000 | 1.0000 |
| Top10 | 16,731 | 0.5376 | 0.1481 | 0.2152 | 0.8717 |
| Board | 16,731 | 2.1221 | 0.1879 | 1.6094 | 2.6391 |
| GDP | 16,731 | 8.5970 | 1.3105 | 5.3725 | 10.8166 |
| Structure | 16,731 | 0.8558 | 0.4093 | 0.1888 | 2.2322 |
| Finlevel | 16,731 | 3.5946 | 1.1689 | 1.9485 | 7.0352 |
Analysis of the empirical results
Baseline regression analysis
Table 2 reports the baseline regression results. Column (1) shows that, without including any control variables, the estimated coefficient of Digtech is positive and statistically significant at the 1% level. Columns (2) and (3) indicate that, after sequentially adding firm-level and region-level control variables, the estimated coefficients of Digtech declines slightly but remains positive and statistically significant at the 1% level. These results suggest that digital intelligence technology adoption significantly enhances the SCS of manufacturing firms, thereby supporting Hypothesis H1.
Table 2.
Baseline regression results. Standard errors clustered at the firm level are reported in parentheses. *P < .10, **P < .05, ***P < .01, the same applies to the following.
| Variables | Dependent variable: SCS | ||
|---|---|---|---|
| (1) | (2) | (3) | |
| Digtech | 0.0124***(0.0013) | 0.0085***(0.0011) | 0.0080***(0.0011) |
| Size | 0.0110***(0.0021) | 0.0110***(0.0021) | |
| Age | -0.0189***(0.0061) | -0.0191***(0.0060) | |
| Growth | -0.0041**(0.0020) | -0.0043**(0.0019) | |
| Lev | -0.0304***(0.0079) | -0.0298***(0.0078) | |
| Roa | 0.0584***(0.0161) | 0.0569***(0.0160) | |
| Cash | 0.0104(0.0130) | 0.0126(0.0128) | |
| Subsidy | 0.0077***(0.0013) | 0.0076***(0.0013) | |
| Soe | 0.0086***(0.0033) | 0.0078**(0.0032) | |
| Top10 | -0.0084(0.0092) | -0.0109(0.0092) | |
| Board | 0.0084(0.0072) | 0.0092(0.0070) | |
| GDP | 0.0070***(0.0014) | ||
| Structure | 0.0004(0.0040) | ||
| Finlevel | -0.0037**(0.0016) | ||
| Constant | 0.1140***(0.0019) | -0.2046***(0.0392) | -0.2470***(0.0400) |
| Industry/Year FE | Yes | Yes | Yes |
| N | 16,731 | 16,731 | 16,731 |
| R² | 0.1639 | 0.2741 | 0.2857 |
Robustness checks
Instrumental variable approach
Following Deng et al.40, we employ an instrumental variable strategy to address potential endogeneity arising from reverse causality. Specifically, the interaction term between the number of fixed-line telephones per 100 people in each city in 1984 and the lagged city-level Internet penetration rate is used as an instrument for digital intelligence technology adoption(Digtech_IV). The two-stage least squares(2SLS) method is applied to mitigate endogeneity concerns. Column (1) of Table 3 reports the first-stage estimation results, showing that Digtech_IV has a significantly positive effect on digital intelligence technology adoption at the 1% level, confirming the relevance of the instrumental variable. The LM and F statistics indicate that the model does not suffer from underidentification or weak instrument problems. Column (2) presents the second-stage results, in which the estimated coefficient of Digtech remains significantly positive at the 1% level. This finding suggests that the baseline results remain robust after controlling for potential reverse causality.
Table 3.
Robustness test results.
| Variables | (1) | (2) | (3) | (4) | (5) |
|---|---|---|---|---|---|
| Digtech | SCS | SCS | SCS | SCS | |
| Digtech_IV | 0.0031***(0.0004) | ||||
| Digtech | 0.0635***(0.0094) | 0.8666***(0.2537) | 0.0065***(0.0012) | ||
| DTtech | 0.0086***(0.0012) | ||||
| Constant | -3.7814***(0.2162) | -0.0175(0.0416) | -8.0734(7.9385) | -0.2604***(0.0399) | -0.2360***(0.0412) |
| Control | Yes | Yes | Yes | Yes | Yes |
| Industry/Year FE | Yes | Yes | Yes | Yes | Yes |
| N | 16,211 | 16,211 | 16,731 | 16,731 | 14,420 |
| R² | 0.6753 | 0.0797 | 0.2867 | 0.2750 | |
| Kleibergen-Paap rk LM | 68.352*** | ||||
| Kleibergen-Paap rk Wald F | 67.710(> 16.38) | ||||
Alternative measurement of the dependent variable
To mitigate potential bias arising from the sensitivity of the entropy weight method to indicator volatility or extreme values, this study remeasures the SCS of manufacturing firms using principal component analysis(PCA). The regression results reported in column (3) of Table 3 show that the estimated coefficient of Digtech remains significantly positive at the 1% level, indicating that the baseline results are robust to alternative measurements of the dependent variable.
Alternative measurement of the independent variable
Following Yang et al.41, this study constructs an alternative measure of digital intelligence technology adoption from two dimensions: breadth and depth. The breadth dimension includes 15 keywords related to technologies such as big data, cloud computing, blockchain, and artificial intelligence. The depth dimension further extracts 70 keywords reflecting the depth of digital intelligence technology adoption across four aspects—production, management, marketing, and products—including terms such as intelligent manufacturing, intelligent operations, intelligent recommendation, and data products. We then count the frequency of all keywords appearing in the MD&A section of firms’ annual reports and measure digital intelligence technology adoption(DTtech) as the natural logarithm of the aggregated keyword frequency after adding one. The regression results reported in column (4) of Table 3 show that the estimated coefficient of DTtech remains significantly positive at the 1% level, further confirming the robustness of the baseline results.
Alternative sample specification
Considering that firms in highly digitalised industries may possess inherent advantages in digital intelligence technology adoption, which could bias the estimation results due to industry structure effects, this study re-estimates the model after excluding firms in the computer, communication, and other electronic equipment manufacturing industries. The results reported in column (5) of Table 3 show that the estimated coefficient of Digtech remains positive and statistically significant at the 1% level, indicating that the baseline results are stable and reliable and robust to changes in the sample composition.
Mechanism tests and heterogeneity analysis
Mediation effect analysis
Risk management capability channel
We measure manufacturing firms’ Risk using the logarithmically transformed DIB internal control index score. The regression results reported in column (1) of Table 4 show that the estimated coefficient of Digtech is positive and statistically significant at the 1% level, indicating that digital intelligence technology adoption significantly enhances firms’ risk management capability. The results in column (2) further show that, after incorporating risk management capability into the regression, the estimated coefficient of Risk is positive and statistically significant at the 1% level, suggesting that improvements in firms’ risk management capability contribute to higher SCS. At the same time, the estimated coefficient of Digtech on SCS remains positive and statistically significant at the 1% level, but its magnitude declines relative to the baseline regression reported in column (3), indicating that risk management capability plays a partial mediating role in the relationship between digital intelligence technology adoption and SCS. Taken together, these regression results demonstrate that digital intelligence technology adoption enhances manufacturing firms’ SCS by strengthening their risk management capability. Therefore, Hypothesis H2 is supported.
Table 4.
Mechanism test results.
| Variables | (1) | (2) | (3) | (4) | (5) |
|---|---|---|---|---|---|
| Risk | SCS | SCC | SCS | SCS | |
| Digtech | 0.0048***(0.0015) | 0.0078***(0.0011) | 0.0067*(0.0037) | 0.0078***(0.0011) | 0.0081***(0.0012) |
| Risk | 0.0254***(0.0051) | ||||
| SCC | 0.0241***(0.0069) | ||||
| DI | 0.0131*(0.0073) | ||||
| Digtech_DI | 0.0067*(0.0035) | ||||
| Constant | 6.0167***(0.0541) | -0.3996***(0.0516) | 0.8944***(0.1016) | -0.2686***(0.0398) | -0.2412***(0.0404) |
| Control | Yes | Yes | Yes | Yes | Yes |
| Industry/Year FE | Yes | Yes | Yes | Yes | Yes |
| N | 16,731 | 16,731 | 16,731 | 16,731 | 15,444 |
| R² | 0.1909 | 0.2880 | 0.3362 | 0.2894 | 0.2860 |
Supply chain coordination capability channel
We measure manufacturing firms’ SCC using inventory turnover, measured as the ratio of operating costs to average inventory holdings. The regression results reported in column (3) of Table 4 show that the estimated coefficient of Digtech is positive and statistically significant at the 10% level, indicating that digital intelligence technology adoption significantly enhances firms’ supply chain coordination capability. The results in column (4) further show that, after incorporating supply chain coordination capability into the regression, the estimated coefficient of SCC is positive and statistically significant at the 1% level, suggesting that improvements in firms’ supply chain coordination capability contribute to higher SCS. At the same time, the estimated coefficient of Digtech on SCS remains positive and statistically significant at the 1% level, but its magnitude declines relative to the baseline regression reported in column (3), indicating that supply chain coordination capability plays a partial mediating role in the relationship between digital intelligence technology adoption and SCS. Taken together, these regression results demonstrate that digital intelligence technology adoption enhances manufacturing firms’ SCS by improving their supply chain coordination capability. Therefore, Hypothesis H3 is supported.
Moderating effect analysis
Based on the theoretical analysis above, this study empirically examines whether improvements in regional digital infrastructure strengthen the positive effect of digital intelligence technology adoption on the SCS of manufacturing firms. First, following Lan et al.36, we construct a composite index of regional digital infrastructure development(DI) using the entropy weight method. This index is based on five indicators: internet penetration(the number of broadband internet subscribers per 100 people), the development of related human resources(the proportion of employees in computer services and software industries relative to total urban employment), related output performance(per capita total telecommunications business volume), mobile phone penetration(the number of mobile phone users per 100 people), and digital financial development(the Digital Financial Inclusion Index). We then augment the baseline model (1) by including the interaction term between digital intelligence technology adoption and regional digital infrastructure (Digtech_DI). The results reported in column (5) of Table 4 show that the estimated coefficient of Digtech_DI is positive and statistically significant at the 10% level, indicating that higher levels of regional digital infrastructure significantly strengthen the positive effect of digital intelligence technology adoption on firms’ SCS. Therefore, Hypothesis H4 is supported.
Heterogeneity analysis
Regional heterogeneity
We divide the sample into firms located in eastern regions and those located in central and western regions according to the geographic location of the cities in which firms operate. The regression results reported in columns (1) and (2) of Table 5 show that the estimated coefficient of digital intelligence technology adoption for firms in eastern regions is 0.0083, which is larger than the corresponding coefficient of 0.0067 for firms in central and western regions. Both coefficients are positive and statistically significant at the 1% level, and the difference between the two coefficients is confirmed by a Fisher test for between-group coefficient differences. These results indicate that digital intelligence technology adoption has a stronger effect on enhancing SCS for firms located in eastern regions. One possible explanation is that firms in eastern regions possess more pronounced advantages in terms of the completeness of digital infrastructure, the availability of digital intelligence technology inputs, the maturity of supply chain networks, and the stock of specialised human capital, which makes it easier for them to embed digital intelligence technologies into various stages of their supply chains and to form stable application scenarios. As a result, digital intelligence technologies can play a more substantial role in promoting SCS in eastern-region firms.
Table 5.
Heterogeneity test results. The P-value is calculated using the Fisher combined test based on 1,000 bootstrap replications.
| Variables | Dependent variable: SCS | |||
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| Eastern region | Central and western regions | High supply chain concentration | Low supply chain concentration | |
| Digtech | 0.0083***(0.0013) | 0.0067***(0.0020) | 0.0070***(0.0014) | 0.0081***(0.0015) |
| Constant | -0.2717***(0.0521) | -0.0879(0.0660) | -0.1588***(0.0491) | -0.2869***(0.0495) |
| Control | Yes | Yes | Yes | Yes |
| Industry/Year FE | Yes | Yes | Yes | Yes |
| N | 11,215 | 5,516 | 8,277 | 8,454 |
| R² | 0.3083 | 0.3162 | 0.2516 | 0.3239 |
| P-value | 0.060 | 0.092 | ||
Heterogeneity by supply chain concentration
Supply chain concentration is measured as the average of the combined purchasing and sales shares of a firm’s top five suppliers and top five customers, as disclosed in firms’ annual reports. Based on the industry–year median, the sample is divided into firms with high supply chain concentration and firms with low supply chain concentration. The regression results reported in columns (3) and (4) of Table 5 show that the estimated coefficient of digital intelligence technology adoption for firms with low supply chain concentration is 0.0081, which is larger than the corresponding coefficient of 0.0070 for firms with high supply chain concentration. Both coefficients are positive and statistically significant at the 1% level, and the difference between the two coefficients is confirmed by a Fisher test for between-group coefficient differences. These results indicate that digital intelligence technology adoption has a stronger effect on enhancing SCS for firms with lower supply chain concentration. A plausible explanation is that firms with lower supply chain concentration tend to have more dispersed upstream and downstream relationships, face higher degrees of information asymmetry and greater coordination costs, and therefore rely more heavily on digital intelligence technologies to enhance supply chain visibility, reduce transaction risks, and strengthen coordination. As a result, digital intelligence technology adoption plays a more pronounced role in improving SCS for firms with lower supply chain concentration.
Further analysis: Spillover effects of supply chain relationships
As core firms within supply chains(i.e., focal firms), manufacturing enterprises not only control key transactional information and credit resources but also play a dominant role in resource allocation, risk transmission, and coordinated governance along the supply chain. Therefore, it is necessary to further examine, from a supply chain relationship perspective, whether improvements in the SCS of focal firms spill over to enhance the SCS of their upstream suppliers and downstream customers.
Following Fang et al.42, we construct a focal firm-upstream supplier(downstream customer)–year dataset using information on the top five suppliers and customers disclosed by focal firms. The sample is restricted to cases in which both upstream and downstream firms are publicly listed companies, yielding a final dataset of 4,420 valid observations. In Table 6, columns (1) and (2) take the SCS of focal firms as the key explanatory variable, while the dependent variables are the SCS of upstream supplier firms and downstream customer firms, respectively. The regression results show that the SCS of focal firms has a significantly positive effect on the SCS of both upstream suppliers and downstream customer firms at the 1% significance level. These findings indicate that the positive effect of digital intelligence technology adoption on the SCS of focal firms propagates along supply chain networks, generating positive spillover effects on upstream and downstream firms. In other words, by enhancing their own SCS, focal firms can effectively drive and empower the entire supply chain ecosystem toward coordinated improvements in both security and efficiency.
Table 6.
Supply chain spillover effect test results.
| Variables | (1) | (2) |
|---|---|---|
| Suppliers’ SCS | Customers’ SCS | |
| Focal firms’ SCS | 0.1945***(0.0575) | 0.4115***(0.0605) |
| Constant | -0.5206***(0.1593) | -0.3950***(0.1034) |
| Control | Yes | Yes |
| Industry/Year FE | Yes | Yes |
| N | 1,634 | 2,786 |
| R² | 0.3615 | 0.3699 |
Discussion and implications
Discussion of findings
Based on micro-level data from Chinese manufacturing firms, this study systematically examines the impact of digital intelligence technology adoption on firms’ SCS and its underlying mechanisms. Unlike prior studies that primarily investigate firm management performance or supply chain resilience from the perspectives of digital technologies or individual intelligent technologies7,12,35, this study conceptualises digital intelligence technology adoption as an integrated technological system that combines data elements, intelligent algorithms, and business scenarios, and incorporates it into the analytical framework of SCS. In doing so, it extends the research perspective on SCS from the standpoint of technological governance.
The empirical results show that digital intelligence technology adoption significantly enhances firms’ SCS, and this finding remains robust across a variety of robustness checks. This result is consistent with prior evidence suggesting that digital technologies help mitigate uncertainty shocks and improve supply chain stability5,12,28. However, this study further provides micro-level empirical evidence from Chinese manufacturing firms, demonstrating that digital intelligence technologies not only improve firms’ emergency response capabilities but also enhance their long-term SCS by reshaping the underlying logic of supply chain operations.
With respect to the underlying mechanisms, this study finds that digital intelligence technology adoption influences SCS through two complementary channels: strengthening firms’ risk management capability and enhancing supply chain coordination capability. These findings directly address the limited attention in existing research to how technologies are translated into SCS outcomes16,32. Moreover, the results suggest that digital intelligence technologies do not directly substitute for risk management or coordination activities; instead, they empower firms’ governance capabilities and inter-firm coordination mechanisms, shifting SCS management from ex post responses toward ex ante early warning and process-based control. Further moderation analysis indicates that regional digital infrastructure significantly amplifies the positive effect of digital intelligence technology adoption on SCS. This finding aligns with studies showing that digital infrastructure enhances the economic effects of digital technologies by reducing information acquisition costs and facilitating the flow of data elements8,36, and it reveals the contextual dependence of digital intelligence–enabled SCS from a regional perspective.
In addition, heterogeneity analyses show that the security-enhancing effect of digital intelligence technology adoption is more pronounced for firms located in eastern regions and for firms with lower supply chain concentration. These results are consistent with prior findings that regional differences in digital development affect firms’ digitalisation outcomes18,40 and that greater supply chain structural complexity intensifies risk exposure23,24. More importantly, from a supply chain network perspective, this study finds that improvements in SCS among focal firms significantly enhance the SCS of both upstream and downstream partners, indicating that SCS exhibits clear network spillover effects. While existing studies have largely focused on the transmission of supply chain risks within networks1,3,26,28, this study complements the literature by highlighting “positive governance spillovers” and revealing the pivotal role of focal firms in SCS governance under digital intelligence empowerment.
Theoretical implications
First, unlike existing studies that primarily examine SCS from the perspectives of digital transformation or individual intelligent technologies12,18,23, this study adopts digital intelligence technology as a composite technological paradigm and incorporates it into the analytical framework of SCS. The findings show that digital intelligence technologies do not merely enhance SCS by improving informatization or automation levels; rather, they reshape the governance logic of SCS through multi-source data integration, intelligent analytics, and cross-chain coordination. In doing so, this study extends the theoretical framework of technology-enabled SCS.
Second, by focusing on two key mechanisms—risk management capability and supply chain coordination capability—this study reveals and empirically verifies the internal mechanism through which digital intelligence technologies promote SCS by “strengthening proactive risk prevention capabilities and enhancing cross-chain coordination efficiency.” This contributes to addressing the limited attention in existing research to the mechanisms underlying the role of digital intelligence technologies in enhancing SCS5. Moreover, this study identifies the moderating role of regional digital infrastructure, thereby enriching research on the boundary conditions of the governance effects of digital intelligence technologies and deepening the understanding of the “technology-organisation-environment” alignment mechanism.
Third, by examining the spillover effects of improvements in SCS among focal firms on upstream and downstream partners, this study extends SCS research from the firm level to the supply chain network level20–24. The results demonstrate that SCS exhibits significant network externalities, thereby advancing the theoretical understanding of SCS from a “firm attribute” to a “network attribute.”
Practical implications
The findings of this study offer important managerial implications for manufacturing firms. First, in promoting the application of digital intelligence technologies, firms should avoid treating them merely as tools for informatization or automation. Instead, digital intelligence technologies should be regarded as critical enablers for enhancing risk identification, early warning, and collaborative governance capabilities. By embedding these technologies into risk management processes and supply chain coordination mechanisms, firms can systematically improve their SCS. Second, firms should place greater emphasis on data sharing and coordination mechanisms with upstream and downstream partners, fully leveraging the synergistic effects of digital intelligence technologies in planning coordination, resource allocation, and emergency response, thereby strengthening the overall stability and shock resistance of the supply chain. Third, while improving their own SCS, focal firms should proactively assume governance responsibilities within the supply chain by empowering partners through technology, information sharing, and coordination mechanism building, thus driving upstream and downstream firms to jointly enhance SCS and achieve coordinated security across the supply chain ecosystem.
From a policy perspective, the findings of this study provide valuable insights for more targeted government interventions. On the one hand, governments should accelerate the development of digital infrastructure to reduce institutional and technological barriers to the adoption of digital intelligence technologies by firms, thereby creating a favourable external environment for digital intelligence-enabled SCS. On the other hand, policymakers can implement differentiated support strategies to prioritise the deployment of digital intelligence technologies in firms and regions characterised by more dispersed supply chain structures and higher risk exposure, thereby improving the efficiency of policy resource allocation. In addition, governments can strengthen supply chain information disclosure systems and support focal firms in playing a coordinating governance role, thereby fostering a multi-actor, collaborative SCS governance framework underpinned by digital intelligence technologies.
Conclusion
Based on data from Chinese A-share listed manufacturing firms from 2012 to 2024, this study systematically examines the impact of digital intelligence technology adoption on firms’ SCS and its underlying mechanisms from a micro-level perspective. The findings indicate that, amid an increasingly complex and volatile external environment, digital intelligence technologies have become an important technological foundation and governance tool for enhancing firms’ SCS.
Specifically, first, digital intelligence technology adoption significantly improves the SCS of manufacturing firms, and this conclusion remains robust after a series of robustness checks. Second, digital intelligence technology adoption promotes SCS by strengthening firms’ risk management capability and enhancing supply chain coordination capability; moreover, the higher the level of regional digital infrastructure, the stronger the positive effect of digital intelligence technology adoption on manufacturing firms’ SCS. Third, heterogeneity analyses reveal that the positive effect of digital intelligence technology adoption on SCS is more pronounced for firms located in eastern regions and for firms with lower supply chain concentration. Fourth, improvements in SCS achieved by focal firms through digital intelligence technology adoption generate significant supply chain spillover effects, further enhancing the SCS of their upstream suppliers and downstream customer firms.
Research limitations and further research
First, regarding sample selection, this study focuses on listed manufacturing firms in China, which implies a certain degree of concentration in terms of industry coverage and firm size. While listed firms provide relatively comprehensive and reliable publicly available information, the generalisability of the findings to non-manufacturing firms, non-listed firms, and other countries or institutional contexts remains to be further examined. Future research could extend the sample scope by incorporating small and medium-sized enterprises, service-sector firms, or cross-country datasets to conduct more systematic comparative analyses of the universality and heterogeneity of digital intelligence technologies in enhancing SCS.
Second, in terms of research methods and analytical perspectives, this study primarily employs large-sample econometric approaches to identify the overall statistical relationship between digital intelligence technology adoption and SCS. Such methods may be limited in capturing the specific operational mechanisms and governance details through which digital intelligence technologies function in concrete supply chain contexts. Future studies could adopt case studies, interviews, or mixed methods approaches to investigate typical firms or key supply chain nodes in greater depth, thereby providing richer insights into how digital intelligence technologies operate in practical scenarios such as risk identification, early warning, and collaborative decision-making, and thus complement and deepen the empirical findings of this study.
Third, with respect to mechanism extension, although this study identifies risk management capability and supply chain coordination capability as two important channels through which digital intelligence technologies enhance SCS, digital intelligence technologies may also influence SCS through other mechanisms, such as organisational restructuring, governance innovation, or changes in decision-right allocation. Future research could further broaden the scope of mechanism analysis by examining organisational governance and institutional arrangements, and systematically explore how digital intelligence technologies reshape the formation logic and evolutionary processes of SCS.
Author contributions
Z.Y. conceived the research idea, collected the data, and drafted the initial manuscript. H.L. reviewed the research design, provided methodological guidance, and secured funding support. C.X. assisted with the empirical analysis and contributed to revising the manuscript. All authors reviewed and approved the final manuscript.
Funding
This research was funded by the Ministry of Education Humanities and Social Sciences Research Planning Fund Project “Mechanisms, Pathways, and Policy Research on Digital Supply Chain Finance Enhancing Supply Chain Security” (24YJA790031), and the 20th Student Scientific Research Project of Jiangxi University of Finance and Economics “The Impact of Digital Intelligence Technology Adoption on Corporate Supply Chain Security and Improvement Strategies” (20251211215356766).
Data availability
The panel data used in this study are obtained from the CSMAR(Link: https://data.csmar.com/), Wind(Link: https://wind.com.cn/), CNRDS(Link: https://www.cnrds.com/), and CEIN(Link: https://db.cei.cn/) databases. The datasets generated and analysed during the current study are available from the corresponding author upon reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The panel data used in this study are obtained from the CSMAR(Link: https://data.csmar.com/), Wind(Link: https://wind.com.cn/), CNRDS(Link: https://www.cnrds.com/), and CEIN(Link: https://db.cei.cn/) databases. The datasets generated and analysed during the current study are available from the corresponding author upon reasonable request.