Influencing Factors of Emergency Supply Chain Digitization from the Perspective of Sustainable Development ()
1. Introduction
In the digital era, emerging information technologies—including cloud computing, artificial intelligence, the Internet of Things, blockchain and digital twin are rapidly transforming logistics service scenarios. This transformation is clearly evidenced by the extensive application of digital technologies across a wide range of logistics activities, thereby accelerating the digitalization of supply chains. In the field of emergency management, traditional emergency supply chains often focus only on short-term efficiency while neglecting long-term environmental, social, and economic impacts. Digital technologies not only provide new insights but also technical tools for addressing relevant issues in traditional emergency supply chain management. Dubey et al. (2020), Chen & Lin (2021), and Longo et al. (2023) also emphasized the necessity and urgency of applying digital technologies to address challenges in emergency supply chain management. Therefore, studies on emergency supply chain digitalization become particularly important.
Theoretical studies indicate that most researchers use empirical studies and modelling approaches to investigate the issues-related to emergency supply chain digitalization from an operational standpoint. However, what are the key factors influencing emergency supply chain digitalization remains open question. Only by clarifying these critical factors and their connections can decision-makers effectively allocate available resources and help managers formulate appropriate strategies, thereby improving the sustainability of emergency supply chain digitalization. By leveraging the insights of Corbett et al. (2022), Anjomshoae et al. (2023), and Chowdhury et al. (2024), the sustainable development theory is verified to be a new perspective to explore the influencing factors of emergency supply chain digitalization. In this context, identifying influencing factors and internal relationships of emergency supply chain digitalization from the perspective of sustainable development has become an urgent and promising research problem.
In recent years, scholars have conducted meaningful explorations on emergency supply chain digitalization. For example, regarding the multi-objective spatial optimization location problem of urban emergency rescue facilities, Zhao & Liu (2018) developed a user-friendly decision support tool that integrates multi-objective optimization algorithms, artificial intelligence techniques, and GIS technology, aiming to facilitate the optimization of emergency facility locations in large urban areas. From a data-driven perspective, Liu et al. (2020) constructed a single-objective linear programming model for emergency logistics network in the context of the COVID-19 pandemic. This model aims to minimize the emergency cost over the decision period and is solved using a hybrid enumeration search and genetic algorithm. Longo et al. (2023) proposed a simulation-based decision framework that leverages the digital supply chain twin paradigm to enhance resilience and sustainability in response to COVID-like crises. Dubey et al. (2020) applied blockchain technology to establish swift trust and cooperation among disaster relief actors and investigated how blockchain influences supply chain transparency among these actors, aiming to improve the traceability and transparency of material and financial flows in disaster relief operations. Chen & Lin (2021) leveraged the advantages of big data in processing massive amounts of information to construct an emergency logistics resource scheduling model in the aftermath of earthquakes. This model considers the coordination and scheduling of multiple transportation modes. A genetic algorithm was employed to solve the model, aiming to improve the accuracy of emergency resource allocation, shorten the delivery time of emergency supplies, and reduce property losses.
In the field of sustainable emergency supply chains, Karuppiah et al. (2021) highlighted five critical challenges in sustainable humanitarian supply chains: facility location selection, short lead times for emergency supplies, spread of rumor, rapid emergence of new clusters, and doubt concerning the available remedy, while identifying public-private partnerships as the optimal strategy. Through the development of a decision support model, Chowdhury et al. (2024) identified the absence of contingency planning, corruption and political interference, and inadequate social-environment awareness as critical barriers to sustainable humanitarian supply chain management. Anjomshoae et al. (2023) made a systematic review of sustainable humanitarian supply chains, revealing that their sustainability encompasses multiple dimensions, including supply network configuration, coordination mechanisms, partnership, and performance measurement. By summarizing a 1-day workshop on sustainable humanitarian supply chain management, Corbett et al. (2022) concluded that the optimization of evaluation metrics and incentive mechanisms, along with the coordination between local and global organizations’ functions, constitute critical factors influencing the effective integration of sustainability into humanitarian operations. Patil et al. (2021) conducted an in-depth analysis of the challenges in achieving sustainability within humanitarian medical supply chains, highlighting the critical need for stakeholders to address material, operational and logistical issues, while the study proposes long-term collaboration and the adoption of cash-based donations as effective alternatives to traditional product donation approaches.
The Analytic Network Process (ANP) is a multi-criteria decision-making method that accounts for interdependence among factors. It is used to solve complex weight determination problems. By constructing a network-based model, ANP can more realistically reflect the feedback and interrelationships among factors in actual decision-making, thus providing a systematic analytical approach for examining the interconnections among influencing factors of emergency supply chain digitalization from the perspective of sustainable development. This method has received extensive attention from scholars in various fields. For example, Saputro et al. (2023) used the Panjalu region of Indonesia as a case study and integrated multidimensional scaling with ANP to examine sustainable strategies for rural tourism development in the area. Zhou et al. (2019) introduced probabilistic linguistic term sets to gather opinions in a short time frame, applied fault tree analysis to evaluate failures, and used ANP to derive standard weights for the severity of potential consequences, aiming to select the optimal relief plan to reduce losses during emergencies. Peng et al. (2022) integrated a consistent linguistic fuzzy preference relation-based ANP with the fuzzy TOPSIS method to construct a comprehensive performance evaluation model for rural homestays, and conducted an empirical analysis on rural homestays in Zhejiang Province. The goal was to fill the research gap in homestay performance and enhance their competitiveness and sustainability in the post-COVID-19 era.
According to the aforementioned literature, the following conclusions can be drawn. Firstly, most scholars have focused on applying various digital technologies to address operational-level issues within emergency supply chains. Although some studies have explored the digitalization of emergency supply chains, research specifically examining the influencing factors of emergency supply chain digitalization remains limited. Secondly, current research on sustainable emergency supply chains primarily centers on using quantitative methods such as mathematical programming to address emergency resource allocation and disaster waste management, exploring influencing factors of emergency supply chains from the perspective of sustainable development is relatively scarce. Thirdly, the ANP has been widely applied in theoretical enhancements and practical extensions. However, its application in the field of humanitarian supply chains, especially in identifying influencing factors of emergency supply chain digitalization from the perspective of sustainable development is still inadequate. To address the above issues, this study first identifies influencing factors of emergency supply chain digitalization from a sustainable development perspective by literature review and expert interviews. Then, the ANP is used to evaluate the importance or weights of these influencing factors from six dimensions: policy, society, economy, technology, organization, and security. Finally, targeted recommendations are proposed to provide a theoretical foundation for advancing the digitalization of emergency supply chains from a sustainability perspective.
2. Analytic Network Process
The ANP, developed by Professor Saaty of the United States as an extension of the Analytic Hierarchy Process (AHP), is designed to address the complex evaluation of influencing factors. ANP consists of a control layer and a network layer. The control layer includes the decision goal and decision criteria, while the network layer composes internal and external elements under various criteria that exhibit interdependencies. ANP overcomes the limitation of AHP, which only presumes elements are mutually independent and organized in a strict hierarchical structure. It enables systematic pairwise comparisons of all elements under multiple criteria, thereby enhancing analytical rigor and strengthening real-world applicability for decision-making contexts. In this regard, ANP offers an effective methodological framework for analyzing influencing factors and their interrelationships of emergency supply chain digitalization from the perspective of sustainable development. The ANP implementation process in emergency supply chain digitalization entails five key steps: identification of influencing factors, construction of the network hierarchy structure, construction of the judgment matrix, development of the unweighted and weighted supermatrix, and calculation of the limit matrix.
2.1. Identification of Influencing Factors
Through a comprehensive literature review and expert interviews, influencing factors of emergency supply chain digitalization from the perspective of sustainable development are identified across multiple dimensions. These factors are further refined and validated based on expert feedback, resulting in a finalized set of influencing factors. In addition, the experts evaluate the pairwise interrelationships among these factors influencing emergency supply chain digitalization, thereby constructing a relationship matrix that reflects the dependencies among the influencing factors.
2.2. Construction of the Network Hierarchy Structure
The identified influencing factors of emergency supply chain digitalization from the perspective of sustainable development are initially categorized into control layer and network layer components. The control layer consists of the goal and decision criteria, with all criteria being directly governed by the goal element. This layer serves three primary functions: organizing the hierarchical structure, establishing weight assignment mechanisms through defined criteria and evaluation standards, and guiding the computational processes of the network layer to ensure the system’s coherence and scientific rigor. While decision criteria are optional in the control layer, but the presence of at least one goal element is mandatory, thereby forming a typical hierarchical structure. The network layer comprises subordinate influencing factors controlled by the control layer, forming an internally interdependent network structure. It focuses on modeling and calculating the specific elements and their interrelationships, handling the actual data processing and information transfer between hierarchical levels.
The key difference lies in that the control layer emphasizes the macrolevel framework design and strategic control, functioning at the decision-making level, whereas the network layer concentrates on micro-level node connections and weight calculations, operating at the execution and computational level. Through their coordinated interaction, the model can effectively achieve hierarchical decomposition and comprehensive multi-factor evaluation.
In this context, the network hierarchy structure is drawn. Each cluster contains its specific elements, with lines representing the connections between node elements. The element at the tail of an arrow exerts the influence on the one at the head, indicating two forms of dependency: external and internal.
2.3. Construction of the Judgment Matrix
1) Expert Scoring
Based on the relationship matrix obtained through expert interviews, a series of judgment matrices are constructed to evaluate the relative importance among all influencing factors. Then, additional experts are invited to assign scores to the elements in these matrices. The collected data undergo consistency verification to ensure the reliability and validity of the judgment matrices.
2) Construction of the Unweighted Supermatrix
Following the method used for constructing the judgment matrices, all relevant matrices are constructed and calculated to derive their corresponding normalized eigenvectors. These normalized eigenvectors, obtained from the pairwise comparison matrices, are then aggregated to form the unweighted supermatrix.
3) Construction of the Weighted Supermatrix
By aggregating the normalized weights derived from the judgment matrices of all primary indicators, the cluster weight matrix can be obtained. Multiplying this cluster weight matrix with the previously constructed unweighted supermatrix yields the weighted supermatrix.
2.4. Calculation of the Limit Supermatrix
In ANP, the introduction of feedback mechanisms and interdependencies increases the complexity of determining indicator weights. The relative importance between two influencing factors can be evaluated through both direct and indirect comparisons. For instance, Wij denotes the direct comparison between indicators i and j, while ∑WikWkj reflects their indirect relationship mediated by other indicators. These intricate indirect relationships are captured through iterative calculations of the supermatrix. Therefore, the weights of indicators are ultimately derived by computing the limit supermatrix, which is obtained by raising the weighted supermatrix to successive powers until convergence, i.e., W = limWk. This process can be executed using the SuperDecisions software.
3. Analytic Network Model and Solution for Emergency
Supply Chain Digitalization
3.1. Model Construction
1) Identification of Influencing Factors for Emergency Supply Chain Digitalization from the Perspective of Sustainable Development
Firstly, this paper concludes the indicators proposed in the existing literature. Then, insights from experts in Table 1 in the field of public safety and emergency management, and supply chain management are extended to revise or modify the indicators. In this context, this study categorizes the key influencing factors of emergency supply chain digitalization into six categories comprising seventeen indicators from the perspective of sustainable development.
Specifically, the first category is policy, including strategic planning and standardization construction. The second factor is society, including trust level, responsiveness, and coordination mechanism. The third one is the economic factor, including resource investment and cost-benefit. The fourth one is the technological factor, including innovation capability, IT level, data sharing efficiency, and intelligence level. The fifth factor is organization, including organizational
Table 1. Expert information.
Expert |
Education background |
Gender |
Academic title |
Research interest |
Expert 1 |
Doctoral degree |
Male |
Professor |
Emergency supply chain |
Expert 2 |
Doctoral degree |
Male |
Professor |
Sustainable supply chain |
Expert 3 |
Doctoral degree |
Male |
Associate professor |
Digital supply chain |
Expert 4 |
Doctoral degree |
Female |
Associate professor |
Emergency supply chain |
Expert 5 |
Doctoral degree |
Male |
Associate professor |
Sustainable humanitarian supply chain |
collaboration, resource integration, and strategic implementation capability. The sixth factor is security, including data security, system reliability, and environmental risk control.
Thus, influencing factors of emergency supply chain digitalization and their specific meanings are presented in the following Table 2.
Table 2. Influencing factors of emergency supply chain digitalization from the perspective of sustainable development.
Primary indicators |
ID |
Secondary indicators |
ID |
Definition |
Source |
Policy |
A |
Strategic planning |
A1 |
Whether the digitization of emergency supply chains explicitly incorporates sustainable development goals to ensure that resource allocation, technology application, and long-term social and environmental benefits are well coordinated. |
Van Wassenhove (2006) Bag et al. (2020) Chowdhury et al. (2024) |
Standardization construction |
A2 |
Whether digital technology regulations and management standards
are unified and embedded with sustainable indicators such as
green and low-carbon criteria to ensure alignment with
long-term development requirements. |
Behl & Dutta (2019) Chowdhury et al. (2024) |
Society |
B |
Trust
level |
B1 |
Level of acceptance of digital emergencies and ethics of social justice and sustainability among businesses, governments and citizens, etc. |
Woldt et al. (2019) |
Responsiveness |
B2 |
Speed of response and efficiency of material mobilization of social agents in emergency situations. |
Woldt et al. (2019) |
Coordination mechanism |
B3 |
Mechanisms for cooperation between different social actors and
for sharing resources. |
Anjomshoae et al. (2023) |
Economy |
C |
Resource investment |
C1 |
Whether the allocation of financial, technical and other resources
in the digitization of emergency supply chains is sufficient and
whether it can guarantee the sustainable operation of the system
and take into account the long-term benefits, etc. |
Patil et al. (2021) |
Cost-benefit |
C1 |
Whether the cost of digital construction and operation is reasonable and can maximize economic, social, and environmental benefits
while enhancing emergency efficiency. |
Patil et al. (2021) Damoah et al. (2021) |
Technology |
D |
Innovation capability |
D1 |
Whether enterprises or industries can continuously develop and apply new technologies during the digital transformation process. |
Dubey et al. (2020) |
IT level |
D2 |
The advancement and reliability of digital infrastructure,
to meet the dual requirements of efficient response and
long-term sustainable development. |
Najjar et al. (2019) Dubey et al. (2020) |
Data sharing efficiency |
D3 |
Whether the real-time and accuracy of cross-departmental
and cross-level data collaboration can support optimized
resource allocation and fair distribution. |
Zhao & Liu (2018) Dubey et al. (2020) |
Intelligence
level |
D4 |
The depth of AI, IoT, and other technology applications in
achieving precise forecasting, dynamic optimization,
and resource waste reduction. |
Najjar et al. (2019) |
Organization |
E |
Organizational collaboration |
E1 |
Whether digital platforms enable seamless collaboration
among subjects in emergency supply chains, forming a unified
and efficient sustainable action network. |
Karuppiah et al. (2021) Tseng et al. (2021) Anjomshoae et al. (2023) |
|
|
Resource integration |
E2 |
Whether all organizations can optimize the allocation of dispersed resources like manpower and materials in the digitalization process
to improve overall rescue efficiency and long-term resilience. |
Anjomshoae et al. (2023) Kashav & Garg (2024) |
Strategic implementation capability |
E3 |
Whether the organizational structure and processes adapt to digital strategies, ensuring sustainable development goals are effectively transformed into specific actions and outcomes. |
Karuppiah et al. (2021) Corbett et al. (2022) |
Safety |
F |
Data
security |
F1 |
The protection strength of sensitive data in the digitalization of emergency supply chains, and whether it can prevent leaks during sharing and collaboration while safeguarding the rights of all parties. |
Dubey et al. (2020) |
System
reliability |
F2 |
The stability and interference resistance of digital platforms,
ensuring sustainable operation and maintenance of
key functions under extreme environments. |
Longo et al. (2023) |
Environmental risk control |
F3 |
Whether the digital construction process can
reduce energy consumption and pollution to
avoid secondary environmental hazards. |
Rehman Khan et al. (2022) Chowdhury et al. (2024) |
To explore the interrelationships among influencing factors, experts have to assess the existence and nature of dependencies between distinct elements. Through surveys and interviews with multiple experts, these interrelationships can be systematically identified in the form of dependency matrix regarding influencing factors of emergency supply chain digitalization, as shown in Table 3.
Table 3. Dependency matrix regarding influencing factors.
|
A1 |
A2 |
B1 |
B2 |
B3 |
C1 |
C2 |
D1 |
D2 |
D3 |
D4 |
E1 |
E2 |
E3 |
F1 |
F2 |
F3 |
A1 |
0 |
1 |
0 |
1 |
1 |
1 |
1 |
0 |
0 |
1 |
0 |
1 |
1 |
1 |
1 |
0 |
0 |
A2 |
1 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
B1 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
0 |
1 |
0 |
1 |
1 |
1 |
0 |
0 |
0 |
B2 |
0 |
0 |
1 |
0 |
1 |
1 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
1 |
0 |
B3 |
0 |
0 |
1 |
1 |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
1 |
0 |
C1 |
1 |
0 |
1 |
1 |
1 |
0 |
1 |
1 |
1 |
0 |
1 |
0 |
1 |
1 |
0 |
0 |
1 |
C2 |
1 |
0 |
0 |
1 |
0 |
1 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
1 |
1 |
D1 |
1 |
0 |
0 |
1 |
1 |
1 |
1 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
D2 |
0 |
1 |
0 |
1 |
1 |
1 |
1 |
1 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
D3 |
1 |
1 |
0 |
1 |
1 |
0 |
1 |
1 |
1 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
D4 |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
1 |
1 |
1 |
1 |
1 |
0 |
E1 |
0 |
0 |
1 |
1 |
1 |
0 |
1 |
0 |
1 |
1 |
1 |
0 |
1 |
1 |
1 |
1 |
0 |
E2 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
1 |
0 |
0 |
1 |
E3 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
1 |
1 |
1 |
F1 |
1 |
1 |
0 |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
0 |
1 |
0 |
1 |
1 |
F2 |
0 |
1 |
0 |
1 |
1 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
1 |
1 |
0 |
0 |
F3 |
0 |
1 |
0 |
1 |
1 |
1 |
1 |
0 |
0 |
1 |
0 |
1 |
1 |
1 |
1 |
0 |
0 |
According to the obtained interrelationships among influencing factors, the SuperDecisions software can be used to generate a visual representation of the relationships among the elements, as shown in Figure 1.
Figure 1. Influencing factors relationships.
2) Construction of the Network Hierarchy Structure
According to the ANP, the identified factors influencing the digitalization of emergency supply chains from a sustainable development perspective are categorized into the control layer and network layer, respectively. Based on the relationships among these factors as outlined in the dependency matrix, the network hierarchy structure is subsequently constructed, as shown in Figure 2.
3) Matrix Construction
Combing the obtained network hierarchy structure, the importance judgment matrices for all influencing factors are first constructed through expert interviews. When these matrices passed a consistency check, the complete set of data for the importance of factors influencing the digitalization of emergency supply chains is obtained. By combining the normalized vectors of all the importance judgment matrices, the unweighted supermatrix can be constructed, as shown in Table 4.
By normalizing the vector of scores generated from expert evaluations of the importance judgment matrix across all dimensional indicators, the cluster weight matrix can be derived, as shown in Table 5.
By multiplying the cluster weight matrix with the unweighted supermatrix, the weighted supermatrix can be obtained, as shown in Table 6.
4) Solution of the limit matrix
Based on the weighted supermatrix derived above, the limit matrix can be obtained by multiplying the weighted supermatrix with itself infinitely, as shown in Table 7. The limit matrix presents the final weights of factors influencing the digitalization of emergency supply chains from the perspective of sustainable development.
In conclusion, the final results are shown in Table 8.
Table 4. Unweighted supermatrix.
|
A1 |
A2 |
B1 |
B2 |
B3 |
C1 |
C2 |
D1 |
D2 |
D3 |
D4 |
E1 |
E2 |
E3 |
F1 |
F2 |
F3 |
A1 |
0.00 |
1.00 |
0.00 |
1.00 |
0.75 |
1.00 |
1.00 |
0.00 |
0.00 |
0.80 |
0.00 |
0.14 |
0.75 |
0.67 |
0.80 |
0.00 |
0.83 |
A2 |
1.00 |
0.00 |
0.00 |
0.00 |
0.25 |
0.00 |
0.00 |
0.00 |
1.00 |
0.20 |
0.00 |
0.86 |
0.25 |
0.33 |
0.20 |
1.00 |
0.17 |
B1 |
0.00 |
0.00 |
0.00 |
0.83 |
0.67 |
0.00 |
0.00 |
0.00 |
0.00 |
0.63 |
0.00 |
0.63 |
0.63 |
0.63 |
0.00 |
0.00 |
0.00 |
B2 |
0.00 |
0.00 |
0.75 |
0.00 |
0.33 |
1.00 |
0.00 |
0.83 |
0.75 |
0.24 |
0.67 |
0.24 |
0.24 |
0.24 |
0.00 |
0.83 |
0.20 |
B3 |
0.00 |
0.00 |
0.25 |
0.17 |
0.00 |
0.00 |
0.00 |
0.17 |
0.25 |
0.14 |
0.33 |
0.14 |
0.14 |
0.14 |
0.00 |
0.17 |
0.80 |
C1 |
0.83 |
0.00 |
1.00 |
0.88 |
1.00 |
0.00 |
1.00 |
0.86 |
0.13 |
0.00 |
0.80 |
0.00 |
0.83 |
0.13 |
0.00 |
0.00 |
0.00 |
C2 |
0.17 |
0.00 |
0.00 |
0.13 |
0.00 |
1.00 |
0.00 |
0.14 |
0.88 |
1.00 |
0.20 |
1.00 |
0.17 |
0.88 |
0.00 |
1.00 |
0.00 |
D1 |
0.83 |
0.00 |
0.00 |
0.58 |
0.58 |
0.66 |
0.58 |
0.00 |
0.74 |
0.65 |
0.65 |
0.58 |
0.58 |
0.58 |
0.58 |
0.58 |
0.58 |
D2 |
0.00 |
0.80 |
0.00 |
0.23 |
0.23 |
0.26 |
0.23 |
0.65 |
0.00 |
0.23 |
0.23 |
0.23 |
0.23 |
0.23 |
0.23 |
0.23 |
0.23 |
D3 |
0.17 |
0.20 |
0.00 |
0.13 |
0.13 |
0.00 |
0.13 |
0.23 |
0.17 |
0.00 |
0.12 |
0.13 |
0.13 |
0.13 |
0.13 |
0.13 |
0.13 |
D4 |
0.00 |
0.00 |
0.00 |
0.06 |
0.06 |
0.08 |
0.06 |
0.12 |
0.09 |
0.12 |
0.00 |
0.06 |
0.06 |
0.06 |
0.06 |
0.06 |
0.06 |
E1 |
0.00 |
0.00 |
0.70 |
0.70 |
0.70 |
0.00 |
0.70 |
0.00 |
0.70 |
0.70 |
0.70 |
0.00 |
0.86 |
0.83 |
0.75 |
0.67 |
0.80 |
E2 |
0.75 |
0.83 |
0.19 |
0.19 |
0.19 |
0.86 |
0.19 |
0.89 |
0.19 |
0.19 |
0.19 |
0.83 |
0.00 |
0.17 |
0.00 |
0.00 |
0.00 |
E3 |
0.25 |
0.17 |
0.11 |
0.11 |
0.11 |
0.14 |
0.11 |
0.11 |
0.11 |
0.11 |
0.11 |
0.17 |
0.14 |
0.00 |
0.25 |
0.33 |
0.20 |
F1 |
0.88 |
0.67 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.70 |
0.70 |
0.70 |
0.70 |
0.70 |
0.00 |
0.70 |
0.00 |
0.75 |
0.88 |
F2 |
0.00 |
0.24 |
0.00 |
0.83 |
0.67 |
0.00 |
1.00 |
0.21 |
0.21 |
0.21 |
0.21 |
0.21 |
0.00 |
0.21 |
0.20 |
0.00 |
0.13 |
F3 |
0.13 |
0.09 |
0.00 |
0.17 |
0.33 |
0.00 |
0.00 |
0.08 |
0.08 |
0.08 |
0.08 |
0.08 |
0.00 |
0.08 |
0.80 |
0.25 |
0.00 |
Table 5. Cluster weight matrix.
|
Policy |
Society |
Economy |
Technology |
Organization |
Security |
Policy |
0.299 |
0.070 |
0.049 |
0.088 |
0.049 |
0.070 |
Society |
0.000 |
0.027 |
0.079 |
0.438 |
0.079 |
0.027 |
Economy |
0.130 |
0.308 |
0.269 |
0.149 |
0.269 |
0.308 |
Technology |
0.448 |
0.431 |
0.436 |
0.234 |
0.436 |
0.431 |
Organization |
0.038 |
0.042 |
0.135 |
0.037 |
0.135 |
0.042 |
Security |
0.084 |
0.121 |
0.032 |
0.055 |
0.032 |
0.121 |
Table 6. Weighted supermatrix.
|
A1 |
A2 |
B1 |
B2 |
B3 |
C1 |
C2 |
D1 |
D2 |
D3 |
D4 |
E1 |
E2 |
E3 |
F1 |
F2 |
F3 |
A1 |
0.00 |
0.34 |
0.00 |
0.07 |
0.05 |
0.05 |
0.05 |
0.00 |
0.00 |
0.07 |
0.00 |
0.01 |
0.04 |
0.03 |
0.08 |
0.00 |
0.08 |
A2 |
0.30 |
0.00 |
0.00 |
0.00 |
0.02 |
0.00 |
0.00 |
0.00 |
0.09 |
0.02 |
0.00 |
0.04 |
0.01 |
0.02 |
0.02 |
0.07 |
0.02 |
B1 |
0.00 |
0.00 |
0.00 |
0.02 |
0.02 |
0.00 |
0.00 |
0.00 |
0.00 |
0.27 |
0.00 |
0.05 |
0.05 |
0.05 |
0.00 |
0.00 |
0.00 |
B2 |
0.00 |
0.00 |
0.05 |
0.00 |
0.01 |
0.08 |
0.00 |
0.40 |
0.33 |
0.10 |
0.32 |
0.02 |
0.02 |
0.02 |
0.00 |
0.02 |
0.01 |
B3 |
0.00 |
0.00 |
0.02 |
0.00 |
0.00 |
0.00 |
0.00 |
0.08 |
0.11 |
0.06 |
0.16 |
0.01 |
0.01 |
0.01 |
0.00 |
0.00 |
0.03 |
C1 |
0.11 |
0.00 |
0.82 |
0.27 |
0.31 |
0.00 |
0.29 |
0.14 |
0.02 |
0.00 |
0.13 |
0.00 |
0.23 |
0.03 |
0.00 |
0.00 |
0.00 |
C2 |
0.02 |
0.00 |
0.00 |
0.04 |
0.00 |
0.28 |
0.00 |
0.02 |
0.13 |
0.15 |
0.03 |
0.27 |
0.05 |
0.24 |
0.00 |
0.31 |
0.00 |
D1 |
0.37 |
0.00 |
0.00 |
0.25 |
0.25 |
0.30 |
0.27 |
0.00 |
0.17 |
0.15 |
0.17 |
0.25 |
0.26 |
0.25 |
0.37 |
0.25 |
0.36 |
D2 |
0.00 |
0.41 |
0.00 |
0.10 |
0.10 |
0.12 |
0.11 |
0.17 |
0.00 |
0.05 |
0.06 |
0.10 |
0.10 |
0.10 |
0.15 |
0.10 |
0.14 |
D3 |
0.07 |
0.10 |
0.00 |
0.06 |
0.06 |
0.00 |
0.06 |
0.06 |
0.04 |
0.00 |
0.03 |
0.06 |
0.06 |
0.06 |
0.09 |
0.06 |
0.08 |
D4 |
0.00 |
0.00 |
0.00 |
0.03 |
0.03 |
0.04 |
0.03 |
0.03 |
0.02 |
0.03 |
0.00 |
0.03 |
0.03 |
0.03 |
0.04 |
0.03 |
0.04 |
E1 |
0.00 |
0.00 |
0.08 |
0.03 |
0.03 |
0.00 |
0.10 |
0.00 |
0.03 |
0.03 |
0.03 |
0.00 |
0.12 |
0.11 |
0.05 |
0.03 |
0.05 |
E2 |
0.03 |
0.04 |
0.02 |
0.01 |
0.01 |
0.12 |
0.03 |
0.04 |
0.01 |
0.01 |
0.01 |
0.11 |
0.00 |
0.02 |
0.00 |
0.00 |
0.00 |
E3 |
0.01 |
0.01 |
0.01 |
0.00 |
0.00 |
0.02 |
0.02 |
0.00 |
0.00 |
0.00 |
0.00 |
0.02 |
0.02 |
0.00 |
0.02 |
0.01 |
0.01 |
F1 |
0.07 |
0.06 |
0.00 |
0.00 |
0.00 |
0.00 |
0.00 |
0.04 |
0.04 |
0.04 |
0.04 |
0.02 |
0.00 |
0.02 |
0.00 |
0.09 |
0.15 |
F2 |
0.00 |
0.02 |
0.00 |
0.10 |
0.08 |
0.00 |
0.04 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.00 |
0.01 |
0.04 |
0.00 |
0.02 |
F3 |
0.01 |
0.01 |
0.00 |
0.02 |
0.04 |
0.00 |
0.00 |
0.01 |
0.00 |
0.00 |
0.01 |
0.00 |
0.00 |
0.00 |
0.15 |
0.03 |
0.00 |
![]()
Figure 2. Network hierarchy structure.
Table 7. Limit supermatrix.
|
A1 |
A2 |
B1 |
B2 |
B3 |
C1 |
C2 |
D1 |
D2 |
D3 |
D4 |
E1 |
E2 |
E3 |
F1 |
F2 |
F3 |
A1 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
A2 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
B1 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
0.02 |
B2 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
B3 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
0.04 |
C1 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
0.14 |
C2 |
0.09 |
0.09 |
0.09 |
0.09 |
0.09 |
0.09 |
0.09 |
0.09 |
0.09 |
0.09 |
0.09 |
0.09 |
0.09 |
0.09 |
0.09 |
0.09 |
0.09 |
D1 |
0.19 |
0.19 |
0.19 |
0.19 |
0.19 |
0.19 |
0.19 |
0.19 |
0.19 |
0.19 |
0.19 |
0.19 |
0.19 |
0.19 |
0.19 |
0.19 |
0.19 |
D2 |
0.11 |
0.11 |
0.11 |
0.11 |
0.11 |
0.11 |
0.11 |
0.11 |
0.11 |
0.11 |
0.11 |
0.11 |
0.11 |
0.11 |
0.11 |
0.11 |
0.11 |
D3 |
0.05 |
0.05 |
0.05 |
0.05 |
0.05 |
0.05 |
0.05 |
0.05 |
0.05 |
0.05 |
0.05 |
0.05 |
0.05 |
0.05 |
0.05 |
0.05 |
0.05 |
D4 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
E1 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
E2 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
E3 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
F1 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
F2 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
0.03 |
F3 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
0.01 |
Table 8. Final weights of influencing factors.
Indicators |
Primary indicator |
Secondary indicator |
Ranking |
Policy |
Strategic planning |
0.069 |
0.041 |
8 |
Standardization construction |
0.027 |
10 |
Society |
Trust level |
0.196 |
0.020 |
14 |
Responsiveness |
0.140 |
2 |
Coordination mechanism |
0.036 |
9 |
Economy |
Resource investment |
0.227 |
0.134 |
3 |
Cost-benefit |
0.093 |
5 |
Technology |
Innovation capability |
0.372 |
0.193 |
1 |
IT level |
0.106 |
4 |
Data sharing efficiency |
0.047 |
7 |
Intelligence level |
0.026 |
13 |
Organization |
Organizational collaboration |
0.073 |
0.014 |
15 |
Resource integration |
0.011 |
17 |
Strategic implementation capability |
0.048 |
6 |
Safety |
Data security |
0.063 |
0.026 |
12 |
System reliability |
0.027 |
11 |
Environmental risk control |
0.011 |
16 |
3.2. Result Analysis
According to Table 8, the importance of control layer factors influencing the digitalization of emergency supply chains from a sustainable development standpoint follows this descending order: technology, economy, society, organization, policy, and security. The importance of network layer factors follows this descending order: innovation capability, responsiveness, resource investment, IT level, cost-benefit, strategic implementation capability, data sharing efficiency, strategic planning, coordination mechanism, standardization construction, system reliability, data security, intelligence level, trust level, organizational coordination, environmental risk control, and resource integration.
In Table 8, the varying weights of influencing factors of emergency supply chain digitalization reflect their differential importance or prioritization when decision-makers develop emergency management strategies in practice. Indicators with relatively low wights, such as resource integration and environmental risk control, should not be considered negligible. Their lower weight values indicate a reduced priority as evaluated by experts within the specific context of this study. In practical applications, the significance of these factors may depend on the circumstance, industry focus, or regional priorities. Therefore, decision-makers should interpret them with contextual sensitivity and dynamically adjust their emphasis based on the actual needs of the implementation environment.
In the control layer, technology and economy exert the most immediate influence on the digitalization of emergency supply chains from a sustainable development perspective. The adaptation of technological applications with economic feasibility often needs to be balanced to provide stable support for the sustainable development of emergency supply chains. The integration of a long-term evaluation mechanism within the system enables assessment of whether digitalization-related investments substantively improve emergency response efficiency, thereby avoiding ineffective expenditure. Overemphasizing advanced technologies while neglecting implementation costs or operational constraints may result in systemic inefficiencies. Conversely, excessive budget constraints prioritizing short-term gains can severely compromise system flexibility and long-term scalability.
In the network layer, factors interact dynamically, with innovation capability emerging as the pivotal factor. Digital innovation capability plays a vital role in enhancing the efficiency of emergency supply chains. To improve emergency response efficiency, continuous advancement in the technology is imperative. Strategic initiatives such as strengthening digital infrastructure, establishing open data platforms, and integrating intelligent algorithms contribute positively to various emergency scenarios. For example, the Internet of Things facilitates real-time location tracking and condition monitoring of critical supplies. AI algorithms optimize routing to accelerate response times. Blockchain enhances information transparency and traceability to a certain degree. However, these technologies entail significant risks. Overreliance on a single technological solution may cause systemic disruption in the event of unexpected cyberattacks or platform failures. Therefore, technological upgrades must incorporate rigorous assessment of system stability and recovery capabilities. Proactive establishment of disaster recovery mechanisms and verifiable adaptability to diverse emergency scenarios are critical implementation prerequisites.
Overall, the digitalization of emergency supply chains constitutes a complex system engineering endeavor characterized by significant cross-dimensional network interactions and necessitating multidimensional collaborative advancement. In detail, policy guidance serves as the institutional foundation for the digitalization of emergency supply chains. Social acceptance fosters a favorable implementation circumstance. Economic investment underpins the infrastructure development. Technological innovation propels the transformation and upgrading of emergency supply chains. Organizational collaboration optimizes emergency resource allocation. Security assurance permeates the lifecycle. Future progress in emergency supply chain digitalization demands the dismantling of single-dimensional development constraints and a strategic focus on constructing a multidimensional collaborative ecosystem. This can be facilitated by the synergistic interplay of policy incentives and social engagement, the reciprocal reinforcement of technological innovation and economic investment, and the coordinated advancement of organizational restructuring alongside security assurance. Upon identifying crucial influencing factors, implementing targeted interventions becomes imperative to effectively elevate the level of emergency supply chain digitalization within the framework of sustainable development.
4. Strategies for Enhancing Emergency Supply Chain
Digitalization from the Perspective of Sustainable
Development
According to the weights in Table 8, it can be seen that technological, economic, and social dimensions are the most important factors influencing emergency supply chain digitalization from a control layer standpoint. Technological, economic, and social influences on the digitalization of emergency supply chains are on a decreasing trend.
4.1. Strategies for Enhancing Emergency Supply Chain
Digitalization from a Technological Perspective
1) Strengthen Technological Support and Build a Digitalized Emergency Management System
Technological capability, as the core driving force behind the modernization of emergency supply chains, directly determines the efficiency and precision of disaster response. The heart of emergency supply chain digitalization lies technological integration and innovation in application. Strategic efforts should be directed towards advancing frontier technologies such as artificial intelligence, blockchain, big data, and the Internet of Things. It is essential to support interdisciplinary and cross-sector collaborative research initiatives to strengthen the independent controllability of crucial technologies. Moreover, long-term and stable cooperation mechanisms among enterprises, universities, and academic institutions should be encouraged, with the goal of jointly developing technology innovation platforms that facilitate the seamless integration of digital tools from research and development to practical application.
2) Accelerate the Construction and Promotion of Digital Infrastructure Such as Intelligent Scheduling Systems, Visual Tracking Platforms, and Distributed Data Storage Systems
In terms of standard construction, it is urgent to promote the implementation of unified interface protocols, data format specifications, and platform docking mechanisms to solve issues like platform fragmentation and data silos, thus truly achieving technological interconnectivity, system linkage, and efficient collaboration.
3) Focus on Breakthroughs in Key Technologies Such as Big Data Analysis, AI Prediction, and Blockchain Traceability
Drawing on the successful experience of Zhejiang Province’s “Flood Control Brain” system, it is recommended to deploy an intelligent disaster warning platform nationwide. This would enable real-time monitoring of disaster risks and accurate prediction of material needs. On this basis, an emergency technology standard system should be established to ensure data interoperability between different systems, thereby avoiding the formation of information silos.
4.2. Strategies for Enhancing Emergency Supply Chain
Digitalization from an Economic Perspective
Digital upgrades often come with high investment and risk. In emergency scenarios, this demands higher efficiency in resource allocation. The government should guide diversified funding to participate in infrastructure construction and digital transformation of emergency supply chains. This can be achieved by encouraging social capital through modes like public-private partnerships. To incentivize participation, differentiated tax exemptions and green credit channels can be explored to ease the digitalization costs for enterprises. A scientific and reasonable cost-benefit evaluation system should be established for the digitalization of emergency supply chains, focusing not only on economic benefits but also on environmental performance and social impact. The goal is to promote forming a “green, low-carbon, and efficient” idea in resource allocation. The government can guide enterprises to explore data-driven cost optimization paths. For instance, enterprises can use digital twin and big data technologies to enhance scheduling efficiency, maximizing resource utilization and minimizing redundancy and waste.
4.3. Strategies for Enhancing Emergency Supply Chain
Digitalization from a Social Perspective
Firstly, a multi-entity collaborative governance network needs to be established to improve social mobilization capabilities. Efficient operations of emergency supply chains involve not only governments and enterprises but also social forces. The central government could lead in forming relevant emergency organizations, integrating efforts from enterprises, communities, NGOs, and other parties. Regular cross-entity joint drills should be conducted. Additionally, residents’ understanding and support for the digitalization of emergency management system must be enhanced through various channels and forms of popular science education and policy promotion, guiding public participation in volunteer services, information feedback, and material collection. An open platform should be built to encourage diverse entities such as research institutions, social organizations, and industry associations to engage in co-constructing the supply chain, data collaboration, and supervisory feedback, fostering the development of a collaborative governance social mechanism. Using blockchain technology, a trusted disaster information sharing platform can be established to publicly disclose the flow of emergency materials in real-time and eliminate social trust barriers. For private enterprises involved in emergency response, incentives such as tax exemptions or government procurement priorities can be provided. Furthermore, legislation should ensure the legal status of volunteer organizations, clarify their action privileges during emergency response to prevent disordered rescue efforts from causing resource waste. It also should strengthen trust-building across departments and industries. Open and transparent data sharing mechanisms and a strengthened platform liability traceability system can gradually establish a trust-based cooperation mode, enhancing overall collaborative efficiency and social responsiveness.
5. Conclusion
To explore the key influencing factors of emergency supply chain digitalization from the perspective of sustainable development, this study initially identified relevant factors from six dimensions—policy, society, economy, technology, organization, and security through a systematic literature review. Subsequently, based on expert interviews, six primary factors and seventeen secondary factors were screened and finalized. Using the ANP, experts evaluated the interrelationships among these factors and assessed their relative importance within each dimension, thereby quantifying the weight of each factor. Based on the analytical results, this study proposed targeted policy recommendations aimed at addressing issues in the digitalization of emergency supply chains.
Future studies can be extended from the following aspects. Firstly, this paper uses literature review and expert interviews to select 17 influencing factors of emergency supply chain digitalization from the viewpoint of sustainable development. In practice, the digitalization of emergency supply chains is quite complex, the criteria to evaluate influencing factors are different for different decision-makers. In the future, constructing a relatively comprehensive indictors is a promising topic. Secondly, the identified influencing factors and their corresponding weights are initially proposed based on expert judgment and subjective evaluation, without being specifically tailored to any particular national or regional context. In practical applications, these factors and weights are intended to serve as a foundational framework that can be adapted and refined according to the unique characteristics and contextual conditions of different countries or regions. The following work is to concentrate on scenario-dependent influencing factors and the corresponding weights. Thirdly, this study uses ANP to evaluate the influencing factors of emergency supply chain digitalization considering sustainability from a static or short-term perspective. In practice, this approach does not fully capture the evolving nature of emergency supply chain digitalization, just providing a snapshot of the current state. Thus, investigating this problem from a dynamic or time-sensitive perspective is an important direction for future research.
Funding
This paper was partially supported by Higher Education Teaching Reform Research Project of Chongqing Technology and Business University Grant No. 2022113, and Undergraduate Innovation and Entrepreneurship Training Planning Grant No. 202311799012.