supply chain finance a literature review

Supply Chain Finance Revisited: A Critical Review with Future Prospects

28 Pages Posted: 9 Dec 2018

Georgios Vousinas

National Technical University of Athens

Date Written: November 19, 2018

Supply Chain Finance (SCF) is a relatively recent thinking in Supply Chain Management (SCM) literature. Major Interest in SCF has steadily increased since the past decades and especially during the global financial crisis of 2008. However, SCF places the focus of research on the interconnection among SCM, corporate value and financial performance, away from the myopic perspective of managing solely the cost when studying financial aspects of SCM. Despite the crisis-related research interest and the growing importance of SCF, academic contributions on the subject remain vague, while scarce research efforts have been identified toward the systematic documentation of its core concepts and the development of a “general theory” of SCF. This paper aims to redefine the term SCF by shedding light on theoretical ambiguities, provide an up-to-date systematic literature review of the SCF concept and identify research gaps. The goal is to also highlight emerging areas like the “Supply Chain Financial Bullwhip Effect” and Blockchain Technology.

Keywords: Supply Chain Finance, financial performance, Supply Chain Financial Bullwhip Effect, Blockchain, crisis

JEL Classification: G23

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Georgios Vousinas (Contact Author)

National technical university of athens ( email ).

Heroon Polytechniou 9 Zografou Athens, 15780 Greece

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Supply chain finance: a systematic literature review and bibliometric analysis

Xu, Xinhan , Chen, Xiangfeng , Jia, Fu , Brown, Stephen , Gong, Yu and Xu, Yifan (2018) Supply chain finance: a systematic literature review and bibliometric analysis. International Journal of Production Economics , 204 , 160-173 . ( doi:10.1016/j.ijpe.2018.08.003 ).

Supply Chain Finance (SCF) is an effective method to lower financing costs and improve financing efficiency and effectiveness, and it has gained research momentum in recent years. This paper adopts a systematic literature review methodology combined with bibliometric, network and content analysis based on 348 papers identified from mainstream academic databases. This review provides insights not previously fully captured or evaluated by other reviews on this topic, including key authors, key journals and the prestige of the reviewed papers. Using rigorous bibliometric and visualisation tools, we identified four research clusters, including deteriorating inventory models under trade credit policy based on the EOQ/EPQ model; inventory decisions with trade credit policy under more complex situations; interaction between replenishment decisions and delay payment strategies in the supply chain and roles of financing service in the supply chain. Based on the clusters identified, we carried out a further content analysis of 112 papers, identifying research gaps and proposing seven actionable directions for future research. The findings provide a robust roadmap for further investigation in this field.

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The improvement strategy of fresh produce supply chain resilience based on extenics

Roles Data curation, Formal analysis, Writing – original draft

Affiliations School of Economics and Management, Guangxi Normal University, Guilin, China, School of Economics and Management, Heilongjiang Bayi Agricultural University, Daqing, China

Roles Funding acquisition, Project administration, Supervision

Affiliation School of Economics and Management, Guangxi Normal University, Guilin, China

Roles Conceptualization, Funding acquisition, Writing – review & editing

* E-mail: [email protected] (LL); [email protected] (XL)

Roles Formal analysis, Funding acquisition, Writing – review & editing

Affiliations School of Economics and Management, Guangxi Normal University, Guilin, China, School of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China

• Zhangzheyi Liao, 
• Chaoling Li, 
• Lin Lu, 
• Xiaochun Luo

• Published: September 30, 2024
• https://doi.org/10.1371/journal.pone.0309008
• Reader Comments

Nowadays, the world is in turmoil, climate and environmental problems are prominent, the import and export of fresh agricultural products are restricted, etc. The impact of the growing demand for fresh agricultural products and healthy lifestyle choices, and fresh agricultural products are essential for people’s daily life, which are perishable, fragile, seasonal, and other unstable factors. Therefore, when the fresh produce supply chain faces various pressures and difficulties, how to enhance the resilience of the supply chain against various problems and risks with flexible and multi-dimensional strategies and methods has become the focus of extensive attention. This kind of problem is a typical contradictory problem, and previous studies have failed to achieve good results. In this paper, based on extenics, we are able to one-dimensionalise the multi-dimensional contradictory problems and multi-dimensionalise the one-dimensional contradictory problems to solve such problems in a scientific and effective way. Firstly, taking fresh agricultural products supply chain enterprise M as the research object, we constructed the fresh agricultural products supply chain enterprise toughness system and identified the toughness state of each index. Secondly, we found the low-evaluation toughness indexes that need to be solved and constructed a extension model of incompatible problems of enterprise toughness. Thirdly, we analysed the objectives and conditions of toughness incompatible problems of fresh agricultural products supply chain enterprises numerically and quantitatively, and then, with the objective of toughness improvement, we analyzed the correlation of the condition basic-elements of incompatible problems and carried out extension transformations. Again, the objectives and conditions of the incompatible problems of fresh produce supply chain enterprises are analysed numerically and quantitatively, and with toughness enhancement as the objective, the correlation analysis and extension transformation of the condition basic-elements of the incompatible problems are implemented to generate the set of toughness enhancement strategies that can solve the incompatible problems in a multidimensional and scientific way. Finally, the optimal toughness enhancement strategies are selected through the superiority evaluation and composed into a new strategy to enhance the toughness of the fresh produce supply chain. Combined with extenics calculations and screening, a new strategy for supply chain resilience enhancement of fresh agricultural products was finally formed. The existing problems are solved from six aspects: product supply type, product demand, product supply efficiency, human resource quantity, production and processing equipment, and logistics guarantee ability. It provides a certain reference significance for the fresh agricultural products supply chain toughness enhancement, and helps enterprises to strengthen their competitiveness and sustainability through the enhancement of toughness.

Citation: Liao Z, Li C, Lu L, Luo X (2024) The improvement strategy of fresh produce supply chain resilience based on extenics. PLoS ONE 19(9): e0309008. https://doi.org/10.1371/journal.pone.0309008

Editor: Muhammad Khalid Bashir, University of Agriculture Faisalabad, PAKISTAN

Received: November 15, 2023; Accepted: August 4, 2024; Published: September 30, 2024

Copyright: © 2024 Liao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: This work was supported by Guangxi Key R&D Plan (2022AB34029) and by the Research Fund Project of Development Institute of Zhujiang-Xijiang Economic Zone, Key Research Base of Humanities and Social Sciences in Guangxi Universities (ZX2023051). The Guangxi Key R&D Plan (2022AB34029) awarded to Chaoling Li and Lin Lu. The Research Fund Project of Development Institute of Zhujiang-Xijiang Economic Zone, Key Research Base of Humanities and Social Sciences in Guangxi Universities (ZX2023051) awarded to Xiaochun Luo. Role of Funder: Chaoling Li plays a role in investigation, data curation and analysis, manuscript writing and revision. Lin Lu plays a role in study design, data collection and analysis, project management, publication decisions and review of manuscript. Xiaochun Luo plays a role in study design, data analysis, supervision and editing of manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

In today’s globalised and information-based business environment, there are many unstable factors, such as inter-regional conflicts and trade wars, public health emergencies and environmental and ecological pollution and other irresistible problems occur from time to time. Agriculture is the basic industry of national economy, and its stable and healthy development is of great significance [ 1 ]. In a complex world of interdependence and mutual influence, it is difficult to quickly restore the supply chain system to a normal state if any part of the system has problems. This will not only lead to large-scale disruption in the fresh agricultural products logistics supply chain, but may even paralyse the entire supply chain system, bringing huge economic losses to the participants of the entire supply chain. It can be seen that the resilience of fresh agricultural products supply chain has not only become one of the indispensable key factors for the normal production of people as well as the success of enterprises or the realisation of national strategic deployment. Due to the special characteristics of fresh agricultural products, such as easy to wear and tear, regionality, and strong product variability, fresh agricultural products have been deteriorating over time in terms of nutritional value, weight, flavour, and colour [ 2 ]. This makes its supply chain system have higher integrity and complexity compared with the supply chain system of other commodities, and the resistance and adaptability to exogenous shocks is weaker. In order to cope with this challenge, it is of great practical significance to conduct resilience evaluation research on fresh produce supply chain. Resilience evaluation refers to the comprehensive assessment of the structure, function and performance of the supply chain system, analysing its adaptability and stability under the changes of the external environment, so as to provide a scientific basis for supply chain management.

For the fresh agricultural products supply chain resilience enhancement strategy can be evaluated and explored from the following three main aspects. First, the resilience of the supply chain structure, fresh agricultural products supply chain structure including production, processing, storage, transport, sales and other links [ 3 ], need to ensure that in the face of market fluctuations, policy adjustments, natural disasters and other uncertainties, the links can be flexibly adapted to ensure the stable operation of the overall supply chain. Secondly, the resilience of supply chain function, fresh agricultural products supply chain function mainly includes information transmission [ 4 ], resource allocation [ 5 ], risk management [ 6 ] etc., which needs to have the ability to respond and adjust quickly when facing various problems to ensure the efficient operation of the supply chain. Finally, the resilience aspect of supply chain performance, fresh agricultural products supply chain performance mainly includes on-time delivery rate, inventory turnover rate, cost control [ 7 – 9 ] etc., which needs to have the ability to maintain good performance when facing pressure to ensure the overall competitiveness of the supply chain.

By understanding the various aspects of fresh produce supply chain resilience, we can better understand the strengths and weaknesses of the fresh produce supply chain system, so as to formulate targeted improvement measures to improve the supply chain’s ability to resist risk and cope with uncertainty. At the same time, the resilience enhancement strategy can also provide us with a decision-making basis to help managers and enterprises make more reasonable and effective strategic choices when facing market changes. Based on the principle of topology, the article seeks to find the most enhancement strategies for the resilience enhancement strategy of fresh agricultural products supply chain through the transformation of primitive logic.

In conclusion, the research on the resilience enhancement strategy of fresh produce supply chain is of great practical significance. By improving the resilience of fresh agricultural products supply chain, it can strengthen the scale, standardisation and intensification of fresh agricultural products production, break the information barriers between urban and rural areas and logistics barriers, make the fresh agricultural products supply chain more precise and standardised, better promote the effective docking and cooperation between the supply and demand of fresh agricultural products, and promote the sustainable development of agriculture and fresh agricultural products economy. This paper takes fresh produce supply chain enterprise M as an example, evaluates its supply chain resilience system and constructs and optimises the problem model for the resilience indicators that do not meet the standard. In summary, the article identifies the following two research objectives

• (1) To construct the index system of fresh produce supply chain and clarify the relevant calculation methods, so as to provide support for effectively selecting the optimisation index and the most optimal fresh produce supply chain;

(2) Combined with the resilience evaluation indexes, the intelligent selection optimisation method of fresh produce supply chain is proposed through data mining and computation, which is free from the inherent stereotypes of strategy generation and realises the strategy generation of resilience optimisation of fresh produce supply chain.

This study makes three major contributions to the field of fresh produce supply chain: There are three major contributions to the field of supply chain research. We address a question that has been neglected and understudied in previous research: how to improve fresh produce supply chain resilience? Firstly, based on the past fresh agricultural products supply chain resilience system, this paper carries out scientific rationality innovation and construction, enriches the relevant theories and methods of fresh agricultural products supply chain research, determines the feasibility of the system, and provides new practical guidance and valuable insights for fresh agricultural products supply chain resilience research. Secondly, based on the deep reinforcement learning algorithm, this paper proposes a specific algorithm for optimising the fresh agricultural products supply chain. Finally, this paper optimises and restructures the supply chain toughness of fresh agricultural products based on the characteristics of fresh agricultural products supply chain based on the knowledge of extenics, and the innovation of toughness enhancement strategy improves the objectivity and effectiveness of the supply chain.

2. Literature review

2.1. fresh produce supply chain research status.

The supply chain is closely linked to a wide range of participants, such as producers, processors, wholesalers, retailers, consumers, governments and non-profit organisations. It covers multiple steps in the planting, breeding, harvesting, processing, storage, transport and marketing of fresh produce. Research on fresh produce supply chains generally began in the 1990s, with scholars focusing on exploring the challenges faced by agricultural logistics and marketing models, and actively seeking solutions and innovative approaches to promote their development. However, there is a relative lack of research on the resilience of fresh produce supply chains. Mari [ 10 ] argued that significant post-harvest losses occur in the fresh produce supply chain. Perishability is one of the main factors affecting the quality of fruits and vegetables. Xiao [ 11 ] considered the problem of perishability in the fresh produce supply chain that results in some products being unsaleable, analysed two business models: the pull model and the push model, and investigated the optimal decisions of the supply chain members. Porat [ 12 ] in order to cope with the problem of losses during the retailing and consumption period, made a study of the logistic and cold chain management, retailing packaging and technological innovations, encouraging post-harvest researchers to be more actively involved in logistics and food supply chain operations and conducting multidisciplinary research. Hughes [ 13 ] defined fresh produce supply chain in the direction of supply chain synergy and co-operation. The fresh produce supply chain is a network of business generated from its production to distribution process, that is, a collection of transactions occurring in the process of from farmers to clients. By combining the fresh produce The collection of resources in the supply chain forms a kind of chain network with transaction function, and the individuals in the chain can reach the partnership. In order to achieve an efficient fresh produce supply chain, the freshness and loss of fresh produce are guaranteed within a certain controllable range. Cai [ 14 ] pointed out that in order to guarantee the quality and quantity of the produce, the distributors need to consider the order, freshness work, and selling price, as well as the producer’s wholesale price, the cost of freshness, and the damage of transport. In addition, the producer needs to determine the wholesale price based on the distributor’s order quantity. Kamble [ 15 ] argued that with the growing importance of agricultural supply chains, agricultural supply chains are designed and operated more tightly regulated and closely monitored to adapt to new regulatory environments and consumer demands, and that further research and development of new models and methodologies are needed to meet the complexity and diversity of agricultural supply chains. Accorsi [ 16 ] considered the food supply chain as an ecosystem, and using a regional potato supply chain as an example, the study found interdependencies between infrastructure, production, distribution, and environmental resources. Besik [ 17 ] developed a model of an integrated, multilevel competitive agricultural supply chain network in which agribusinesses and processors compete to sell their differentiated products. Carstens [ 18 ] summarised the pathways through which fresh produce can be contaminated in the supply chain, both pre-harvest and post-harvest. Yu [ 19 ] investigated the impact of outsourcing patterns on supply chain decision-making and profitability by modelling the game of fresh produce supply chains, driven by the widespread use of outsourcing of cold-chain services. Omar [ 20 ] reviewed the crop-based agricultural production and distribution planning field and explored the main contributions in the field, exploring the applicable models for agricultural supply chains in different environments from the perspective of fresh and non-fresh agricultural products.

2.2. State of the art in supply chain resilience research

In the 2000s, the study of resilient supply chains gradually attracted extensive attention from scholars. With the continuous development and change of the global economy, the importance of supply chain management has become more and more prominent. Resilient supply chain as an effective means of coping with uncertainty and risk has received a great deal of attention from academics. Wieland [ 21 ] argues that resilience is not only related to the ability of the system to "bounce back" after an impeding event, but also the ability to adapt and transform, and that resilience of the supply chain is no longer understood as stability but as adaptation and transformation. Hosseini [ 22 ] introduced the concept of resilience, which refers to the ability to be resistant enough to withstand disruptions and recover quickly from them. Taghikhah [ 23 ] implemented an extension of the new concept of supply chains for agricultural products and modelled the adaptive behaviours of farmers, food processors, retailers, and customers. Gerken [ 24 ] synthesised how pest management ("IPM") strategies may help to improve the management of pests. IPM) strategies may help to improve the adaptive capacity and resilience of managing agricultural supply chains under climate change. Aslam [ 25 ] analysed the role of supply chain flexibility in the development of supply chain resilience. Aboah [ 26 ] identified flexibility, collaboration, adaptability and resourcefulness as key elements in assessing resilience at the level of the individual chain actors, and adaptability as a general level resilience assessment of the food system as a relevant element as it takes into account changes in the state of supply chain resilience Leat [ 27 ] explored the importance of the stability and sustainability of the pork supply chain, as one of the key industries in the region, for ensuring food security and promoting economic development. Anastasiadis [ 28 ] viewed the Greek wine supply chain as a systematic and holistic approach from the grapes to the shelf process; the product and the complex flow of information; wine supply chain and stakeholders and concluded that improving the resilience of the Greek wine industry remains an important issue. Feng [ 29 ] analysed the risk factors of the fresh grape supply chain from the perspective of sustainable development and assessed the risk level using an optimised BP neural network to provide recommendations for constructing a fresh grape supply chain with controllable risk and sustainable development. Marusak [ 30 ] explores how regionalised food supply chains can increase the resilience of the US food supply system, using logistics best practices to provide efficient and reliable distribution to consumers under normal conditions and during disasters in response to large-scale public problems like the COVID-19 pandemic. Boyaci- Gunduz [ 31 ] discusses panic buying observed during the crisis, food shortages and price spikes, and a review of food security and sustainability emphasises the importance of supply chain resilience. However, during development, we face a number of key risks and challenges such as climate impacts, fluctuating market demand, and transport depletion. To cope with these risks, we need to build a resilient fresh produce supply chain to ensure stable operations in the face of uncertainty. Wieland [ 32 ] used SEM to empirically investigate that communication and collaborative relationships have a positive impact on resilience, whereas integration does not have a significant effect. Scholten [ 33 ] found that information sharing, collaborative communication, co-created knowledge, and joint relational endeavours increase supply chain resilience by increasing visibility, speed, and flexibility. Ambulkar [ 34 ] found that firms with supply chain disruptions need to have the ability to reconfigure their resources or have a risk management resource infrastructure to foster resilience. Brandon-Jones [ 35 ] found that supply chain connectivity and information sharing resources lead to supply chain visibility capabilities to increase resilience.

2.3. Current status of research on extenics

Extenics [ 36 , 37 ] is a wide-ranging and original transversal discipline that encompasses mathematics, philosophy, and engineering. It provides some new possibilities as well as methods and ideas for solving specific paradoxical problems in a more multidimensional and high-dimensional perspective with formal models. Cai, Yang [ 38 ] and others summarised the Extenics in theory into three major categories: Basic-element theory, Extension set theory and Extension Logic. Based on the primitive expansion model, Li [ 39 ] argued that the characteristics of things are collected through information technology, and then systematically expanded many innovative thinking directions. Zhou [ 40 ] helped college students to adapt to their lives and conduct research by analysing Matter-element, Affair-element and Relation-element of the Basic-element theory. Ruo [ 41 ] applied the Basic-element theory and investigated intelligent knowledge representation based on this theory method, which studies the characteristics of things and their corresponding characteristic quantities as a whole, uses primitive elements to formally describe things, behaviours and relationships, and builds an extended model to represent the knowledge. Li [ 42 ] in order to solve the problems in the traditional social network analysis model, uses the theory of Basic-element to use multidimensional object elements to represent the characteristics and nodes of a complex social network, and then later on integrates them in an advantageous way.

2.4. Research gap

In the current research, most of the research on the resilience elements of the fresh produce supply chain is to analyse and solve the problem of the supply resilience of individual varieties of fresh produce, or to carry out a simple quantitative analysis of a few resilience indicators of specific enterprises, which are not sufficiently influential and applicable to the resilience enhancement of other companies, and the selection of resilience indicators and resilience enhancement strategies lack a certain degree of scientific and comprehensive methodology only through a single research methodology as an entry point. Only through a single research method as an entry point, the selection of resilience indicators and resilience enhancement strategies lack a certain degree of science and comprehensiveness, failing to effectively help companies from a professional perspective to solve the contradictions between the current goals and the resilience conditions of the supply chain in all aspects.

Accordingly, in the current agricultural environment, it is particularly important to find ways to explore and resolve the incompatibility of supply chain resilience in the production of agricultural products. With the development of globalisation and the diversification of consumer demands, fresh produce supply chains are facing more and more challenges, which put forward higher requirements for supply chain resilience, i.e., the supply chain is able to maintain its operation and efficiency in the face of pressures and shocks so as to ensure a stable supply of fresh produce. In order to construct an enterprise resilience enhancement strategy, the article constructs a resilience enhancement strategy generation method with both expertise and applicability, which not only starts from the perspective of professional knowledge to understand the operation mechanism of the supply chain and the challenges it faces, but also takes into account the applicability of the enhancement strategy, which is able to adapt to different supply chain environments and enterprise needs. It provides customised resilience enhancement strategies for enterprises to help them cope with various challenges and improve the resilience of the supply chain, and provides scientific and effective guidelines for resilience enhancement strategies for fresh produce supply chain enterprises.

3. Model establishment and solution

3.1. basic-element model.

3.2. Extension modelling of contradictory problems

For some relatively complex and difficult to use a basic-element clear description of things, extenics can be matter-element, affair-element and Relation-element combination into a composite element, the use of different descriptive characteristics of the basic-element more clearly to describe the establishment of understandable model.

3.3. Integrated evaluation model

3.3.1. classical domains, section domains and objects to be evaluated..

3.3.2. Dependent function.

3.3.3. Comprehensive dependent function.

3.4. Extension transformation and extension analysis

Extension transformation of extensible analysis mainly tries to apply purposeful and process-oriented extension transformation to the goal and condition basic-element or complex elements in the face of contradictory problems, but before expanding the transformation, it should also consider whether the goal and condition of the problem to be solved can be transformed, and if it can be transformed, then it can find feasible problem-solving channels by opening up a multi-dimensional perspective, which is a method of problem solving and decision making. extensible analysis is a method of problem solving and decision making. extensible analysis is based on the principles of divergence analysis, correlation network, implication system and expandability analysis, and it is used to explore the possibilities of various expandable transformations by replacing, adding, deleting, expanding, reducing, decomposing and copying basic-element or complex elements of the goals and conditions of the problem to be solved in the existence of contradictory problems.

By constructing the primitive model of the above expansion analysis method. We want to better find a feasible way to solve the problem. The basic transformations of extension transformations mainly imply five methods, which are substitution transformation T Γ = Γ′, Increase and decrease transformations TΓ 0 = Γ or T 1 Γ = Γ ⊙ Γ 1 , Addition and deletion transformations T Γ = αΓ, Subdivision transformation T Γ = {Γ 1 , Γ 2 , …, Γ n } which Γ 1 ⊕ Γ 2 ⊕…⊕Γ n = Γ, and replication transformation T Γ = {Γ, Γ*} In the basic extension transformation method, there exists the transformation T that changes Γ 0 into another object of the same kind Γ multiple objects Γ 1 , Γ 2 ,…, Γ n , and is called T as the extension transformation of the object Γ 0 .

4. Numerical calculation and analysis of results

4.1 establishment of fresh agricultural products supply chain resilience evaluation index system.

In order to establish a scientific and accurate supply chain resilience index system for fresh agricultural products, this paper selects and constructs a supply chain resilience evaluation index system for fresh agricultural products by reviewing relevant literature and other research results and combining the characteristics of the supply chain with the current situation of supply chain resilience of fresh agricultural products and its development potential, following the principles of scientificity, independence and measurability. The whole evaluation index system is divided into four levels: target level, guideline level, indicator level and indicator interpretation. The evaluation of fresh produce supply chain resilience is selected as the target layer, three dimensions of fresh produce resilience, capital resilience and internal resilience are selected as the criterion layer from the whole life cycle of production and operation of fresh produce supply chain, and 13 evaluation indicators are selected as the indicator layer. The evaluation index system for the resilience of the fresh product supply chain is shown in Table 1 .

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https://doi.org/10.1371/journal.pone.0309008.t001

4.2. Calculation of weights for indicators of supply chain resilience for fresh produce

Table 2 below specifies the comparison of quantitative values between indicators.

https://doi.org/10.1371/journal.pone.0309008.t002

Construct the judgment matrix to facilitate the two-by-two comparison of indicators, and then use the expert scoring method to assess the relative importance of each indicator, through the expert scoring will be the indicator two-by-two comparison of the value of the final can be calculated at all levels of indicators of the weight value of ω and the judgment matrix of the largest characteristic root of the λmax, and consistency test, when the CR< 0.1 the judgment matrix to meet the consistency. Calculating the weights of the indicators of the fresh produce supply chain lays the foundation for the subsequent calculation of the correlation function and the evaluation of the indicator intervals.

In this paper, a panel of five professionals engaged in as well as having rich theoretical experience in the supply of fresh produce was formed to make comparisons between the indicators through expert scoring, and to make a more scientific guide to the subsequent scoring of the indicators, and the information of the experts is shown in Table 3 .

https://doi.org/10.1371/journal.pone.0309008.t003

4.2.1. Calculate the weight of the first level indicators.

The judgment matrix for the first level indicators of fresh product resilience B1, financial resilience B2, and internal resilience B3 is shown in Table 4 .

https://doi.org/10.1371/journal.pone.0309008.t004

4.2.2. Calculate the weights of secondary indicators.

Construct the weight determination matrix of the secondary indicator system of fresh produce resilience B 1 , product quality resilience C 1 , product supply type C 2 , product being demanded C 3 , product supply efficiency C 4 , product supply time C 5 . The calculation results are shown in Table 5 .

https://doi.org/10.1371/journal.pone.0309008.t005

Construct the weight determination matrix for the secondary indicator system of financial resilience B2, financing capacity C 6 , nodal transport costs C 7 , and maintenance costs C 8 . The calculation results are shown in Table 6 .

https://doi.org/10.1371/journal.pone.0309008.t006

Construct the weight determination matrix for the secondary indicator system of internal resilience B 3 , human resources security C 9 , procurement security C 10 , production and processing equipment C 11 , logistics security C 12 , and information flow C 13 . The calculation results are shown in Table 7 .

https://doi.org/10.1371/journal.pone.0309008.t007

According to the results of the calculation of the indicators of each indicator layer, the comprehensive weights of the evaluation indicators of the resilience of the supply chain of the production of agricultural products are obtained as shown in Table 8 .

https://doi.org/10.1371/journal.pone.0309008.t008

4.3. Comprehensive evaluation of extenics

4.3.1. classical and sectional domains..

The classical extension domain divides the evaluation of fresh produce supply chain resilience indicators into 5 quantitative value intervals, when an indicator C i is in the interval [0, 1], it means the indicator is very poor, in the interval [1, 2], it means the indicator is poor, in the interval [2, 3], it means the indicator is ordinary, in the interval [3, 4], it means the indicator is good, and in the interval [4, 5], it means the indicator is excellent. The [0, 5] of the section field represents the range of values that the indicator can take.

4.3.2. Construct matter-element to be evaluated.

Analysing the value of the comprehensive evaluation dependent function for more levels of enterprise M, it is concluded that regarding the evaluation interval evaluation level e = 4, the value of the comprehensive dependent function at this time is positive, indicating that the resilience evaluation result of this fresh produce supply chain enterprise M is good. Regarding the evaluation level e = 5, only two resilience evaluation indexes are positive, indicating that only two indexes are in the excellent state. Through the overall analysis, although the comprehensive tenacity score of this enterprise has reached the good interval, many indexes still have a lot of room for improvement to reach the excellent interval.

4.4. Modelling incompatible problems of fresh agricultural products

4.4.1. constructing the incompatible problem model..

From the analysis of the dependent function of the indicators of each evaluation level, in the resilience evaluation results of enterprise M, the product supply type C2, product supply efficiency C4 and logistics security C12 in the evaluation level e = 3 when the dependent function value is positive, which indicates that these evaluation indicators scores are located in the ordinary interval, the evaluation object of enterprise M needs to optimise the indicators, so as to arrive at the enterprise’s resilience of the overall score interval, and then strive to punch the excellent. Then strive to punch excellent.

Product quality resilience C1, product demand C3, product supply time C5, financing capacity C6, maintenance fee C8, human resources security C9. These resilience indicators have a positive dependent function value at evaluation level e = 4, which indicates that the scores of these evaluation indicators are in the good range, proving that these indicators have a lot of potential for development, and they are the reserve force of the excellent indicators, which can be used for the transformation of enterprise M into a high resilience fresh produce supply chain enterprise. high resilience fresh produce supply chain enterprise. Production and processing equipment C11 has a dependent function value of 0 at evaluation levels e = 3 and e = 4, which indicates that the indicator is at the boundary of the two evaluation levels, and there is still room for improvement of the indicator through the analysis of the specific score of the indicator.

The goal is to improve the supply chain resilience of fresh produce firm M. When the goal is constant, we make the incompatible problem dissolve by changing the conditions of the problem.

4.4.2. Establishment of core problem.

According to the dependent function value of supply chain resilience evaluation and each index score for the selection of indicators to carry out the establishment of the nuclear problem, this paper selects the lower score has a lot of room for improvement of a few indicators, the product supply type C 2 , the product is demanded C 3 , the product supply efficiency C 4 , human resources security C 9 , the production and processing equipment C 11 , the logistics security C 12 is in the ordinary or good grade range, some of these indicators still have a lot of room for improvement, enterprise M wants to do supply chain resilience improvement, it is necessary to optimise the indicators. Some of these indicators still have a lot of room for improvement, and Enterprise M needs to optimise the indicators if it wants to achieve supply chain resilience.

In order to achieve the goal of the enterprise M resilience to improve the nuclear problem, we need to combine the reality of the enterprise and the weight of the indicator table to divide the classical domain of each condition base element, the expert group to analyse and formulate the goal of the indicator to improve the classical domain of the target interval, which are divided into the nuclear problem of the condition base element of the target classical domain of the target interval: the type of supply of the target classical domain of the product interval for the (3.5, 4), the product is demanded for the target classical domain of the interval for the (3.5, 4.2), product supply efficiency objective classical domain interval is (3.2, 4), human resources security objective classical domain interval is (3.6, 4.3), production and processing equipment (3.5, 4.5), logistics security objective classical domain interval is (3.2, 4).

The compatibility function of the present indicator scores with the classical domain of their respective targets can be obtained from the above equations as: k(x 1 ) = -1.1, k(x 2 ) = -0.5714, k(x 3 ) = -1, k(x 4 ) = -0.6429, k(x 5 ) = -0.5, k(x 6 ) = -1.425.

The total compatibility function of the core problem at this point k ( p ) < 0, we need to perform extension and subdivision transformations of the conditional basic-elements of the core problem to solve the incompatibility problem at this point.

4.5. Extension and subdivision transformations

In order to enhance the M resilience of fresh produce supply chain enterprises to solve the incompatible problem, firstly, the extensible analysis method is used to analyse the complex relationship network of conditional basic-elements in a scalable way, so as to construct the correlation network of conditional basic-elements, and then, the "leaves" on the branch trunks in the correlation network are analysed by dispersion analysis, and the dispersed basic-elements are subjected to scalable transformations, so that the basic-elements of the strategies that can enhance the resilience of the supply chain can be generated eventually.

By searching the database of supply chain resilience elements, correlation analysis was conducted to find out the relevant elements that have a positive influence on each condition element, and to find out the types of product supply l 01 related to product development l 011 and market information collection l 012 .

In this case, if there exists the dependent function k ( v ( T 01 l 01 )) ≥ 0 of the conditional basic-elements after the extension transformation, then it means that the transformation is feasible.

In this case, if there exists the dependent function k ( v ( T 02 l 02 )) ≥ 0 of the conditional basic-elements after the extension transformation, then it means that the transformation is feasible.

In this case, if there exists the dependent function k ( v ( T 03 l 03 )) ≥ 0 of the conditional basic-elements after the extension transformation, then it means that the transformation is feasible.

ln this case, if there exists the dependent function k ( v ( T 05 l 05 )) ≥ 0 of the conditional basic-elements after the extension transformation, then it means that the transformation is feasible.

ln this case, if there exists the dependent function k ( v ( T 06 l 06 )) ≥ 0 of the conditional basic-elements after the extension transformation, then it means that the transformation is feasible.

4.6. Superiority evaluation constitutes the optimal resilience enhancement strategy

By solving the problem of resilience incompatibility of fresh produce supply chain enterprise M and forming a new set of resilience enhancement strategies, and finally combining the calculation and screening of relevant professional knowledge to form a new resilience enhancement strategy for fresh produce supply chain. The resilience enhancement strategy solves the six contradictory problems of insufficient richness of product supply types, lack of product demand, low product supply efficiency, low human resources, weak production and processing equipment, and poor logistics protection ability in six aspects of fresh agricultural products supply chain enterprises.

Enrichment of product supply variety. The increase in variety contributes to the strengthening of supply chain resilience, and the increase in product variety also expands the business and market area, which increases the consumer experience and improves customer satisfaction when meeting different consumer needs. In order to occupy more market share to achieve the goal of profitability, the diversification of product categories in the face of fluctuating market environment, can effectively reduce the risk, to protect the supply chain’s continued stability. Therefore, enterprises can attract funds from more sources to support product research and development activities, and the collection of information on product categories should also obtain data from comprehensive and accurate information platforms to assist in the enrichment of product categories.

Guarantee the supply of products that are in demand. Can guarantee the stability of each link in the supply chain, on-time delivery of the required products to help reduce inventory costs, reduce the excess inventory crisis, and effectively improve the operation of funds, product supply and demand security requirements of high standards of co-operation to promote the development of enterprises to the high resilience of the system to enhance the resilience of the supply of products and reliability, and can be timely to the supply chain to make timely adjustments to the problems that arise in one of the links to reduce the interruption of supply chain and the resulting production It can make timely adjustments to problems in one part of the supply chain and reduce production stagnation and order delays caused by supply chain disruption. Long-term stable product supply also helps to improve the enthusiasm and loyalty of suppliers, and provide better service and support for enterprises. The product supply chain can be transformed from sales strategy and inventory management. Sales strategy, as one of the core elements of product supply, directly affects the operational efficiency and profitability of the enterprise. Adjustment of sales strategy will cause changes in market demand, which in turn affects the ability to supply products. And an effective inventory management system can ensure the stability of product supply, meet customer demand, reduce inventory costs and improve the competitiveness of enterprises.

Improve the efficiency of product supply chain. For the customer side, the improvement of efficiency can make the product faster to the hands of consumers, and enhance consumer satisfaction and trust. On the supply chain side, it also helps to improve competitiveness. Under the environment of global marketisation, efficient product supply can better meet the rapid market demand and flexibly adjust the supply chain links. Therefore, the optimisation of transport and order management processes is an important factor affecting the efficiency of the supply chain. The use of advanced logistics technology to improve the loading rate of transport means can effectively shorten the transport time and increase the number of transports, which can well enhance the transport efficiency. Secondly, the perfect optimisation of the order guarantee can understand the change of customer’s demand in time, and improve the accuracy of the order and the timeliness of the product delivery.

Optimise personnel training mode. Through the scientific, reasonable and systematic training of managers and employees in supply chain enterprises, employees can not only master more professional knowledge and skills, but also enhance teamwork ability and improve work enthusiasm, thus promoting the overall optimisation and upgrading of supply chain resilience. Therefore, personnel training is an important means to enhance the supply chain resilience, but also a key factor in the sustainable development of enterprises.

Optimise production and processing equipment. Improvement of production and processing equipment can reduce production costs, improve the efficiency and flexibility of the production line, reduce carbon emissions in the production and processing process and pollution of the environment, but also to ensure the safety of the production and processing process, to reduce the cost of enterprise consumption and to ensure the stability of its operations and competitiveness, but also to protect the safety and health of employees, to reduce the production of hidden safety hazards and the risk of delays caused by insufficient productivity, and to greatly enhance the resilience of the supply chain. Greatly enhance the resilience of the supply chain.

Enhance the ability of logistics security. The choice of logistics distribution channels affects the transport efficiency and transport costs, the attributes of different products should choose different and effective channels, the appropriate distribution channels can minimise the waste of time and cost, improve the efficiency of logistics distribution, secondly, the level of professional skills of the distribution staff also affects the quality of logistics distribution, professional and efficient distribution staff will pay attention to the nature of the distribution products and product protection, and have a better communication skills and service consciousness. Better communication skills and service consciousness, can communicate effectively with customers, timely solution to deal with unexpected problems. Therefore, enterprises in the logistics distribution should pay attention to channel selection and personnel training, and constantly improve their own logistics management capabilities.

5. Conclusions

Nowadays, facing the problem of unstable factors such as international society and ecological and climatic environments, fresh agricultural products are essential food sources in people’s daily life and account for an important part of the national economy, market consumers’ awareness of the stability and sustainability of the quality of the supply of fresh agricultural products has been gradually strengthened, and the national policy has also given important instructions on the various aspects of the supply chain of fresh agricultural products, therefore, the resilience of the fresh agricultural products supply chain has become the main goal of supply chain development. Therefore, the resilience of fresh produce supply chain has become the main goal of supply chain development. However, when improving the resilience, we should consider the resources, structure, environment and other complex elements of the supply chain, evaluate the resilience state of the supply chain from the scientific, efficient, multi-dimensional and quantitative perspectives, and implement optimisation strategies for the resilience problems. In this paper, with the help of the theoretical method of topology, through the data calculation and analysis of the evaluation of supply chain resilience, the index of low resilience is expanded and analysed with extension transformations, and the method of generating extension strategies for the conditioned basic-elements of the nuclear problem is constructed.

5.1. Impact

According to the characteristics and importance of the fresh produce supply chain, this paper selects the three facets of fresh produce resilience, capital resilience and internal resilience to construct the enterprise resilience evaluation system, and through the calculation of the index weights and correlation function values, it applies quantitative analysis to objectively and efficiently evaluate the resilience state of the fresh produce supply chain enterprises, and explores the incompatibility of the enterprise’s resilience enhancement goal with the contradiction of the existing conditions.

In order to reach a topical solution to the incompatible problem of improving the resilience of fresh produce supply chain, this paper expands the analyses and topical transformations of the existing condition basic-elements, which gives more inspiration to the enterprise M to solve the incompatible problem of the current resilience condition basic-elements.

The practical significance of this study is to give fresh produce supply chain enterprises a more multi-dimensional perspective to analyse and solve the deficiencies in the supply chain operation process, to better cope with the complexity and uncertainty brought by the supply chain, and to guarantee the national strategic deployment and stable social development with efficient and transparent supply chain resilience.

5.2. Limitations and future prospects

Although this paper generates resilience enhancement strategies for fresh produce supply chain enterprises from a multidimensional and scientific perspective, there are still some shortcomings, this study is subject to subjective conditions, it is difficult to construct a resilience indicator system with a comprehensive, scientific and objective system in terms of indicator selection, and in the generation of resilience enhancement strategies, the basic-elements are subjective and easy to formulate unclear, expanding the analysis of the condition of basic-elements and expanding the transformations. The generation efficiency is not high, and many conditional basic-elements that cannot solve the compatibility problem will be generated.

In summary, in the future, in the construction of supply chain resilience index system of fresh agricultural products, the artificial intelligence recommendation system can be used to screen and identify the indexes with the advantages of objectivity and high efficiency, and optimize the process of expanding analysis and extension transformation, so that the intelligent system can screen the more relevant and influential basic-elements, and improve the effectiveness and scientificity of the resilience strategy generation.

Supporting information

https://doi.org/10.1371/journal.pone.0309008.s001

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Blockchain and supply chain finance: a critical literature review at the intersection of operations, finance and law

Ilias ioannou.

1 Centre for Commercial Law Studies, Queen Mary University of London, 67-69 Lincoln’s Inn Fields, London, WC2A 3JB UK

Guven Demirel

2 School of Business and Management, Queen Mary University of London, The Bancroft Building, Mile End Road, London, E1 4NS UK

Associated Data

Not applicable.

In the current environment, where the Covid-19 pandemic has exposed the vulnerabilities of the incumbent paper-based trade and supply chain finance systems, digital transformation pledges to alleviate the friction on international trade. Here, we provide a timely review of state-of-the-art industry applications and theoretical perspectives on the use of blockchain as the medium toward digitalisation for supply chain finance systems. We argue that blockchain technology has an innovation promoting role in supply chain finance solutions through reducing inefficiencies and increasing visibility between different parties, which have hitherto constituted the main challenges in this sphere. Based on a review of the academic literature as well as an analysis of the industrial solutions that have emerged, we identify and discuss the financial, operational and legal challenges encountered in supply chain financing and the promise of blockchain to address these limitations. We discuss the bottlenecks as well as the benefits of blockchain and identify some necessary conditions required for the emergence of blockchain-enabled trade and supply chain financing, such as the establishment of co-opetition among supply chain actors, integration with IoT systems for data quality, and reform of regulatory and legal frameworks. We conclude by identifying promising research directions about the implementation process, inviting further research into the transformation of business models toward a more collaborative nature.

Introduction

An important but still relatively undervalued use case of blockchain technology is Supply Chain Finance (SCF). Up to 80 % of international trade transactions require trade and SCF to provide liquidity and risk mitigation [ 42 ]. The financing of trade transactions was estimated by the European Commission to be worth USD 10 trillion in 2017 alone [ 98 ]. It includes both various methods for the discharge of the payment obligation as well as techniques and practices for the optimisation of the working capital invested in supply chain transactions, such as receivables purchase techniques or accounts payable-centric finance. However, the ingrained reliance of trade and supply chain financing on paper-based documentation has driven up costs and caused inefficiencies. Fragmented processes, discordance of regulations, and the increased risk of fraud contribute together to a USD 1.5 trillion supply-demand gap in the financing of trade [ 2 ], which, if left unresolved, is expected to exceed USD 2.4 trillion by 2025 [ 155 ].

While SCF is difficult to obtain for many stakeholders in an ordinary business environment, the ongoing pandemic and global recession magnify the existing pain points and barriers in SCF and pose new ones of unprecedented scale [ 73 ]. Most of the problems being faced today originate from the paper medium used in SCF and relate to the delivery and the handling of physical documents, the lack of staff, the inability to print, and business closures due to lockdown restrictions [ 96 , 108 ]. Moreover, the necessity of validating the originality of documents and the legal matters that emanate from jurisdictions requiring wet-ink signed payment obligations and transport documents have challenged the industry’s capacity to deal with this unrivalled disruption on a global scale [ 73 ]. The existing gap in the financing of trade, which according to the International Financing Corporation of the World Bank Group is now anticipated to exceed USD 4 trillion [ 74 , 121 , 139 ], is set to double.

In essence, SCF techniques aim to reliably establish the creditworthiness of the buyer of goods and approve that the sellers of goods have manufactured and shipped them [ 9 ]. The past five years have witnessed a proliferation of research, initiatives and discussions regarding blockchain as the medium toward digitalisation of the supply chain [ 85 – 87 , 120 ]. Significant advancements have been made and the obstacles are gradually being removed, improving the efficacy of information flow in the supply chain and increasing the flexibility of the financial supply chain [ 17 , 33 , 48 ], both of which run alongside the physical supply chain [ 157 ]. The aim of this review is to complement the literature’s interest in the usability of blockchain in international trade and to identify the main drivers and challenges of digital transformation within the trade and supply chain finance industry.

In this context, there are several reasons for undertaking a critical literature review on the interface of blockchain and supply chain finance. First, the industry has expressed a keen interest in adopting new technologies and SCF is well oriented in innovative financing solutions. Second, the growing body of academic literature [ 23 ] and the emerging range of supply chain financing systems deserve a review, which will illuminate the benefits and the limitations of blockchain SCF procedures. Third, whilst previous research focuses either on blockchain implementation in supply chain operations [ 15 , 160 ] or on analysing supply chain financing solutions [ 10 , 54 , 158 ], this is the first specialised review combining the literatures on both blockchain and SCF, and it uncovers knowledge from companies that are pioneering blockchain in their SCF products.

We focus on three research questions in providing an overview of the research on blockchain technology in SCF:

The remainder of this paper is organised as follows. Section 2 summarises the basic concepts related to SCF and blockchain technology, providing an account of the various SCF techniques as well as an introduction to blockchain foundations. The findings of our analysis of the literature are provided in Sect. 3 , revealing the state-of-the-art developments in both theory and practice. To that end, Sect. 4 discusses the insights the literature offers for the barriers and pain-points of SCF systems, the ways blockchain can alleviate these, and the implementation challenges for the adoption of blockchain-based SCF systems. This section goes on to identify promising research directions using a cross-disciplinary perspective, and it concludes by presenting the limitations of this study. The review concludes with a summary of the main contributions in Sect. 5 .

Supply chain finance

SCF is a micro-finance concept defined as the use of financial instruments, practices, and technologies for optimising the management of the working capital and liquidity tied up in supply chain processes between collaborating business partners [ 19 ]. According to Xu et al. [ 158 ] and Ali et al. [ 4 ], the term was introduced by Stemmler [ 131 ], who explained that SCF constitutes an essential part of supply chain management (SCM) and aims to integrate finance with the supply chain operations. The appeal of SCF is to mitigate the payment and performance risks and to concurrently offer to the supplier accelerated receivables and to the buyer protracted credit [ 23 , 54 ]. It is distinct from trade finance, which is an overarching term describing the financing of trade in general [ 141 ] and which is traditionally associated with financing techniques governed by rules published by the International Chamber of Commerce (ICC), such as the Uniform Rules for Collections (URC 522) for Documentary Collections, Uniform Rules for Demand Guarantees (URDG 758) for Guarantees, and the Uniform Customs and Practice for Documentary Credits (UCP 600) for Letters of Credit (L/Cs) [ 62 ].

While the financial supply chain usually refers to the discharge of the payment obligation by the buyer upon receipt of evidence of contractual performance by the seller [ 57 ], SCF is a more complex notion and scholars have taken a range of different approaches. According to Hofmann [ 67 ], SCF is an approach of two or more organisations ‘to jointly create value through means of planning, steering and controlling the flow of financial resources on an inter-organisational level’. Similarly, Pfohl and Gomm [ 111 ], define SCF at the inter-company level as the ‘integration of financing processes to increase the value of all participating companies’. A comprehensive literature review dealing with the various definitions of SCF, and its specific solutions, is provided by Gelsomino et al. [ 54 ], who identified two major perspectives: financial-oriented, which refers to short-term receivables and payables SCF solutions provided by financial institutions, and supply chain-oriented perspective, which extends SCF scope to include the capitalisation of inventories and financing provided by non-banks [ 22 , 54 ]. While earlier reviews [ 54 , 158 ] cover papers from 2000 to 2016, this paper focuses on current developments in the field, specifically blockchain-enabled solutions.

SCF solutions are designed to increase the visibility and the availability of cash and reduce its cost for all supply chain partners [ 58 , 60 ] with a view to optimise the management of financial flows at the supply chain level [ 55 ]. Some scholars focus more on the central role that banks play in SCF [ 31 , 93 , 157 ], defining SCF as the set of products that a financial institution offers to facilitate the management of the material and information flows in a supply chain [ 21 ]. Others consider technology an essential component in the SCF scheme, describing it as financial services solutions stemming from technology service providers [ 36 , 89 ]. In the operations management literature, SCF solutions have been classified with respect to the party that provides the financing, i.e. trade credit, buyer finance and inter-mediated finance [ 10 , 34 , 135 ]. All the aforementioned elements are summarised in a definition suggested by the Global Supply Chain Finance Forum (GSCFF), which describes SCF as ‘the use of financing and risk mitigation practices and techniques to optimise the management of working capital and liquidity invested in supply chain processes and transactions’ [ 56 ]. This article will hereinafter build upon this definition and use the relevant terminology suggested by the GSCFF, which applies irrespective of the role or the existence of an intermediary and the specific enabling technology.

At a basic level, SCF consists of receivables purchases (receivables discounting, forfaiting, factoring and receivables securitisation), payables finance (dynamic discounting, reverse factoring and reverse securitisation) and borrowing using trade credit/accounts receivables as collateral (loan or advance against receivables, distributor finance, inventory finance and pre-shipment finance) [ 31 ]. In Table ​ Table1, 1 , we provide the most commonly used definitions and the synonyms of the SCF techniques based on the classification recommended in Global Supply Chain Finance Forum [ 56 ].

Categorisation of supply chain finance solutions

Despite the variations among these mechanisms, a common feature of all SCF techniques is their need to access and process trustworthy trade data [ 57 , 92 ]. This is because SCF is an event-driven financing solution in that each intervention in the financial chain is ‘triggered’ by an event in the physical chain [ 4 , 165 ]. For example, receivables purchase techniques require access to reliable trade documentation which can verify the receivables, such as invoices or e-invoices [ 57 ]. Similarly, loan or advance-based techniques require access to data that can evidence the expectation of repayment such as, purchase order confirmations, transport documentation and warehouse receipts, while the trigger event in payables financing solutions is usually proved with the approval of the invoice from the buyer [ 69 ]. The coupling of information and material flows enables financers to reduce both the financial and operational risks within the supply chain and mitigate the credit risk [ 10 , 92 ], thereby enabling capital-constrained firms to access capital sooner and at lower rates [ 31 , 93 ]. This work investigates how the adoption of blockchain technology increases visibility into reliable trade data and allows businesses to form partnerships and accelerate cash flows throughout the financial supply chain.

Foundations of blockchain technology

Blockchain is a digital distributed ledger of time-stamped series of data records that is stored on a cluster of computers where no single entity has control, and the information is visible to all parties [ 52 , 137 ]. Transactions are broadcasted to the network and the full-node participants validate them directly through the operation of a consensus mechanism [ 7 ]. The full-node participants or miners validate whether there is a successful delivery from the sender to the recipient and examine the veracity of the signed acknowledgements provided by the intermediate nodes [ 63 ]. An encryption method secures data against unauthorised interference to ensure censor-resistance and to safeguard sensitive information [ 41 ]. A key aspect of blockchain is its anti-double spending feature, which ensures that a person transferring an asset in the form of unspent transaction outputs/inputs [ 7 ] or in the form of a balance within an account [ 8 ] cannot transfer the same asset more than once [ 137 ].

Blockchains are classified as permission-less (‘public’) and permissioned, in alignment with the extent to which nodes may be involved in the consensus process [ 52 , 164 ]. In a permission-less blockchain, such as Bitcoin or Ethereum, anyone can run as a pseudonymous full node, make contribution, and receive awards pursuant to the corresponding rules. Permissioned blockchains can be further categorised into private and consortium-based blockchains. Simply put, consortium Blockchains, such as the Hyperledger project, have a governance structure and consensus procedures controlled by pre-set nodes in the system [ 20 ]. In private blockchains, which can be built on Hyperledger Fabric [ 6 ] or Corda [ 64 ], for example, access is controlled by a single organisation [ 137 ]. A comparison of key features among different types of blockchain is provided in Chang et al. [ 25 ] and Tasca and Tessone [ 137 ], who argue that the extent of decentralisation is weaker in permissioned blockchains, but the speed of transaction validation is faster [ 146 ]. It is noted that an extensive discussion regarding the differences and the similarities between different blockchains of the same class/type regarding their appropriateness for SCF techniques is, to the best of our knowledge, absent from the literature.

From a technical perspective, blockchain comprises a decentralised data infrastructure employing a cryptographic hash function [ 45 ]. It can be considered as an infrastructure layer that runs on top of the internet and which is suitable for recording, tracing, monitoring, and transacting all type of assets on a global scale [ 149 ]. The first blockchain application was a data protocol for keeping the chronological records of Bitcoin transactions [ 105 ]. Since then, blockchain technology has been hailed as an ingenious innovation with countless possibilities for applications in numerous areas [ 41 , 136 ]. In this regard, the digitisation of documents and the tokenisation of assets into the blockchain can help dismantle financing barriers and pain points in international trade transactions. In the next sections we will examine how blockchain can address existing inefficiencies in trade and supply chain finance processes based on a detailed review of the extant literature.

Contribution to the literature

Blockchain technology is a significant high-tech breakthrough that may revolutionise SCF. This paper is one of a few works that endeavour to illuminate the positive disruption caused by blockchain for trade and supply chain finance processes. The review examines the existing research on the subject matter and highlights the identified gaps in the literature. It proposes a re-examination of the subject matter through the prism of foundational concepts and results from supply chain management (SCM), economics, legal analysis and platform theory. The provided practical and theoretical insights can be conducive to reflection by SCF practitioners and serve as a base for future academic studies on blockchain adoption in SCF.

Current developments in blockchain supply chain finance

This section presents the scientific publications identified through the research protocol outlined in Appendix  1 and the state-of-the-art business developments. Some common themes observed in the literature and in practice are summarised in this section. The areas in which blockchain provides most value to SCF will be explored in the next section.

Academic literature

Although blockchain is still in its nascence, its capacity for trade and supply chain finance has already been acknowledged in the academic literature, where related value-added activities are being mapped and several implementation systems have been proposed. Bogucharskov et al. [ 17 ] have proposed a blockchain prototype of a documentary Letter of Credit (L/C). Similarly, Chang et al. [ 26 ] and Tsiulin et al. [ 144 ] discuss modern blockchain-supported L/C services built on a consortium blockchain, while Chang et al. [ 25 ] recommend the re-engineering of L/Cs via smart contracts, which is argued to improve the performance of the payment process and enhance the overall supply chain efficiency.

Chen et al. [ 30 ] leverage blockchain, alongside systems and technologies such as cloud computing and the Internet of Things (IoT), to establish an integrated SCF platform running as-a-service for the automotive retail industry. The platform, called Blockchain auto SCF, provides equal visibility on transactions and collateral custody information to interested parties and collaborates with financial institutions to supply inventory financing and purchase order financing [ 30 ]. Yu et al. [ 162 ] move beyond the performance analysis of operations under the existing SCF techniques and propose a new model for SCF that enables a platform-based financer to offer the best SCF solutions under different conditions and to optimise service fees and price setting based on the client’s opportunity cost rate for self-guarantee. This is achieved by leveraging reliable information stored in a blockchain that demonstrate to the financer, based on the customer’s operational information, the sufficiency of the credit or assets. The proposed model also enables the customer to mortgage its assets, which can range from raw materials to finished products, and transfer these assets to the financer in case of default, all happening in an integrated manner on the blockchain [ 162 ].

In their analysis, Omran et al. [ 107 ] describe the use cases of blockchain for reverse factoring and dynamic discounting. Reverse factoring can be optimised because blockchain enables invoice status information to be transferred securely, allowing financiers to offer high-frequency financing services for any transaction value at lower risk [ 107 ]. In conjunction with smart contracts, blockchain can improve the access to reliable real-time information and automate decision-making through the integration of financial and informational flows in supply chains [ 93 , 157 ]. That way, the risk premium of an early payment financing proposal can be continuously adjusted at each step of the material flow [ 107 ]. Hofmann et al. [ 69 ] discuss applications in various buyer-led SCF techniques and examine a new solution that implements blockchain-based reverse-securitisation. Specifically, they propose the issuing and post-trade clearing and settlement processing of the asset-backed securities that require various intermediaries, data reconciliations and manual intervention to be issued directly into the blockchain as digital assets, thereby switching the ultimate record of ownership from central depositories and custodians onto a blockchain. By doing so they expound an effective and instantaneous clearing and settlement mechanism leading to lower financing costs [ 69 ]. Moreover, Li et al. [ 94 ] introduce a blockchain use-case in logistics finance to tackle financing shortages for SME retailers. They propose a blockchain-enabled logistics finance execution platform, whereby retailers, suppliers, commercial institution financers and third-party logistics providers can arrange inventory financing by leveraging dynamic pledge of warehouse operations [ 94 ]. Du et al. [ 45 ] integrate the characteristics of blockchain to solve the problem of non-trust and information asymmetry among the participants in the supply chain and present a solution for warehouse receipts financing through a service platform, which has already been active for a year and has served more than 500 companies in China with an accumulated transaction value of USD 1.2 billion.

The benefits of blockchain in eliminating or reducing information asymmetry have recently been analysed using game theory in Chod et al. [ 35 ] and Lee et al. [ 91 ]. Based on a signalling game between a buyer and a bank, Chod et al. [ 35 ] show that signalling operational quality through larger purchase order quantities leads to less disruptions than cash signalling in the form of inflated loan requests. Inventory signalling requires the bank to verify supply chain transactions, which calls for the use of blockchain. Accordingly, Chod et al. [ 35 ] introduce a Bitcoin-based low-cost transaction verification protocol that maintains privacy. The study postulates that a high-type buyer is more likely to adopt blockchain if its reliability increases, if the product has no salvage value, e.g. highly customised or perishable, if its market size increases, and if the verification costs are lower. Focusing on transaction costs, Choi [ 36 ] shows that blockchain-based transactions in a newsvendor setting lead to higher profit than a bank-mediated trade, if the blockchain transaction costs are sufficiently lower than the bank charges. Lee et al. [ 91 ] compare dynamic interest rates with uniform interest rates in an abstract multi-stage trade finance setting where the bank may benefit from blockchain by reducing the information asymmetry or improving the efficiency of information flows. When there are long delays in collecting reliable information, the blockchain is required for the dynamic interest rates to be rewarding [ 91 ]. The academic studies on blockchain SCF are summarised in Table ​ Table2 2 .

List of articles identified through the research protocol

Industrial projects and initiatives

The use of blockchain for SCF is being explored by incumbent market leaders as well as start-up companies. Many proof-of-concepts, piloting, or entering production schemes have been developed in the last five years. The purpose of this section is to analyse these newly emerging blockchain projects in trade and supply chain finance and to identify how they enhance existing processes. Table ​ Table3 3 presents a list of popular blockchain-enabled SCF initiatives identified through a practical case-based research on the grey literature.

List of selected popular blockchain-based projects in supply chain finance

The findings indicate that reviewed projects can be compiled into categories according to the problems they are trying to solve. For example, We.Trade, Skuchain, and eTradeConnect utilise various business models to enhance existing processes and provide better SCF products through sharing of information and digitisation of the relevant paper-based documentation. Blockchain is also being used under Letters of Credit (L/C) by the Contour network, Financle Trade Connect, and TradeFinex, which are among the most popular trade finance projects in the industry. Similarly, the Marco Polo Network, which consists of 30 banks, aims to facilitate SCF solutions via a DLT-based platform inter alia by providing distributed data storage and bookkeeping, identity management, and asset verification [ 109 ]. In this context, the Digital Ledger Payment Commitment (DLPC) provides a payment undertaking in digital form on a blockchain for use in any trade finance transaction, which is legally binding, enforceable, negotiable and independent in a sense that it is not contingent on the underlying trade transaction [ 43 ]. Komgo and Clipeum do not only offer digital trade finance-related products, but also Know-Your-Customer (KYC) compliance services which enable the transmission of data stored in a blockchain-based platform among the participating entities on a need-to-know basis [ 39 , 129 ]. Some projects, such as Chained Finance, Halotrade, Skuchain, Hyperchain and Ant Blockchain Open Alliance leverage DLT to enhance financial transparency of micro, small and medium-sized enterprises (MSME) [ 151 ]. Skuchain, specifically, utilises a blockchain system to enhance buyer’s visibility into their inventory and provide better financing to MSMEs by allowing them to get financing at the buyer’s cost of capital, whereas Hyperchain can digitise the accounts receivable, store them in the blockchain, and based on secure information sharing allows MSMEs to benefit from the credit status of the core enterprises, such as large manufacturers. To solve the issue of inter-operability among the various blockchain-based networks and other technology platforms, organisations, such as TradeFinex and the International Chamber of Commerce’s (ICC) Digital Trade Standards Initiative (DSI), are focusing on technical standardisation [ 109 ]. An extensive analysis of each identified project is beyond the scope of this study withal. In the following section the review combines information extracted from these projects and the literature to underline how specific features of blockchain technology can address existing inefficiencies in SCF.

Findings and discussion

This section analyses both the academic literature and blockchain-based SCF projects from the perspectives of (i) pain points and barriers in existing SCF processes, (ii) the promise of blockchain-driven SCF solutions, and (iii) implementation challenges.

Pain points and barriers in supply chain finance

Considering that blockchain solutions apply to different existing problems, understanding the pain points and barriers in SCF processes is necessary to perceive how blockchain can revolutionise SCF. The analysis of the selected literature suggests that lack of visibility in physical supply chain processes, time consuming and inefficient manual paperwork, regulatory and compliance related costs, the risk of fraud, and high transaction costs are essential barriers in SCF in general.

Lack of supply chain visibility

The visibility across the supply chain has been shown to be a crucial requirement for trust, collaboration, and coordination in supply chains, resulting in the stabilisation of material flows, reduction in demand distortion and increased efficiency and agility [ 12 , 29 , 51 , 133 , 161 ]. For supply chain finance, the end-to-end visibility of financers into the material flows as well as the financial flows from invoice to cash is essential [ 35 , 88 ]. However, even the biggest corporations lack the capacity to access reliable and up-to-date information throughout their extended supply networks [ 103 , 153 ]. The principal cause of high financing rates and transaction costs in the incumbent trade and supply chain finance processes is the risk premium due the lack of transparency in credit evaluation processes [ 65 , 93 ]. Moreover, the limited visibility does not only ignite more than 25,000 disputes in SCF every year with USD 100 million tied up at any given time [ 15 ], but also hampers the collection of receivables for the core firm [ 47 , 92 ]. The lack of visibility impedes trust and commitment among supply chain partners [ 46 , 119 ] and foments moral hazard problems [ 34 ] as well as more general adverse effects of information asymmetry [ 35 , 91 ], which result in sub-optimal operational decisions that expose stakeholders in supply chains to financial risks [ 10 , 13 , 127 ]. As a result, many actors in the chain operate in opacity and a large group of MSMEs are precluded from SCF [ 45 ], especially if they do not transact directly with the core enterprises [ 93 ].

Laborious and inefficient processing of manual paperwork

The ingrained dependence of SCF on paper-based documentation has driven up costs and caused inefficiencies in SCF [ 2 , 36 , 117 , 144 ]. Sequential input and manual checking of the paper documentation is costly and error prone [ 25 , 30 ], and results in delays in invoice reconciliation as well as in the receipts of payments [ 103 ]. Costs occur from the complexity of inter-organisational supply chain collaboration and intra-firm cross-functional coordination [ 124 , 165 ]. Tedious, time-consuming and opaque document flows that use a computer-paper-computer manual operation model [ 85 ] introduce errors and risks [ 155 ], resulting in high administrative costs [ 25 ] and expensive billing operations [ 15 ]. The cost of processing this paperwork is estimated to be between 5 and 10 percent of the transaction value [ 148 ].

Regulatory and compliance-related barriers

One of the biggest hurdles of the existing SCF processes is the regulatory requirements that have been imposed on financial institutions [ 74 , 103 , 117 ]. According to a survey conducted by the Asian Development Bank (ADB), which investigated the reasons behind the rejection of financing applications by banks, 76 % of the surveyed banks highlighted the cost and complexity of conducting Anti-Money-Laundering (AML) and KYC checks as the principal barriers in expanding their trade and supply chain finance operations [ 1 ]. Considering that the approval of SCF applications is manual and complex, usually only the most well-known applicants are currently being approved, while MSMEs applications remain under-served [ 2 , 74 ]. Therefore, AML/KYC compliance procedures increase transaction costs and lower the profit margin, thereby reducing the chances of SCF applications being accepted and causing a shortage of SCF around the globe [ 62 ].

Risk of fraud

The massive amount of money and documents changing hands in trade and supply chain finance transactions render them susceptible to attack from fraudsters [ 15 , 30 , 74 ]. The risk of fraud can be defined as the possibility that the receivable does not exist or varies from how it is represented [ 62 ]. L/Cs, purchase orders, invoices, warehouse receipts, and bills of lading (B/Ls) are all subject to tampering and alteration [ 14 , 98 ]. Some common types of trade finance fraud are multiple invoicing, over-invoicing, duplicate B/Ls that are financed multiple times, forged B/Ls and L/Cs, and backdating of transport documents [ 28 , 62 ] or even repeated pledges and empty pledges caused by asymmetric information and adverse selection [ 25 , 93 ]. Fraudulent trade and supply chain financing deals plague SCF as evidenced by the USD 10 billion uncovered fraudulent deals only in China during the year of 2014 [ 62 ].

Corresponding benefits of blockchain-driven supply chain finance

The barriers and challenges highlighted above have created a need for digitalisation in the SCF sphere. As discussed in previous parts, blockchain integration emerges as the most promising drive towards digitalisation of the SCF processes. Blockchains pledge to streamline the flow of information in supply chains and achieve the synchronisation of material, information, and financial flows [ 10 , 95 ]. In the following, we analyse the ways the blockchain-driven SCF has been proposed or shown to address the challenges above based on the review of the academic studies and the industry applications summarised in Tables  2 and ​ and3, 3 , respectively.

End-to-end supply chain visibility

The increased supply chain visibility has been presented as a pillar of blockchain technology [ 9 , 113 ]. Due to the integrity and immutability of records, blockchain enables real-time trade and cargo information from a single source of truth [ 92 , 150 ]. For example, Tradelens provides real-time visibility of the progress of goods and documents in the container transportation industry through its blockchain ecosystem [ 78 ]. Visibility provides transparency, which is crucial for orchestrating SCF programs [ 92 ] as it solves issues of information asymmetry within the supply chain that drive financing costs higher [ 45 , 93 ]. Since the SCF decisions and premiums are driven by the fluctuation of credit risk [ 55 ], information transparency provided by blockchain enables financers not only to view the credit history of the applicant [ 47 ], but also to monitor other related operational and financial data, such as order quantities, latest warehouse, shipping, and payment statuses [ 69 ], thereby gauging their risk estimations dynamically [ 91 ]. The traceability of collaterals in providing SCF solutions is a key benefit distinguishing blockchain ecosystems from other existing platforms [ 9 , 30 ]. It could also provide an unacknowledged applicant, such as an SME, with the opportunity to evidence its creditworthiness to a financer, thereby securing favourable financing terms with improved operational performance [ 35 , 36 ].

Increased speed and operational efficiencies enabled by digitalisation, smart-contracts, and the Internet of Things (IoT)

The promises to expedite transactional processes and to lower the overall costs of financing bring substantial benefits to all stakeholders involved in an SCF transaction [ 25 ]. Hofmann et al. [ 69 ] argue that the combination of blockchain with IoT can maintain device connectivity and deliver material flow tracing across the supply network so to adjust the risk premium throughout the shipping process. IoT enables feeding the blockchain with instant information via sensors, rather than having to rely on human ‘oracles’ to transmit data about the physical movement of goods [ 26 ]. This application involves using Radio Frequency Identification (RFID) tags, GPS tags, and other chips in the form of installed detectors throughout the physical chain [ 147 , 159 ] to achieve real-time monitoring and tracking of data [ 120 ], which can be leveraged by smart contracts to automate the execution of transactions [ 93 , 149 ]. The latter constitute automatable and enforceable agreements that can run on blockchains by coding various contractual terms into computer code [ 24 , 134 ]. Undoubtedly, there is a resemblance between the programmable nature of smart contracts and the state-contingent character of traditional trade finance procedures, such as documentary collections and L/Cs [ 17 ]. For example, trade finance techniques, are usually designed to release a tranche by detecting that some pre-determined conditions have been met, such as that a B/L has been sent or that a shipment has been made [ 25 , 159 ]. The flexibility of smart contracts renders them suitable to automate further SCF solutions, such as receivables or payables finance. Automation is achieved through implementing staged trigger points for key events for a range of SCF solutions [ 69 , 93 , 112 ], resulting thus in efficient, transparent and cost-effective flow of information and value [ 150 ].

In practice, numerous initiatives have been vigorously researching blockchain-supported proposals that tackle the inefficiencies occurring from manual processing of information in trade finance (see cases from Komgo to Marco Polo in Table ​ Table3). 3 ). For instance, by utilising a blockchain-based network that links all the entities involved in a L/C transaction, platforms like Finacle Trade Connect and Contour have achieved to reduce the end-to-end processing time by 90 per cent. Similarly, Komgo promotes structured data fields instead of documents in its platform, so that it can streamline seamlessly the entire document workflow in trade finance transactions in its platform. More ambitiously, TradeFinex provides a marketplace for peer-to-peer trade and SCF transactions utilising cryptocurrencies. The BAFT DLPC provides a legally binding digital payment commitment in fiat currency, which can inter-operate with Skuchain to digitalise L/Cs and other trade and SCF transactions and automate execution of these instruments through smart-contracting [ 156 ].

Reduced regulatory costs

Blockchains constitute distributed trustworthy databases, shared by a community, which can be used for KYC, Customer Due Diligence (CDD), and AML purposes [ 25 , 117 ]. The key functionality for financers of an immutable ledger, in which near real-time data are recorded, is the provision of reliable evidence about new clients, such as IDs and any relevant background documentation [ 69 , 159 ]. Process integrity, disintermediation and decentralisation can enable secure information sharing amongst various parties [ 120 ], thereby rendering it possible to eliminate duplication of regulatory compliance processes, such as KYC checks, by sharing the existing information on a blockchain so that other financers would no longer need to execute the same controls manually [ 52 , 107 ]. Blockchain can, thus, enable a system where all financers simultaneously hold KYC data and benefit from economies of scale resulting from checks needing to be undertaken only once [ 11 , 164 ]. As evidenced in Table  3 , some blockchain projects, such as Clipeum or Komgo, are building platforms where the members can upload KYC documents and authorise other participants to consult these documents upon request on a need-to-know basis [ 39 , 66 ]. Therefore, blockchain could assist in credit checks, diminish compliance costs, and, thus, simplify the establishment of SCF programs.

Mitigated fraud risk

As explained in Han et al. [ 62 ] and Lawlor [ 90 ], the primary aim of tokenising trade documents on a blockchain is to avoid fraud and double-financing issues. As an immutable and shared registry [ 150 ], blockchain can preserve the integrity and authenticity of the trading background, including shipping and warehouse status and purchase order data, which are vital for SCF techniques [ 93 ]. Each document is hashed and time-stamped to create an original identifier, and, if a malicious actor attempts to use the same document for financing purposes through the platform, that identifier signals the previous case of financing to all parties [ 69 ]. Thus, blockchains limit forgery and multi-financing issues in, for example, inventory financing, pre-shipment financing, advance against receivables and distributor finance techniques [ 69 ], thereby enhancing SMEs credibility to obtain financing from previously hesitant financers [ 94 ].

Implementation challenges to further adoption of blockchain technology in the SCF sphere: toward a more collaborative business model?

Thus far, this paper discusses how blockchain technology can transform trade and supply chain finance processes. This section reveals the challenges associated with blockchain implementation in this environment, which are summarised in Table  4 .

Implementation challenges for blockchain adoption in supply chain finance

Business implementation challenges

A decentralised and immutable database which enables SCF stakeholders to securely share peer-to-peer digital trade documentation and tokenised assets entails a paradigm shift toward automation, real-time risk management, and cheap, efficient, and inclusive financing at reduced administrative cost [ 9 , 26 ]. However, there is evidence of opposition from incumbent economic leaders within the banking system to the blockchain transformation in SCF out of fear of being cut-off [ 149 ] or of missing revenue streams [ 101 ]. Other actors are unwilling to share valued information and reluctant to the total transparency provided by blockchain [ 82 , 149 ]. Given that production costs, order quantities and transaction prices are usually perceived as trade secrets, privacy concerns will be a major problem in SCF should visibility be achieved [ 45 ]. Hence, parties that extract information rent are expected to be reluctant to take part in blockchain platforms that decrease information asymmetry.

Saberi et al. [ 120 ] analysed inter-organisational blockchain implementation challenges, alongside intra-organisational, system related, and external to the supply chain challenges. They identified information sharing issues, cultural differences, and challenges in coordination and communication that impede collaboration in supply chains [ 120 ]. Kouhizadeh et al. [ 86 ] detect the complexity of blockchain technology and the need for re-engineering of business processes across the supply chain in an orchestrated manner as the inter-organisational barriers, in addition to the aforementioned confidentiality and security concerns. Korpela et al. [ 85 ] focus on the requirements for the digital supply chain transformation to succeed. Companies must develop their business model to maximise effectiveness in leveraging blockchain in their business offerings and should establish information model platforms to achieve inter-operability and integration among multiple internal platforms of various organisations [ 85 ]. As discussed in supply chain collaboration literature based on EDI, CPFR, and RFID technologies [ 50 , 114 , 122 ], the industry must develop standards which would enable business-to-business (B2B) process connectivity so that members in SCF transactions can exchange original documents and conduct transactions online [ 147 ]. Lastly, integration channel intermediaries, similar to EDI or SWIFT operators, are needed to reconcile data formats and distribute information across the various blockchain systems of independent organisations [ 85 ]. In this regard, several industrial projects (e.g. TradeFinex and Digital Trade Standards Initiative in Table ​ Table3) 3 ) explicitly refer to the need for standardisation as a prerequisite to utilise blockchain in SCF.

Managerial implementation challenges

Despite that blockchain provides for networked applications across an ecosystem of companies, with no single party controlling the application [ 142 ], to ensure that a company’s systems are compatible with blockchain SCF platforms requires surmounting some managerial challenges. Batwa and Norrman [ 15 ] discovered that the lack of acceptance in the industry, lack of technological maturity, and the need for collaboration and coordination among competing parties are the main obstacles for blockchain integration in SCF processes. Likewise, Queiroz and Fosso Wamba [ 115 ] discuss implementation challenges through the prism of technology acceptance models in order to understand the individual behaviours in IT adoption based on performance expectancy, effort expectancy, facilitating conditions, perceived usefulness, and trust among supply chain actors. Other scholars suggest institutional theory, diffusion of innovations theory [ 118 ], theory of planned behaviour, technology readiness and the classical technology acceptance model [ 80 ] to explain the reasons why a particular organisation adopts a new and disruptive technology [ 86 , 150 , 159 ].

In this context, Iansiti and Lakhani [ 71 ] developed a blockchain applicability model based on how innovative technologies are naturally being adopted. To this end, Wang et al. [ 150 ] propose using sense-making in assisting managerial decision-making, which refers to the process of developing specific assumptions, expectations, and an awareness of the said technology [ 147 ], which then frame the actions of the decision makers towards it [ 99 ]. Wang et al. [ 150 ], thus, focus on managers’ prospective sense-making perspectives and extricate their views on the issues that may negatively influence blockchain diffusion through interviews with 14 supply chain experts. Numerous stakeholders who may develop conflicting objectives would be involved in a blockchain platform. Therefore, cultural hurdles against new innovations, data ownership and intellectual property issues, the lack of standards, costly implementation, security issues and regulatory uncertainties present barriers to blockchain deployment in SCF [ 120 , 159 ]. In this regard, a solution to overcome these challenges has been suggested arguing that government-led initiatives and a paradigm shift toward a more collaborative business model in the industry could convince top management in organisations aboard blockchain SCF platforms [ 150 ].

Technical implementation challenges

Lu and Xu [ 97 ] and Kouhizadeh et al. [ 86 ] discuss technical issues, such as usability, energy consumption, size and bandwidth and throughput latency, while Wang et al. [ 149 ] point out that despite the immutable character of blockchain, hacking is still possible [ 163 ]. In a similar fashion, Kshetri [ 87 ] highlights the technological immaturity of sensor devices, the borderline between the physical and virtual worlds, and the high degree of computerisation that might not be accessible in some parts of the world. Moreover, Babich and Hilary [ 9 ] underline the ‘garbage in, garbage out’ weakness, namely the issue that there might be discrepancies between the information recorded in the blockchain and the physical state due to mistakes or intent. Although IoT is often presented as the solution to the flaw of introducing erroneous data into the blockchain [ 27 ], the technological risks of the system are not sufficiently discussed in the extant literature. For instance, the system is vulnerable to fraudulent activities by malignant actors, who may separate the sensor from the rest of the cargo to automatically trigger the release of an unlawful payment.

Legal implementation challenges

Despite the continuous development and improvement of the technology to achieve digitalisation in SCF, the absence of enabling regulatory and legal frameworks and broadly accepted standards may impede blockchain diffusion in SCF. For example, both Article 3 of the Uniform Commercial Code (UCC) in the US and its ancestor, Article 3 of the English Bills of Exchange Act 1882, apply only to ‘written’ bills of exchange and promissory notes, thereby not covering bills of exchange, promissory notes, and other negotiable instruments or payment commitments that are in digital form and registered via blockchain in SCF [ 116 ]. Similarly, the market practice in international trade is currently dependent on paper negotiable bills of lading and other paper documents of title [ 138 ] as there is uncertainty regarding the legal value of digitally issued documents of title [ 57 ].

Other legal issues relate to the legal enforceability of smart contracts and to the legal liabilities of decentralised blockchain platforms, with respect to whom is responsibility attributable for platform-related risks, such as system malfunctions, leakage of sensitive information, insuring against risks and non-compliance with regulations, including data protection regulations [ 41 ]. Further legal issues that need to be addressed include the legal status of blockchain records and the issue of synchronicity between the state of the blockchain and the legal status, which might be different due to the occurrence of fraud or incapacitation [ 125 ]. The situation is further complicated as blockchain-driven SCF operates worldwide, which requires numerous parties to comply with different national laws, regulations, and institutions [ 61 ].

Current solutions rely on private legal frameworks established through multipartite agreements-contracts to establish rights and liabilities [ 57 ]. However, without coherency and unification, the market is vulnerable to fragmentation. Hence, the adoption of a stable legal environment is imperative for blockchain-based trade and supply chain finance to succeed [ 15 , 150 ]. Even though the SCM literature does take into account legal considerations in abstract, as a general factor that impedes blockchain adoption in SCF [ 86 , 150 ], there is limited in-depth consideration of the specific legal issues that arise and affect the feasibility of each theoretical proposition.

Critique and future avenues of research

Building on the proposition of Saberi et al. [ 120 ] that supply chain governance mechanisms must be further evaluated for effectiveness in understanding blockchain-based supply chains, it is argued herein that future research should integrate some overlooked analytical frameworks and employ empirical methods as well as mathematical modelling in order to investigate blockchain implementation challenges further and propose solutions.

Global supply chain management

According to the idealised view of supply chain management, supply chains are perceived as networks of organisations that collaborate together to produce competitive advantage [ 37 ]. However, firms might get stuck in long-term adversarial relationships with their suppliers, making them susceptible to opportunistic behaviour due to information asymmetry [ 83 ]. As discussed above, blockchain promises to address this issue by ensuring trust through immutability of records and transparency. However, this necessitates the participation of the stakeholders in the first place. Mechanisms for incentivising blockchain participation remains a major strategic challenge and an open research question [ 124 ]. Therefore, further theoretical development is needed to understand the conditions for the establishment of blockchain-based SCF networks.

Platform theory and strategic management

We suggest that blockchain SCF networks may be conceptualised as ecosystem platforms [ 76 ], which consist of members that are themselves other organisations and operate as evolving organisations or meta-organisations [ 3 ] that shift along a continuum of different innovation configurations [ 53 ]. This means that potential innovators of complementary products can utilise Application Programming Interfaces (APIs), to build compatible complements [ 40 ]. As Google or Facebook have developed and shared APIs to encourage independent software developers to build applications [ 53 ], blockchain platforms can provide the necessary open APIs in the form of flexible script code system to encourage participants to code smart-contracts and offer innovative payment and financing solutions [ 93 ]. For instance, companies may promote Tradelens and create complementary services, such as smart contracts and other decentralised SCF applications, on top of its platform for their clients. This may enable new SCF channels, such as an open market for financing of invoices [ 47 ]. To this end, Choi [ 36 ] reported new blockchain-enabled SCF solutions in which participants conduct transactions peer-to-peer using cryptocurrency and concluded that these solutions can yield higher expected profits and lower level of operational risk compared to existing SCF techniques.

Co-opetition strategy

The current debate regarding the appropriate strategies and operational practices for the use of blockchain in SCF can be improved by drawing on the co-opetition strategy that combines competition and cooperation to leverage on the shared resources [ 18 , 154 ]. As we have seen in Table ​ Table3, 3 , most of the projects trying to leverage blockchain technology within the financial supply chain sphere are essentially consortia. Being a network-based endeavour, blockchain technology is facilitating cooperation between competitors. In this regard, a V-form organisational structure has been suggested as ‘an outsourced, vertically integrated organisation’ tied together by blockchain [ 5 ]. This form of organisation is comprised of an ecosystem of fully independent companies which coordinate and audit their activities through DLT [ 41 , 79 ]. Future research could focus on the notion of co-opetition with a view of determining the organisational conditions under which a blockchain SCF network is feasible and stable. Game theoretical network formation models [ 75 ] provide an analytical framework for such an endeavour, and can help identify SCF methods, market structure, and economic conditions under which blockchain-based SCF can be established.

Legal analysis

Another promising direction of research is the articulation of the legal implementation challenges, which is already underway by one of the authors. For instance, the lack of a sufficient legislative and regulatory framework for blockchain alternatives to paper trade documentation begets a risk of a legal void surrounding the use of blockchain SCF platforms. The key legal issues raised by the development and the use of blockchain records operating on global trade platforms need to be explored by legal scholars in order to establish how would the legislative and regulatory environment need to change to ensure legal enforceability of blockchain-based SCF solutions.

Information systems and empirical analysis

Further studies could investigate the underlying technology in more depth. For example, a comparative study regarding the appropriateness of different blockchains of the same type (e.g. Hyperledger Fabric and Corda) for SCF would be an important contribution. Currently, most academic studies investigate blockchain and SCF by utilising either conceptual or simulation methods. Future studies should consider more mathematical modelling and empirical studies to develop an analytical understanding of the key factors that drive the relationships between different types of flows and stakeholders with conflicting interests acting on networked systems. For instance, there has been little empirical investigation into the blockchain impact on terms of return on investment and realised customer value [ 143 ] or on its impact on critical supply chain properties, such as network risk and resilience.

Limitations

Finally, a few limitations of this literature review need to be considered. First, this review focuses on the impact of blockchain technology on SCF. The authors acknowledge that the choice of keywords might have excluded some relevant blockchain articles. Here, we aimed to provide a concise discussion of the implications of blockchain in trade and supply chain finance, while a comprehensive discussion on the broader benefits and challenges of blockchain for SCM is beyond the scope of this article and has been provided elsewhere [ 27 , 113 , 115 , 149 ]. Second, most industrial projects are at their early stages; hence, there is limited empirical data on the results of these projects. Thus, the conclusions have to be drawn from the analysis of the projects based on restricted information in the public domain as well as theoretical discussions in the literature. Third, the academic literature on blockchain-enabled SCF is in its infancy and the publications are dispersed over journals in various fields and topics. This review provides a starting point for future studies that may quantify the significance of the various implementation challenges, identify causal relationships among them, and suggest possible solutions to effectively manage blockchain adoption in trade and supply chain finance.

Conclusions

The current pandemic has made clear that digitalisation and platform-enabled change is the only way forward for international commerce [ 106 , 126 ]. It forced corporations and banks to digitalise their operations, seek digital alternatives to wet-ink documentation and understand the inefficiencies of the existing internet solutions and internal systems [ 72 ]. This crisis might evolve into an opportunity for the industry to acknowledge the need for creative technology solutions, and to invest in and embrace blockchain resources toward digitalisation [ 73 ].

This review contributes to the SCF literature by articulating the rationale behind blockchain adoption. It has enriched this emerging field by discussing several theoretical studies and industry blockchain applications. This paper is one of the first to consolidate the state-of-the-art of blockchain applications in trade and supply chain financing. By elucidating the current perspectives in academia and practice, the areas where blockchain may bring value to trade and supply chain finance have been identified. This review sets out to explore how blockchain technology may transform SCF by exploring the answers to three research questions.

The first research question (RQ1) concerned the key barriers and pain points that hold back innovation in existing SCF processes and contribute to the growing financing gap of international commerce. Our literature review found that the lack of visibility into supply chain material flows, the inefficient manual processes, the paper-based documentation, the burden of compliance with regulations, and the risk of fraud are the main bottlenecks in existing SCF processes. The second research question (RQ2) probed how blockchain combined with related technologies, such as smart-contracts and IoT, can provide solutions to these inefficiencies. Via an analysis of the academic literature, grey literature, and blockchain use cases, the expected gains from blockchain adoption in trade and supply chain finance were identified to include the provision of end-to-end supply chain visibility, the increased operational efficiencies, the reduced transaction and regulatory costs and the mitigation of fraud-related risks. Our review allowed us to further capture several blockchain implementation challenges in SCF at the frontier of practice, ranging from business and managerial implementation challenges to technical and regulatory issues, which were the focus of RQ3. On this question the research attempted to introduce a novel viewpoint in the discussion, suggesting that future academic literature can examine blockchain adoption challenges in SCF through game theoretical models and using the concept of co-opetition, which is tailored to blockchain platforms wherein many competing companies participate and collaborate.

To our knowledge, this study is one of the very few to have contemplated the implementation challenges for blockchain adoption in SCF. It brings valuable insights about SCF and blockchain, thus placing a foundation to motivate further cross-disciplinary research on this emerging technology and range of financing solutions. It will also help practitioners to further understand where and how blockchain may revolutionise SCF processes and stimulate managers to develop strategies and employ the necessary changes that are required for blockchain-driven SCF to succeed. Considering the nascent nature of the technology, regulators can either instigate and mould the development of blockchain-based SCF solutions through pro-innovation policies and regulations or constrain their impact by strict over-regulation. Therefore, understanding how to regulate blockchain-based projects presupposes an analysis of its novel use-cases [ 41 ]. Our review provides such an analysis of blockchain-based solutions in trade and supply chain financing, along with a state-of-the-art examination of the theoretical solutions, thus enhances the ability of the regulators to identify further legal issues that might emerge and design laws and mechanisms that will facilitate innovation.

Acknowledgements

We would like to thank Prof. Miriam Goldby whose detailed comments and constructive feedback on an earlier draft helped improve and clarify this manuscript. All errors remain our own

APPENDIX: Materials and methods

Considering the rapidly evolving nature of blockchain technology and the paucity of publicly available results on the implementation of blockchain-supported SCF, we have used critical literature review methodology to be able to generate new perspectives [ 140 ]. To conduct a transparent and reproducible critical literature review, the process suggested by Torraco [ 140 ] and Snyder [ 128 ] has been adopted, which was extended by some elements of the PRISMA statement (see Fig. ​ Fig.1 1 ).

Procedural steps of the search protocol for the academic literature, Source: Moher et al. [ 102 ]

The review covers the state-of-the-art use of blockchain in SCF in the past five years, 2016 to 2020. Primary data is collected through a systematic search and review of the literature [ 59 ], while additional data is collected from grey literature. To avoid biases stemming from omitted literature, the articles were located through keyword search in the core collection of Web of Science of terms related to SCF, trade finance, and more generally, supply chain and international trade. Considering the inter-disciplinary nature of the topic and the diversity of the outlets, no constraints were imposed on specific fields or journals. Additional papers were identified through the bibliography of the relevant articles found by the initial keyword search. Finally, the so-called ‘grey literature’ and reports commissioned by public institutions were also examined to capture the current state of industrial applications, which were located through searches in Google and Google Scholar, supplemented by insights gained by attending several industry events and virtual presentations organised by the International Chamber of Commerce (ICC), the World Trade Organisation (WTO), the International Trade and Forfaiting Association (ITFA), the Bankers Association for Finance and Trade (BAFT) and other organisations and industry associations over the summer 2020, in which market leaders discussed their current efforts.

A.1 Keyword selection

A research protocol was created to search for all relevant papers on the topic and closely related areas. The terms used in the final selection were determined after some pilot searches, where multiple possible combinations of search strings and keywords were tested. After this iterative trial and error process, the search protocol was formulated as shown in Table ​ Table5 5 .

The search string and keywords selection

As our topic consists of three elements (i.e. blockchain technology, supply chain, and finance), three groups of search terms were included to ensure that all three aspects are fully captured. We included not only the term blockchain in the first group, but also related concepts, such as Distributed Ledger Technology (DLT) or smart contracts, which are sometimes used interchangeably. To narrow down the scope to supply chain processes and international trade transactions, the second group consisted of supply chain and platform-related terms, including keywords such as ‘supply chain’, ‘trade’ and ‘ecosystem’. As the majority of these keywords can be applied in different themes, they were combined with a third string of keywords consisting of finance-related terms. That way, the second group should always be related with both blockchain technology (first group) and financial perspectives (third group). Specific SCF solutions, ‘factoring’, ‘forfaiting’, ‘discounting’, ‘receivables’ and ‘payables’, as discussed in Sect.  2 , were also included among the finance related terms. Finally, main trade finance methods and payment mechanisms used in international trade transactions, such as ‘letter of credit’, ‘open account’ and ‘bank payment obligation’, were added. This literature could not be neglected in the present review, because trade finance is not only highly related [ 54 , 160 ] but it also partially overlaps with the concept of SCF [ 21 , 84 ]. Consequently, these keywords were searched for in the scientific article titles, abstracts, author’s keywords, and the keywords-plus field.

A.2 Article selection criteria and process

After employing the above-mentioned research protocol, 493 studies were returned by the keyword search. Specific exclusion criteria were then applied to identify the directly relevant articles. Articles that were written in any language other than English, editorials, calls for papers, book reviews, articles with missing abstracts, and preliminary studies were excluded to ensure transparency, validity, and academic rigour [ 128 ]. Moreover, articles for which the focus fell fully under disciplines other than economics, finance, law, and business and management, e.g. computer science or electrical engineering, were removed. In addition to peer-reviewed academic journals, the search included the proceedings of leading international conferences. Furthermore, certain popular books and book chapters on blockchain, such as Chuen and Deng [ 38 ], De Filippi and Wright [ 69 ], Hacker et al. [ 41 ], Hofmann et al. [ 61 ] were included to better understand how blockchain is framed within the popular literature. Consequently, 161 studies were obtained.

Following the guidelines of Snyder [ 128 ], the literature review can be conducted in phases by reading abstracts first, making selections, and then reading full-text articles, before making the ultimate selection. Papers that discuss mainly different topics, e.g. cryptocurrency markets and Bitcoin’s price fluctuations, or that focus solely on specific sectors, e.g. use of blockchain in healthcare were discarded. As illustrated in Fig. ​ Fig.1, 1 , 51 research papers were retrieved and downloaded.

A full text analysis for finer selection of the candidate papers was employed to align the content of the selected papers with the focus of the review. Twenty-two papers were removed from the poll as they were not directly associated with blockchain implementations in SCF. Publications that discussed features of blockchain that support explicitly SCF received further scrutiny pursuant to their relevance, quality and academic rigour. Ultimately, the selected corpus of core publications consisted of 13 records, which are summarised in Table  2 and discussed in Sects. 3 and 4 of the main text.

As academic studies tend to fall behind the practical implementation of technological innovations, relying merely on academic literature would give a rather constringed view of the topic, especially considering the industry is teeming with blockchain projects. Therefore, the above list is supplemented by a desk-based research on blockchain-supported projects and an analysis of documents beyond academic publishing, such as industry-produced research, to provide a solid overview for understanding how blockchain technology is practically being used in SCF. This led to the identification of the 16 blockchain-based SCF projects discussed in the paper.

This study was supported by the Economic and Social Research Council (Grant no. ES/P000703/1).

Availability of data and material

Code availability, declarations.

The authors declare that they have no conflict of interest.

Contributor Information

Ilias Ioannou, Email: [email protected] .

Guven Demirel, Email: [email protected] .

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• Published: 30 September 2024

Substitution effect of Asian economies on China’s industrial and supply chains: from the perspective of global production network

• Lizhi Xing   ORCID: orcid.org/0000-0001-9554-5414 1 , 2 ,
• Shuo Jiang 1 ,
• Simeng Yin 1 &
• Fangke Liu 1  

Humanities and Social Sciences Communications volume  11 , Article number:  1304 ( 2024 ) Cite this article

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Metrics details

• Complex networks
• Development studies

This paper views China Production Network (CPN) as an integral component of the Global Production Network (GPN) and constructs null model (GIVCBN-I model) and the counterfactual model (GIVCBN-II or GIVCBN-III model) to identify and measure the potential industrial relocation risk exposure of CPN based on the Multi-Regional Input-Output (MRIO) table. The main findings are as follows. Firstly, Altasia will induce more of China’s industrial and supply chains to break compared with ASEAN, which also means that strong industrial complementarity exists between Japan, South Korea, India, Bangladesh and the ASEAN member countries, enabling Altasia to have a more significant substitution effect on China. Secondly, the counterfactual models’ network-level characteristic indicators are worse than those of the null model in economic terms, suggesting that removing trade barriers for intermediate goods within Altasia could lead to the decoupling of industrial sectors in the CPN, thereby accelerating the trend of China’s industrial and supply chains relocating offshore. Thirdly, according to the comparison results of node-level characteristic indicators, Altasia has weakened China’s influence scope, profitability, and robustness of risk within the global industrial and supply chains, but mainly concentrating on its resource-intensive and labor-intensive sectors rather than capital-intensive and technology-intensive sectors, which indicates that some China’s industrial sectors still maintain substantial competitive advantages in the GPN. In sum, this paper provides theoretical guidance for identifying and analyzing the trends of industrial relocation in the Asia-Pacific region and helps industrial policymakers deepen the understanding of regional economic integration and its impact.

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Introduction.

Profound adjustments have been observed in the global industrial division of labor in recent years, with new trends such as inward contraction, localized relocation, and regional clustering. The global stage is witnessing a fierce competition among economies for dominance in the industrial and supply chains. Some economies have even resorted to political and economic measures to promote re-shoring, near-shoring, and friend-shoring of their industrial layouts, underlining the urgency and importance of this competition. According to a report by The Economist in February 2023, fourteen Asian economies are on the verge of potentially supplanting China as the focal point of the global industrial and supply chains amidst escalating tensions between China and the United States. These economies, collectively referred to as Alternative Asian Supply Chain , or the so-called Altasia , include the vast majority of the Association of Southeast Asian Nations (ASEAN) except Myanmar, as well as Japan and South Korea in East Asia, and India and Bangladesh in South Asia.

Importance of Altasia

The Economist suggested that Altasia could collectively rival or surpass China regarding economic and manufacturing capabilities. However, this issue needs to be explored from multiple perspectives. On the one hand, China has become a global production center after more than three decades of effort. It is not realistic for Altasia to replace China overnight. On the other hand, as China’s demographic dividend fades and production costs rise, the ASEAN, which occupies the dominant position in Altasia, continued to promote export-oriented economic expansion in recent years and continuously expand the scale of foreign investment by relying on population, resources, and policy advantages. Some labor-intensive manufacturing industries transfer from China to less developed Asian economies. Meanwhile, the world is undergoing the Fifth International Industrial Transfer . The United States tried to rely on Asia-Pacific and Indo-Pacific regions to implement a Friend-Shoring strategy to reshape the “De-Sinicized” supply chain, which resulted in a passive industrial transfer process for China. However, to cope with the blockade and oppression of the United States, China is also taking the initiative to transfer and export industries to Southeast Asian economies. Data shows that in recent years, although China’s share of imports in the United States has declined, its share in the added value of consumer goods in the United States has increased because imports from emerging markets often include components from China. Taking laptops as an example, from 2017 to 2022, the number of laptops imported by the United States from Vietnam surged. At the same time, the number of laptop components imported by Vietnam from China also increased significantly.

To highlight the significance of Altasia and ASEAN in the restructuring of Global Value Chain (GVC) , this paper systematically reviews the economic cooperation agreements in the Asia-Pacific and Indo-Pacific regions, as shown in Fig. 1 . Clearly, Asian economies have become strategic partners actively sought after by China, the United States, and Japan, around which multilateral trade agreements and economic cooperation frameworks such as Regional Comprehensive Economic Partnership (RCEP) , Indo-Pacific Economic Framework for Prosperity (IPEF) , and Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) have emerged. With the trend of regional economic integration, Asian economies are also exploring and forming new internal cooperation mechanisms to gain more significant benefits in competing with major powers.

Altasia is merely a hypothesis, and China–Japan–South Korea FTA has not been achieved, so we differentiate them by yellow font. To emphasize the major contributors to the global economy, this paper marks the names of the countries ranked in the top five of global GDP in red font.

Advantages of Altasia

While it’s true that no single economy within this group can replace China, Altasia, as a collective, stands as a robust and competitive alternative. For global manufacturers seeking new production bases outside China due to geopolitical risks with the United States, Altasia offers a stable and reliable option, backed by its diverse industrial structure and a demographic dividend that is set to expand.

Altasia’s strength lies in its diverse internal industrial structure. Each member economy brings a unique advantage to the table. Japan, South Korea, and Taiwan excel in technology; Singapore boasts robust logistics; Indonesia and Malaysia are resource-rich; Vietnam, Thailand, and India are developing investment policies; and the Philippines, Bangladesh, Myanmar, Laos, and Cambodia offer significantly lower labor costs compared to China. In the post-pandemic era, transnational enterprises are increasingly diversifying their industrial and supply chains to enhance risk resilience and profitability. Altasia with its array of strengths, is undoubtedly a compelling choice.

Altasia’s demographic dividend is even more advantageous. Its workforce is about 1.5 times larger than China’s, with 10 million more college-educated people. Taking Southeast Asian economies as the representative, compared with the rapidly aging China, Indonesia, Malaysia, Vietnam, and the Philippines have a younger population structure that will expand for a considerable period. While the labor cost is Altasia’s most advantageous aspect, in terms of labor efficiency, China’s average workweek is 45 h, higher than all Southeast Asian economies, and the average labor performance is 2.3 times higher than that of Southeast Asian economies. By comparing the leading macroeconomic indicators of Altasia and China (as shown in Fig. 2 ), we can find that Altasia’s comprehensive capabilities and growth potential are comparable to China’s (Hong et al. 2023 ).

Population data are from World Population Review (retrieved from https://worldpopulationreview.com/ ); GDP, land, export and import data are from World Bank Open Data (retrieved from https://data.worldbank.org/ ); Foreign reserve data are from World Investment Report (retrieved from https://unctad.org/ ).

In summary, China possesses advantages that Altasia economies cannot surpass in the short term. First, China boasts a vast economic scale with a solid manufacturing base and extensive domestic market, enabling it to regain its significant position in the global economy post-pandemic gradually. Second, China has demonstrated considerable strength in technological innovation and infrastructure development, particularly in high-speed rail and electronic payments. Third, China’s geographical proximity to the Altasia economies facilitates bilateral or multilateral trade, reducing logistics costs and accelerating the circulation of goods and services. Fourth, deep cultural connections and historical ties between China and these countries play a bridging role in promoting mutual cultural exchanges and economic cooperation. This sense of cultural closeness also helps establish and maintain trust-based business relationships.

Disadvantages of Altasia

Altasia currently holds certain comparative advantages over China in labor and manufacturing costs, which provides a solid foundation for its competitiveness. While it may not be able to close the gap with China in the short term, these advantages should not be overlooked.

As shown in Fig. 3 , it is evident that Altasia economies exhibit distinct disadvantages in comparison to China across six key metrics. These disadvantages are apparent in areas such as:

Countries like Japan, South Korea, Vietnam, Malaysia, and Indonesia have significantly lower exports of goods and services, suggesting less global trade influence than China.

Although South Korea and Vietnam have substantial high-tech exports, other nations like Indonesia lag considerably behind, indicating a gap in high-tech industry capacity.

Japan is the only country with total reserves exceeding one trillion USD, while other economies like Malaysia and Vietnam have much smaller reserves, highlighting a disadvantage in economic buffering capacity.

While Indonesia shows a high contribution rate of 4.4%, other countries like Malaysia (1.1%) and South Korea (1.3%) contribute less to global economic growth, which may reflect lesser economic dynamics or influence.

There is a drastic difference in the number of patent applications, with Japan and South Korea filing hundreds of thousands, whereas countries like Vietnam and Malaysia file significantly fewer, suggesting lower innovation output.

The volume of air freight measured in billion ton-kilometers also shows disparities; Japan and South Korea manage more substantial air freight operations, whereas nations like Vietnam and Indonesia handle much less, indicating less developed logistics and air transport capabilities.

Export of goods and services, high-technology exports, and total reverses data are from World Bank Open Data (retrieved from https://data.worldbank.org/ ); Contribution rate to world economic growth data is from World Economic Outlook Databases (retrieved from https://www.imf.org/ ); Patent application data are from International Patent System (retrieved from https://www.wipo.int/ ); Freight transported by air data is from Global Outlook for Air Transport (retrieved from https://www.iata.org/ ).

These metrics collectively indicate that despite their strengths, these Asia-Pacific economies face considerable disadvantages in these areas compared to China, affecting their competitive stance and economic resilience on a global scale. Therefore, without external forces driving Altasia to become a tightly integrated economic entity, it could not pose a threat to China in a short period. However, an external force has emerged: the U.S. trade policy towards China.

Importantly, all Altasia economies have forged close economic ties with China, which is their leading trading partner as detailed in Fig. 4 . China is not only a source of raw materials and components but also a significant market for final products. This interdependence, a result of decades of economic integration and specialization of regional production networks, suggests that diversifying parts of the supply chain away from China may be a goal for specific industries or companies. Maintaining diplomatic and economic relations with China is necessary for regional peace and prosperity.

The relevant data are from UN Comtrade Database (retrieved from https://comtradeplus.un.org/ ).

Literature review

Although Altasia is a fictional economy, its substitution effect on China’s industrial and supply chains is becoming increasingly apparent in the context of Global Value Chain (GVC) restructuring. Essentially, this substitution effect represents an international industrial transfer from China to Altasia, driven by changes in the comparative advantages of these emerging economies and the United States’ trade policy towards China. This section provides an overview of the new world economic order and introduces what we can do with network science to study this new economic phenomenon.

Industrial transfer in the process of globalization and regionalization

Industrial Transfer , an economic process in which enterprises in one country or region shift specific industries’ production, sales, or even R&D to others (Shafik and Bandyopadhyay 1992 ; Tian et al. 2019 ), is often accompanied by the transfer of market, technology, and cross-regional investment. Driven by the market mechanism, production factors will flow to sectors with high productivity or high growth rates, and the resulting Structural Dividend and Technology Spillover significantly impact the productivity of enterprises in various economies (Timmer and Szirmai 2000 ; Yue et al. 2022 ), driving the economic development of the whole country or region. The process of industrialization has brought significant changes globally, promoting the industrial transfer from developed economies to less developed ones. Since the reform and opening up, China has played a pivotal role, undertaking the large-scale international industrial transfer of developed economies. Its comparative advantage, rooted in various factors (Jiang et al. 2018 ), has made China the biggest beneficiary of the Fourth International Industrial Transfer , underscoring its global economic influence.

Traditional theories, such as the Flying Geese Paradigm (Akamatsu 1937 ), Core and Periphery Theory (Prebisch 1962 ), Product Life Cycle Theory (Vernon 1966 ), Marginal Industry Expansion Theory, Labor-Intensive Industry Transfer Theory (Lewis 2015 ) and New Economic Geography Theory (Krugman 1991 ) and so on, mainly involve the vertical and horizontal international industrial transfer under the background of globalization, but not the current regionalization. As the scale of GVC restructuring expands in depth and breadth, the academic community introduced the concept of the GVC (Gereffi et al. 2005 ), as well as developed the GVC Accounting System (also known as Value-Added Trade Accounting System ) to study the processes of value creation and distribution in globalized production. This analytical framework represents a significant theoretical extension of Input-Output Analysis (IOA) . Since the twenty-first century, the compilation and public release of Multi-Regional Input-Output (MRIO) tables have enriched the theoretical framework of IOA and pioneered a new research paradigm for GVC accounting. With the continuous updates and improvements of the MRIO databases, using MRIO tables to account for the value of industrial transfer has become an essential method for studying this phenomenon from a macro perspective in recent years.

MRIO tables contain rich information on inter-regional industrial and supply chain linkages, providing a basis for assessing the direction and intensity of industrial transfer in the GVC. For example, Liu et al. ( 2011 ) and Gao et al. ( 2018 ) proposed methods for calculating the value of inter-regional industrial transfer based on the IOA framework; Gao et al. ( 2022 ) introduced a framework for measuring the scale of global industrial transfer from a production perspective; Yin et al. ( 2024 ) developed a model for measuring international industrial transfer, distinguishing between domestic and foreign-funded enterprises. Empirical research focusing on this aspect is relatively scarce, and the MRIO databases exhibit considerable lag, forcing most studies to infer future trends from past economic phenomena.

Friend-shoring strategy’s impact on industrial transfer among Asian economies

In recent years, the trade policy implemented by the United States towards China has disrupted the normal patterns of international industrial transfer. On the one hand, the United States has committed to “De-Sinicization” to ensure the security of its critical industrial and supply chains; on the other hand, this Cold War mentality also aims to curb the rapid development of China’s economy. Since 2021, the Biden administration has vigorously promoted its strategy of “Friend-Shoring” and “Near-Shoring” to strengthen supply chains and reduce reliance on geopolitical rivals. This enforced a gradual shift of production capacity from some China-based, manufacturing-centric enterprises to Southeast Asian and Latin American economies, where production costs are more advantageous. In this context, Friend-Shoring Strategy refers to the United States shifting its industrial and supply chains from countries like China and Russia to political allies such as ASEAN, East Asia, and South Asia, centered around its national security strategy. Implementing this policy could lead to the exclusion of China from some crucial strategic industrial sectors and cause China to become disconnected and marginalized in the GVC. Given Altasia’s unique geographical location, it could become a force under the influence of European and American countries to contain China, adversely affecting the security of China’s industrial and supply chains.

From an academic perspective, research on the impact of friend-shoring strategy remains largely theoretical. Boeckelmann et al. ( 2023 ) emphasize the conditions for the emergence of the concept of friend-shoring from a GVC perspective, addressing major economic and environmental issues and the limitations of its implementation. Banaszyk ( 2023 ) elaborates on how reshoring and friend-shoring are significant factors in changing the geography of international industrial and supply chains. On the empirical side, Vivoda and Matthews ( 2023 ) have indicated that friend-shoring is a practical approach to resolving the issues of industrial and supply chains for essential mineral products in the West.

In summary, current studies on the impact of the United States’ friend-shoring strategy and Altasia on China are limited. Most of them rely on international trade data from the United Nations Commodity Trade Statistics Database (UN Comtrade), making them difficult to distinguish the effects of other exogenous variables, such as the COVID-19 pandemic and the Russia-Ukraine conflict. Most critically, these studies often can only infer which industrial sectors in China might be affected through import and export data, without precisely identifying whether the trend of regional economic integration in Asia, guided by U.S. policies, has led to the relocation of China’s industrial and supply chains.

Network science’s contribution to the study of industrial transfer

Network Science is a meaningful way to represent international trade flows (De Benedictis and Tajoli 2011 ; Fronczak and Fronczak 2012 ; Nemeth and Smith 1985 ; Smith and White 1992 ). Its essential advantage is in capturing the complex structure of interactions between a large number of economies and their industrial sectors. This method cannot only identify the characteristics of trade networks but also provide a comprehensive global perspective, offering solutions to various problems in observation, modeling, and forecasting (Fagiolo et al. 2013 ; Medo et al. 2018 ; Saracco et al. 2015 ; Schweitzer et al. 2009 ; Squartini et al. 2018 , Chaney 2014 ; Ren et al. 2020 ). In recent years, network science and World Economics have been deeply integrated, resulting in significant progress in the GVC Network Accounting System (Xing et al. 2016 , 2017a , b , 2018a , b , 2019 , 2020 , 2021a , b ; Xing and Han 2022 ). This system not only reflects the global economic system’s complexity, topological structure, hierarchy, and synergy but also analyzes the essence of various socio-economic phenomena from the Systems Theory perspective. As its most important module, the Global Production Network (GPN) describes the trade of intermediate goods with a high degree of generalization and restoration of the global industrial and supply chains. It has the dual characteristics of structural complexity and evolutionary complexity. Therefore, studying industrial transfer from a complex network perspective can better depict and analyze the complex real-world economic issues.

The intrinsic motivations of industrial transfer can be explained through the Local-World Evolving Network (LWEN) model (Li and Chen 2003 ). Barabási and Albert proposed the BA model in 1999, which is a simple fusion of two mechanisms: growth and preferential attachment, and it explains the evolutionary mechanism of the topological structure of complex systems in the real-world (Barabási and Albert 1999 ). However, the evolutionary complexity of some systems is not only derived from their structural complexity but also related to certain characteristics of the evolving entities themselves. For example, in the global economic system, primarily carried by the GPN (Ernst and Kim 2001 ), Global Trade Network (GTN) (Liu et al. 2021 ), and Global Financial Network (GFN) (Coe et al. 2014 ), various economic ties between countries (regions) and their industrial sectors are not established at the global level according to the preferential attachment mechanism but tend to develop selectively within multilateral trade agreements or regional economic cooperation organizations where information is relatively transparent and risks are relatively controllable. The construction algorithm of the LWEN model (Wang et al. 2012 ):

Growth: the network initiates with \({m}_{0}\) nodes and \({e}_{0}\) edges. With each addition of a new node to the network, it introduces \(m\) additional edges that connect the new node to existing nodes.

Local-World Preferential Attachment: to simulate the local-world phenomenon, for each new node added, a subset of \(M\) existing nodes \(M\ge m\) is randomly selected from the network to form a “local world”. The new node then connects to \(m\) nodes within this local world. The probability of connecting to a \({v}_{i}\) within the local world is determined by the preferential attachment probability \({\prod }_{{local}}K\left(i\right)\) , defined as:

where \(K\left(i\right)\) and \(K\left(j\right)\) represent the degree of \({v}_{i}\) and \({v}_{j}\) respectively, and the summation in the denominator extends over all nodes within the local world.

This formula captures the essence of preferential attachment within a localized subset of the network by mimicking how real-world networks grow and evolve. At each moment, the new node selects \(m\) nodes to connect with from its local world according to the preferential attachment principle, rather than choosing from the entire network as in the BA model. The rule for constructing a node’s local world is determined by the actual situation—For the global economic system, the multilateral trade agreements signed by a nation or the economic cooperation organizations it belongs to can be used as the basis for division, such as the RCEP, IPEF, CPTPP, APEC, ASEAN, DEPA and so on. In summary, building the GPN is one of the best ways to analyze the issue of industrial transfer on a global scale.

Methodology

Data structure of mrio table.

In a global economic environment increasingly characterized by fragmented and internationally distributed production processes and by trade in intermediates, the necessity for pertinent quantitative information of sufficient granularity is more urgent than ever. As mentioned above, MRIO tables quantitatively depict international trade among multiple regions and the corresponding flows of intermediate and final goods, reflecting the production technology connections and supply-demand balance relationships among various industrial sectors within economies. Also, MRIO tables can be extended according to scholars’ research focuses. In recent years, various international organizations and academic institutions have compiled and released new MRIO tables, further promoting the development of GVC accounting in application fields. At the same time, as MRIO tables vividly illustrate the intricate production-consumption relationships among various economies and their industrial sectors in a chessboard-like balance sheet pattern, they are conducive to combining network science models and algorithms to analyze GVC and provide powerful tools for researching vertical specialization, international trade, and industrial structure optimization.

As shown in Fig. 5a , the final demand and intermediate use sections of the global MRIO table constitute the complex topological structures of the GTN and GPN, respectively. The MRIO table is the most comprehensive data structure for studying the efficiency of distribution and production in the global economic system. It explains which consumers use the various resources input into the production system and how much they consume. Additionally, they can link regional and sectoral outputs with satellite accounts such as energy use, carbon emissions, and environmental pollutants, providing a data foundation for extensive research on environmental impacts. Economists have developed a mature GVC accounting system using the global MRIO table (Hummels et al. 2001 ; Koopman et al. 2010 ; Timmer et al. 2012 ; Wang et al. 2017 ; Mi et al. 2018 ).

a An example of the MRIO table including two countries with two sectors; b its multi-layer structure; c intermediate goods flowing among countries and sectors. The typical MRIO table includes three areas: value-added, intermediate use, and final demand. The whole global economic system can be extracted into a multi-layer network, as shown in ( b ), which includes three layers: the value-added layer, the intermediate use layer, and the final demand layer. The intermediate use layer depicts the topological structure of GPN, in which the nodes are industrial sectors, and the edges are the intermediate goods from the upstream sectors to the downstream ones.

Currently, representative world MRIO databases include the World Input-Output Database (WIOD), the OECD-WTO Database on Trade in Value-Added (OECD-TiVA), Analytical Multinational Enterprises and Global Value Chains Database (OECD-AMNE); the Eora Multi-Region Input-Output Table Database (Eora-MRIO), the Asian Development Bank (ADB) Multi-Regional Input-Output Tables (ADB-MRIO) and China Emission Accounts and Datasets (CEADs). The basic information of these MRIO databases is shown in Table 1 .

From the perspective of data structure: the WIOD, OECD-TiVA (Timmer et al. 2015 ), Eora-MRIO (Lenzen et al. 2013 ), ADB-MRIO, and EMERGINGV2 in the CEADs databases are all Inter-Country Input-Output (ICIO) tables, which can be used to study the transfer of industries from all enterprises in a country/region to foreign industries; the CHNMRIO and CHNCLMRIO in the CEADs database are Inter-Region Input-Output (IRIO) tables, which can expand specific countries or regions in the ICIO tables to obtain Regionally Extended Multi-Region Input-Output (RE-MRIO) tables, thereby considering the trend of industrial transfer within countries or regions; the OECD-AMNE database is a Multi-Regional Multiple-Input Multiple-Output (MRMIMO) table, which can further distinguish between domestic and foreign-funded enterprises based on the ICIO tables.

To facilitate analysis work related to the Asia and the Pacific Region, the ADB has produced MRIO tables building on the WIOD database (Timmer et al. 2015 ) to include 29 Asian economies, namely: Armenia, Australia, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, Fiji, Georgia, Hong Kong, China, India, Indonesia, Kazakhstan, Kyrgyz Republic, Lao People’s Democratic Republic, Malaysia, Maldives, Mongolia, Nepal, New Zealand, Pakistan, People’s Republic of China, Philippines, Republic of Korea, Singapore, Sri Lanka, Taipei, China, Thailand, and Vietnam. As a result, ADB compiles MRIO tables at current prices, and at constant 2010 prices for 62 economies and aggregated Rest of the World (RoW). This has facilitated the production and analysis of global value chain related statistics for Asian economies. Economies explicitly identified in the ADB-MRIO account for at least 93% of the world Gross Domestic Product.

This paper focuses on the impact of Asian economies on the China Production Network (CPN) , and requires high timeliness of data. Therefore, considering the characteristics of the above MRIO databases, this paper ultimately adopted the intermediate uses section of ADB2023(E62) as the modeling data. The covered countries or regions and industrial sectors are detailed in Tables 2 and 3 .

Network modeling and extracting

This paper views the industrial sectors of various countries or regions as nodes, and their IO relationships via industrial and supply chains as edges, with the trade volume of intermediate goods reflecting the strength of these relationships as weights. When the total number of nodes in the network is \(N\) , the node set is represented as \(V=\{{v}_{1},{v}_{2},\cdots {,v}_{N}\}\) , the edge set as \(E=\{{e}_{11},{e}_{12},\cdots ,{e}_{{ij}}{,\cdots {,e}_{N(N-1)},e}_{{NN}}\}\) , and the weight set as \(W=\{{w}_{11},{w}_{12},\cdots ,{w}_{{ij}}{,\cdots {,w}_{N(N-1)},w}_{{NN}}\}\) . While the GPN applies the weight set \(W\) to display the adjacency matrix, each row refers to the distribution of intermediate goods output from an upstream sector to several downstream sectors, and each column the intermediate goods input obtained by a downstream sector from several upstream sectors. Since the graph \(G=\left(V,E,W\right)\) reflects the division of labor that each industrial sector undertakes and portrays the intermediate goods trade among economies in the GPN, this paper names it the Global Industrial Value Chain Network (GIVCN) model, as shown in Fig. 5c .

Obviously, the GIVCN models are highly dense (approximately fully connected) weighted directed networks, so data dimensionality reduction is required to visualize the network’s topological structure changes. Considering the substantial heterogeneity of intermediate goods trade between upstream and downstream sectors, this paper uses the X-Index Filtering Algorithm (XIFA) to extract a sub-network from the GIVCN model (Xing and Han 2022 ), which is named the Global Industrial Value Chain Backbone Network (GIVCBN) and represented as \(\acute{G}=\left(V,\acute{E},\acute{W}\right)\) . Therefore, this paper builds 16 GIVCBN models based on the intermediate uses section of ADB2023(E62) from 2007 to 2022. Each model includes 2205 industrial sectors, distributed across 62 economies and RoW. The topological structure of the latest model is shown in Fig. 6 .

This figure was generated by inputting the adjacency matrix of the GIVCBN-2022 model into the Gephi software for visualization.

Furthermore, to reflect the potential impact of Altasia and ASEAN on China’s industrial and supply chains, we need to extract three types of GIVCBN models from the GIVCN model, and the extraction methods and basic settings are shown in Fig. 7 :

GIVCBN-I Model : firstly, the XIFA algorithm is applied to prune the GIVCN model. Secondly, the industrial sectors of Altasia are merged, treating them as a loosely integrated entity. Finally, a dichotomized network model containing 1750 nodes is obtained, as shown in Fig. 8 .

GIVCBN-II Model : firstly, the industrial sectors of Altasia are merged to form a tightly integrated entity in the GIVCN model. Secondly, the XIFA algorithm is applied to prune this network. Finally, the resulting GIVCBN model is dichotomized, as shown in Fig. 9 .

GIVCBN-III Model : firstly, the industrial sectors of nine ASEAN member countries are merged (Myanmar is not included in Altasia), that is, ASEAN member countries are regarded as a whole. Secondly, XIFA algorithm is applied to prune this network. Finally, ASEAN member countries are merged with the remaining relevant countries or regions in Altasia, as shown in Fig. 10 .

The XIFA algorithm is used to prune the network of an individual network (Ego Network) to extract its backbone network. Assuming that the key nodes with red outlines (assigned weights of 10, 5, 5, 5, 5 respectively) can be merged into a super node, then in ( a ), except for the node with a weight of 10, the other nodes have disappeared in the pruning process, so the weight assigned to the super node after merging is only 10. If, as in ( b ), the merger is carried out first to obtain a super node with a weight of 30, then pruning will result in a completely different backbone network.

This figure was generated by inputting the adjacency matrix of the GIVCBN-I-2022 model into the Gephi software for visualization.

This figure was generated by inputting the adjacency matrix of the GIVCBN-II-2022 model into the Gephi software for visualization.

This figure was generated by inputting the adjacency matrix of the GIVCBN-III-2022 model into the Gephi software for visualization.

The GIVCBN-II or GIVCBN-III models treat the inner industrial sectors of Altasia economies or ASEAN member countries as a closely integrated whole, which allows them to compete for more intermediate goods resources in turn. As a result, the relatively less efficient industrial and supply chains in other economies will be removed, while Altasia’s industrial sectors, initially located in the “Periphery” or “Semi-Periphery” of GPN, become closer to the “Core”. Compared to the GIVCBN-I model, the GIVCBN-II or GIVCBN-III model demonstrates the network clustering in Altasia and the network disintegration in the remaining economies. Therefore, by comparing the adjacency matrices of the GIVCBN-I model and the GIVCBN-II or GIVCBN-III model, we can identify which of China’s industrial and supply chains are disrupted due to the substitution effect of Altasia or ASEAN, manifesting as the risk exposure of CPN.

The network topological structures of the three types of GIVCBN models are not random but formed according to specific rules, ensuring the reliability of our findings. This non-triviality allows us to reflect the heterogeneity of each model, which can be measured by both network-level and node-level indicators. By comparing the measurement results of the real-world network (GIVCBN-I model) and the artificial networks (GIVCBN-II or GIVCBN-III model), we can confidently quantify the extent to which different modeling considerations impact the network topology structure.

Network-level measurements

Average path length.

The Average Path Length (denoted as \({APL}\) ) measures the average shortest distance between all industrial sectors in the GIVCBN model, which describes the degree of separation among nodes. In network studies, the distance between nodes is generally defined as the minimum number of edges that must be traversed from \({v}_{i}\) to \({v}_{j}\) . A path whose length equals the distance between two nodes is called the Shortest Path, denoted by \({d}_{{ij}}\) . If there is no such path in the network, it is denoted as d ij  = ∞. The formula for average path length is given by:

where \({APL}\in \left[1,\right.\left.\infty \right)\) . When \({APL}=1\) , all pairs of nodes are directly connected, forming a fully connected network.

A low \({APL}\) value close to 1 indicates a “small-world” effect, which means industrial sectors are closely linked to each other, facilitating quick intermediate goods trades. This is often seen within specific domestic production network or that of an economy formed through regional economic integration. On the contrary, a high \({APL}\) value significantly greater than 1 suggests that the GIVCBN model has economies or industrial sectors that are relatively isolated or require more steps to reach, which can impact the efficiency of international trade transactions.

Average clustering coefficient

The Clustering Coefficient (denoted as \(C\) ) measures the degree to which nodes in an undirected network tend to cluster together, indicating the familiarity among nodes. In the GIVCBN model where direction is ignored, the \(C\left(i\right)\) describes the close relationships between the \({v}_{i}\) and its directly connected neighbors. Numerically, it is the ratio of the number of edges between these adjacent nodes to the maximum possible number of edges. The formula for clustering coefficient of \({v}_{i}\) is given by:

where \(A\left(i\right)\) is the actual number of edges between adjacent nodes of \({v}_{i}\) , and \(K\left(i\right)\) is the degree of \({v}_{i}\) . If \({v}_{i}\) has only one or no neighboring node, i.e., \(K\left(i\right)=1\) or \(K\left(i\right)=0\) , \(A\left(i\right)=0\) , and the numerator and denominator of the formula are both 0.

The average value of these ratios across the network is known as the Average Clustering Coefficient (denoted as \(\left\langle C\right\rangle\) ), which is equal to:

On the one hand, a high \(\left\langle C\right\rangle\) value indicates that many industrial sectors have interconnected upstream and downstream sectors, suggesting a high potential for localized intermediate goods trade relationships within the geographically adjacent regions or the same multilateral trade agreement. This phenomenon implies the formation of industrial clustering. On the other hand, a low \(\left\langle C\right\rangle\) value suggests that the GIVCBN model has a more tree-like structure, with industrial sectors having fewer interconnected sectors, which might impede the spread of intermediate goods but could enhance system robustness against localized attacks.

The Asymmetry (denoted as \({ASY}\) ) generally refers to a characteristic of directed networks where the relationships between nodes are not reciprocal. This concept is crucial in understanding the dynamics and structure of networks where the direction of interaction plays a significant role. An asymmetric relationship in the GIVCBN model where the direction is emphasized implies that: if there is an intermediate goods export from sector \(i\) in one country to sector \(j\) in another, there isn’t necessarily a corresponding one from sector \(j\) to sector \(i\) . The formula for asymmetry is given by:

where \({K}^{{IN}}\left(i\right)\) and \({K}^{{OUT}}\left(i\right)\) are the in-degree and out-degree of \({v}_{i}\) respectively. If \({ASY}=0\) , the in-degree of each node is equal to the out-degree, and \({ASY} > 0\) proves that the in-degree and out-degree of the nodes in the network are inconsistent, and the network is asymmetric.

A higher \({ASY}\) value indicates a significant disparity in the sources and destinations of intermediate goods within a given production network. This means that after an industrial sector obtains intermediate goods from \(N\) other sectors for production, the number of downstream sectors that receive its output will be significantly greater than or less than N . This phenomenon reflects a low degree of vertical specialization, which in turn negatively affects the efficiency of the production network.

Node-level measurements

Degree centrality.

The Degree Centrality (denoted as \({DC}\) ) measures the number of direct industrial connections an industrial sector has in the GIVCBN model, intuitively reflecting its influence scope in the industrial and supply chains. The degree of a node is the number of edges directly connected to other nodes in the network. The greater the degree of a node, the more important it is, indicating that the node has a higher \({DC}\) value. In a network with \(N\) nodes, the maximum possible degree value of a node is \(N-1\) . The formula for degree centrality of \({v}_{i}\) is given by:

In the GIVCBN model, the higher the \({DC}\) value of an industrial sector, the more sectors that establish industrial connections with it, and therefore its status in the network is relatively higher. Since the GIVCBN model is a directed network, the number of direct connections between an industrial sector and the upstream and downstream sectors on the global industrial and supply chains needs to be reflected separately by the In-Degree Centrality and the Out-Degree Centrality , represented by \({{DC}}^{{IN}}\left(i\right)\) and \({{DC}}^{{OUT}}(i)\) respectively.

Betweenness centrality

The Betweenness Centrality (denoted as \({BC}\) ) measures the ability of an industrial sector to act as a hub for value-added brought by intermediate goods trade in the GIVCBN model. According to Burt’s theory of structural holes, it reflects the profitability of sectors in the industrial and supply chains. If there are \({d}_{{jk}}\) shortest paths between a pair of nodes, and \({d}_{{jk}}\left(i\right)\) of them pass through \({v}_{i}\) , then the contribution rate of \({v}_{i}\) to the betweenness of this pair of nodes is \({d}_{{jk}}\left(i\right)/{d}_{{jk}}\) . By summing the contribution rates of \({v}_{i}\) to the betweenness of all pairs of nodes in the network and dividing by the total number of node pairs, we can obtain the betweenness of \({v}_{i}\) . The formula for betweenness centrality of \({v}_{i}\) is given by:

where \({d}_{{jk}}\) is the number of shortest paths from \({v}_{j}\) to \({v}_{k}\) , and \({d}_{{jk}}\left(i\right)\) is the number of those paths that pass through \({v}_{i}\) .

According to its concept and formula, \({BC}\) can be used to quantitatively analyze the potential control ability of industrial sectors over intermediate goods trade. In other words, the greater the \({BC}\) value of an industrial sector, the stronger its ability to control value flows and the information advantage it possesses. If an industrial sector with high \({BC}\) value is removed or restricted, there will be a significant impact on the network topology and transmission efficiency.

Closeness centrality

The Closeness Centrality (denoted as \({CC}\) ) measures the industrial agglomeration relationship between an industrial sector and all its upstream or downstream sectors in the GIVCBN model. A higher \({CC}\) value indicates that an industrial sector has a higher robustness of risk , which means it has the ability to withstand uncertainties arising from upstream or downstream sectors in the GPN. Numerically, the \({CC}\) value is equal to the reciprocal of the sum of the shortest distances from one node to all other nodes. The formula for closeness centrality of \({v}_{i}\) is given by:

where \({d}_{{ij}}\) is the shortest distance from \({v}_{i}\) to \({v}_{j}\) .

When the sum of the shortest distances from \({v}_{i}\) to all other nodes is minimized, the \({CC}\left(i\right)\) value is maximized. The node with the highest \({BC}\) has the greatest control over the flow of value in the network, while the node with the highest \({CC}\) value has the best observational perspective on the flow of value. In the GIVCBN model, if an industrial sector is very close to other industrial sectors, it will be able to conduct intermediate goods trade efficiently and quickly, thereby greatly enhancing the robustness of its related industrial and supply chains. Furthermore, this concept can be expanded used to assess the industrial agglomeration phenomena formed by specific industrial sectors with their upstream and downstream sectors through In-Degree Closeness Centrality and Out-Degree Closeness Centrality , represented by \({{CC}}^{{IN}}(i)\) and \({{CC}}^{{OUT}}(i)\) respectively.

This section primarily employs counterfactual analysis and complex network characteristic indicators to explore the risk exposures within the CPN. It quantitatively assesses the extent to which strengthening Altasia or ASEAN into a powerful regional economic alliance could diminish China’s function and status in the GPN. Additionally, it empirically analyzes the risk of industrial chain relocation occurring in several kinds of factor-intensive industries of China.

Risk exposure of China’s industrial and supply chains

To sort out the risk exposure of China’s industrial and supply chains from a topological structure perspective, particularly the upstream and downstream industrial sectors that might face decoupling or disruption, this paper compares all the linkages in the CPN between the counterfactual model and the null model of GIVCBN. It was found that some of these linkages were retained in the extraction process of the null model but were deleted in the counterfactual model due to their decreased relative importance. Due to space limitations, the paper uses chord diagrams to list the comparative results from 2019 to 2022, as shown in Figs. 11 and 12 . Each chord in the chord diagram represents an industrial and supply chain that exists in the null model but disappears in the counterfactual model, indicating potential risk exposures within the CPN when Altasia becomes a reality. It is important to note that the GIVCBN-III model is closer to reality than the GIVCBN-II model, in which ASEAN member countries are promoting regional trade and investment through multilateral agreements and joint market plans, such as the ASEAN Free Trade Area (AFTA) . These countries work together to reduce trade barriers and strengthen regional economic integration, which enhances economic interconnectivity among them and strengthens their connections with other global economies.

The width of the chord is proportional to the intermediate goods trade volume corresponding to the missing linkage in the industrial and supply chains as represented in the original MRIO table. From a to d are chord diagrams for the years 2019 to 2022, respectively.

From a to d are chord diagrams for the years 2019 to 2022, respectively.

By comparing the GIVCBN-I model (null model) and the GIVCBN-II or GIVCBN-III model (counterfactual model), the risk exposure in CPN can be observed due to the substitution effect of the Altasia, namely the decoupling phenomena between industrial sectors. Tables 4 and 5 provide statistics on the risk exposure of CPN under two scenarios. The first column in Tables 4 and 5 lists the 35 industrial sectors as upstream sectors at risk, and each subsequent column lists the names of the downstream sectors at risk in specific years. If, in any year, an upstream sector forms a risk exposure with at least one downstream sector, the table will list the names of these downstream sectors. In this case, the combination of upstream and downstream sectors reflects the specific industrial and supply chains where decoupling phenomena have occurred. If an upstream sector does not form a risk exposure with any downstream sector in a particular year, it is indicated with “—”.

From the results of comparing GIVCBN-I with GIVCBN-II and GIVCBN-III respectively, we can observe the following facts. First of all, considering all the Altasia-related economies as a whole will cause about 25 of China’s industrial and supply chains to be broken, while considering ASEAN as a whole will cause less than 15. We believe that the reason behind this result lies in the strong industrial complementarity between Japan, South Korea, India, and Bangladesh and the ASEAN member countries, which enables Altasia to have a more significant substitution effect on China. Secondly, the risk exposure of CPN primarily relates to resource-intensive and labor-intensive industries such as “Agriculture, Hunting, Forestry and Fishing (S01)”, “Wood and Products of Wood and Cork (S06)”, “Real Estate Activities (S29)”. In contrast, technology-intensive industries like “Chemicals and Chemical Products (S09)” and “Electrical and Optical Equipment (S14)” are almost unaffected. This indicates that Southeast Asian countries are likely to undertake the production and processing segments of China’s lower and middle-end industries. Simultaneously, China is vigorously developing high-end manufacturing with higher technological content and value-added to replace the low-end excess capacity that is transferred to Southeast Asian countries.

Changes in the macroscopic topological structure of CPN

In this section, several network-level indicators of three types of GIVCBN models were calculated, respectively, and their ratios in the counterfactual model and null model were used to gauge the influence of Altasia and ASEAN on the CPN.

As shown in Fig. 13 , the ratio of the APL in both counterfactual models to that in the null model is greater than 1, which indicates that the formation of Altasia will extend China’s industrial and supply chains, increase the distance between production and market endpoints, and decrease the efficiency of value creation through the circulation of intermediate goods. In comparison, if ASEAN can establish close cooperation with East and South Asian countries, it will not only directly lead to more of China’s industrial and supply chains relocating to Altasia economies. However, it will also indirectly deepen the separation among China’s internal industrial sectors.

This figure shows the trend of APL changes in CPNs from 2007 to 2022, where the purple bars represent the ratio of APL values in CPN from the GIVCBN-II model to those in the GIVCBN-I model, and the red bars represent the ratio of APL values in CPN from the GIVCBN-III model to those in the GIVCBN-I model.

Notably, the APL exhibits a fluctuating trend and has been continuously rebounding since 2018. This phenomenon indicates that China is adjusting its industrial layout in response to the GVC restructuring. On the one hand, China continuously strengthens economic and trade cooperation with ASEAN member countries through the RCEP, which is led by itself, exporting some of its excess capacity in resource-intensive and labor-intensive industries to Southeast Asian countries. On the other hand, China is also strategizing its overseas layouts through the international business strategy. According to a February 2024 analysis of trade data by the Financial Times, China is shipping more goods to the United States via Mexico to circumvent the high tariffs imposed by the Trump administration and retained by the Biden-led White House. Additionally, China has trade surpluses with Vietnam, Singapore, and the Philippines, and these countries’ trade surpluses with the United States are also expanding, indicating that Chinese manufacturers continue to benefit from the demand of American consumers for their products with competitive prices.

Figure 14 clearly illustrates that the ratios of \(\left\langle C\right\rangle\) in the two scenarios are all lower than 1, indicating that Altasia is causing a significant break in the interconnection between some upstream and downstream sectors within the CPN. The GIVCBN-II model consistently shows lower results than the GIVCBN-III model, suggesting that the close cooperation among ASEAN countries is also contributing to the partial decoupling of China’s industrial and supply chains. However, the impact of this is dwarfed by the formation of Altasia, which is causing a more substantial shift in the landscape.

This figure shows the trend of changes in CPNs from 2007 to 2022, where the purple bars represent the ratio of values in CPN from the GIVCBN-II model to those in the GIVCBN-I model, and the red bars represent the ratio of values in CPN from the GIVCBN-III model to those in the GIVCBN-I model.

With Southeast Asian countries as the majority, Altasia has become the biggest beneficiary of the fifth international industrial transfer due to the superposition of human costs and security factors. According to relevant statistics, the total Foreign Direct Investment (FDI) attracted by Southeast Asian countries reached a record $222.5 billion in 2022. The United States is the global leader in capital investment projects in Southeast Asia, spending $74.3 billion to build factories and other projects between 2018 and 2022, mainly hoping to diversify its supply chain by reducing its over-reliance on China. Nevertheless, under the strategic influence of the “De-Sinicization” strategy implemented by the United States, China is not passively responding but actively relocating its industrial and supply chains to Southeast Asian countries or Mexico. During the same period, China’s investment also reached $68.5 billion, aiming to maintain exports to the United States and Europe through Southeast Asian factories under the complex international background. Therefore, Altasia has become an essential broker between significant economies.

As a result, under the premise that China and the United States would jointly seek Altasia as a strategic partner, China’s position as the “World’s Factory” began to decline. This is specifically manifested in the industrial and supply chains of the CPN being less dense than in the past, which is indicated by a decrease in the network’s \(\left\langle C\right\rangle\) value.

According to the definition, a higher value of ASY indicates a lower degree of vertical specialization in the division of labor within the production network. As shown in Fig. 15 , the ratios of ASY in the two scenarios are all higher than 1. Moreover, in 2020, the ratio of the GIVCBN-II model to the GIVCBN-I model even exceeded 1.4. The fact behind this phenomenon is that the COVID-19 pandemic has severely impacted the coupling relationship between the CPN and the GPN, and significantly reduced China’s capacity to export intermediate goods to the global supply chains. Simultaneously, the substitution effect of Altasia for China has begun to manifest.

This figure shows the trend of ASY changes in CPNs from 2007 to 2022, where the purple bars represent the ratio of ASY values in CPN from the GIVCBN-II model to those in the GIVCBN-I model, and the red bars represent the ratio of ASY values in CPN from the GIVCBN-III model to those in the GIVCBN-I model.

The asymmetrical nature of different countries’ responses to COVID-19 further complicated global supply chain issues. After the outbreak of the COVID-19 pandemic, China instituted one of the strictest containment strategies in the world, known as the “Zero COVID” approach. With production halted in one of the world’s most productive countries, COVID-19 shocked the global economy into widespread unpredictability and shortages. In this context, companies and governments are reconsidering their reliance on “Made in China” and are beginning to diversify their supply sources to enhance resilience. At the same time, in response to the pandemic and its economic impacts, many countries have introduced policies and incentives aimed at attracting FDI and encouraging domestic manufacturing. Countries in Southeast Asia, Mexico, and India have implemented favorable tax regimes, subsidies, and infrastructure improvements, making them more competitive as alternative manufacturing locations.

Changes in the microscopic topological structure of CPN

Although global economic integration has experienced a period of rapid development, the industrial and supply chains of Southeast Asia have long been positioned as “Semi-Periphery” within the GPN, maintaining a relatively independent internal structure. In recent years, the United States’ policy and strategy towards China have shifted, as evidenced by leveraging strategic frameworks such as the CPTPP and the IPEF to unite Southeast Asian countries. This move aims to turn them into new manufacturing hubs and proxies in the Asia-Pacific region, resulting in a trend of both active and passive relocation of industrial supply chains from China.

To quantitatively analyze the extent to which Altasia has weakened China’s influence scope , profitability , and robustness of risk within the global industrial and supply chains, this paper integrates the calculation results of five types of network centrality indicators from 2007 to 2022. In this section, the Probability Density Curve (PDF) was used to evaluate the overall impact on Altasia and China based on the Mean ( \({\boldsymbol{\mu }}\) ) and Standard Deviation ( \({\boldsymbol{\sigma }}\) ) of the normal distribution.

Once Altasia becomes a tight economic organization, both its and China’s industrial sectors will see a reduced influence scope on the upstream ( \({\mu }_{3}=0.835\) and \({\mu }_{1}=0.878\) in Fig. 16 ) and downstream ( \({\mu }_{3}=0.772\) and \({\mu }_{1}=0.892\) in Fig. 17 ) sectors across all other economies. If ASEAN is considered solely as a tight core, then the negative impact of Altasia on China would be significantly reduced ( \({\mu }_{1} < {\mu }_{2}\) in Figs. 16 and 17 ), and the variability among the data points representing the influence scope of Altasia’s and China’s internal industrial sectors would decrease ( \({{\sigma }_{4} < \sigma }_{3}\) and \({{\sigma }_{2} < \sigma }_{1}\) in Figs. 16 and 17 ). Obviously, the anti-globalization trend generated by ASEAN is weaker than that of Altasia.

The probability density function curve is drawn based on data from all relevant industrial sectors from 2007 to 2022, reflecting the potential impact capacity of Altasia and ASEAN on China over more than a decade.

The settings in this figure are the same as those in Fig. 16 .

Under the premise of Altasia as a tightly integrated economic organization, its and China’s overall profitability of industrial sectors would see a slight decline ( \({\mu }_{3}=0.966\) and \({\mu }_{1}=0.962\) in Fig. 18 ). Altasia’s industrial sectors, dispersed across different economies, exhibit more significant variability among data points ( \({\sigma }_{3}=0.493\) in Fig. 18 ). However, under the premise of a tight integration among ASEAN member countries, the overall profitability of Altasia’s industrial sectors shows a more noticeable decrease ( \({\mu }_{4}=0.843\) in Fig. 18 ), while that of China slightly increases ( \({\mu }_{2}=1.025\) in Fig. 18 ). This result demonstrates that by deepening economic and trade cooperation with ASEAN through the RCEP, China can gain incredible benefits in the global industrial and supply chains.

Overall, after the formation of either a complete (including ASEAN, East Asian, and South Asian) or partial (only ASEAN) tight industrial and supply chain coupling in Altasia, the overall robustness of China’s industrial sectors against uncertainties from upstream ( \({\mu }_{1}=0.973\) and \({\mu }_{2}=0.987\) in Fig. 19 ) and downstream ( \({\mu }_{1}=0.978\) and \({\mu }_{2}=0.988\) in Fig. 20 ) do not show a significant decline. However, in the former scenario, there is more significant variability among the data points ( \({{\sigma }_{1} > \sigma }_{2}\) in Figs. 19 and 20 ), indicating that some industrial sectors experience different levels of impact. Even if Altasia prompts the relocation of industrial chains within China, China maintains close economic ties with other economies worldwide and leverages these connections to hedge against external risk shocks.

Changes in the specific industrial sectors of CPN

This paper selects seven industries as empirical analysis objects based on their intensity of factors, including resource-intensive industries [“Agriculture, Hunting, Forestry and Fishing (PRCS01)”, “Mining and Quarrying (PRCS02)”, “Food, Beverages and Tobacco (PRCS03)”], labor-intensive industries [“Textiles and Textile Products (PRCS04)”], capital-intensive industries [“Basic Metals and Fabricated Metal (PRCS09)”], and technology-intensive industries [“Chemicals and Chemical Products (PRCS12)”, “Electrical and Optical Equipment (PRCS14)”]. In this section, the boxplots are used to illustrate the fluctuation intervals of these factor-intensive industries.

Degree centrality measures a sector’s direct industrial linkages in the production network. The in-degree centrality reflects the importance of an industrial sector as the demander of intermediate goods. In contrast, the out-degree centrality reflects the importance of a sector as the supplier of intermediate goods. Altasia’s industrial substitution effect on China shows a certain regularity from the in-degree or out-degree centrality perspective, as shown in Figs. 21 and 22 . First, the ratios of the three resource-intensive industries are significantly less than 1, indicating that Altasia economies have seriously weakened China’s influence scope in these fields. Second, Altasia’s direct impacts on China’s capital-intensive and technology-intensive industries are relatively small, especially when the latter’s “Electrical and Optical Equipment (PRCS14)” sector supplies intermediate goods. Altasia will only partially replace the functions and positions of these factor-intensive industries in China. Third, the ratio of “Agriculture, Hunting, Forestry and Fishing (PRCS01)” is the smallest when China plays the role of supplier, indicating that Altasia has replaced China as the main export region for such intermediate goods.

The boxplots provide a visualization of the median line, reflecting the extent of the industrial substitution effect caused by Altasia (GIVCBN-II/GIVCBN-I) or ASEAN (GIVCBN-III/GIVCBN-I) on China. The smaller the ratio, the stronger the substitution effect. Besides, the icons on the horizontal axis in the boxplots represent PRCS01, PRCS02, PRCS03, PRCS04, PRCS09, PRCS12, PRCS14 sectors, respectively. a Temporal distribution of in-degree centrality (GIVCBN-II/GIVCBN-I). b Temporal distribution of in-degree centrality (GIVCBN-III/GIVCBN-I).

a Temporal distribution of out-degree centrality (GIVCBN-II/GIVCBN-I). b Temporal distribution of out-degree centrality (GIVCBN-III/GIVCBN-I).

From an overall perspective, compared with Altasia, ASEAN has a weaker industrial substitution effect on China, which indicates that economies such as Taiwan, India, Japan, and South Korea play an essential role in Altasia. Taking “Electrical and Optical Equipment” as an example, Taiwan, South Korea, and Japan own these core upstream sectors that provide critical intermediate goods for developing China’s electronic equipment manufacturing industry. Taking India as another example, based on the window period of global geopolitical tensions, India has attracted foreign capital to set up factories. Since 2020, India’s electronic products’ average annual export growth rate has been 30.4%, and its importance as a global electronic products trade node is rising. In addition, in the context of global value chain restructuring and COVID-19, Japan’s critical industrial and supply chains are broken, which promotes strengthening the critical industrial chain’s localization and starting the supply chain reform—the “China + 1” strategy. However, it is challenging to replicate a mature manufacturing industry chain and supply chain quickly, so this strategy is not easy to achieve.

Betweenness centrality measures the extent to which an industrial sector acts as a bridge for the flow of products and services in the GPN, and its ratio of the counterfactual model to the null model for different industrial sectors reflects the extent to which their hub function is disturbed. From the perspective of betweenness centrality, the ratios of six representative sectors in China are all less than 1, indicating that they are affected by the industrial substitution effect of a hypothetical economic entity, as shown in Fig. 23 . Generally speaking, the most vital industrial substitution effect is mainly in the resource-intensive industry, among which the industrial relocation trend of “Agriculture, Hunting, Forestry and Fishing (PRCS01)” is the most obvious. In contrast, the ratio of “Electrical and Optical Equipment (PRCS14)” exceeds 1, reflecting that Altasia not only does not weaken PRCS14’s hub function but actually plays a significant role in enhancing its profitability in the GPN. This underscores the dynamic nature of industrial substitution effect and its potential to reshape the global production landscape.

a Temporal distribution of betweenness centrality (GIVCBN-II/GIVCBN-I). b Temporal distribution of betweenness centrality (GIVCBN-III/GIVCBN-I).

Taking ASEAN as a whole, the ratios of most industrial sectors are greater than 1, especially the capital-intensive and technology-intensive industries, indicating that ASEAN will strengthen the profitability of these sectors in China. In recent years, China has taken the initiative to transfer some low-value-added and labor-intensive industrial sectors, such as clothing, toys, mechanical and electrical products processing and assembly, to Vietnam, India and other Asian neighboring economies, while increasing investment in higher value-added and more technological innovation linkages in the industrial chain, so as to provide intermediate goods with higher value for Southeast Asia. Since Japan and South Korea’s scientific and technological strengths are in the leading position in the world (such as South Korea’s semiconductor and Japan’s precision machine tools), Altasia has a relatively significant substitution effect for China’s technology-intensive industry, while ASEAN mainly undertakes low-end sectors, and even rely on China’s industrial and supply chains to a large extent.

Closeness centrality, a measure used in network analysis, quantifies the extent to which an industrial sector is central to the GPN. In our analysis, the ratio of the counterfactual model to the null model is close to 1, indicating that China’s status as a global production center has not been affected by the apparent spillover effect of other economies in the region, as shown in Figs. 24 and 25 . First of all, China has the world’s largest manufacturing industry and the most complete industrial chain, which can independently complete the whole process from raw material procurement to manufacturing, and then to sales and service, making it impossible for enterprises from other countries or regions to truly implement the so-called “China exit strategy”. Second, China’s dominance in the high-tech field is not a recent development. Despite attempts by the United States to limit investment in China’s science and technology, American high-tech enterprises like Apple and Boeing still heavily depend on China’s industrial and supply chain. This is a testament to China’s long-standing technological prowess. It has taken over three decades for China to become a global production center, and the notion of any region replacing it overnight is unrealistic. Third, China has more advantages than other economies in terms of the business environment, such as its robust legal system and favorable tax policies, and top talents, with a large pool of highly skilled workers and a strong emphasis on education. In addition, the high degree of integration and deep cooperation with the industrial chain of some economies (such as Japan and South Korea) makes it more challenging for these economies to shake China’s central position.

a Temporal distribution of in-degree closeness centrality (GIVCBN-II/GIVCBN-I). b Temporal distribution of in-degree closeness centrality (GIVCBN-III/GIVCBN-I).

a Temporal distribution of out-degree closeness centrality (GIVCBN-II/GIVCBN-I). b Temporal distribution of out-degree closeness centrality (GIVCBN-III/GIVCBN-I).

Discussion and conclusion

If, under the influence of geopolitics, ASEAN can form closer economic and trade cooperation with East Asia and South Asia, then the resulting Altasia will indeed affect the security of China’s industrial and supply chains, particularly having a noticeable substitution effect on resource-intensive and labor-intensive industries. Therefore, we can see that China is accelerating the construction of the “Dual Circulation” new development pattern, aiming to enhance the resilience and security of industrial and supply chains, and achieve long-term sustainable development. To address the industrial substitution effect caused by Altasia on China, this study proposes specific policy recommendations as follows:

China should start to deepen trade and investment ties with countries that the United States prioritizes for nearshoring and offshoring, such as Vietnam, Thailand, and Mexico. These countries often require Chinese equipment and components for processing and production, while China also needs their resources and markets. Supporting Chinese manufacturing enterprises to increase overseas investment can effectively respond to the profound adjustments in the global industrial and supply chains by the United States and significant changes in international trade, thus mitigating the adverse impacts of “De-Risking” strategies on Chinese enterprises. At the same time, China should continue to increase investments in domestic high-tech and high-value-added manufacturing industries, providing Southeast Asia and Mexico with intermediate products of higher added value. In the trend towards diversified supply chains, China should achieve win-win cooperation with these countries to jointly promote the optimization and development of the global industrial and supply chains.

For resource-intensive industries at the highest risk of industrial relocation, relevant trade policies should be introduced to prevent excessive supply chain outsourcing and avoid the phenomenon of “Hollowing Out” in China. For technology-intensive industries, China should strengthen the substitution of domestic products for imported ones, promoting the manufacturing industry’s high-end, intelligent, and green development. It should actively cultivate strategic emerging industries such as new energy, new materials, advanced manufacturing, and electronic information, and proactively plan for future industries like brain-like intelligence, quantum information, deep-sea space, hydrogen energy, and energy storage to promote modern industrial clusters and accelerate the formation of new productive forces. At the same time, to enhance the local resilience of China’s industrial and supply chains, further improvements should be made in the foreign investment environment, strengthening intellectual property protection and reducing market access barriers to attract multinational corporations to stay in China.

Actively cultivate regional and domestic value chains to build an independent, controllable, safe, and reliable complete industry chain system that can effectively withstand shocks from domestic and international contingencies, emergencies, and adverse effects, avoiding disruptions, blockages, and even breaks in the supply chain whenever possible. China should seize the post-pandemic window of opportunity for economic recovery and rely on the Belt and Road Initiative to build “China+X” industrial chains, implementing a more proactive and open strategy. By utilizing RCEP to construct a regionally dominated industrial chain network and actively striving to join CPTPP, China can more proactively expand international exchanges and cooperation, gaining more influence, decision-making power, and initiative in restructuring the global value chain. Through active participation in the reform and construction of the global governance system, China can contribute more Chinese wisdom, solutions, and strength to restructuring the global value chain, continuously enhancing China’s influence and rule-making capacity in global governance.

Research outlook

Due to the dominance of processing trade by foreign enterprises, which is the main mode of foreign trade for emerging economies (such as China), it plays a crucial role in the development of the national economy. In recent years, the large-scale capacity transfer by foreign enterprises has threatened the industrial security of many countries and regions. MRIO tables contain rich inter-regional industrial and supply chain linkage information, providing a basis to judge the direction and intensity of industrial shifts on the GVC. However, most studies based on MRIO tables follow the “territorial” rules of national economic accounting, which treat the value-added activities of multinational corporations’ branches within the host country the same as those of domestic enterprises, leading to inaccurate assessments of the negative impacts of multinational corporations’ capacity transfers on the industrial structures of specific economies. Therefore, it is not only a necessity but also a promising opportunity to establish an IO model that reflects the relationships between domestic and foreign enterprises, and scholars have already made beneficial attempts in this direction, paving the way for more accurate and comprehensive assessments of the global industrial landscape.

In 2019, the OECD released the OECD-AMNE database, which, using the bilateral distribution matrix of multinational corporations’ outputs, transforms the investment returns in the host countries’ territorial added value to the parent country’s entitled added value. This has become the optimal approach for depicting GVC activities under the conditions of multinational corporations’ influence, fully reflecting the adverse effects of the anti-globalization process on multinational corporations and their production networks in their respective countries. The data structure of the OECD-AMNE database’s bilateral distribution matrix is a typical Multiple-Input Multiple-Output (MIMO) network, thus these types of MRIO tables can be referred to as Multi-Regional Multiple-Input Multiple-Output (MRMIMO) tables. Research utilizing the OECD-AMNE database has begun to show preliminary results (Cadestin et al. 2018 ; Andrenelli et al. 2018 ; Zhu et al. 2022 ; Duan and Cai 2022 ).

In light of the current clear trend of anti-globalization and the unstable geopolitical landscape, to further understand the extent to which multinational corporations’ diversified layout strategies are reshaping the global industrial division system, and their impact on the security and resilience of GPN, it is necessary to distinguish between domestic and foreign enterprises within the industrial sectors. Therefore, in future research, we will establish a type of heterogeneous network based on MRMIMO tables that differentiates corporate ownership and the upstream and downstream relationships in the industrial and supply chains. We will measure the impact of foreign enterprises’ capacity transfers on the resilience of GPN using various research methods such as time series analysis, counterfactual analysis, and game theory analysis to assess future trends.

Data availability

The datasets analyzed during the current study are available in the ADB Key Indicators Database (KIDB) repository, https://kidb.adb.org/globalization . And the datasets generated during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We acknowledge financial support from the National Natural Science Foundation of China (72471007, 71971006, 72273009), the Beijing Municipal Social Science Foundation, China (23ZGB005), the R&D Program of Beijing Municipal Education Commission (KZ202210005013), and the Study Abroad Foundation of China Scholarship Council (202306540140).

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Xing, L., Jiang, S., Yin, S. et al. Substitution effect of Asian economies on China’s industrial and supply chains: from the perspective of global production network. Humanit Soc Sci Commun 11 , 1304 (2024). https://doi.org/10.1057/s41599-024-03797-6

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A comprehensive review and mapping citrus supply chains from a sustainability perspective across the european union, middle east, and africa.

1. Introduction

• What are the recent research topics in the studied regions?
• Which research methodology is applied in the research field?
• Which echelons of the citrus supply chain are studied?

Click here to enlarge figure

2. Citrus Production and Exports in European Union, Middle East, and Africa

2.1. european union, 2.2. middle east, 2.3. africa, 3. methodology, 4. bibliometric analysis results, 5. content analysis results, 5.1. research topics.

5.2. Research Methodology

5.3. supply chain echelons, 6. citrus supply chain structure, 7. conclusions and future work, 7.1. conclusions, 7.2. future work, author contributions, data availability statement, conflicts of interest.

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Beshara, S.; Kassem, A.; Fors, H.; Harraz, N. A Comprehensive Review and Mapping Citrus Supply Chains from a Sustainability Perspective across the European Union, Middle East, and Africa. Sustainability 2024 , 16 , 8582. https://doi.org/10.3390/su16198582

Beshara S, Kassem A, Fors H, Harraz N. A Comprehensive Review and Mapping Citrus Supply Chains from a Sustainability Perspective across the European Union, Middle East, and Africa. Sustainability . 2024; 16(19):8582. https://doi.org/10.3390/su16198582

Beshara, Sherin, Ahmed Kassem, Hadi Fors, and Nermine Harraz. 2024. "A Comprehensive Review and Mapping Citrus Supply Chains from a Sustainability Perspective across the European Union, Middle East, and Africa" Sustainability 16, no. 19: 8582. https://doi.org/10.3390/su16198582

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Understanding the Drivers of Industry 4.0 Technologies to Enhance Supply Chain Sustainability: Insights from the Agri-Food Industry

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• Published: 26 September 2024

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• Guoqing Zhao   ORCID: orcid.org/0000-0003-4553-2417 1 ,
• Xiaoning Chen 2 ,
• Paul Jones 1 ,
• Shaofeng Liu 2 ,
• Carmen Lopez 3 ,
• Leonardo Leoni 4 &
• Denis Dennehy 1  

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The sustainability of agri-food supply chains (AFSCs) is severely threatened by regional and global events (e.g., conflicts, natural and human-made disasters, climate crises). In response, the AFSC industry is seeking digital solutions using Industry 4.0 (I4.0) technologies to enhance resilience and efficiency. However, why I4.0 adoption remains stubbornly low in the agri-food industry remains poorly understood. To address this gap, this study draws on middle-range theory (MRT) and uses thematic analysis, the fuzzy analytic hierarchy process, total interpretive structural modelling, and fuzzy cross-impact matrix multiplication applied to classification to produce insights from nine case studies in China that have invested in I4.0 technologies to improve their AFSC sustainability. New drivers of I4.0 unique to the agri-food industry are identified, showing how I4.0 can contribute to the environmental, economic, and social dimensions of AFSC sustainability. The results have implications for AFSC researchers and practitioners with an interest in supply chain sustainability.

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1 Introduction

Agri-food supply chains (AFSCs) are inherently complex systems involving various stakeholders (e.g., suppliers, farmers, processors, wholesalers, distributors, and retailers) who engage in agriculture-related activities to move products across the chain from ‘farm to fork’ (De Carvalho et al., 2022 ; Zhao et al., 2024 ). In contrast to the supply chains of other foods (e.g., tinned food), agri-food products are characterized by perishability, seasonality, and short life cycles, and require specialized transportation and storage conditions are required to maintain product quality (Zissis et al., 2017 ).

AFSCs are critical achieving the United Nations Sustainable Development Goal 2, to end hunger, achieve food security and improved nutrition and promote sustainable agriculture. Despite their importance, AFSCs’ sustainability is threatened by regional and global challenges. For example,to deal with the predicted population growth, urbanization, and consumption, agri-food production will need to increase by 70% by 2050 (Spanaki et al., 2021 ). At the same time, the environmental effects of agri-food system may increase by 50% to 90% by 2050, reaching levels beyond planetary boundaries that define a safe operating space for humanity (Springmann et al., 2018 ). Agri-food systems have environmental impacts: for example, excessive use of agrichemicals to increase productivity may contaminate water supplies and the agricultural sector contributes 21% of global greenhouse gas emissions (SDWF, 2023 ).

In this study, we draw on nine case studies in China that have invested in I4.0 technologies to improve their AFSC sustainability. China offers a unique context for this study. Almost 99 billion US dollars-worth of agricultural products were exported from China in 2023, yet it has become the largest importer of agricultural products in the world (Statista, 2024 ). This over reliance on imports has arisen because the Chinese agricultural industry can no longer meet increased demand owing to a scarcity of arable land, making it less competitive in an open trade environment (Stastista, 2024 ).

I4.0 technologies have the capability to enhance AFSCs’ sustainability by improving stakeholder collaboration, enhancing information sharing, augmenting decision making and creating value (Gebhardt et al., 2022 ; Huber et al., 2022 ). Recent studies (e.g., Chatterjee et al., 2023 ; Chou & Shao, 2023 ; Margherita & Braccini, 2023 ) have explored various aspects of I4.0 and supply chain sustainability such as factors mediating between I4.0 and supply chain practices, sustainable supply chains and the circular economy, and evaluation of sustainability performance. Despite the important contributions of these studies, a holistic understanding of the drivers of I4.0 applications and their impact on the three pillars of sustainable performance (environmental, social and economic) is lacking (Srhir et al., 2023 ). Furthermore, few studies have used a range of techniques to analyze the drivers of I4.0 technology deployment to achieve AFSC sustainability (Agrawal et al., 2022 ; Taddei et al., 2022 ; Yadav et al., 2022 ).

Another deficiency in knowledge relating to I4.0 and its effects on AFSCs is that extant studies focus largely on manufacturing industries, where understanding of its adoption and implementation is well developed (Yadav et al., 2022 ). As the agri-food industry is distinct from manufacturing industry, its challenges to adopting I4.0 and understanding its contributions to AFSC sustainability are less well understood (Birkel & Muller, 2021 ; Tseng et al., 2018 ). Therefore, identifying and prioritizing drivers are warranted to better understand the potential of I4.0 technologies in AFSCs. Several literature reviews (e.g., Agrawal et al., 2022 ; Srhir et al., 2023 ; Taddei et al., 2022 ; Yadav et al., 2022 ) highlight the need to employ different analytical techniques to gain a deeper understanding of the enablers and drivers of I4.0 technology deployment to achieve sustainable supply chains. Our literature review reveals that only six of the 56 primary papers we identified focus on analyzing drivers, enablers, success factors, decision frameworks, or facilitators to achieve sustainable supply chain, green supply chain, circular economy, or sustainable development. This study addresses this gap by conducting an empirical study of I4.0 technology deployment to achieve AFSC sustainability using multiple analytical techniques.

Against this background, in this study we aim to answer three interrelated research questions (RQs).

RQ 1. What drivers facilitate the adoption of I4.0 in AFSCs?

RQ 2. How are these drivers prioritized?

RQ 3. On Which of these drivers should AFSC practitioners focus?

To answer these questions, we conducted three phases of research: first, semi-structured interviews were conducted with AFSC practitioners in China to identify drivers facilitating the adoption of I4.0 (RQ1). Next, the fuzzy analytic hierarchy process (AHP) was used to rank the drivers and evaluate their weightings in relation to the three pillars of AFSC sustainability (RQ2). Finally, interpretive structural modelling (TISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis was conducted to identify key drivers by building a hierarchical framework and categorizing the drivers based on their driving and dependence power (RQ3).

This study advances understanding of the deployment of I4.0 technology to achieve AFSC sustainability and suggests adoption routes for AFSC practitioners. Furthermore, by identifying two key drivers of I4.0 adoption not mentioned in previous AFSC studies, and aggregating several agri-food industry-specific drivers rarely mentioned in previous studies, we provide a more holistic understanding of this important phenomenon that impacts all societies.

The remainder of this paper is structured as follows. First, background literature to middle-range theory (MRT), and a systematic literature review on the applications of I4.0 in the context of AFSCs is provided. Next, the data collection and data analysis methods are explained. Then, the analysis and findings are presented. Followed by a discussion, implications, and opportunities for future research. The paper ends with a conclusion.

2 Literature Review

2.1 middle-range theory (mrt).

Various theories have been used to explore the relationship between I4.0 and sustainability issues. For example, Abdul-Hamid et al. ( 2021 ) investigate drivers of I4.0 in a circular economy by deploying ecological modernization theory, which posits that advanced technologies can improve value added on both economic and environmental dimensions. Karmaker et al. ( 2023 ) explore the impact of I4.0 on sustainable supply chain performance through the resource-based view (RBV), which assumes that firms gain competitive advantage by controlling scarce and valuable resources. Other theories frequently used to understand supply chain sustainability in an I4.0 context, include institutional theory, dynamic capabilities (DC), innovation diffusion theory, social network theory, and information processing theory. These are useful for exploring a wide range of phenomena by defining relationships and concepts, but are criticized by scholars for focusing on phenomena operationalized at a high level of abstraction with little functional context or specificity (Stank et al., 2017 ). This results in weak understanding of why and when the investigated phenomena occur.

MRT differs from other theories by restricting explanation of causal connections to a subset of phenomena operating within a given context (Pellathy et al., 2018 ). It focuses on understanding how and why constructs are related, and under what conditions, thereby helping to consolidate knowledge in a particular domain. For example, Burns et al. ( 2023 ) develop an MRT to understand motives for and controls on insider computer abuse, and Hassan and Lowry ( 2015 ) call for MRT to be used in more information systems research. Formal MRT has three essential elements: (1) conducting research within a specific domain of knowledge; (2) building or establishing relationships based on existing findings within that domain; and (3) concentrating on causal mechanisms and the contexts in which they produce outcomes (Pawson & Tilley, 1997 ). MRT was suited to our study for several reasons. First, MRT aims to extend knowledge within a mature discipline, and the topic of our study has already been explored by various scholars (Bhatia & Kumar, 2022 ; Lu et al., 2022 ; Mastrocinque et al., 2022 ). Second, we aim to understand I4.0 deployment to achieve AFSC sustainability by exploring various drivers. In this case, drivers can be considered as the enabling environment and deployment of I4.0 technology can be considered as a mechanism to jointly achieve AFSC sustainability, thus fulfilling the MRT framework of mechanisms + context = outcomes (Pawson & Tilley, 1997 ). Third, accumulated knowledge of I4.0 and supply chain sustainability can be leveraged to establish relationships between variables. To build a theoretical framework empowered by MRT, we first examined this accumulated knowledge in order to formulate appropriate research questions (Craighead et al., 2024 ). For example, in this study, our research questions focused on identification and prioritization drivers that facilitate the adoption of I4.0 in AFSCs. Having derived our research questions, we then contextualized our MRT to determine how to engage with the theory. The three basic approaches are induction, deduction and abduction. An inductive approach was appropriate because it examines meanings, processes, or contexts that are difficult to quantify. For example, we aimed to understand why and how drivers are related, and under what conditions that these drivers can be used to facilitate the adoption of I4.0 technologies. Since it would have been difficult to gain a deep understanding through a quantitative approach, we began by analyzing rich qualitative data to reveal drivers facilitating the adoption of I4.0 technologies. We then employed two research techniques to further explore our key findings to draw out their theoretical and practical implications. Finally, we linked the mechanisms, contexts, and outcomes to formulate a conceptual MRT framework as shown in Fig.  1 .

Conceptual framework

2.2 Applications of I4.0 Technologies in AFSC Management

I4.0, representing the fourth industrial revolution, was originally proposed in 2011 to upgrade and reshape the manufacturing sector by integrating advanced information technologies (Lu et al., 2022 ). The aims are to maximize production efficiency to satisfy customized individual needs for products and services, enhance flexibility and connectivity, and minimize production costs by establishing smart, automated, integrated, and intelligent manufacturing processes (Zhang et al., 2021a , 2021b ). Several I4.0 technologies have been widely discussed and applied, including facial recognition enabled by AI to enhance value in the travel and tourism industry (Gupta et al., 2023 ), IoT-based information systems for logistics 4.0 (Tang et al., 2023 ), and smart city management using big data analytics (BDA) powered by AI-machine learning (Alahakoon et al., 2023 ). Yin et al. ( 2018 ) concludes that I4.0 comprises seven technologies, whereas Tang and Veelenturf ( 2019 ) identify six. Zheng et al. ( 2021 ) propose ten I4.0 technologies: IoT, cyber-physical systems, BDA, cloud technology, AI, blockchain, simulation and modeling, augmented/virtual reality, automation and industrial robots, and additive manufacturing. The lists of I4.0 technologies in the literature lack consistency, perhaps for two reasons. First, scholars understand I4.0 design principles differently, resulting in diverse I4.0 technologies. For example, Qin et al. ( 2016 ) and Alguliyev et al. ( 2018 ) suggest six key characteristics of I4.0 technologies: decentralization, modularity, interoperability, virtualization, real-time support, and service orientation. However, Aoun et al. ( 2021 ) consider only three characteristics: vertical networking of smart production systems, horizontal integration of global value chain networks, and through-life engineering across the entire value chain. Second, industries have unique characteristics, and therefore emphasize different technologies in accelerating I4.0 adoption. For example, the logistics industry may focus on blockchain technology, the healthcare industry may concentrate on BDA, and the maritime industry may strengthen automation through robotics. Based on a critical review of papers published in reputable journals and consideration of I4.0 characteristics, our synthesis of existing works consists of eleven I4.0 technologies (see Table  1 ), adding drones to Zheng et al. ( 2021 ) list.

AFSCs are facing challenges such as food price volatility, quality and safety issues, food wastage and loss, and food fraud (Zhao et al., 2022 ). I4.0 technologies have been applied to alleviate or tackle these challenges. For example, self-driving robots have been utilized for automatic spraying of pesticides and crop harvesting (Javaid et al., 2022 ), machine learning algorithms have been used for crop and soil monitoring, and BDA has been applied to track and anticipate environmental impacts on agricultural outputs (Ranjha et al., 2022 ). IoT is widely used to monitor and control food processing equipment and can be utilized with AI to take corrective actions to avert machine breakdowns (Dadhaneeya et al., 2023 ). Pele et al. ( 2023 ) propose an IoT- and blockchain-based framework that can be applied to AFSC logistics. This promises several benefits, including reducing the number of middlemen and building trust at the intra-company level, creating transparency and reducing errors at the inter-company level, and reducing cost and delivery times at the customer level. Duong et al.’s ( 2020 ) summary of applications of robotics and autonomous systems in AFSCs suggests that applications commonly integrate these technologies into AFSCs to achieve food quality, safety, and waste reduction, and enhance supply chain efficiency and analysis. Based on a review of over 80 journal articles, Sharma et al. ( 2020 ) identify that machine learning has been adopted in four phases of AFSC management: pre-production (e.g., irrigation management and analysis of soil properties), production (e.g., disease detection and weather prediction), processing (e.g., demand and quality management), and distribution (e.g., transportation and retail management). I4.0 is a relatively new concept encompassing many technologies. Each has unique features that allow its application in different phases of AFSCs including farming, processing, distribution, and retailing. For example, IoT, sensors, smartphones, and machine learning can be applied to the production phase for irrigation management (Kamienski et al., 2019 ), and 3D printing is characterized by layer-by-layer material deposition directly from a pre-designed file, can be applied at the food processing stage for customized food design and personalized food nutrition (Liu et al., 2017 ); IoT, blockchain, and sensors can be used in the distribution phase for traceability (Zhao et al., 2019 ); and BDA, smartphones, and cloud computing can be used in the retailing phase to predict consumer preferences (Erevelles et al., 2016 ). Evidence of how I4.0 applications are used in AFSCs are listed in Table  1 , whereby three ticks (√√√) indicate strong evidence, one tick (√) indicates weak evidence, and no tick indicates no evidence.

2.3 I4.0 Technologies and Supply Chain Sustainability

Previous studies have examined the impact of I4.0 on supply chain digitalization and performance analysis, its utilization to improve supply chain productivity, and barriers to its deployment to achieve supply chain sustainability (Agrawal et al., 2022 ; Bag et al., 2021 ; Gebhardt et al., 2022 ). In research on the relationship between I4.0 and sustainability, particular attention is given to I4.0’s contributions to the three pillars of sustainability. Some papers take a general perspective, while others concentrate on specific factors. For example, Ghobakhloo et al. ( 2020 ) indicate 16 opportunities provided by I4.0 for sustainability, including some frequently mentioned by other scholars, such as greenhouse gas emissions reduction, energy and resource sustainability, human resource development, and social welfare enhancement. According to Naseem and Yang ( 2021 ), I4.0 empowers product planning and scheduling, storage, and distribution, purchasing and sourcing, and production processes, thereby enhancing the environmental, social, and economic sustainability of supply chains. The topic of supply chain digitalization and performance analysis focuses on I4.0 technology implementation and its implications for supply chain performance. For example, Sengupta et al.’s ( 2022 ) case study illustrates how blockchain technology improves supply chain resilience and generates income opportunities for those in poor fishing communities. Mesquita et al. ( 2022 ) highlight the integration of lean and I4.0 to achieve environmental sustainability, another important topic closely linked with both I4.0 and supply chain productivity. Amongst many conceptual and empirical studies relevant to this topic are Fragapane et al.’s ( 2022 ) examination of the role of autonomous robotics in increasing the productivity and flexibility of production networks and Enrique et al.’s ( 2023 ) study of arrangements of I4.0 technologies to achieve different purposes (e.g., manufacturing flexibility, process quality, and productivity). Papers on challenges or barriers to I4.0 deployment to achieve supply chain sustainability focus on identifying, prioritizing, linking, and clustering them using various analytical and modeling techniques. Finally, subtopics relevant to I4.0 and the circular economy include theoretical models for implementing of I4.0 in the context of the circular economy and case studies exploring intersections between the circular economy and I4.0 (Awan et al., 2021 ; Chauhan et al., 2021 ).

This research area is fragmented because supply chain sustainability is a broad term comprising many elements (environmental, social, and economic), and can be achieved through various capabilities, such as collaboration, coordination, and supply chain integration (Piccarozzi et al., 2022 ). Although extant literature explores a range of topics relating to I4.0 technologies and supply chain sustainability, further investigation of the drivers of I4.0 deployment will advance understanding of their integration into sustainable supply chains (Srhir et al., 2023 ; Taddei et al., 2022 ).

3 Systematic Review of the Literature on Drivers of I4.0 Technology Deployment to Achieve Supply Chain Sustainability

Consistent with previous reviews of the literature on achieving supply chain sustainability using I4.0 technologies (Birkel & Muller, 2021 ; Piccarozzi et al., 2022 ; Srhir et al., 2023 ), a search string of 18 keywords was used to identify the drivers of I4.0 technology deployment (see Fig.  2 ).

SLR process

Characteristics of the 56 primary papers identified are presented in Table  2 .

Our systematic literature review revealed many drivers reported in previous studies, as well as new drivers (highlighted in bold) emerging from this phase of our study (see Table  3 ).

4 Research Methodology

We adopted a qualitative approach to analyze the drivers of I4.0 technology deployment to achieve AFSC sustainability, which promised several advantages. First, a qualitative approach potentially provides a deeper understanding of the phenomenon would be gained from a quantitative study. Second, qualitative data can capture the diversity of environments or situations. In our study, we analyzed various drivers, captured through qualitative interviews. Third, qualitative data can help to generate new ideas, concepts, and theories (Jogulu & Pansiri, 2011 ). We addressed criticism that qualitative data may be subject to credibility and reliability issues by employing multiple data analysis techniques, including thematic analysis, fuzzy AHP, TISM, and fuzzy MICMAC analysis (see Fig.  3 ).

Research methodology adopted

4.1 Data Collection Method

Semi-structured interviews, simply defined as purposeful conservations (Burgess, 1984 ), are a widely used qualitative research method allowing researchers and participants to explore a pre-determined set of research questions (Saunders et al., 2009 ). We adopted this method for several reasons. First, our conservations with interviewees were guided by a pre-defined list of open-ended questions, providing a set of themes on which to focus, while also allowing us to probe interesting and relevant issues (Barriball & While, 1994 ). This critical advantage over structured and unstructured interviews enabled elicitation of more valuable and complete information on the topic. Second, participants were provided with sufficient opportunities to speak freely during the interviews, even on sensitive topics, thereby helping to generate highly meaningful information and reveal novel aspects (McIntosh & Morse, 2015 ). For example, government subsidies may be one driver of farmers’ use of advanced agricultural facilities, and this approach allowed us to discuss the amounts of subsidies they received from the government. Finally, a high response rate was achieved by ensuring that participants were able to answer all the questions.

4.2 Data Analysis Techniques

Four complementary data analysis techniques (thematic analysis, fuzzy AHP, TISM, and fuzzy MICMAC analysis) were employed in this study. Each is presented in order of use in this study.

Thematic analysis: This technique was used to generate drivers based on the data collected from the semi-structured interviews. Thematic analysis is an easily grasped, widely accepted, and foundational method for conducting qualitative analysis, used mainly to identify, describe, organize, and report themes found within a dataset (Braun & Clarke, 2006 ). It was adopted for several reasons. First, the results of thematic analysis are easily understood by members of the public with low educational levels, which suited our research context and would enable broad impacts on the agri-food industry. Furthermore, thematic analysis enables key features of a large dataset to be summarized (Nowell et al., 2017 ), and was thus suited to this study, which produced 130 pages of transcripts from 26 semi-structured interviews. Thematic analysis is also useful for generating insights into aspects and highlighting similarities and differences across diverse participants (King, 2004 ).

Fuzzy AHP : The results of our thematic analysis were used as inputs into fuzzy AHP. Our aims were to prioritize the drivers and understand the contributions of I4.0 technologies to different dimensions of AFSC sustainability. AHP is a widely applied multiple-criteria decision-making (MCDM) method for prioritizing alternatives hierarchically (Awasthi et al., 2018 ). We integrated fuzzy sets with AHP because this helps to tackle the imprecision of AHP while retaining its advantages (Liu et al., 2020 ). Other prioritization methods are available but could not be applied in this study owing to various limitations. For example, the interpretive ranking process (IRP) is an effective MCDM method used to rank a set of variables, but the process becomes difficult with more than 10 variables and the interpretive process is highly subjective (Mangla et al., 2018 ). Data envelopment analysis (DEA), a powerful mathematical model for ranking alternatives in multi-criteria decision analysis, is better suited to performance measurement activities (Mardani et al., 2017 ).

TISM : This technique was used to identify key drivers by constructing interrelationships between them, thereby helping to understand potential routes through which AFSC practitioners might effectively deploy I4.0 technologies to achieve AFSC sustainability. TISM is an effective qualitative modeling method widely deployed to build hierarchical frameworks to illustrate interactions between variables (Sushil, 2012 ). It offered several advantages for this study. For example, TISM enables interpretation of links between two variables, which is lacking in ISM (Jena et al., 2017 ). The decision-making trial and evaluation laboratory (DEMATEL) can be used to identify cause-effect relationships between variables by building structural models, but it has limited applicability (Si et al., 2018 ), whereas TISM can be used in a range of areas. Finally, ANP is effective in revealing interdependencies between variables in a network-based system but, unlike TISM, it relies heavily on experts’ judgments and experience (Zhao et al., 2020 ).

Fuzzy MICMAC: This technique was used to classify drivers and validate the TISM model based on each driver’s driving and dependence power. We adopted this method for several reasons. We initially used (non-fuzzy) MICMAC analysis to categorize variables based on binary relationships. However, one drawback of MICMAC is that it does not evaluate the strength of relationships between two variables, thereby causing imprecision (Mota et al., 2021 ). Thus, fuzzy MICMAC analysis was applied to strengthen our sensitivity analysis. Furthermore, TISM and fuzzy MICMAC analysis have previously been combined to analyze issues relating to supply chain sustainability (Luthra & Mangla, 2018 ).

5 Empirical Data Collection

Our data collection was conducted in province of China between November 2021 and March 2022. Shandong was suited to this study as its vegetable production has been ranked first among China’s 34 provinces since 2015. More than 80 million tons of vegetables were produced in 2021 (Ministry of Agriculture and Rural Affairs, 2021 ). Purposive sampling (Creswell, 2014 ) was used to identify individuals with extensive experience relating to the AFSC industry. As a result, 26 qualified individuals agreed to participate in semi-structured interviews (see Table 4 ).

All interviews were recorded with permission, using voice memos on iPhone 13, and many probing questions were asked to enable participants to clarify their answers. Each interview lasted between 75 and 120 min to give participants sufficient time to elaborate on their answers. 48 h of digital recordings were collected.

6 Data Analysis and Findings

6.1 identification of drivers through thematic analysis.

The thematic analysis consisted of five steps (see Fig.  4 ). The first step was verbatim transcription of all digital recordings, which produced four to six pages of transcript per recording. A total of 130 pages of transcripts was generated from the 26 interviews. Second, each transcript was read several times to increase familiarity with the data before generating initial codes. Third, during the coding process, data relevant to drivers of I4.0 technology deployment to achieve AFSC sustainability were coded inductively. NVivo 13 was used to assist in the coding process by highlighting, tagging, and naming data extracts. Next, codes extracted from the coding process were organized into groups by considering their interrelationships, and these overarching themes were labelled. These themes were then organized into higher-level aggregate dimensions by considering links between themes, which were named using established constructs from existing literature on supply chain sustainability (Martins & Pato, 2019 ). Next, we refined the codes and themes by checking for links between codes, themes, and different levels of themes. During this process, an iterative approach was adopted, moving back and forth between relevant theory and data. Finally, we used King and Horrocks’s ( 2010 ) framework to organize the empirical evidence into first-order codes, second-order themes, and aggregate dimensions. Table 5 presents a sample of the empirical evidence on drivers of AFSC sustainability in the I4.0 context.

The thematic analysis process

The results of the thematic analysis pinpointed 13 drivers of I4.0 technology deployment to achieve AFSC sustainability. For example, from a social perspective, AFSC practitioners deploy I4.0 technologies to assist in reducing work intensity, labor headcount, and human exposure to pesticides, strengthening farmers’ agri-tech skills training, and improving working conditions. From an environmental perspective, deploying I4.0 technologies in AFSCs has positive effects in reducing carbon emissions and groundwater pollution, and reducing waste by controlling resource competition. From an economic perspective, the drivers identified are enhancing the efficiency of water and fertilizer use, acquiring government subsidies for agricultural facilities, improving product safety and farms’ productivity, reducing labor costs, and accelerating circular agriculture.

6.2 Prioritization of Drivers using Fuzzy AHP

Fuzzy AHP was used to prioritize the identified drivers to gain a better understanding of the management of I4.0 technologies to achieve AFSC sustainability. This consisted of five steps.

Step I: Defining and structuring the objective . One of our research objectives was to rank the drivers to understand the contribution of each to AFSC sustainability in relation to applying I4.0 technologies. This objective was decomposed into a hierarchical structure, with the objective in the top level, followed by categories of drivers in the middle level (social, environmental, economic) and the drivers of each category in the bottom level.

Step II: Constructing a fuzzy judgment matrix   \(\widetilde{E}\) . Fuzzy judgment matrix  \(\widetilde{E}\)  is a pairwise comparison matrix obtained by pairwise comparison of categories of drivers and the drivers in each. Appendix 1 shows the linguistic scales used to conduct pairwise comparisons. In this study, we produced five fuzzy judgment matrices because we sought to understand the relative importance of drivers in each category, the categories of drivers, and the global ranking of drivers.

Step III: Calculating the fuzzy weights of each criterion . We followed Buckley’s ( 1985 ) method to calculate the fuzzy weights of each criterion. In the following,  \({\widetilde{E}}_{ij}\)  is the fuzzy comparison value of criterion i to criterion j ,  \({\widetilde{r}}_{i}\)  is the geometric mean of the fuzzy comparison value of criterion i to each criterion, and  \({\widetilde{w}}_{i}\)  is the fuzzy weight of the ith criterion.

Step IV: Hierarchical layer sequencing . The final fuzzy weight of each alternative was calculated through hierarchical sequencing:

Where \({\widetilde{r}}_{ij}\) is the fuzzy weight value of the j th criterion to the i th driver. \({\widetilde{u}}_{i}\) can be indicated by a triangular fuzzy number, \({\widetilde{U}}_{i}= \left(l, m, u\right).\)

Step V: Ranking drivers . The final fuzzy weight values of drivers are represented in terms of fuzzy numbers. Thus, we followed Lee and Li’s ( 1988 ) method to defuzzify and rank the fuzzy numbers.

The fuzzy AHP analysis reveals the contributions of I4.0 technology deployment to achieving AFSC sustainability. The rankings of categories of drivers, the drivers in each category, and the global rankings of the specific drivers are shown in Table  6 . The economic category is ranked first among the three categories of drivers, with a relative weighting of 0.5784. This means that AFSC practitioners are most concerned about the economic benefits of deploying I4.0 technologies, for several reasons. First, the cost of intelligent agricultural technical equipment is too high because applications of I4.0 technologies have just begun and production of this kind of equipment has not yet reached scale. For example, a water and fertilizer integration system will be expensive when integrated with customized automatic controls, PH value detection, and wireless mobile controls. Second, most AFSC practitioners work in small and medium-sized enterprises (SMEs) and are reluctant to apply these technologies unless they guarantee significant income increases. As one interviewee stated: “intelligent agricultural equipment can only be applied by a farmer who has more than 200 or 300 acres of farmland, because the increased profits can cover the cost of this equipment” . Enhancing the efficiency of water and fertilizer is ranked first among the five drivers in this category,, followed by improving product safety and farms’ productivity, reducing labor costs, accelerating circular agriculture, and acquiring government subsidies for agricultural facilities. For example, from the perspective of saving water, applying a water and fertilizer integration system and a drip irrigation system may reduce water use by more than 70%.

The environmental category of drivers is second in the priority list, with a relative weighting of 0.2942. The Chinese government’s science and technology-supported action plan is to reach peak carbon emissions by 2030 and achieve carbon neutrality by 2060 (Ministry of Science and Technology, 2022 ). Therefore, technologies such as advanced sensors, intelligent greenhouses, IoT, and remote controls should be used to monitor and reduce carbon emissions. For example, light, humidity, carbon dioxide, acidity, and irrigation monitoring sensors are applied in intelligent greenhouses to manage crops precisely. One interviewee stated: “In the intelligent greenhouses, the heat flow can be controlled and used effectively. For example, if the ground temperature reaches above 12 degrees, we can grow warm-loving crops, and if the temperatures are between 6 and 8 degrees, we can grow cold-resistant crops” . The three drivers in this category in rank order are reducing carbon emissions, reducing groundwater pollution, and reducing waste by controlling resource competition.

Finally, the social category of drivers is ranked in last among the three categories. We assume that these category of drivers have received least attention owing to China’s hierarchical cultural value orientation. In this cultural environment, people view competition as good, and are required to obey the expectations of those in higher-status roles (Schwartz, 2006 ). For example, the 996 working hour system implemented by some companies in China requires employees to work from 9am to 9 pm, six days per week. Under the 13th Five-Year Plan, the Chinese government proposed several tasks relating to agriculture, including increasing the informatization of agricultural equipment, improving agricultural support and protection systems, and enhancing the safety of agricultural products. Thus, I4.0 technologies, such as intelligent greenhouses, advanced sensors, and IoT, are applied to reduce work intensity and improve working conditions. However, blockchain technology and automatic tractors are not widely deployed for several reasons. First, Chinese farmers are aging, with the majority aged between 45 and 55, and are relatively unwilling to learn new knowledge: “Farmers are relatively high in age level and relatively low in knowledge structure. Therefore, both model application and equipment maintenance are relatively lacking” . Second, no standardized model can be used to apply these technologies because soil and weather conditions vary in different areas. Third, applying these technologies will significantly increase the costs of terminal logistics, particularly for blockchain technology applications. Amongst the five drivers in this category: reducing work intensity is ranked first with a relative weighting of 0.4331, reducing human exposure to pesticides is ranked last with a relative weighting of 0.0576, and reducing labor headcount, improving work conditions, and strengthening farmers’ agri-tech skills training are ranked from second to fourth. As one interviewee stated: “Local governments have provided training for new farmers, part of which includes information technology courses (e.g., technical equipment, IoT, blockchain, organizational models, and application models)” .

6.3 Generation of Key Drivers through TISM

Simply understanding the contributions of I4.0 technology deployment to AFSC sustainability is insufficient, as more than 80% of businesses in AFSCs are SMEs, so most AFSC practitioners lack the resources necessary to implement these technologies. The focus must therefore be on key resources, drivers, and enablers to initiate I4.0. We used TISM to identify the key drivers by constructing a hierarchical model, implementing a nine-step process.

Step I: Identification and definition of drivers . This step involved identifying and defining the drivers to be modeled. The 13 drivers identified through the thematic analysis were used as inputs into the TISM process.

Step II: Determination of contextual relationships . Our research objective was to identify key drivers to provide practical guidance to AFSC practitioners seeking to initiate I4.0. To fulfill this objective, a contextual relationship between two drivers was defined as “Driver A will enhance or enable Driver B.”

Step III: Interpretation of relationships . Two professors in operations management who had been collaborating with the agri-food industry for more than 20 years were involved in interpreting relationships between pairs of drivers. Their opinions were initially captured to determine whether “Driver A will enhance or enable Driver B”. If their answer was “yes”, a follow-up question was asked: “In what way will Driver A enhance or enable Driver B.” Capturing the experts’ opinions, enabled us to obtain in-depth knowledge of relationships between drivers.

Step IV: Interpretive logic of pair-wise comparison . We conducted pair-wise comparisons of the 13 drivers identified to obtain an interpretive logic-knowledge base. Each driver was individually compared with all the other drivers. The two professors’ opinions were captured to rate relationships between two drivers by coding them as “Y” for yes and “N” for no. Further interpretation was required if the relationship between two drivers was “yes”. The knowledge base for this study consisted of n × (n-1) = 13 × (13–1) = 156 rows, where n represents the number of drivers.

Step V: Reachability matrix and transitivity test . The initial reachability matrix was obtained by transforming “Y” codes in the knowledge base into “1” and “N” codes into “0”. We then transformed the initial reachability matrix into a final reachability matrix by conducting a transitivity test: if driver A relates to driver B, and driver B relates to driver C, then driver A necessarily relates to driver C. The initial and final reachability matrices are shown in Appendix 2 .

Step VI: Level determination by partitioning the reachability matrix . This step was performed to determine the level of each driver in the TISM model by obtaining each driver’s reachability and antecedent sets in the final reachability matrix. The reachability set for a particular driver consists of the driver itself and other drivers that it will enhance or enable, whereas a driver’s antecedent set consists of the driver itself and other drivers that will enhance or enable it. The intersection set of each driver consists of common elements between the reachability and antecedent sets. If the elements in the reachability and intersection sets are the same, the driver is placed in the top level of the TISM model. The level partitioning process was performed until the level of each driver had been determined (see Appendix 3 ).

Step VII: Digraph development . We developed a digraph by allocating the drivers to their respective levels and drawing direct links according to the relationships shown in the final reachability matrix. Only important transitive links were retained following discussion with the two professors.

Step VIII: Interpretive matrix . A binary interpretive matrix was developed by translating all interactions in the digraph into 1 in the respective cell. The appropriate interpretation was selected from the interpretive logic-knowledge base to interpret relationships between pairs of drivers.

Step IX: TISM model of drivers . A TISM model of the drivers was developed (see Fig.  5 ) by allocating the drivers to different layers of the framework, linking them with solid and dotted lines, and interpreting each link.

TISM model of drivers

The TISM analysis of drivers resulted in a seven-level hierarchical model. Strengthening farmers’ agri-tech skills training (S4) and government subsidies for agricultural facilities (C2) are located at level VII of the TISM hierarchy, reducing work intensity (S1), reducing human exposure to pesticides (S3), and reducing groundwater pollution (E2) are at level I, and the other drivers are spread from levels II to VI. Drivers located at lower levels of the model can enable more other drivers of the system, whereas those occupying higher levels of the model require more other drivers to achieve them. The analysis reveals two key drivers of the system: strengthening farmers’ agri-tech skills training (S4) and government subsidies for agricultural facilities (C2). One interviewee stated: “The local government spends more than ¥6 million per year to support agricultural technology, smart greenhouses, and other professional training. Furthermore, government subsidies are provided to exemplary agricultural enterprises because they act as links between farmers and agricultural research institutes and have a strong willingness to apply I4.0 technologies” . Applications of I4.0 technologies in agriculture, such as IoT, water and fertilizer integration systems, advanced sensors, and smart greenhouses, have positive effects in reducing water and agrichemical use, and enhancing mechanized and automatized agriculture, thereby reducing waste (E3), improving working conditions (S5), and accelerating circular agriculture (C5). Specifically, a water and fertilizer integration system may significantly increase the efficiency of water and fertilizer use (C1). As one interviewee stated: “The application of a water and fertilizer integration system can achieve more than 70% of water saving, which is critical for North China because they generally lack water” . Other benefits achievable by deploying I4.0 technologies include reducing labor costs (C4), reducing labor headcount (S2), reducing carbon emissions (E1), improving product safety and farms’ productivity (C3), reducing work intensity (S1), reducing human exposure to pesticides (S3), and reducing groundwater pollution (E2).

6.4 Categorization of Drivers using Fuzzy MICMAC Analysis

We used fuzzy MICMAC analysis to critically analyze the scope of each driver by considering its driving and dependence power (Bhosale & Kant, 2016 ). Two primary considerations led us to adopt this method. First, AFSC practitioners must understand the scope of each driver when they are implementing I4.0 technologies to achieve AFSC sustainability. Adopting some drivers may achieve synergies, or they may conflict with other drivers, thereby reducing effective achievement of AFSC sustainability. Second, fuzzy MICMAC analysis was implemented as a complement to TISM because the latter tends not to consider the strength of relationships between pairs of drivers. For example, relationships between two drivers were coded as “0” or “1” during the TISM implementation, with “0” representing no relationship, and “1” representing a relationship between the two drivers. However, other aspects of relationships need to be considered, as some relationships may be strong, some very strong, and some weak (Zhao et al., 2020 ). Our fuzzy MICMAC analysis was conducted in three steps.

Step I: Development of a binary direct relationship matrix . We obtained the binary direct relationship matrix (see Appendix 4 (a)) by converting the diagonal entries of Appendix 3 (a) into 0.

Step II: Establishment of a fuzzy direct relationship matrix . We employed fuzzy set theory to increase the sensitivity of analysis. Potential interactions between pairs of drivers can be qualitatively defined by linguistic variables on 0–1 scale, with 0 – indicating no influence, 0.1 – very low influence, 0.3 – low influence, 0.5 – medium influence, 0.7 – high influence, 0.9 – very high influence, and 1 – complete influence. The two professors involved in step III of the TISM analysis were asked to re-rate the relationships between drivers using these values. Based on their opinions, we superimposed these new values onto the binary direct relationship matrix to obtain the fuzzy direct relationship matrix (see Appendix 4 (b)).

Step III: Generation of a fuzzy MICMAC stabilized matrix . We followed Kandasamy et al.’s ( 2007 ) method to conduct fuzzy matrix multiplication, which is a process for generalizing Boolean matrix multiplication. According to fuzzy set theory, when two fuzzy matrices are multiplied, the outcome is still a fuzzy matrix. The matrix was multiplied repeatedly until the driving and dependence power of each driver was constant. We used the following rule to conduct the multiplication process:

Following this rule and using MATLAB to calculate the matrices, we obtained the fuzzy MICMAC stabilized matrix shown in Appendix 4 (c). We then produced a scatter chart to portray each driver based on the sum of its driving and dependence power (see Fig.  6 ).

Fuzzy MICMAC analysis of drivers

Based on the fuzzy MICMAC analysis results, we clustered the 13 drivers into four categories: independent, linkage, autonomous, and dependent.

Independent drivers cluster: Drivers in this cluster are characterized by strong driving but weak dependence power. The five independent drivers are strengthening farmers’ agri-tech skills training (S4), government subsidies for agricultural facilities (C2), reducing waste by controlling resource competition (E3), improving working conditions (S5), and accelerating circular agriculture (C5). These drivers can enable or enhance other drivers and are the root cause of all drivers, thereby improving the performance of I4.0 technology deployment to achieve AFSC sustainability. Strengthening farmers’ agri-tech skills training (S4) and government subsidies for agricultural facilities (C2) should be critically considered, as they have the highest driving power and are located at the lowest level of the TISM hierarchy. However, it is difficult to reskill and upskill farmers, because aging farmers may be reluctant to receive new knowledge. One interviewee stated: “Most young people have gone out to work, leaving some 50 to 60, or even 70-year-olds who are still farming, and it is difficult for these people to accept new knowledge”.

Dependent drivers cluster: Drivers in this cluster are characterized by strong dependence but weak driving power. Unlike independent drivers that mainly enable or enhance other drivers, dependent drivers have the fewest opportunities to enable others. They are strongly dependent on other drivers for their achievement, and therefore appear at a relatively high level of the TISM hierarchy. The seven dependent drivers are reducing work intensity (S1), reducing human exposure to pesticides (S3), reducing groundwater pollution (E2), reducing carbon emissions (E1), improving product safety and farms’ productivity (C3), reducing labor costs (C4), and reducing labor headcount (S2).

Linkage drivers cluster: Drivers in this cluster have relatively strong driving and dependence power and are characteristically as unstable. They act as links between independent and dependent drivers; therefore, any changes in the lower level of independent drivers may affect these drivers and further influence the higher level of dependent drivers. Only one linkage driver is identified in this study: enhancing the efficiency of water and fertilizer use (C1).

Autonomous drivers cluster: Drivers in this cluster are characterized by relatively weak driving and dependence power. They are considered to have few or even no connections with other drivers, and thus have little influence on the system. There are no drivers in this cluster, which means that all the drivers identified are effective for deploying I4.0 technologies to achieve AFSC sustainability.

7 Discussion

This study generates insights into the deployment of I4.0 technologies to achieve AFSC sustainability, thus addressing our three questions. First, we identify 13 drivers that facilitate I4.0 deployment to achieve AFSC sustainability, including some rarely mentioned in previous literature. Second, we prioritize the drivers by ranking the categories of drivers, drivers within each category, and their global ranking. Third, we generate models of drivers’ interrelationships and categorizations, and thereby provide insights into which should be given critical attention.

Our study makes several contributions to existing knowledge. First, it contributes by identifying new drivers of I4.0 technology deployment to achieve sustainable AFSCs. For example, we find that reducing work intensity, reducing human exposure to pesticides, reducing groundwater pollution, and enhancing the efficiency of water and fertilizer use are seldom mentioned in previous studies (see Table  2 ). However, other drivers are supported by the extant literature. Yadav et al. ( 2020 ) highlight that sustainable human resource management, continuous monitoring of emissions reductions, and green design and disposal systems are drivers of I4.0 technology deployment to achieve sustainability in manufacturing organizations. Our study confirms that the agri-food industry is adopting I4.0 technologies to reduce labor costs, headcount, and carbon emissions, and to reduce waste by controlling resource competition. Bhatia and Kumar ( 2022 ) find that improving the efficiency of the manufacturing process, product quality, consumption of resources, and information sharing are success factors for deploying I4.0 technologies in India’s automobile industry. Our study supports their results by highlighting that enhancing the efficiency of water and fertilizer use and increasing product safety and farms’ productivity are drivers of I4.0 deployment in China’s agri-food industry. Rad et al. ( 2022 ) reveal that training and new competencies, top management support, and knowledge development are enablers of I4.0 technology deployment. Our study partially supports their results by confirming that AFSC stakeholders implement I4.0 technologies to strengthen their agri-tech skills. Srhir et al. ( 2023 ) highlight that I4.0 technologies can enhance various aspects of supply chain sustainability, including improved productivity and value creation opportunities on the economic dimension, better water management, efficient use of energy, and reduced carbon emissions on the environment dimension, and good working conditions on the social dimension. However, their study is a literature review, and therefore lacks industry-specific drivers. Our study confirms agri-food industry-specific drivers, including improving working conditions, enhancing the efficiency of water and fertilizer use, reducing groundwater pollution, and accelerating circular agriculture.

Second, our driver prioritization results also provide new understandings. For example, in Jamwal et al.’s ( 2021 ) study of a sustainability framework for I4.0, their prioritization results give the economic dimension the highest weighting, and the environmental dimension the lowest. Our study partially supports their results by highlighting that Chinese AFSC stakeholders are more concerned about the economic dimension of AFSC sustainability when deploying I4.0 technologies, followed by the environmental and social dimensions. Sharma et al.’s ( 2021 ) study of the impact of I4.0 adoption on sustainability shows that productivity, reduced emissions, and non-invasive interactions are ranked first on the economic, environmental, and social dimensions of sustainability, respectively. However, our results differ in prioritizing enhancing the efficiency of water and fertilizer use, reducing groundwater pollution, and reducing work intensity on these three dimensions of sustainability. This contrast illustrates that various sustainability frameworks for I4.0 have been proposed because different countries have differing I4.0 strategies (e.g., China’s Made in China 2025 and India’s Digital India) and diverse cultural value orientations, and specific industries have unique characteristics.

Third, we identify that strengthening farmers’ agri-tech skills training and government subsidies for agricultural facilities are two key drivers of I4.0 technology deployment to achieve AFSC sustainability. This finding differs from most existing studies. For example, Krishnan et al. ( 2021 ) propose that top management interest in implementing I4.0 is critical, Harikannan et al. ( 2021 ) suggest that societal pressure and public awareness are of prominent importance, and Kumar et al. ( 2022 ) state that environmental regulations for sustainability, adequate labor laws for less-skilled workforces in the digital environment, and continuous support and commitment from top management are key. Our study differs from these in considering specific characteristics of the Chinese agri-food industry. First, more than 60% of farmers in China are over the age of 45, and older individuals tend to be less receptive to new knowledge and skills. Second, national, provincial, and local governments have agri-tech extension and service centres that act as knowledge brokers between knowledge providers and agri-food industry practitioners. However, these exist in name only in many places. Third, with China’s hierarchical value orientation, agri-food industry practitioners are expected to use intelligent agricultural equipment, so more subsidies are provided to those willing to do so. Accordingly, we conclude that simply receiving governmental support or subsidies is insufficient, and that reskilling or upskilling of agri-food industry practitioners is also necessary.

7.1 Theoretical Contributions

Although studies have integrated various theories to explore I4.0 adoption to achieve supply chain sustainability. Widely adopted theories include RBV, the practice-based view (PBV), and DC. For example, Bag et al. ( 2021 ) adopt DC and PBV to understand why adopting I4.0 may facilitate sustainable supply chain management. Their results indicate that the mediating role of 10R (e.g., refuse, reuse, rethink, and repurpose) principles has positive impacts on sustainable supply chain performance. Belhadi et al. ( 2022 ) combine DC and PBV to understand how I4.0-enabled practices can help to achieve sustainable supply chains. They conclude that the adoption of I4.0 enables digital business transformation, organizational ambidexterity (OA), and circular business models, thus contributing to supply chain’s sustainable performance. Erboz et al. ( 2022 ) adopt the theoretical lens of RBV to understand the relationship between I4.0 adoption and sustainable supply chain performance. They conclude that I4.0 adoption activates supply chain integration, and that the latter contributes to supply chain sustainability latter contributes to supply chain sustainability. Appendix 5 presents empirical studies focusing on I4.0 enabled sustainable supply chains.

Despite previous studies have adopted various theories to explore the topic, most concentrate on post-I4.0 adoption conditions to examine the mediating roles of mechanisms or capabilities that can be used to leverage supply chain sustainability. For example, Umar et al. ( 2022 ) explore the impact of I4.0-enabled sustainable green supply chain practices on supply chain sustainability and Khan et al. ( 2023 ) investigate how I4.0 adoption impacts on the, environmental and economic performance of supply chain sustainability. Less understood is when I4.0 technologies can be successfully adopted and thus help to tackle sustainability challenges and achieve supply chain sustainability. Our study differs from most of the previous studies and takes an initial step in shedding light on pre-I4.0 adoption conditions, highlighting the social, economic and environmental forces that may enable I4.0 adoption. Therefore, this study contributes to MRT by explaining how mechanisms (adoption of I4.0 technologies) + contexts (social, economic, and environmental forces) = achievement of AFSC sustainability (see Fig. 7 ). Other studies (e.g., Bag et al., 2021 ; Erboz et al., 2022 ; Khan et al., 2023 ; Margherita & Braccini, 2023 ; Strandhagen et al., 2022 ) suggest that adoption of I4.0 can be used as a mechanism and posit some general contexts (e.g., manufacturing, shipping building, textile and agri-food) in which it can be used, but fail to highlight specific contexts for achieving supply chain sustainability. However, we still find several studies do adopt the framework of mechanisms + context = outcomes. For example, in the context of lean and sustainable manufacturing, the ambidextrous innovation capabilities generated by the context may facilitate I4.0 adoption and contribute to the development of sustainable supply chains (Dixit et al., 2022 ). Coercive, normative, and mimetic pressures may facilitate exploration or exploitation orientations and thereby encourage I4.0 technology adoption (Gupta et al., 2020 ). Our results also indicate that strengthening farmer’s agri-tech skills training and government subsidies for agricultural facilities are two key contextual forces enabling I4.0 technologies.

Evaluated conceptual framework of 14.0 technology adoption

7.2 Implications for AFSC Practice

This study has two key implications for practice. First, the drivers and the prioritization framework can be used by AFSC practitioners to better understand the benefits of I4.0 technology deployment. For example, it has positive effects on lowering groundwater pollution and carbon emissions, reducing work intensity and human exposure to pesticides, enhancing water and fertilizer use and reducing labor costs. This is critical for AFSC practitioners to understand because China has promised to achieve peak carbon emissions before 2030 and to fight climate change. Thus, these results should be widely disseminated across policymakers, AFSC practitioners, research institutes, and wider society to maximize their impacts. Second, governments should focus on agri-tech skills training and providing subsidies to accelerate applications of I4.0 technologies. Chinese AFSC practitioners might gain knowledge and skills from agricultural equipment manufacturers and agricultural research institutes, but most practitioners do not trust these bodies, believing that they lack experience. Thus, knowledge brokers, and especially non-profit knowledge brokers, should be established to work to improve sharing of knowledge and skills. For example, the Chinese government should make national, provincial, and local agri-tech extension and service centres work more effectively to share knowledge and skills with AFSC practitioners. Regarding subsidies, these are currently only given to agricultural equipment manufacturers. Governments should also consider giving subsidies to knowledge brokers, based on performance indicators such as the number of educated AFSC practitioners.

7.3 Limitations and Future Research Directions

As with all research, our study has limitations that must be acknowledged. First, we collected data specific to the agri-food industry in China, limiting the generalizability of the results. Future studies might use large-scale surveys to collect data from other countries or regions that are also actively pursuing I4.0 technologies, thereby enabling cross-cultural comparisons and a broader understanding of the drivers. Second, this study does not distinguish between different agri-food industry contexts (e.g., meat processing, canned food processing), limiting deeper understanding of a specific context. Future studies should encompass a wider range of agri-food industry contexts, such as collecting data from a range of agri-food industry practitioners focusing on crops, livestock, and fisheries to gain a more comprehensive understanding of how I4.0 technologies impact on various sectors of the agri-food industry.

Third, in this study we used two MCDM techniques (fuzzy AHP and fuzzy-TISM-MICMAC) to analyze our drivers, but the results are not definitive. Other MCDM techniques might be applied to enrich and deepen understanding, such as the best–worst method to determine the most and least desirable drivers or DEMATEL to analyze cause-effect relationships between the drivers or VIekriterijumsko KOmpromisno Rangiranje (VIKOR) to rank and select from a set of drivers. Combining two or more MCDM techniques is useful for balancing the shortcomings of any single method, validating the findings, and providing a more robust understanding of the relative importance of drivers (Velasquez & Hester., 2013 ). Fourth, we conducted a cross-sectional survey to collect data from November 2021 to March 2022, providing limited understanding of the rapidly evolving nature of I4.0. Future research might adopt a longitudinal approach to capture the evolving nature of I4.0 technology adoption in AFSCs.

8 Conclusion

This study was motivated to identify and understand drivers of I4.0 deployment unique to AFSC sustainability. Using several quantitative analytical techniques, these drivers were weighted based on the environmental, economic, and social dimensions of AFSC sustainability. A conceptual framework was developed to provide AFSC practitioners with a holistic understanding of I4.0 technology deployment across the three dimensions of AFSC sustainability. The results also have implications for AFSC researchers as we make a call to action for future research to focus on AFSC sustainability across regions. Specifically in the context of developing countries as there is a stubbornly low number of studies that are being published from a Southern perspective, as such studies can inform national and international interventions to achieve sustainability.

Data Availability

Data will be made available on request.

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Appendix 1 Fuzzy linguistic scales

Appendix 2 Initial and final reachability matrices

• Note : * represents transitivity

Appendix 3 Partitioning of the reachability matrix into different levels

Appendix 4 Matrices to perform fuzzy MICMAC analysis

Appendix 5 Empirical studies focus on I4.0 enabling sustainable supply chains

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Zhao, G., Chen, X., Jones, P. et al. Understanding the Drivers of Industry 4.0 Technologies to Enhance Supply Chain Sustainability: Insights from the Agri-Food Industry. Inf Syst Front (2024). https://doi.org/10.1007/s10796-024-10539-1

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