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Article

Empowerment of Digital Technology for the Resilience of the Logistics Industry: Mechanisms and Paths

by
Jifeng Zhang
,
Zirui Yang
and
Bing He
*
School of Business, Jiangsu Ocean University, Lianyungang 222005, China
*
Author to whom correspondence should be addressed.
Systems 2024, 12(8), 278; https://doi.org/10.3390/systems12080278
Submission received: 19 June 2024 / Revised: 25 July 2024 / Accepted: 30 July 2024 / Published: 31 July 2024
(This article belongs to the Special Issue Developing Resilient Systems: Engineering Solutions for a Changing World)
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Versions Notes

Abstract

:
Digital technology, acting as an engine for industrial development, propels the rapid integration of data elements and the swift iteration of digital logistics technology, significantly enhancing logistic resilience. Exploring the impact of digital technology on the resilience of logistics helps strengthen the latter’s ability to withstand external shocks. Based on the correlation between digital technology and the resilience of the logistics industry, this study measured their levels in 275 Chinese prefecture-level cities from 2011 to 2020, showing that the former significantly improved the latter, a conclusion which remained valid after robustness tests. The mechanism analysis results showed that promoting industrial collaborative agglomeration was an important part of this process, while the threshold model analysis found that the impact on resilience had nonlinear characteristics. The heterogeneity test results showed that digital technology had a significant resilience-promoting effect in the eastern and northern coastal regions, as well as in the middle reaches of the Yellow and Yangtze Rivers. Accordingly, it is necessary to accelerate digital technology integration with the logistics industry and formulate differentiated development strategies. This study provides a theoretical foundation for exploring the relationship between digital technology and logistics resilience, as well as policy recommendations.

1. Introduction

The report of the 20th National Congress of the Communist Party of China emphasizes the acceleration of building a modern economic system, with a focus on enhancing supply chain resilience. As a crucial player in cities’ supply chains, the logistics industry connects production and circulation, integrating transportation, warehousing, distribution, and delivery services. Its ability to respond to external shocks has become a core component of supply chain resilience. In recent years, China’s logistics industry has seen continuous improvements in scale and efficiency. According to “The China Logistics Yearbook” and “The China Statistical Yearbook” in 2021 (details in the note1), the total social logistics earnings reached CNY 33.52 trillion, with a steady decrease in logistics costs, and the ratio of total logistics costs to GDP dropped to 14.6%. However, logistics challenges, such as resource misallocation, facilities’ structural imbalances, and network bottlenecks, persist [1], leading to a lack of resilience in the logistics sector when facing external shocks, and, in turn, industrial and supply chain vulnerabilities and interruption risks. Therefore, addressing how to minimize the impact on the logistics industry and enhancing its resilience and adaptability to innovation at the level are crucial for economic development.
Digital technology, the material and technical foundations of the digital economy, is deeply integrated into various sectors. It leads to a comprehensive digital transformation in lifestyle and governance [2], reshaping traditional industrial forms and continuously adopting new models. The “14th Five-Year Plan and Vision 2035 Outline” of China emphasizes the need to “deepen the digital transformation of the service industry and cultivate new growth points such as crowdsourced design, smart logistics, and new retail”. The new digital revolution wave has led to the logistics industry exhibiting significant digitization, information technology, and diversification characteristics. The emerging digital technologies, such as information transmission and big data, effectively enhance the information transmission speed, reduce the communication costs, and strengthen their ability to capture market demand, respond promptly, and agilely adjust, profoundly impacting the logistics industry’s resilience [3]. Considering the above, this study’s key questions are as follows: Does digital technology empower resilience enhancement in logistics? What are the mechanisms and pathways underlying its effects? Investigations related to these topics can help remove the industry obstacles and establish a modern, intelligent, and green logistics system adaptive to supply and demand.
Research at the provincial or agglomeration level may not yield universal conclusions. Drawing on economic resilience studies, this paper innovatively focuses its research perspective by taking 275 prefecture-level cities in China as subjects, measuring their logistics resilience from 2011 to 2020, and analyzing the influencing factors and mechanisms through which digital technology enhances said resilience. It aims to better leverage advantages such as resource circulation and locational conditions, providing references for logistics industry upgrade and policy formulation, thereby promoting logistics resilience improvement in agglomerations.
The remainder of this paper is structured as follows: The Section 2 summarizes the relevant literature on logistics and the digital economy. The Section 3 constructs a theoretical framework for the impact of the digital economy on resilience and proposes hypotheses. The Section 4 details the research design, primarily introducing the econometric model, variables, descriptive statistics, and data sources. In the Section 5, empirical tests and further analyses are presented to investigate digital economy’s mechanisms and impact on logistics resilience. Finally, the Section 6 summarizes the findings and offers policy recommendations.

2. Literature Review

The concept of resilience was initially applied in engineering. Perrings [4] defined resilience as the ability of a system to maintain its organizational structure after perturbations to certain state variables of given values. Reggiani et al. [5] introduced resilience into economic systems, pointing out that it is the capacity of a system to absorb external shocks without altering its fundamental functions. Some scholars have studied the factors influencing economic resilience, mainly reflected in aspects such as industrial diversity, agglomeration, and technological innovation. The evidence shows that based on the theoretical framework of evolutionary economic geography, industrial diversity acts as an automatic stabilizer by dispersing risks and resisting shocks [6]. Others have argued that the externalities of industrial agglomeration enhance knowledge and information spillover among enterprises, contributing to economic resilience, and that innovation is the driving force of economic growth and the core element influencing industrial resilience [7,8,9].
The existing literature on resilience in the logistics industry mainly focuses on the definition and spatiotemporal differences. Some scholars defined efficiency and productivity as the key factors in assessing logistics resilience [10], arguing that production efficiency can enhance the flexibility of logistics, allowing it to adapt to technological changes and ensuring an optimal operational status [11,12]. From the idea of spatial and temporal heterogeneity, Yu et al. [13] defined the integration of temporal changes in logistics delivery time as the resilience characterization quantity. They studied e-commerce logistics resilience under the impact of the epidemic and found that the logistics delivery time exhibited a “decay wave” characteristic. From the green development perspective, Xie [14] measured logistics resilience in cities along the Yangtze River using the entropy weight method. Based on the logistics economy, community connection, and innovation capacity, Jin et al. [15] used factor analysis to measure logistics resilience, pointing out that it is continuously increasing, with imbalances. From the perspective of digital logistics infrastructure input factors, Zuo [16] constructed evaluation indicators for logistics resilience. Using geographical analysis, they identified the driving factors of digital logistics green development, revealing that the level of logistics resilience was higher in the East.
Digital technology research has become a relatively mature field. Bharadwaj et al. [17] defined digital technology as a combination of information, computing, and communication technologies, with its core advantage being its ability to connect data islands. More specifically, digital technology is influencing innovation in the traditional industries and the real economy through characteristics such as substitution and collaboration [18,19]. Banister and Stead [20] believed that the combination of modern information technology with traditional logistics could greatly streamline transportation costs in the logistics industry and improve commodity turnover and logistics efficiency. Shao et al. [21] argued that the large-scale ICT investment triggered by digital technology upgrades empowers the optimization of enterprise capital structure and achieves cost reduction in production. Moreover, Zhao et al. [22] explored the relationship between digital logistics, supply chain resilience, and circulation industry chain resilience, finding a positive correlation between internal, client, and supplier elasticity in digital logistics and industry chain resilience.
Currently, digital technology’s rapid development has garnered widespread attention in terms of its potential to address the existing logistics industry shortcomings. Research on digitization and logistics industry development focuses on several aspects, among which is the way in which the application of digital logistics technology effectively addresses historical drawbacks in logistics enterprises, such as a low operational efficiency and significant information asymmetry [23,24]. Other scholars have analyzed the development of digital logistics technology and its role in macroeconomic growth. Additionally, the application of digital logistics technology helps logistics enterprises, particularly small- and medium-sized ones, achieve digital transformation [25]; for instance, the digital canvas model (DCM) enables logistics enterprises to identify genuine customer needs, achieving differentiation from competitors [26], while crowdsourced logistics systems match the most suitable logistics solutions based on user demands [27], and logistics digital twin (DT) frameworks assess potential impact through the exchange of physical and virtual data [28]. Secondly, digital technology facilitates logistics industry development. MacCarthy and Ivanov [29] found that digitization directly impacts both the logistics industry and structural optimization, promoting the high-quality development of the former. Büyüközkan and Ilıcak [30] pointed out that the industrial Internet promoted the transformation of the traditional logistics industry, with digital operation and industrial ecosystems becoming important features of intelligent logistics. Thirdly, regarding the integration of the digital economy and the logistics industry, Gu and Zhang [31] measured the level of intelligence in the logistics industry by constructing an index system, pointing out that this level continued to improve, but remained low.
It is worth noting that digital technology in the logistics industry may lead to a series of potential negative consequences. For example, the adoption and competitive timing of digital logistics technology pose significant challenges for logistics enterprises [32]. Furthermore, tracking product information using digital technology or assisting logistics decision making with DT technology [33] imposes high requirements on data transmission networks’ security, potentially resulting in consequences, such as data theft and manipulation, mismatching errors, and failure of traceability channels [34]. While the integration of digital technology and the logistics industry brings many opportunities to enhance logistics resilience, it also introduces certain challenges.
In summary, the academic community has conducted extensive research on digital technology integration and the logistics industry, although many of these have theoretical outcomes and often provide qualitative descriptions of logistics digitalization, but lack a corresponding empirical study [35,36]. Moreover, there is a lack of studies focusing on logistics industry resilience in the context of digital technology. The marginal contribution of the current study lies in the following aspects: First, it constructs an evaluation index system for logistics industry resilience based on the resistance, recovery, adaptation, and innovation transformation. Second, considering that the previous studies mostly focused on logistics resilience at the provincial level [13,14,15], this study integrates digital technology with prefecture–city-level logistics resilience, benefitting from a more abundant sample size. This enhances the theoretical significance and practical value. Third, within the digital integration context, this study integrates digital technology and logistics industry resilience into the research framework. Additionally, it overcomes the limitations of a traditional analysis by examining the effects mediating the enhancement of logistics industry resilience. This offers a policy approach to promote logistics and related industries.

3. Theoretical Analysis and Hypotheses

3.1. Direct Effects of Digital Technology on Logistics Industry Resilience

Digital technology may drive production development, exerting positive impacts on the logistics industry through industrial strength, market demand, and technological innovation. In terms of industrial strength, digital technology aids logistics enterprises in overcoming geographical constraints by leveraging advantages in information search costs [37], facilitating the coordination of supply and demand information between industries, and enhancing logistics industry resilience against potential emergent risks. From the perspective of industry subjects, technological progress is one of the essential drivers of economic development. The penetration of digital technology has transformed the traditional operational models and technological foundations, driving optimization across logistics industry chain stages, improving transfer and transportation in the middle stage, and enhancing the distribution of goods in the last stage, thereby reducing the risk of disruption. From a micro-level perspective, digital technology has facilitated the formation of the consumer Internet [38] and has assisted the logistics industry by algorithm matching and enabling point-to-point delivery at terminal courier stations, consequently improving industrial efficiency.
In the market demand dimension, digital technology reduces the customer cost [39], stimulates market logistics enterprises’ vitality, and enhances logistics vitality. This ensures that the logistics industry has sufficient market demand and internal drive to self-adjust and recover when facing uncertain shocks. On the one hand, the emergence of e-commerce platforms allowed for supply transparency, timeliness, and convenience. Utilizing cloud platforms for logistics digitalization and network transformation enhances logistics services’ cost-effectiveness, boosting market demand and providing support for the logistics industry. On the other hand, the combination of digital technology with traditional logistics accelerates technological innovation, intrinsically driving industry transformation and upgrading. It also creates new business models and reconstructs service channels for logistics enterprises. Simultaneously, it alleviates issues such as the difficulty in obtaining information [40], effectively lowering the market-entry barriers for logistics enterprises, attracting many companies. Moreover, it also rationalizes the matching of market logistics supply and demand, enhancing industry resilience for coping with environmental shocks and adapting to recovery by bolstering demand support [41].
In the technological innovation dimension, digital technology possesses permeability and synergy [42]. Digital transformation effectively enhances the management and service efficiency of logistics enterprises, leading to innovations in big data processing and information analysis methods. Furthermore, the functions of the logistics industry gradually shift from commodity distribution to resource aggregation and from exchange intermediaries to intelligent management [43]. Simultaneously, the development of digital technology facilitates the integration of traditional logistics with digital technology applications. This enables the logistics industry to benefit from new dividends. In the short term, it stabilizes the economic level and improves operational efficiency. In the long term, it supports the digital transformation of industries and promotes sustainable development, leading to Hypothesis H1.
H1. 
Digital technology has a facilitating effect on logistics industry resilience.

3.2. Mediating Effects of Digital Technology on Logistics Industry Resilience

Industrial synergy clustering refers to the simultaneous aggregation and distribution of two or more related industries in close geographic proximity, achieving increased resource allocation efficiency, and enhancing the quality of economic development. On the one hand, the upgrading of digital technology endows traditional industries with inclusiveness and shared benefits, attracting the spontaneous aggregation of productive service industries and manufacturing industries, facilitating complementary advantages between industries [44]. This drives up the degree of interconnection, gradually rationalizes the industrial division of labor, and optimizes industrial collaboration mechanisms, thereby promoting industrial synergy clustering. On the other hand, industrial synergy clustering greatly shortens the distance between the supply and the demand by bringing together productive service and manufacturing industries in geographical proximity and functional interaction, therefore enhancing logistics efficiency [45,46]. Moreover, it enhances the ability of the logistics industry to prepare for potential emergencies, respond rapidly during emergencies, and recover afterward.
H2. 
Digital technology enhances logistics industry resilience by promoting industrial synergy clustering.

3.3. Threshold Effects of Digital Technology on Logistics Industry Resilience

The impact of digital technology on logistics industry resilience may not exhibit a purely linear relationship. Variables such as digital technology and industrial synergy clustering may exhibit “threshold effects”. According to “Metcalfe’s Law”, participants in the digital economy can obtain geometrically increased benefits from Internet development. In the initial stages of digital technology integration and the logistics industry, digital technologies can mitigate geographical distance constraints on cross-regional logistics cooperation, enabling stakeholders to share convenient network services, promoting the transition to open collaboration in the logistics industry [47]. This facilitates easier alignment between the supply and demand sides, leading to expansion in the commodity transportation scope. Logistics enterprises gain economies of scale, effectively improving logistics demand and operational efficiency, enhancing logistics industry resilience [48]. However, as the integration of digital technology in the logistics industry deepens, the multiplier effect of the former on the resilience of the latter is expected to gradually stabilize. Based on this, Hypothesis H3 is proposed.
H3. 
The impact of digital technology on logistics industry resilience has threshold effects.
The research framework is illustrated in Figure 1.

4. Research Design

4.1. Model Construction

4.1.1. Baseline Regression

Based on the analysis above, the baseline regression model is as follows:
Resit = α0 + α1Techit + λControlit + τi + μt + εit
Resit represents logistics industry resilience; Techit represents digital technology; Controlit represents the control variables; εit represents the error term; α0 is the constant term; and α1 is the coefficient of interest in this study. If digital technology grows logistics industry resilience, then α1 is positive. Furthermore, after Hausman’s test, this study decides to employ the two-way fixed effects model.

4.1.2. The Mediation Effect

In the existing research, scholars primarily utilize traditional regression analysis (REG) as the main method for intermediary testing [49,50]. However, structural equation modeling (SEM) can address many of its limitations. Using regression analysis to test for potential mediation effects may have significant drawbacks. Compared to structural equation modeling, the standard errors of the mediation pathway coefficients obtained using the REG method are larger. Furthermore, SEM employs maximum likelihood estimation to obtain unbiased and consistent estimates, while also providing overall model fit tests and independent parameter estimation tests. Based on these, SEM allows for model refinement to achieve a better fit, providing a more precise examination of the extent of mechanism effects compared to traditional regression models [51]. Therefore, this study adopts the SEM method to analyze the mediation effects, with the measurement model shown in Equation (2) and the structural model shown in Equations (3) and (4):
X = δXθ + Φ
Y = δYϑ + φ
ϑ = Zϑ + Ϛθ + ϖ
X and Y represent exogenous observable and endogenous variables, respectively; δX and δY are factor-loading matrices; θ and ϑ represent exogenous and endogenous latent variables, respectively; Z represents the relationship between the endogenous latent variables; Ϛ represents the coefficients representing the relationship between the exogenous latent variables; and Ϛ, φ, and ϖ are the residual terms.

4.1.3. Threshold Effect

Considering the threshold effect, this study establishes the model as follows:
Resit = β0 + β1Techit × I (Adjit ≤ ρ) + β2Techit × I (Adjit > ρ) + λControlit + τi + μt + εit
Adj represents the threshold variable, and I (…) is an indicator function that takes value of 0 or 1 based on the condition inside the parentheses. Equation (5) is used to calculate a single-threshold model, which can be expanded to a double- or multiple-threshold model. The other variables are the same as in Equation (1).

4.2. Indicator Selection

4.2.1. Index System of Logistics Industry Resilience

Regarding economic resilience, Martin [52] constructed an index system based on evolutionary resilience, covering four aspects: resistance, recovery, adaptation, and renewal. Moreover, some scholars measured economic resilience from three perspectives: self-adaptation, innovation transformation, and risk resistance [53]. Therefore, this study constructed an index system for measuring logistics industry resilience from the perspective of economic strength to withstand and recover from shocks, demand support for adapting to unstable external environments, and the industry’s ability to innovate and upgrade. In terms of tertiary indicators, this study further refines the calculation methods based on the connotations and characteristics of the secondary indicators. For instance, infrastructure construction often has a strong capacity to withstand external risk impacts; hence, this study chose the area of logistics warehouses as one of the measures for “Resistance and Recovery Capacity” according to Wang et al. [54]. Per capita GDP reflects the city’s production capacity more comprehensively compared to the total GDP. Regions with higher per capita GDP have more stable supply and demand in the logistics industry when risks arise, thereby having a higher capacity to withstand risks. Therefore, this study includes the level of per capita GDP in the measurement for “Adaptation and Adjustment Capacity” [14,15]. In “Innovation and Transformation Capacity”, this study selects indicators, such as the number of granted patents [15] and per capita R&D expenditure [16], to measure the innovation potential of the logistics industry. The selection methods for other indicators are similar, and the details are shown in Table 1.
Firstly, the KMO value was 0.871, which is greater than 0.5. Additionally, Bartlett’s test yielded a p-value < 0.01. These results indicate that the data are suitable for factor analysis. Secondly, the main factors were extracted, and the factor-loading matrix was computed. Finally, considering that the Urban Statistical Yearbook and China Logistics Yearbook of many central and western cities did not publish relevant statistical indicators for 2021 and beyond (such as express delivery service volume and logistics warehouse area), using linear interpolation to fill in the missing data may seriously affect the precision of the results. Therefore, this study decides upon the year 2020 as the final sample year. Based on the calculated main factor scores, logistic resilience was calculated for each city.

4.2.2. Indicator System for Digital Technology

In this study, we constructed an indicator system for digital technology spanning five dimensions: the Internet penetration rate, the number of employees, and the output value in the information technology industry, the mobile phone penetration rate, and digital finance development. After standardizing and reducing the dimensionality of the indicators, factor analysis was employed, and the system is shown in Table 2.

4.2.3. The Mediating Effect of Industrial Collaboration Agglomeration

To explore industrial collaboration agglomeration (ICA)’s mediating effect on digital technology’s impact on logistics resilience, this study referred to research by Wang and Wang [71]. By combining this with the classification details of productive service industries provided by the National Bureau of Statistics in 2019, the following formula for calculating the industrial collaboration agglomeration variable between the productive service and manufacturing industries was used:
ICAit = 1 − |Agmait − Agseit|/(Agmait + Agseit)
where Agma and Agseit represent the locational entropy of manufacturing and productive service industries, respectively, calculated based on the proportion of employment in various industries. A higher ICA value indicates a stronger degree of ICA in a city.

4.2.4. Control Variables

Drawing from the existing research, this study employed control variables for three aspects: logistics infrastructure, innovation level, and external support. First, logistics infrastructure included two variables, logistics infrastructure (Pos), representing the degree of distribution infrastructure completeness, measured by the number of post offices, and logistics demand (Dem), i.e., the market demand for industry development, measured by total freight volume of road, waterway, and air transport [72]. Second, the innovation level included the technological innovation level (Sci), representing a city’s innovation ability, measured by the internal expenditure on R&D [73]. Third, external support included two variables, the industrial structure (Thi), representing a city’s degree of industrial structure upgrades, measured by the proportion of tertiary industry employees to the total number of employees, and labor force (Hum), representing the total number of human labor force, measured by the total population [74].

4.3. Data Collection and Description

The data are from the “The China City Statistical Yearbook”, “The China Logistics Yearbook”, “The China Third Industry Statistical Yearbook”, “The China Electronic Information Industry Statistical Yearbook”, and the Digital Inclusive Finance Index (details in the note2). Considering the administrative changes in some cities and data availability, the study excluded cities with severe data deficiencies. The descriptive statistics are shown in Table 3.

5. Empirical Analysis

5.1. Baseline Regression

Columns from (1) to (3) in Table 4 present the results for the OLS, random effects (REs), and fixed effects (FEs), respectively. The Hausman test indicates that this study should adopt the FE model. From Column (3), it can be observed that the coefficient of Tech is positive and significant at the 1% level, indicating that the resilience of logistics increases with the level of digital technology development. This might be because digital technology stimulates the digitization of logistics operations and improves the structural optimization of the logistics industry chain, encouraging logistics resilience improvement. Therefore, Hypothesis 1 is valid.

5.2. Robustness Test

To ensure the reliability of the results, this study conducts several robustness tests. Firstly, considering potential endogeneity, this study controlled a series of city characteristic factors in the baseline model and included “city-time” fixed effects to absorb the unobservable factors that varied across regions and time. Secondly, it employed instrumental variable (IV) methods to further mitigate the adverse effects of endogeneity. Thirdly, considering the multidimensionality of logistics resilience and the measurement methods’ errors, this study replaced the measurement method of the dependent variable. Furthermore, in this study, we conducted outlier removal at the 1% and 99% levels.

5.2.1. 2SLS Test

Following Huang et al. (2006), the IV method was employed in this study [75], constructing an interaction term using the number of fixed-line telephone users per hundred people in 1998 and the previous year’s national Internet penetration rate. The interaction term served as an IV for “Tech” (digital technology) and allowed us to conduct robustness tests 2SLS. While the traditional communication technologies like fixed-line telephones may have an impact on Internet development in cities, fulfilling the relevance requirement for selecting IV variable, due to the widespread use of electronic communication devices like mobile phones, the usage rate of traditional communication tools has been declining rapidly, making their influence on logistics resilience low and, thus, meeting the exclusivity requirement. The IV test results are presented in Columns from (1) to (2) of Table 5. In the first-stage test, the F statistic is 69.16 and significant. In the second stage, the F statistic is 196.28, exceeding the critical value of 8.96. Hence, there is no weak instrument problem. Overall, the estimation of “Tech” is consistent with the previous findings in this study.

5.2.2. Replace the Dependent Variable

To mitigate the impact of the measurement methods, this study follows Xie (2008) [14] in replacing the measurement for logistics resilience. The results in Column (3) of Table 5 show that the digital technology coefficient is positive and significant.

5.2.3. Remove Outliers

To mitigate the influence of extreme outliers, this study conducts the Winsorization of the sample data at the 1% and 99% levels, respectively. The results in Column (4) of Table 5 indicate that the effect of digital technology on logistics resilience remains robust.

5.3. Mediation Analysis

Based on the SEM principle, the model fit was evaluated to determine the presence of a mediation effect. Subsequently, the path coefficients were examined to identify their type. The results in Table 6 indicate that the coefficients of the fitted model for “Tech” (digital technology) and “ICA” (industrial collaboration agglomeration) are significant, suggesting the presence of a mediation effect. Further, Sobel tests were conducted. According to Table 7, the path effect passed the Delta, Sobel, and Monte Carlo tests, and all the results indicate the existence of an indirect effect. In conclusion, technological advancement can facilitate industrial collaboration and agglomeration and promote the development of industrial logistics, improving their resilience. Therefore, H2 is supported.

5.4. Threshold Effect

Digital technology’s impact on logistics resilience may exhibit a threshold effect. Therefore, in this study, we conducted a threshold test on the relevant variables, indicating that digital technology (Tech) and industry agglomeration (ICA) passed the single- and double-threshold tests, respectively. Subsequently, threshold models were established, and the regression results are in Table 8.
Column (1) in Table 9 presents the results, with digital technology as the threshold variable. The digital technology coefficient is positive, and, after crossing the threshold, it slightly decreases, indicating that digital technology has a significant impact on enhancing logistics resilience. However, its stimulating effect on logistics resilience gradually diminishes. Column (2) displays the results with industry agglomeration as the threshold variable, whereby the impact increases after crossing the first threshold, reaches its maximum between the first and second thresholds, and decreases insignificantly after crossing the second threshold, suggesting that as the level of industry agglomeration increases, productive service and manufacturing industries’ integration accelerates. The scale effect of industries leads to closer connections between different enterprise types and, consequently, enhanced logistics resilience. The results indicate that both digital technology and industrial agglomeration play a nonlinear role in promoting logistics resilience. Therefore, H3 is supported.

5.5. Heterogeneity Test

To assess digital technology’s impact on logistics resilience across regions, this study divides the sample into eight economic zones for analysis, as shown in Table 10. The results show that digital technology exhibits a significant effect on logistics resilience in the east, north, Yellow River, and Yangtze River Basin, while in the south and southwest, the coefficients are significantly negative. In the northeastern and northwestern regions, the impact is not significant. Specifically, the influence is most pronounced in the eastern, surpassing that in the northern, Yellow River, and Yangtze River Basin regions, a discrepancy which may stem from cross-regional variations in digital industrialization. The eastern, northern, Yellow River Basin, and Yangtze River Basin regions exhibit more digital technology development and the deeper integration of digital technology with logistics, with digital technology contributing more to enhancing logistics resilience. Meanwhile, the northwestern and southwestern regions face deficiencies in transportation infrastructure and economic development, and, as digital technology is still in nascent stage, it fails to leverage digital dividends.

5.6. Further Research Based on the “Broadband China” Policy

The level of digital technology development often depends on factors such as the industrial structure, the market size, and the degree of openness [76], which can also affect logistics industry resilience, thus influencing the accuracy of empirical analysis. Therefore, to avoid interference from such factors and accurately assess the impact of digital technology on logistics resilience, in this study introduced the “Broadband China” policy as an exogenous policy shock for further investigation.

5.6.1. Background and Model Specification

The “Broadband China” policy selected a total of 120 pilot cities in 2014, 2015, and 2016. This study set up a multi-period DID model to examine the mechanism and impact of the “Broadband China” on logistics industry resilience. The model is as follows:
Resit = α0 + α1Digitalit + λControlit + τi + μt + εit
Digitalit signifies the presence of digital infrastructure. Specifically, D i g i t a l is the policy dummy variable. The meanings of the remaining are the same as in Equation (1).

5.6.2. Empirical Test Results

Initially, a parallel trend test was conducted, showing that the sample met the assumption required for DID analysis. Then, multi-period DID analysis was conducted, as shown in Table 11. Columns (1) and (2), respectively, present the results without and with a control for other variables. The findings demonstrate that the “Broadband China” policy has a significant impact on logistics resilience.

5.7. Discussions

This paper validates the impact of digital technology on logistics resilience. Compared to the existing research, it overcomes the limitations of focusing solely on provincial or selected municipal levels and, unlike many studies, which commonly use the traditional regression models [50,51] with poor fit issues and high standard errors, innovatively applies SEM models to examine the mediating role of industrial agglomeration. It also employs threshold regression analysis to explore the phased characteristics of digital technology and industrial agglomeration in enhancing logistics resilience [22,25]. Furthermore, considering that digital technology is often influenced by other factors that are overlooked in most digital technology-related studies [77], this paper uses the “Broadband China” policy as a proxy variable for digital technology. After controlling for various confounding factors, it confirms that digital technology continues to facilitate logistics resilience, providing an innovative contribution and valuable insights to the existing literature. The research conclusions offer evidence supporting the role of digital technology in enhancing logistics resilience, as well as recommendations.

6. Conclusions and Recommendations

6.1. Conclusions

This study examined the mechanisms and pathways through which digital technology enhanced logistics industry resilience from 2011 to 2020 using panel data from 275 cities. It also investigated the industry mediation and threshold effects of digital technology on logistics industry resilience.
Firstly, digital technology enhances logistics industry resilience. Secondly, a partial mediation effect of industrial synergy clustering exists in the enhancement of digital technology-empowered logistics industry resilience. Thirdly, digital technology and industry agglomeration have a threshold effect on logistics industry resilience. Fourthly, heterogeneity analysis showed that digital technology development in the eastern and northern coastal areas, in the Yellow River Basin, and in the middle reaches of the Yangtze River had a more significant impact on enhancing logistics industry resilience.

6.2. Recommendations

First, the government should develop digital logistics and create a public logistics information platform. Restoring and expanding consumption in the logistics supply chain should be a priority. The government should utilize digital technology to foster digital logistics development and enhance distribution networks’ efficiency. Specifically, logistics platform companies and digital service providers should intensify their efforts in developing cloud platforms for SMEs; bolster the collection, analysis, and application of big data in logistics; and encourage the implementation of a “one-stop” logistics data platform. Furthermore, the government should encourage the use of 5G+ industrial Internet and intelligent robots to digitize logistics components, create diverse application scenarios, and establish a new ecosystem of digital technology-powered intelligent logistics.
Second, the government should break through industry boundaries and achieve deep integration between the logistics and manufacturing industries. Logistics companies should explore innovative collaborative operation models with the relevant industries and provide solutions for their development. The government should actively guide long-term strategic cooperation between manufacturing enterprises and logistics companies, increasing investment in logistics facilities, equipment research, and development. Meanwhile, logistics companies should extend their services beyond traditional logistics operations, establish integrated supply chain service systems, and transform into comprehensive supply chain service providers. They should also support the development of strategic emerging industries and achieve industry synergy, while building an independent supply chain.
Third, the government should implement modern logistics equipment upgrades and solve issues through intelligent solutions. Specifically, the real-time tracking of goods’ location and status can be achieved through technology applications to enhance logistics transparency and efficiency. AI technology applications to optimize supply chain management effectively predict demand fluctuations, improving the decision making speed and accuracy. Simultaneously, logistics enterprises can utilize industrial robots to automate repetitive tasks, enhancing production efficiency and reducing labor costs. Additionally, augmented reality (AR) and virtual reality (VR) applications can simulate logistics operation environments for employee training, improving operational efficiency.
Fourth, the government should optimize the construction of regional logistics hubs and domestic logistics corridors. To begin, the coordinated growth of logistics regions behind schedule should be sped up by studying how big cities have grown, encouraging the efficient linking of regional logistics hubs and building “Four Horizontal and Five Vertical, Two Along and Ten Corridors” logistics networks. In the meantime, we recommend enhancing the quality of digital technology and professional logistics in the northeast, southwest, and northwest regions. It is necessary to implement policies to raise the standard of cold chain logistics services, boost the service capabilities and operational efficiency of relevant agglomerations, and advocate for the strengthening of the logistics industry.
Fifth, the government should increase investment in the application of digital technology and innovative models in the logistics industry. Starting from foundational logistics processes, such as packaging and warehousing, there should be a deep integration of Internet technologies to enhance the overall resilience and efficiency in logistics, such as, for instance, by gradually increasing the use of new energy vehicles equipped with novel onboard systems to achieve full-process transparency and visibility. Simultaneously, introducing third-generation Internet, blockchain, and 6G network architecture technologies to vigorously develop an “Internet + Green + Logistics” model would innovate resilient logistics patterns.

6.3. Limitations and Future Prospects

This research focused on examining the digital economy’s impact on logistics resilience. First, the lack of publicly available data from statistical departments significantly influences the persuasiveness of this study. As previously mentioned, many cities in central and western China have been slow to release data, with statistics for 2021 and beyond still unavailable at the time of writing. These data constitute an essential part of the logistics resilience calculations in Table 1. Using methods such as linear interpolation or ARIMA interpolation to fill in the huge gaps could lead to severe distortions in the measurement results and a substantial decrease in the results’ persuasive power. Therefore, from the perspectives of rigor and authenticity, this paper has set the study’s final year to 2020 rather than the subsequent years. While this method ensures the conclusion’s authenticity, it may not match logistics resilience developments, which is a major drawback. To deal with this data issue, we suggest that future researcher select a specific region (such as the Yangtze River economic zone) as the research sample. The other problems include the explanatory power at the municipal, but not at the finer levels (e.g., county), and the empirical methods need further exploration to understand the mediating mechanisms behind this process, with the prospects involving the theoretical clarification of its fundamental pathways. This would also entail researching the specific impacts of digital economic development on logistics industry sectors and the application of digital elements in the logistics industry chain. Currently, relevant statistical agencies in China have begun accelerating the improvement of data availability (for example, the National Bureau of Statistics of China). Therefore, as the statistical agencies of cities with missing data gradually fill in these gaps using the research methods of this paper combined with the latest data will allow for more timely and persuasive conclusions. Also, future empirical research should seek evidence to support these findings. Currently, plenty of studies are exploring innovation and spillover effects, alongside other specific mechanisms, but there remains a need for extensive empirical research from a resilience perspective.

Author Contributions

Conceptualization, J.Z.; validation, J.Z. and B.H.; data curation, Z.Y.; formal analysis, Z.Y. and B.H.; writing—original draft preparation, J.Z. and B.H.; and writing—review and editing, B.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Project of Haiyan by Lianyungang City (KK22006) and the Research Start-up Fund Project by Jiangsu Ocean University (KQ19060).

Data Availability Statement

Data will be available upon request.

Acknowledgments

Thanks to Western Corner Coffee Shop for the support.

Conflicts of Interest

The authors declare no conflicts of interest.

Notes

1
“The China Logistics Yearbook” is a book published by China Fortune Publishing House in 2023, compiled by the China Federation of Logistics and Purchasing; “The China Statistical Yearbook” is an informative annual publication compiled and printed by the National Bureau of Statistics (https://www.stats.gov.cn/sj/ndsj/, accessed on 23 July 2024). “The China Third Industry Statistical Yearbook” is a book compiled by the National Bureau of Statistics and published by China Statistics Publishing House; “The China Electronic Information Industry Statistical Yearbook” is a book published by the Electronic Industry Press. “The China City Statistical Yearbook” is a descriptive term compiled by each city government to comprehensively reflect the economic and social development of Chinese cities. It is an annual publication that is compiled and published separately for each city. “Digital Inclusive Finance Index” is a report compiled by the “Peking University Digital Inclusive Finance Index” research group.
2
See Note 1 above.

References

  1. Li, M.; Huang, K.; Xie, X.; Chen, Y. Dynamic evolution, regional differences and influencing factors of high-quality development of China’s logistics industry. Ecol. Indic. 2024, 159, 111728. [Google Scholar] [CrossRef]
  2. Haleem, A.; Javaid, M.; Qadri, M.A.; Suman, R. Understanding the role of digital technologies in education: A review. Sustain. Oper. Comput. 2022, 3, 275–285. [Google Scholar] [CrossRef]
  3. Xu, A.; Qian, F.; Ding, H.; Zhang, X. Digitalization of logistics for transition to a resource-efficient and circular economy. Resour. Policy 2023, 83, 103616. [Google Scholar] [CrossRef]
  4. Perrings, C. Ecological Resilience in the Sustainability of Economic Development. Économie Appliquée 1995, 48, 121–142. [Google Scholar] [CrossRef]
  5. Reggiani, A.; Graaff, T.D.; Nijkamp, P. Resilience: An evolutionary approach to spatial economic systems. Netw. Spat. Econ. 2002, 2, 211–229. [Google Scholar] [CrossRef]
  6. Cheng, T.; Zhao, Y.; Zhao, C. What determines the economic resilience of Chinese cities amid pandemic crisis: An economic operating state perspective. Int. J. Disaster Risk Reduct. 2024, 104, 104389. [Google Scholar] [CrossRef]
  7. Zhang, J.; Wang, G.; He, B. Does foreign direct investment affect wage inequality in Chinese manufacturing sector? Appl. Econ. Lett. 2023, 30, 80–83. [Google Scholar] [CrossRef]
  8. Yang, Y.; Lin, Z.; Xu, Z.; Liu, S. The impact of digital finance on regional economic resilience. Pac.-Basin Financ. J. 2024, 85, 102353. [Google Scholar] [CrossRef]
  9. Zhang, J.; Yang, Z.; He, B. Does Digital Infrastructure Improve Urban Economic Resilience? Evidence from the Yangtze River Economic Belt in China. Sustainability 2023, 15, 14289. [Google Scholar] [CrossRef]
  10. Han, Y.; Chong, W.K.; Li, D. A systematic literature review of the capabilities and performance metrics of supply chain resilience. Int. J. Prod. Res. 2020, 58, 4541–4566. [Google Scholar] [CrossRef]
  11. Ambulkar, S.; Blackhurst, J.; Grawe, S. Firm’s resilience to supply chain disruptions: Scale development and empirical examination. J. Oper. Manag. 2014, 33–34, 111–122. [Google Scholar] [CrossRef]
  12. Hosseini, S.; Ivanov, D.; Dolgui, A. Review of quantitative methods for supply chain resilience analysis. Transp. Res. Part E Logist. Transp. Rev. 2019, 125, 285–307. [Google Scholar] [CrossRef]
  13. Yu, J.; Zhang, Y.; Liu, W.; Xiao, Z. Research on spatio-temporal heterogeneity of global cross-border e-commerce logistics resilience under the impact of the COVID-19epidemic. Geogr. Res. 2021, 12, 3333–3348. (In Chinese) [Google Scholar]
  14. Xie, S. Innovation of Resilient Logistics Leading Strategy from the Perspective of Green Economic Development in Yangtze River Economic Belt. J. Nantong Univ. (Soc. Sci. Ed.) 2023, 1, 11–22. (In Chinese) [Google Scholar]
  15. Jin, F.; Yang, B.; Ma, H. Calculation of Logistics Resilience in the Yangtze River Delta Urban Agglomeration. Stat. Decis. 2022, 4, 102–105. (In Chinese) [Google Scholar]
  16. Zuo, H. The influencing factors and improvement paths of digital logistics resilience under the background of green development. J. Commer. Econ. 2023, 14, 71–75. (In Chinese) [Google Scholar]
  17. Bharadwaj, A.; El Sawy, O.A.; Pavlou, P.A.; Venkatraman, N.V. Digital Business Strategy: Toward a Next Generation of Insights. MIS Q. 2013, 1, 204–208. [Google Scholar] [CrossRef]
  18. Adner, R.; Puranam, P.; Zhu, F. What is Different About Digital Strategy? From Quantitative to Qualitative Change. Oper. Res. Manag. Sci. 2022, 4, 253–261. [Google Scholar] [CrossRef]
  19. Wan, Q.; Tang, S.; Jiang, Z. Does the development of digital technology contribute to the innovation performance of China’s high-tech industry? Technovation 2023, 124, 102738. [Google Scholar] [CrossRef]
  20. Banister, D.; Stead, D. Impact of Information and Communications Technology on Transport. Transp. Rev. 2004, 5, 611–632. [Google Scholar] [CrossRef]
  21. Shao, Y.; Xu, K.; Shan, Y.G. Leveraging corporate digitalization for green technology innovation: The mediating role of resource endowments. Technovation 2024, 133, 102999. [Google Scholar] [CrossRef]
  22. Zhao, N.; Hong, J.; Lau, K.H. Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model. Int. J. Prod. Econ. 2023, 259, 108817. [Google Scholar] [CrossRef]
  23. Shee, H.K.; Miah, S.J.; De Vass, T. Impact of smart logistics on smart city sustainable performance: An empirical investigation. Int. J. Logist. Manag. 2021, 32, 821–845. [Google Scholar] [CrossRef]
  24. Gumzej, R. Intelligent logistics systems in E-commerce and transportation. Math. Biosci. Eng. 2022, 20, 2348–2363. [Google Scholar] [CrossRef]
  25. Rojas-García, J.A.; Elias-Giordano, C.; Quiroz-Flores, J.C.; Nallusamy, S. Profitability enhancement by digital transformation and canvas digital model on strategic processes in post-COVID-19 in logistics SMEs. Soc. Sci. Humanit. Open 2024, 9, 100777. [Google Scholar] [CrossRef]
  26. Gupta, A. R&D and Firm Resilience During Bad Times. Soc. Sci. Res. Netw. 2020. [Google Scholar] [CrossRef]
  27. Zhang, L.; Wang, X.; Lin, H.; Piran, M.J. A crowdsourcing logistics solution based on digital twin and four-party evolutionary game. Eng. Appl. Artif. Intell. 2024, 130, 107797. [Google Scholar] [CrossRef]
  28. Belfadel, A.; Hörl, S.; Tapia, R.J.; Politaki, D.; Kureshi, I.; Tavasszy, L.; Puchinger, J. A conceptual digital twin framework for city logistics. Comput. Environ. Urban Syst. 2023, 103, 101989. [Google Scholar] [CrossRef]
  29. MacCarthy, B.L.; Ivanov, D. The Digital Supply Chain; Elsevier: Amsterdam, The Netherlands, 2022. [Google Scholar]
  30. Büyüközkan, G.; Ilıcak, Y. Smart urban logistics: Literature review and future directions. Socio-Econ. Plan. Sci. 2022, 81, 101197. [Google Scholar] [CrossRef]
  31. Gu, C.; Zhang, S. Research on Distribution Dynamics, Regional Differences and Spatial Convergence of Intelligent Development of China’s Logistics Industry. China Bus. Mark. 2023, 3, 17–31. (In Chinese) [Google Scholar]
  32. Mishra, R.; Singh, R.K.; Papadopoulos, T. Linking Digital Orientation and Data-Driven Innovations: A SAP–LAP Linkage Framework and Research Propositions. IEEE Trans. Eng. Manag. 2024, 71, 1346–1358. [Google Scholar] [CrossRef]
  33. Fuller, A.; Fan, Z.; Day, C.; Barlow, C. Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access 2020, 8, 108952–108971. [Google Scholar] [CrossRef]
  34. Vilas-Boas, J.L.; Rodrigues, J.J.; Alberti, A.M. Convergence of Distributed Ledger Technologies with Digital Twins, IoT, and AI for fresh food logistics: Challenges and opportunities. J. Ind. Inf. Integr. 2023, 31, 100393. [Google Scholar] [CrossRef]
  35. Winkelhaus, S.; Grosse, E.H. Logistics 4.0: A systematic review towards a new logistics system. Int. J. Prod. Res. 2019, 58, 18–43. [Google Scholar] [CrossRef]
  36. Song, Y.; Yu, F.R.; Zhou, L.; Yang, X.; He, Z. Applications of the Internet of Things (IoT) in Smart Logistics: A Comprehensive Survey. IEEE Internet Things J. 2021, 8, 4250–4274. [Google Scholar] [CrossRef]
  37. Sardarabady, N.J.; Durst, S. A systematic literature review on the economic impact of digitalization technologies in transport logistics. Transp. Econ. Manag. 2024, 2, 76–89. [Google Scholar] [CrossRef]
  38. Liu, X. Research on consumers’ personal information security and perception based on digital twins and Internet of Things. Sustain. Energy Technol. Assess. 2022, 53, 102706. [Google Scholar] [CrossRef]
  39. Qinqin, W.; Qalati, S.A.; Hussain, R.Y.; Irshad, H.; Tajeddini, K.; Siddique, F.; Gamage, T.C. The effects of enterprises’ attention to digital economy on innovation and cost control: Evidence from A-stock market of China. J. Innov. Knowl. 2023, 8, 100415. [Google Scholar] [CrossRef]
  40. Wang, Y.; He, P. Enterprise digital transformation, financial information disclosure and innovation efficiency. Financ. Res. Lett. 2024, 59, 104707. [Google Scholar] [CrossRef]
  41. Parfenov, A.; Shamina, L.; Niu, J.; Yadykin, V. Transformation of Distribution Logistics Management in the Digitalization of the Economy. J. Open Innov. 2021, 7, 58. [Google Scholar] [CrossRef]
  42. Liu, Z.; Pan, Q. Research on value creation path of logistics platform under the background of digital ecosystem: Based on SEM and fsQCA methods. Electron. Commer. Res. Appl. 2024, 67, 101424. [Google Scholar] [CrossRef]
  43. Parhi, S.; Joshi, K.; Gunasekaran, A.; Sethuraman, K. Reflecting on an empirical study of the digitalization initiatives for sustainability on logistics: The concept of sustainable logistics 4.0. Clean. Logist. Supply Chain 2022, 4, 100058. [Google Scholar] [CrossRef]
  44. Wang, Y.; Li, L. Digital economy, industrial structure upgrading, and residents’ consumption: Empirical evidence from prefecture-level cities in China. Int. Rev. Econ. Financ. 2024, 92, 1045–1058. [Google Scholar] [CrossRef]
  45. Wu, S.; Li, J. The future of cities in the digital economy era: A study on the impact of internet on the agglomeration of producer services in Chinese Cities. China Econ. Q. Int. 2024, 4, 107–118. [Google Scholar] [CrossRef]
  46. Wu, L.; Wan, X.; Jahanger, A.; Li, M.; Murshed, M.; Balsalobre-Lorente, D. Does the digital economy reduce air pollution in China? A perspective from industrial agglomeration. Energy Rep. 2023, 9, 3625–3641. [Google Scholar] [CrossRef]
  47. Yoo, J.W.; Fan, B.; Chang, Y.J. CSR, Digital Transformation, and Internal Control: Three-Way Interaction Effect on the Firm Value of Chinese Listed Companies. Systems 2024, 12, 236. [Google Scholar] [CrossRef]
  48. Macias-Aguayo, J.; McFarlane, D.; Schönfuß, B.; Salter, L. A catalogue of digital solution areas for logistics SMEs. IFAC-Pap. 2022, 55, 1828–1833. [Google Scholar] [CrossRef]
  49. Fang, X.; Liu, M. How does the digital transformation drive digital technology innovation of enterprises? Evidence from enterprise’s digital patents. Technol. Forecast. Soc. Chang. 2024, 204, 123428. [Google Scholar] [CrossRef]
  50. Bie, F.; Yang, Y.; Shen, H.; Zhao, Q. Inclusive digital economy, resource dependence and changes in the urban energy mix: City level analysis from China. Resour. Policy 2024, 92, 105027. [Google Scholar] [CrossRef]
  51. Iacobucc, D.; Saldanha, N.; Deng, X. A Meditation on Mediation: Evidence that Structural Equations Models Perform Better Than Regressions. J. Consum. Psychol. 2007, 2, 139–153. [Google Scholar] [CrossRef]
  52. Martin, R.; Sunley, P. On the Notion of Regional Economic Resilience: Conceptualization and Explanation. J. Econ. Geogr. 2015, 1, 1–42. [Google Scholar] [CrossRef]
  53. Yu, Z.; Li, Y.; Dai, L. Digital finance and regional economic resilience: Theoretical framework and empirical test. Financ. Res. Lett. 2023, 55, 103920. [Google Scholar] [CrossRef]
  54. Wang, W.; Yang, J.; Huang, L.; Proverbs, D.; Wei, J. Intelligent Storage Location Allocation with Multiple Objectives for Flood Control Materials. Water 2019, 11, 1537. [Google Scholar] [CrossRef]
  55. Li, S.; Zhou, Y. Integrating equity and efficiency into urban logistics resilience under emergency lockdowns. Transportation Research. Part E, Logist. Transp. Rev. 2024, 183, 103446. [Google Scholar] [CrossRef]
  56. Gu, X.; Chan, H.K.; Thadani, D.R.; Chan FK, S.; Peng, Y. The role of digital techniques in organisational resilience and performance of logistics firms in response to disruptive events: Flooding as an example. Int. J. Prod. Econ. 2023, 266, 109033. [Google Scholar] [CrossRef]
  57. Balakrishnan, A.S.; Ramanathan, U. The role of digital technologies in supply chain resilience for emerging markets’ automotive sector. Supply Chain Manag. 2021, 26, 654–671. [Google Scholar] [CrossRef]
  58. Xue, L.; Gong, Y.; Yang, B.; Xu, X. Resilience, efficiency fluctuations, and regional heterogeneity in disaster: An empirical study on logistics. Socio-Econ. Plan. Sci. 2024, 93, 101854. [Google Scholar] [CrossRef]
  59. Gupta, H.; Yadav, A.K.; Kusi-Sarpong, S.; Khan, S.A.; Sharma, S.C. Strategies to overcome barriers to innovative digitalisation technologies for supply chain logistics resilience during pandemic. Technol. Soc. 2022, 69, 101970. [Google Scholar] [CrossRef]
  60. Edson, M.C. A Complex Adaptive Systems View of Resilience in a Project Team. Syst. Res. Behav. Sci. 2012, 29, 499–516. [Google Scholar] [CrossRef]
  61. Tsiapa, M.; Batsiolas, I. Firm resilience in regions of Eastern Europe during the period 2007–2011. Post-Communist Econ. 2018, 31, 19–35. [Google Scholar] [CrossRef]
  62. Gu, M.; Yang, L.; Huo, B. The impact of information technology usage on supply chain resilience and performance: An ambidexterous view. Int. J. Prod. Econ. 2021, 232, 107956. [Google Scholar] [CrossRef]
  63. Senbeto, D.L.; Hon AH, Y. Market turbulence and service innovation in hospitality: Examining the underlying mechanisms of employee and organizational resilience. Serv. Ind. J. 2020, 40, 1119–1139. [Google Scholar] [CrossRef]
  64. Jiang, Y.; Ritchie, B.W.; Verreynne, M. Building tourism organizational resilience to crises and disasters: A dynamic capabilities view. Int. J. Tour. Res. 2019, 21, 882–900. [Google Scholar] [CrossRef]
  65. Yaroson, E.V.; Breen, L.; Hou, J.; Sowter, J. Advancing the understanding of pharmaceutical supply chain resilience using complex adaptive system (CAS) theory. Supply Chain Manag. 2021, 26, 323–340. [Google Scholar] [CrossRef]
  66. Wang, S.; Song, Y.; Du, A.M.; Liang, J. The digital economy and entrepreneurial dynamics: An empirical analysis of urban regions in China. Res. Int. Bus. Financ. 2024, 71, 102459. [Google Scholar] [CrossRef]
  67. Xu, X.; Cai, H. The impacts on regional “resource curse” by digital economy: Based on panel data analysis of 262 resource-based cities in China. Resour. Policy 2024, 95, 105152. [Google Scholar] [CrossRef]
  68. Wang, B.; Liu, Y.; Yang, X. Digital Economy and Urban Entrepreneurial Activity. Financ. Res. Lett. 2024, 66, 105649. [Google Scholar] [CrossRef]
  69. Wang, J.; Dong, K.; Dong, X.; Taghizadeh-Hesary, F. Assessing the digital economy and its carbon-mitigation effects: The case of China. Energy Econ. 2022, 113, 106198. [Google Scholar] [CrossRef]
  70. Cai, H.; Wang, Z.; Ji, Y.; Xu, L. Digitalization and innovation: How does the digital economy drive technology transfer in China? Econ. Model. 2024, 136, 106758. [Google Scholar] [CrossRef]
  71. Wang, X.; Wang, Q. Research on the impact of Industrial Collaborative Agglomeration on Regional Economic Growth: Based on the Perspective of Scale Effect and Congestion Effect. Econ. Rev. 2023, 2, 43–58. (In Chinese) [Google Scholar]
  72. Zhao, Y.; Wang, S.; Liu, X.; Tang, X. Effect of the logistics industry on the promotion of China’s position in the global value chain: An international trade perspective. Int. Rev. Econ. Financ. 2023, 86, 834–847. [Google Scholar] [CrossRef]
  73. Zhang, J.; Liu, H.; Wang, J.; Huang, Q. Spatial impact of the digital economy on low-carbon logistics efficiency in RCEP countries. J. Environ. Manag. 2024, 360, 121221. [Google Scholar] [CrossRef]
  74. Liang, H.; Lin, S.; Wang, J. Impact of technological innovation on carbon emissions in China’s logistics industry: Based on the rebound effect. J. Clean. Prod. 2022, 377, 134371. [Google Scholar] [CrossRef]
  75. Huang, Q.; Yu, Y.; Zhang, S. Internet Development and Productivity Growth in Manufacturing Industry: Internal Mechanism and China Experiences. China Ind. Econ. 2019, 8, 5–23. (In Chinese) [Google Scholar]
  76. He, B.; Xu, D.; Nan, G.; Zhang, X.; Yu, X. Does the cross-border e-commerce comprehensive pilot zones policy affect the urban–rural income gap in China? Am. J. Econ. Sociol. 2024. early view. [Google Scholar] [CrossRef]
  77. Tolstykh, T.; Shmeleva, N.; Boev, A.; Guseva, T.; Panova, S. System Approach to the Process of Institutional Transformation for Industrial Integrations in the Digital Era. Systems 2024, 12, 120. [Google Scholar] [CrossRef]
Systems 12 00278 g001
Figure 1. Mind map.
Figure 1. Mind map.
Systems 12 00278 g001
Table 1. Index system of logistics industry resilience.
Table 1. Index system of logistics industry resilience.
PrimarySecondaryTertiaryAttributesReferences
Logistics
Resilience		Resistance and Recovery CapacityTotal wages of employees in transportation, warehousing, and postal services (in billion CNY)+[14]
Freight volume by waterway, railway, and air transport (10,000 tons)+[55]
Road freight volume (10,000 tons)+[56]
Logistics warehouse area (square kilometers)+[54]
Total volume of express delivery services
(Hundred million pieces)		+[55]
The proportion of the tertiary industry output value to the GDP (%)+[57]
Adaptation and Adjustment CapacityPer capita regional GDP (in CNY)+[14,15]
Total retail sales of consumer goods
(In CNY ten thousand)		+[58]
Volume of import and export trade
(In CNY hundred million)		+[59]
Total fiscal expenditure of the logistics industry
(In CNY 100 million)		+[60]
Number of mobile subscribers at the end of the year (in 10,000 households)+[61]
[62]
Total sales of goods in wholesale and retail trade above designated size (in CNY 10,000)+[63]
Innovation and Transformation CapacityNumber of R&D personnel (in 10,000 persons)+[16]
Number of patents granted+[15]
Number of university students per 10,000 persons+[64]
Proportion of education expenditure to local financial budgetary expenditure (%)+[65]
Note: The data come from the Third Industry Statistical Yearbook, various local Statistical Yearbooks, and the Logistics Yearbook of China.
Table 2. Indicator system for digital technology.
Table 2. Indicator system for digital technology.
PrimarySecondaryTertiaryAttributesReferences
Digital TechnologyInternet Penetration RateNumber of Broadband Internet Access Users per 100 persons+[66]
Number of Employees in Information
Technology Industry		Proportion of Employees in Computer Services and Software Industry to Unit Employees+[67]
Output Value of Information Technology IndustryPer Capita Telecommunications Service Volume (in CNY 10,000)+[68]
Penetration Rate of Mobile PhonesNumber of Mobile Phone Users per 100 People (households)+[69]
Development of Digital FinanceChina’s Digital Inclusive
Finance Index		+[70]
Note: The data are sourced from the China Statistical Yearbook and the China Financial Yearbook.
Table 3. Descriptive statistics of variables.
Table 3. Descriptive statistics of variables.
Variable TypeVariable NameMeanS. D.MinMax
DependentResilience of logistics industry (Res)0.00070.658−0.6707.550
Core explanatoryDigital technology (Tech)0.00090.756−0.8306.960
MediatingIndustrial synergy clustering (ICA)2.4160.5560.6114.432
ControlLogistics infrastructure (Pos)4.9020.7050.00027.487
Logistics demand (Dem)1.6512.0370.04455.446
Industrial structure (LnThi)3.9480.2722.7304.550
Level of technological innovation (LnSci)4.7720.9890.6937.166
Number of labor force (Hum)4.5063.2710.20034.160
Table 4. Baseline regression.
Table 4. Baseline regression.
Variables(1) OLS(2) RE(3) FE
Tech0.431 ***
(0.010)		0.120 ***
(0.011)		0.027 ***
(0.010)
Pos0.027 ***
(0.007)		0.022 ***
(0.008)		−0.012
(0.008)
Dem0.051 ***
(0.004)		0.038 ***
(0.003)		0.032 ***
(0.002)
LnThi0.120 ***
(0.026)		0.191 ***
(0.027)		−0.008
(0.031)
LnSci0.076 ***
(0.004)		0.049 ***
(0.005)		0.011 **
(0.005)
Hum0.061 ***
(0.003)		0.103 ***
(0.004)		0.120 ***
(0.005)
FeNoNoYes
N275027502750
R20.7160.3090.420
Note: Robust standard errors are reported in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively, throughout.
Table 5. Robustness test results.
Table 5. Robustness test results.
Variables2SLSReplace Dependent VariableExclude Outliers
(1)(2)(3)(4)
Instru0.061 ***
(0.401)
Tech 0.616 ***
(0.046)		0.001 **
(0.001)		0.035 ***
(0.009)
FeYesYesYesYes
N2750275027502750
R20.4040.6790.3950.386
F69.16196.28
Note: Robust standard errors are reported in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively, throughout.
Table 6. Path coefficient test of fitted model.
Table 6. Path coefficient test of fitted model.
VariablesICARes
Tech0.184 ***
(0.012)		0.411 ***
(0.010)
ICA 0.107 ***
(0.016)
ControlsYesYes
Fe1.605 ***
(0.150)		−1.946 ***
(0.125)
N27502750
Note: Robust standard errors are reported in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively, throughout.
Table 7. SEM mediation effect evaluation.
Table 7. SEM mediation effect evaluation.
MethodDeltaSobelMonte Carlo
Mediation EffectIndirect EffectIndirect EffectIndirect Effect
Standard Error0.0040.0040.004
Z6.3096.3096.309
p<0.001<0.001<0.001
Conf. Interval[0.016, 0.030][0.016, 0.030][0.016, 0.030]
Table 8. Threshold effect.
Table 8. Threshold effect.
Threshold VariableModelFpThreshold ValueCritical ValueBS
10%5%1%
TechSingle27.410.0201.86917.18520.79631.056300
ICASingle24.200.1602.97829.58041.36399.035300
Double28.470.0833.02326.14236.63679.703300
Table 9. Threshold regression model estimation.
Table 9. Threshold regression model estimation.
VariablesRes
(1)(2)
Tech·I (Tech ≤ 1.869)0.077 ***
(0.016)
Tech·I (Tech > 1.869)0.032 ***
(0.011)
Tech·I (ICA ≤ 2.978) 0.056 ***
(0.012)
Tech·I (2.978 < ICA < 3.023) 0.191 ***
(0.032)
Tech·I (ICA ≥ 3.023) 0.004
(0.016)
ControlsYesYes
FeYesYes
N27502750
R20.3120.320
Note: Robust standard errors are reported in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively, throughout.
Table 10. Heterogeneity test.
Table 10. Heterogeneity test.
Variables(1)(2)(3)(4)(5)(6)(7)(8)
EastSouthNorthNortheastYellow River BasinYangtze River BasinSouthwestNorthwest
Tech0.344 **−0.068 ***0.128 **0.0100.059 ***0.084 ***−0.046 **0.024
(0.147)(0.018)(0.050)(0.008)(0.021)(0.019)(0.022)(0.028)
ControlsYesYesYesYesYesYesYesYes
FeYesYesYesYesYesYesYesYes
N240310270330460520430190
R20.6500.6840.4430.5120.2260.7280.5140.673
Note: Robust standard errors are reported in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively, throughout.
Table 11. “Broadband China” policy mechanism test.
Table 11. “Broadband China” policy mechanism test.
Variables(1)(2)
Digital0.089 ***
(0.013)		0.072 ***
(0.012)
ControlsNoYes
FeYesYes
N27502750
R20.2420.427
Note: Robust standard errors are reported in parentheses. *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively, throughout.
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Zhang, J.; Yang, Z.; He, B. Empowerment of Digital Technology for the Resilience of the Logistics Industry: Mechanisms and Paths. Systems 2024, 12, 278. https://doi.org/10.3390/systems12080278

AMA Style

Zhang J, Yang Z, He B. Empowerment of Digital Technology for the Resilience of the Logistics Industry: Mechanisms and Paths. Systems. 2024; 12(8):278. https://doi.org/10.3390/systems12080278

Chicago/Turabian Style

Zhang, Jifeng, Zirui Yang, and Bing He. 2024. "Empowerment of Digital Technology for the Resilience of the Logistics Industry: Mechanisms and Paths" Systems 12, no. 8: 278. https://doi.org/10.3390/systems12080278

APA Style

Zhang, J., Yang, Z., & He, B. (2024). Empowerment of Digital Technology for the Resilience of the Logistics Industry: Mechanisms and Paths. Systems, 12(8), 278. https://doi.org/10.3390/systems12080278

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