Enabling Sustainable Diffusion in Supply Chains Through Industry 5.0: An Impact Analysis of Key Enablers for SMEs in Emerging Economies

Abstract

Industry 5.0 (I5.0) builds upon Industry 4.0 by emphasizing the role of workers in production processes and prioritizing socio-economic-environmental sustainability. It has been shown that I5.0 can enhance sustainability within supply chains (SCs). However, companies in emerging economies, especially small and medium-sized manufacturing enterprises (SMEs), which are crucial to developing economies, face challenges in implementing these concepts. These SMEs are in the early stages of adopting I5.0 to foster sustainability in their SCs and require urgent identification of key I5.0 enablers. Unfortunately, the current literature lacks research on this topic specifically within the context of SMEs in emerging economies. To bridge this gap, this study identifies the enablers of I5.0 that promote sustainability diffusion in SCs, using China’s SME manufacturing sector as a case study. The integrated framework for applying multiple criteria decision-making (MCDM) techniques in this study aims to assist decision-makers in evaluating different options and making optimal choices in a systematic and structured manner when faced with complex situations. The study employs the fuzzy Delphi method (FDM) to identify 15 key I5.0 enablers and categorize them into three clusters. Grey-DEMATEL is subsequently utilized to determine the causal relationships, rank the importance of the enablers, and construct an interrelationship diagram. This study found that ‘availability and functionality of resources’; ‘top management support, active participation, and effective governance’; ‘support from government, regulators, and financial resources’; and ‘introduction of safer and more efficient robotic systems for human–robot interaction and collaboration’ serve as the primary means of resolving issues. Overall, this study helps managers, practitioners, and policymakers interested in I5.0 applications to promote sustainability in the supply chain.

1. Introduction

Since the industrialization of human society, the economic growth of countries worldwide and the development of traditional industrial civilization have brought about a series of serious negative impacts, leading to increased attention towards sustainable development. The concept of sustainable development was first introduced by the United Nations in 1987. Sustainable development can be defined as a form of progress that meets the needs of the present without compromising the ability of future generations to meet their own needs. In 2015, the United Nations established 17 Sustainable Development Goals (SDGs) with the aim of addressing the three dimensions of development—social, economic, and environmental—in an integrated and comprehensive manner by 2030 [1]. Specifically, SDG #9 calls for the construction of resilient infrastructure, the promotion of inclusive and sustainable industrialization, and the fostering of innovation. The triple bottom line of sustainable development consists of the social, economic, and environmental dimensions. Maintaining ecosystem balance and focusing on the depletion and replenishment of natural resources are critical for achieving environmental sustainability. Social sustainability encompasses not only meeting present-day survival and production needs but also ensuring a livable future. In the long term, this guarantees the continued existence of humanity and ensures that all individuals are free from discrimination and enjoy universal human rights. In contrast, economic sustainability aims to secure long-term economic growth while simultaneously safeguarding environmental and social resources [2]. The European Union proposed Industry 5.0 (I5.0) in 2021, which prioritizes the well-being of industrial workers by ensuring that production respects planetary boundaries. This initiative places workers at the heart of the manufacturing process, utilizing new technologies to increase productivity, reduce environmental harm, and enhance the resilience of industrial production, thereby promoting sustainable prosperity [3]. The objective of this initiative is to advance a sustainable, people-centred, and resilient industry that complements Industry 4.0 by prioritizing the well-being of industrial workers and achieving social goals beyond production and the economy in a more sustainable manner [4,5].

Industry 4.0 has been extensively researched in many countries for a considerable period prior to this [6]. The concept was introduced by Germany in 2011 with the aim of using information technology to drive industrial transformation, incorporating digital technologies such as the Internet, big data, and cyber-physical systems [7]. Indeed, Industry 4.0 has significantly boosted global economic development in recent years, enhancing the efficiency and reliability of industrial operations. Digital technologies have spurred numerous reforms in both production and daily life; for example, the deployment of robots throughout the industrial chain and the digitalization and servitization of manufacturing systems [2,8]. From a supply chain (SC) perspective, Industry 4.0 enables organizations to better manage complex and dynamic processes by utilizing digital technologies to create efficient, transparent, adaptive, and resilient systems across all stages of the SC, including new product development, manufacturing, sourcing, planning, logistics, and marketing. In these processes, elements of the SC—including suppliers, manufacturers, and customers—share information on digital platforms, thereby increasing overall efficiency and resilience and reducing risks [9,10]. However, there is growing academic concern that such transformations may eventually conflict with the principles of sustainable development. Since the Second Industrial Revolution, environmental issues have garnered significant attention. Unfortunately, Industry 4.0 has not adequately addressed environmental concerns or the development of sustainable technologies. This is because Industry 4.0 focuses primarily on the automation of manufacturing processes through digital technologies such as the Internet of Things and cyber-physical systems, often overlooking issues related to optimizing human resources and potentially marginalizing employees over the long term. The emphasis on process optimization has led to the marginalization of the workforce, which could face resistance from trade unions and politicians in the future [11,12]. Ignoring issues like natural resource management, social welfare, and ecosystem balance means that good economic sustainability cannot be maintained [13]. Moreover, SC sustainability must not overlook SC resilience, defined as the ability to anticipate disruptions, resist their propagation, and recover through effective response strategies to return to a stable state. Due to the limitations of Industry 4.0 in linking to concepts of social equity and sustainability, there is still a significant gap to be bridged. It is precisely these shortcomings that I5.0 aims to address and improve [14,15].

Although I5.0 builds upon Industry 4.0 technologies, it differs fundamentally in its approach. While Industry 4.0 focused on leveraging technology to create wealth, I5.0 shifts the focus towards sustainable development, emphasizing the control of technology, promoting social and environmental responsibility, and redefining corporate social responsibility. This includes ethical business practises, engagement with environmental issues, and the elimination of social inequality [16]. I5.0 emphasizes harmonious collaboration between humans and machines, prioritizing eco-economics and the efficient use of limited resources while centering technological developments on human needs to enhance quality of life [17]. I5.0 achieves its core objectives of sustainability, human-centricity, and resilience through technological innovations such as cognitive cyber-physical systems, adaptive robotics, and smart wearables. These innovations prioritize the core needs of human workers, significantly enhancing the information capacity, intelligence, stability, and productivity of the workforce in industrial environments [18]. For the manufacturing industry, in the era of I5.0, intelligent production technologies that understand operators and collaborate with them enable efficient production without the fear of replacement [19]. These digital technologies for sustainable emerging practises within the framework of I5.0 enable SC resilience. Technologies such as artificial intelligence combine the triple bottom line of sustainability to monitor key determinants of product quality, enabling faster productivity and reduced production pollution. Additionally, smart environmental sensors collect data on human behaviour to study impacts on employee productivity, well-being, fatigue, and safety [15,20]. This suggests that I5.0 can act as an enabler for spreading sustainability in SCs [21,22]. Ghobakhloo et al. [23] argue that I5.0 builds upon the digitalization benefits of Industry 4.0 to address its shortcomings, particularly in terms of sustainability. By leveraging these advancements, I5.0 aims to mitigate the negative impacts of SC operations on society and the environment, thereby contributing to the sustainable development of SCs.

As emerging economies continue to experience significant global economic growth and development, the influence of small and medium-sized manufacturing enterprises (SMEs) in these regions is increasing within the manufacturing sector. However, due to resource constraints and greater challenges in addressing environmental pressures, SMEs in emerging economies have a more pressing need for I5.0 to drive SC sustainability compared to those in developed economies [24]. As manufacturing in developed economies shifts towards offshoring to take advantage of cheaper labour, quicker access to raw materials, and to maintain efficient SCs, emerging economies have become preferable options. However, the level of industrial digitalization in emerging economies is lower compared to developed countries, and the adoption of I5.0 is not as widespread. This makes the manufacturing sector in emerging economies more susceptible to challenges [25,26,27]. Emerging economies do not possess the robust economic power and industrial levels found in developed economies. Factors such as natural disasters are more likely to disrupt the global SCs of emerging economies, impacting their manufacturing sectors and challenging them with uncertain business environments, stringent government-imposed embargoes, closures of production plants, and a lack of available advanced AI technology, which exacerbates the risk of SC disruptions [28]. In addition, SMEs are often recognized as the predominant form of business, particularly in Asia, one of the fastest-growing economic regions. This region is largely composed of SMEs, which have made significant contributions to the social and economic development of developing Asian countries [29]. It is worth noting that China, as an emerging economy, has ranked first globally in total manufacturing output value for many consecutive years. SMEs constitute 99.7 percent of China’s manufacturing firms, yet they face challenges related to SC resilience and sustainability. The environmental damage caused by these SMEs is not conducive to sustainable development [30,31]. Therefore, increasing the application of I5.0 in SMEs in emerging economies could be beneficial for the manufacturing industry in Asia and, by extension, the global economy.

However, the existing literature lacks a detailed examination of the linkages between I5.0 and SC sustainability practises within SMEs in emerging economies. A review of the Web of Science database revealed that only 56 papers related to I5.0 and SC sustainability have been published to date. Nowadays, I5.0 and SC sustainability have garnered significant attention and have been studied by scholars from various perspectives. However, most of the literature focuses on describing the context of I5.0 to explore model frameworks, challenges, technological developments, theoretical studies, and practical applications of different SCs in terms of sustainability. Typical examples in each area include Wang et al. [32] who argued that in the era of I5.0, the sustainability of personalized SCs becomes an important research topic. They proposed a personalized SC model based on distributed local manufacturing, which enhances SC efficiency through cost reduction, risk mitigation, and responsiveness enhancement. Ivanov [33] utilized literature analysis, framework construction, definitional summarization, and synthesis to propose a framework for I5.0 that integrates resilience, sustainability, and a human-centred perspective. This comprehensive framework aids in understanding and implementing I5.0, supporting future sustainability in manufacturing and SC management. Masoomi et al. [17] discussed the role of I5.0 in addressing the sustainability challenges within renewable energy SCs. Using a hybrid fuzzy best–worst approach and a fuzzy weighted integrated product assessment technique, they determined the weights of sustainability challenges (SDCs) and evaluated the benefits of I5.0 in tackling these SDCs. Kazancoglu et al. [34] outlined the challenges of transitioning from Industry 4.0 to I5.0, focusing on textile and apparel SCs. Their work analyzes the interrelationships between these challenges during the transition to I5.0 from multiple perspectives. Varriale et al. [20] investigated the role of eleven digital technologies (e.g., artificial intelligence, blockchain, and the Internet of Things) in achieving sustainable practises in SC management using a systematic literature review methodology, considering environmental, social, and economic dimensions. In terms of practical applications, Fernández-Miguel et al. [35] discuss the digital moulding approach for additive manufacturing driven by I5.0, which is considered to improve the efficiency, agility, and sustainability of SCs while driving innovation and providing strategic advantages to companies, contributing to the achievement of the Sustainable Development Goals (SDGs). Priyadarshini et al. [36] use a paradox theory perspective to explore the paradoxical tensions that arise when implementing additive manufacturing for healthcare SCs. They manage these tensions from an I5.0 perspective, working towards SC sustainability, facilitating human–machine collaborations, improving system resilience, and reducing the occurrence of risks such as medical errors. From another perspective, Mandal et al. [37], using food SCs as an example, argue that the impact of technology on SC management is growing in the I5.0 era. Technologies such as big data, IoT, and blockchain are integral to the construction of I5.0 where humans and machines will work harmoniously and collaboratively to achieve greater efficiencies, enhancing the resilience of SCs and thus promoting SC sustainability. Ivanov et al. [38] outline the concept of SC sustainability, which extends the understanding of SC resilience by focusing on the long-term viability of the SC and its associated ecosystems. They cite the example of the COVID-19 pandemic, which highlights the necessity of a sustainability perspective. As can be seen from the above, research on I5.0 and SC sustainability is extensive, characterized by cross-industry, multi-method, and multi-directional approaches.

In contrast, the discussion of ‘enablers, enablers, and success factors’ related to the sustainability of the I5.0 SC is very limited in the literature, with only eight articles currently available from a Web of Science search. Some of the articles that have received more attention are as follows: Ghobakhloo et al. [18] found that people have not been able to fully explain how I5.0 can realize its sustainability value. They analyzed these relationships using a combination of Interpretive Structural modelling (ISM) and Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC), identifying potential enablers for the development of I5.0. Dwivedi et al. [39] explored the interactions between I5.0 and circular SCs to achieve sustainable development. They analyzed the criticality and interrelationships of the enablers using an enhanced General Explanatory Structural Model and MICMAC. Dacre et al. [40] noted that there is still a lack of clarity regarding the consideration of this paradigm shift in the manufacturing industry. Thus, they proposed the concept of a conceptual framework for Manufacturing SC 5.0, based on thematic analyses of the literature, including enablers of transformation, impacts on manufacturing SCs, challenges, and outcomes. However, even more noteworthy is the current scarcity of research on the enablers of SC sustainability driven by I5.0, particularly in the context of SMEs in emerging economies. Therefore, it is particularly important to identify I5.0 enablers for SMEs in emerging economies to help promote SC sustainability.

Therefore, this study targets SMEs in the Chinese manufacturing sector for investigation, aiming to explore the impact of SC sustainability enablers on the application of I5.0 among these enterprises. This is because China is not only the largest emerging economy in the world but has also made substantial efforts in sustainable development, providing valuable insights for decision-making on sustainable development in other similar countries [41].

In summary, under the current trend toward sustainable development, manufacturing industries in emerging economies will face increasing challenges. Improving the application of I5.0 in SMEs could have a positive impact on global sustainable development. However, the existing research has not yet focused on the detailed links between I5.0 and SC sustainability practises in SMEs. There is also a lack of research on the factors driving I5.0 for SC sustainability. To bridge this research gap, this study aims to identify the key enablers that promote the adoption of I5.0 in SMEs and enhance SC sustainability in manufacturing industries in emerging economies. The novelty of this study lies in its focus on how I5.0 can effectively drive SC sustainability specifically within the context of SMEs in emerging economies. Although previous studies have acknowledged the benefits of I5.0 in enhancing SC sustainability, they have not examined these effects in the context of SMEs in emerging economies. Furthermore, although the challenges faced by emerging economies in adopting I5.0 have been noted, the critical enablers contributing to the effective adoption of I5.0 in SMEs to achieve widespread SC sustainability have not been explored. Thus, this study offers an opportunity to identify the key enablers that contribute to SC sustainability in the context of I5.0. Specifically, this study addresses the following research question: What are the key enablers of I5.0 for achieving sustainability in SCs?

In order to address the aforementioned research question, this study has the following objectives:

• Investigate the key enablers of I5.0 diffusion sustainability in SCs from the perspective of SMEs in emerging economies.
• Analyze the causal relationships among the identified key enablers.

This study compiles the literature and expert opinions by consolidating the preliminary I5.0 enablers identified through a literature review. Through the distribution of questionnaires and the collection of expert opinions, the ambiguity of expert judgments was addressed using the fuzzy Delphi method (FDM). This method allows for the description of individual expert attributes and explains the semantic structure of the predicted items, thereby enabling experts to more accurately express their views and identify the key enablers. This approach overcame inherent uncertainties and reduced the number of iterations required [42]. On the other hand, due to the limitations of the Decision Synthesis Laboratory method, which cannot handle uncertainty, lacks information, and fails to resolve conflicts between experts or represent fuzzy values around discrete points, the decision outcomes can be significantly impacted. Conversely, grey systems theory compensates for the lack of specificity in expert scoring, allowing the expression of the degree of correlation between factors [43]. Therefore, this study combines grey systems theory with the decision experimentation method to investigate the importance levels of the factors and their causal relationships. The contribution of this study is that the findings will provide a valuable theoretical foundation for policymakers, business practitioners, and future in-depth research.

The remainder of this study is organized as follows:

Section 2 outlines the research methodology. Section 3 details the analysis process. Section 4 presents the results of the study. Section 5 provides conclusions and recommendations for future research.

2. Materials and Methods

2.1. Industry 5.0 Enablers of Diffusion SC Sustainability

There is a notable absence of research on how SMEs in emerging economies can implement I5.0 to enhance SC sustainability. Barriers such as low levels of digitization, limited operational resources, and a lack of relevant expertise make it challenging for SMEs to allocate their limited resources to critical and precise areas. Therefore, to assist SMEs in emerging economies in effectively implementing I5.0 to propagate SC sustainability, it is crucial to understand and analyze the key enablers [44,45]. The following six databases and digital libraries were utilized in this study: ACM Digital Library, IEEE Xplore, ScienceDirect, Scopus, Taylor & Francis Online, and Web of Science. After obtaining the papers, we screened them according to the following criteria:

• To avoid papers that were not closely related to the search, we focused on the subject matter of the paper, specifically the title, abstract, and keywords.
• English-language papers were selected to ensure homogeneity and maintain the international scope of the research.
• Duplicates were removed.
• Papers that did not meet the initial research criteria were excluded.
• Non-JCR publications were removed. Only publications indexed by the Journal Citation Reports (JCR), a frame of reference that certifies the quality of journals, were included.
• The final step involved manual screening by the authors on the remaining papers: titles, abstracts, and keywords were analyzed to ensure relevance to the search topic.

Ambiguous papers were removed to facilitate an in-depth analysis of the literature.

The keywords used in our literature search included: “Industry 5.0”, “small and medium-sized enterprises”, “supply chain sustainability”, “manufacturing”, “emerging economies”, “sustainable manufacturing”, “digital transformation”, and “environmental sustainability”. It is worth mentioning that because I5.0 is a relatively new concept, in order to prevent the omission of some important key driving factors, we adopted a small number of Industry 4.0 articles as references when searching.

We combined these keywords using Boolean operators (AND, OR) to ensure a comprehensive search. For example, one of the search strings was as follows: (“Industry 5.0” AND “SMEs” AND “supply chain sustainability”). We conducted preliminary searches in the six databases and digital libraries mentioned above and obtained a total of 1039 papers. Then, based on the screening criteria, we narrowed down the scope to 48 relevant papers.

Ultimately, through discussions with experts, 27 enablers of I5.0 as an enabler of SC sustainability were identified from the literature.

2.2. Research Gaps

In summary, the current research focuses on five aspects: sustainability challenges, frameworks, technology development, theoretical research, and practical applications. There is a notable absence of literature exploring the enablers of I5.0 for SC sustainability, particularly concerning SMEs in emerging economies. This gap leaves the challenges faced by SMEs in these economies when adopting I5.0 unaddressed. However, SMEs in emerging economies tend to be economically weaker compared to those in developed economies, and industrial digitization is less advanced. Consequently, research based on developed countries or large firms may not be entirely applicable to the context of emerging economies or SMEs. This study argues that providing targeted and robust I5.0 enablers for SMEs in emerging economies is essential to promote SC sustainability. Therefore, this study aims to explore the enablers of I5.0 diffusion sustainability in the SC from the perspective of SMEs in a developing country (China). By identifying the key I5.0 enablers and analyzing the causal relationships among them, this study will assist relevant firms in improving the success rate of implementing I5.0 for promoting SC sustainability.

2.3. Research Process

To identify and analyze the I5.0 enablers for the sustainable development of SMEs, this study employs two analytical methods: the FDM and the grey correlation analysis combined with the decision laboratory method. The specific analysis steps are outlined below: In the first step, the initially identified I5.0 driving factors from the literature review are refined using the FDM to determine the key driving factors. The expert consensus value Gⁱ is calculated, and by setting a threshold for Gⁱ, the final key driving factors are identified and categorized into three types of driving clusters. In the second step, the grey correlation analysis combined with the decision-making laboratory method is utilized to calculate the influence degree D_x, the influenced degree C_x, the centrality degree J_x, and the causality degree K_x. Finally, a causality diagram is constructed using the causality degree and centrality degree. These steps are detailed in the following subsections, and the framework and steps of the research methodology are illustrated in Figure 1.

2.4. MCDM Methods Applied

Multiple criteria decision-making (MCDM) provides a systematic framework for analyzing and evaluating alternatives in complex decision-making scenarios. It allows stakeholders to be involved, enabling decision-makers to overcome the challenges of multi-criterion decision problems through organized methods and tools, thereby making more informed and beneficial decisions [46].

This study proposes an evaluation method that investigates the key enablers for the dissemination of sustainability in SCs from the perspective of small and medium-sized manufacturers in emerging economies under I5.0. The specific methods used in this study are introduced in the following Section 2.4.1 and Section 2.4.2.

In the first part, we design and distribute a questionnaire to experts to gather their opinions. We use the FDM to identify the key enablers for achieving SC sustainability driven by I5.0. The FDM incorporates fuzzy set theory to handle the uncertainty and subjectivity in expert opinions, making the decision process more realistic.

In the second part, based on the key enablers identified in the first part, we again design and distribute a questionnaire to experts. We apply the Grey-DEMATEL method to study the importance and causal relationships of these factors. The Grey-DEMATEL method is particularly suitable for situations where data are incomplete or information is ambiguous, effectively handling the coexistence of partial known and unknown information.

The combination of FDM and Grey-DEMATEL not only addresses the issues of incomplete data and information ambiguity in the decision-making process but also provides decision-makers with a comprehensive and detailed perspective through the integration of expert knowledge and the analysis of causal relationships. This integrated approach enhances the quality and effectiveness of the decision-making process.

2.4.1. Fuzzy Delphi Method

The Delphi method was developed by the RAND Corporation in the United States in the 1960s. It involves collecting anonymous opinions from experts who do not directly communicate with one another but interact solely with the researcher. Typically, this process requires at least 2–3 rounds of consultation and feedback to compile convergent and highly reliable results [47]. However, the Delphi method can suffer from ambiguity and imprecision due to varying interpretations of the issues by individual experts during the iterative collection and feedback rounds, leading to unclear data. The FDM integrates the traditional Delphi method with fuzzy set theory to transform experts’ subjective judgments into more precise numerical values, allowing for a more accurate expression of expert opinions [48]. Utilizing FDM not only resolves the vagueness inherent in expert judgement, facilitating group decision-making, but also reduces the number of iterations required, thereby enhancing the efficiency and quality of the survey process and providing a more comprehensive aggregation of expert insights [49]. The specific steps involved in the FDM are outlined as follows:

Step 1: Identify Relevant Enablers

The initial selection of I5.0 enablers for achieving the sustainable development of SMEs was conducted through expert consultations and a comprehensive literature review.

Step 2: Set Interval Values

An importance scale from 0 to 10 was established for the expert assessments. The minimum value within this range represents the most conservative estimate, while the maximum value reflects the most optimistic estimate.

Step 3: Collect and Collate Expert Opinions

Expert opinions are gathered and compiled. Outliers that exceed twice the standard deviation are eliminated according to Formula (1). Using Formulas (2) and (3), the conservative estimates ${\mathrm{C}}^{\mathrm{i}}$ = (${\mathrm{C}}_{\mathrm{L}}^{\mathrm{i}},{\mathrm{C}}_{\mathrm{M}}^{\mathrm{i}},{\mathrm{C}}_{\mathrm{U}}^{\mathrm{i}}$) and the optimistic estimates ${\mathrm{O}}^{\mathrm{i}}$ = (${\mathrm{O}}_{\mathrm{L}}^{\mathrm{i}},{\mathrm{O}}_{\mathrm{M}}^{\mathrm{i}},{\mathrm{O}}_{\mathrm{U}}^{\mathrm{i}}$) are calculated, respectively.

$$\mathsf{\sigma }=\sqrt{{\displaystyle \frac{1}{\mathrm{n}}}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{X}}_{\mathrm{i}}-\overline{\mathrm{X}}\right)}^2},$$

(1)

$${\mathrm{C}}_{\mathrm{L}}^{\mathrm{i}}=\min \left\{{\mathrm{A}}_{\mathrm{i}}\right\},{\mathrm{C}}_{\mathrm{M}}^{\mathrm{i}}=\sqrt[\mathrm{n}]{{\prod }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{A}}_{\mathrm{i}}},{\mathrm{C}}_{\mathrm{U}}^{\mathrm{i}}=\max \left\{\mathrm{Ai}\right\},$$

(2)

$${\mathrm{O}}_{\mathrm{L}}^{\mathrm{i}}=\min \left\{{\mathrm{B}}_{\mathrm{i}}\right\},{\mathrm{O}}_{\mathrm{M}}^{\mathrm{i}}=\sqrt[\mathrm{n}]{{\prod }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{B}}_{\mathrm{i}}},{\mathrm{O}}_{\mathrm{U}}^{\mathrm{i}}=\max \left\{\mathrm{Bi}\right\}.$$

(3)

The symbols used in the formulas represent the following meanings, as shown in

Table 1. Meaning of formula symbols.

Symbol	Meaning	Symbol	Meaning
$\sigma$	Standard deviation	n	Number of experts
$X_i$	The i-th data point	$\overline{X}$	Arithmetic mean
$C^i$	Most conservative cognitive triangular fuzzy function	$O^i$	Most optimistic cognitive triangular fuzzy function
$C_L^i$	Minimum value of the conservative cognitive value	$O_L^i$	Minimum value of the optimistic cognitive value
$C_M^i$	Geometric mean of the conservative cognitive value	$O_M^i$	Geometric mean of the optimistic cognitive value
$C_U^i$	Maximum value of the conservative cognitive value	$O_U^i$	Maximum value of the optimistic cognitive value
$A_i$	Conservative cognitive value of the i-th item	$B_i$	Optimistic cognitive value of the i-th item

:

Step 4: Calculate Expert Consensus Importance Degree $G^i$.

The grey zone ${\mathrm{Z}}^{\mathrm{i}}$ of the fuzzy relationship is calculated using Equation (4). Subsequently, ${\mathrm{G}}^{\mathrm{i}}$ is determined under three distinct scenarios:

$${\mathrm{Z}}^{\mathrm{i}}={\mathrm{C}}_{\mathrm{U}}^{\mathrm{i}}-{\mathrm{O}}_{\mathrm{L}}^{\mathrm{i}},$$

(4)

When ${\mathrm{Z}}^{\mathrm{i}}$ = 0, Equation (5) is used:

$${\mathrm{G}}^{\mathrm{i}}={\displaystyle \frac{{\mathrm{C}}_{\mathrm{M}}^{\mathrm{i}}+{\mathrm{O}}_{\mathrm{M}}^{\mathrm{i}}}{2}},$$

(5)

When $Z^i$ > 0 and $Z^i$ < $M^i$, Equation (6) is used:

$${\mathrm{G}}^{\mathrm{i}}={\displaystyle \frac{\left({\mathrm{C}}_{\mathrm{U}}^{\mathrm{i}}\times {\mathrm{O}}_{\mathrm{M}}^{\mathrm{i}}\right)-({\mathrm{O}}_{\mathrm{L}}^{\mathrm{i}}\times {\mathrm{C}}_{\mathrm{M}}^{\mathrm{i}})}{\left({\mathrm{C}}_{\mathrm{U}}^{\mathrm{i}}-{\mathrm{C}}_{\mathrm{M}}^{\mathrm{i}}\right)+({\mathrm{O}}_{\mathrm{M}}^{\mathrm{i}}-{\mathrm{O}}_{\mathrm{L}}^{\mathrm{i}})}},$$

(6)

where $M^i=O_M^i-C_M^i$.

When $Z^i$ > 0, and $Z^i$ ≥ $M^i$, the above steps need to be repeated.

Step 5: Set the threshold value, keep the factors greater than or equal to the threshold value, and remove the opposite.

2.4.2. Grey-DEMATEL

The essence of factor analysis lies in identifying key factors and elucidating the interaction mechanisms among these factors. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is particularly well suited for this purpose, as it helps to construct causal relationships between factors through a matrix approach, thereby providing a detailed description of their interrelationships [50]. Therefore, this study adopts the DEMATEL method as one of the primary analytical tools.

However, the DEMATEL method has some limitations. It struggles with handling uncertain situations, lacks robustness in scenarios with scarce information, cannot resolve conflicts among experts, and is not effective in representing the fuzziness around discrete values, which can significantly impact decision outcomes [51]. To overcome these limitations, we chose to integrate Grey System Theory with the DEMATEL method. Grey System Theory is well suited to complement the shortcomings of DEMATEL by transforming semantic variables provided by experts into grey matrices. This transformation process includes standardization and clarification, thereby enhancing the precision of expert evaluations and better capturing the interrelationships among factors [52].

Therefore, this study combines Grey System Theory and the DEMATEL method to investigate the importance and causal relationships of the factors. The specific steps are as follows:

Step 1: Distribute Questionnaires

The key enablers for achieving SC sustainability in the context of I5.0, identified through the FDM, will be used to design and distribute questionnaires, requesting authoritative experts to rate the degree of interaction between the factors. This will establish the direct relationship original rating matrix.

Step 2: Construct Greyscale Matrix

Based on the grey number theory in Grey System Theory, the original rating values in the scoring matrix are transformed into corresponding grey number intervals. The grey semantic scale is shown in

Table 2. Greyscale conversion of semantics.

Expert Semantic	Degree of Influence	Greyscale Interval
No impact	0	[0–0]
Low impact	1	[0–0.25]
Moderate impact	3	[0.25–0.5]
High impact	6	[0.5–0.75]
Very high impact	9	[0.75–1]

. A grey matrix $\otimes A_{\mathrm{xy}}^l$ is then constructed.

Step 3: According to Equation (7), construct the average grey matrix $\otimes {\overline{A}}_{\mathrm{xy}}$.

$$\otimes {\overline{A}}_{\mathrm{xy}}=(\sum l{\displaystyle \frac{\underset{\_}{\otimes }{A}_{xy}^l}{n}},\sum l{\displaystyle \frac{\overline{\otimes }{A}_{xy}^l}{n}})$$

(7)

where 1 ≤ l ≤ n, 1 ≤ x ≤ c, 1 ≤ y ≤ c, n denotes the number of experts, and c denotes the number of enablers. $\underset{\_}{\otimes }{A}_{xy}^l$ denotes the lower limit greyscale matrix, and $\overline{\otimes }{A}_{xy}^l$ denotes the upper limit greyscale matrix.

Step 4: Normalize and Clarify the Average Grey Matrix

According to Equations (8)–(10), the average grey matrix is normalized. Then, the normalized matrix is clarified using Equation (11), resulting in the clarified relationship matrix B_xy.

$$\underset{¯}{\otimes }{\stackrel{•}{A}}_{\mathrm{xy}}=(\underset{¯}{\otimes }{\overline{\mathrm{A}}}_{\mathrm{xy}}-{}_{\mathrm{y}}{}^{\min }\underset{¯}{\otimes }{\overline{\mathrm{A}}}_{\mathrm{xy}})/{\Delta }_{\min }^{\max },$$

(8)

$$\overline{\otimes }{\stackrel{•}{A}}_{\mathrm{xy}}=(\overline{\otimes }{\overline{\mathrm{A}}}_{\mathrm{xy}}-{}_y{}^{\min }\overline{\otimes }{\overline{\mathrm{A}}}_{\mathrm{xy}})/{\Delta }_{\min }^{\max },$$

(9)

$${\Delta }_{\min }^{\max }={}_{\mathrm{y}}{}^{\max }\overline{\otimes }{\overline{\mathrm{A}}}_{\mathrm{xy}}-{}_y{}^{\min }\underset{¯}{\otimes }{\overline{\mathrm{A}}}_{\mathrm{xy}},$$

(10)

$$B_{xy}=\left({\displaystyle \frac{\underset{\_}{\otimes }{\stackrel{•}{A}}_{\mathrm{xy}}(1-\underset{\_}{\otimes }{\stackrel{•}{A}}_{\mathrm{xy}})+(\overline{\otimes }{\stackrel{•}{A}}_{\mathrm{xy}}\times \overline{\otimes }{\stackrel{•}{A}}_{\mathrm{xy}})}{(1-\underset{\_}{\otimes }{\stackrel{•}{A}}_{\mathrm{xy}}+\overline{\otimes }{\stackrel{•}{A}}_{\mathrm{xy}})}}\right),$$

(11)

where $\underset{\_}{\otimes }{\stackrel{•}{A}}_{\mathrm{xy}}$ denotes the normalized lower limit grey matrix, $\overline{\otimes }{\stackrel{•}{A}}_{\mathrm{xy}}$ denotes the normalized upper limit grey matrix, and ${\Delta }_{min}^{max}$ denotes the difference between the maximum value of the upper limit mean grey matrix and the minimum value of the upper limit mean grey matrix.

Step 5: After obtaining the clarified matrix, the row sums and column sums of the matrix are calculated using Equation (12) and Equation (13), respectively. The maximum sum value is selected to obtain the normalized influence matrix N. Finally, the integrated impact matrix T is constructed using Equation (14), where L is the standardized value and I is the identity matrix.

$$L={\displaystyle \frac{1}{{}_{1\le x\le c}{}^{\max }\sum _{\mathrm{y}}^{\mathrm{c}}{\mathrm{B}}_{xy}}}$$

(12)

$$N=L\ast B_{\mathrm{xy}}$$

(13)

$$T=N(I-N{)}^{-1}$$

(14)

where L is the normalized value, N is the normalized impact matrix, and I is the unit matrix.

Step 6: According to Formulas (15)–(18), the degree of influence Dx, the degree of being influenced Cx, the degree of centrality Jx, and the degree of cause Kx are calculated for each factor, respectively.

$$D_x={\sum }_y^c{T}_{xy}(x=1,2,\dots c)$$

(15)

$$C_x={\sum }_y^c{T}_{yx}(x=1,2,\dots c)$$

(16)

$$J_x=D_x+C_x$$

(17)

$$K_x=D_x-C_x$$

(18)

Among them, the meaning and characteristics of the factor indicators are shown in

Table 3. Meaning and characterization of factor indicators.

Factor Indicator	Meaning	Characterization
Influence Degree Dx	Indicates the comprehensive influence of one factor on other factors.	The higher the influence degree, the greater the impact of the factor on others.
Affected Degree Cx	Indicates the extent to which a factor is influenced by other factors.	The higher the affected degree, the more susceptible the factor is to influences from others.
Centrality Jx	Indicates the importance of a factor.	Factors with higher centrality are more critical.
Causality Kx	Indicates the difference between the influence degree and the affected degree of a factor.	If Kx is positive, the factor is a cause factor exerting significant influence on others; if Kx is negative, it is an effect factor highly influenced by others.

:

Step 7: Label the relative position of each driver in the coordinate system to obtain a causality diagram.

3. Enablers Analyses

This section will follow the steps presented in Section 3, thus identifying the key enablers and obtaining the causal relationships between them.

3.1. Identification of Key Enablers Through FDM

By reviewing the relevant literature, this study initially identified 27 I5.0 enablers related to sustainability after combining factors with similar meanings. These enablers were then categorized into three clusters based on the three core elements of I5.0: sustainability, people-centeredness, and resilience [19]. The division of the factors and the sources of the factors are provided in Appendix A 

Table A1. Enablers of I5.0.

Enabler Group	Enabler Label	I5.0 Enablers
Sustainability	w1	Support from government, regulators and financial resources
	w2	Creating business models and new value networks that promote value chain inclusiveness
	w3	Green manufacturing
	w4	Sustainable corporate governance models
	w5	Resource availability and functionality
	w6	Technical governance
	w7	Harnessing digital technologies for sustainable development
	w8	Sustainability performance management
	w9	Stakeholder collaboration and integration
Human-centricity	w10	Creating an environment that enhances employment opportunities, safeguards jobs, and reduces the efficiency of unemployment
	w11	Empowering employees and allowing them the autonomy to develop innovations
	w12	Organizational culture
	w13	Human resource development, training and development programmes for employees
	w14	Enhancement of employees’ digital knowledge and skills
	w15	Improvement of working conditions and employee satisfaction
	w16	Introduction of more flexible, safe, inexpensive, viable, and efficient robotic systems for human–robot interaction, collaboration
	w17	Adherence to and control of product and service quality to enhance customer satisfaction
Resilience	w18	Digitization, transparency, integration and flexibility of the SC and communication and cooperation among relevant players
	w19	Industry flexibility
	w20	Development or introduction of appropriate project management or policy tools
	w21	Real-time communications and real-time dynamic monitoring capabilities
	w22	Flexible, efficient, and intelligent manufacturing systems
	w23	Enhance industrial resilience and improve industrial chain security capability
	w24	Interoperability, i.e., the ability to perform the same functions after changing equipment and manufacturers
	w25	Integration of technology platforms
	w26	Establishment of infrastructure and efficient information management systems
	w27	Top management support, active participation, and effective governance

and Appendix A 

Table A2. Sources of I5.0 Driving Factors.

Enablers
		Sustainability								Human-Centricity									Resilience
No.	Authors	w1	w2	w3	w4	w5	w6	w7	w8	w9	w10	w11	w12	w13	w14	w15	w16	w17	w18	w19	w20	w21	w22	w23	w24	w25	w26	w27
1	(Pasi et al., 2021) [64]	●							●	●	●	●	●					●						●	●	●		●
2	(Yadav et al., 2020) [65]	●		●		●			●		●				●			●						●		●		●
3	(Kiel et al., 2017) [66]	●								●	●	●		●	●							●		●		●
4	(Dev et al., 2020) [67]	●	●				●		●	●	●	●	●		●			●						●
5	(Ghobakhloo, 2020) [2]	●	●				●		●	●	●	●	●											●
6	(Esmaeilian et al., 2020) [68]	●	●				●		●		●	●									●			●	●
7	(Pech and Vrchota, 2020) [69]	●	●				●		●	●		●	●	●	●													●
8	(Maisiri et al., 2021) [70]	●	●				●			●			●	●
9	(Khanzode et al., 2021) [71]	●	●		●	●	●		●	●		●	●					●						●				●
10	(Colacino et al., 2021) [72]							●	●	●		●	●	●				●						●				●
11	(Kamali Saraji et al., 2021) [73]	●			●				●	●	●	●	●					●		●				●
12	(Adamik and Sikora-Fernandez, 2021) [74]	●	●		●	●	●		●	●		●	●	●	●			●
13	(Ghobakhloo et al., 2021) [75]	●	●		●		●	●	●	●	●	●		●										●
14	(Strandhagen et al., 2022) [76]	●				●			●	●				●												●
15	(Toktaş-Palut, 2022) [77]	●			●	●							●	●										●		●
16	(Adebanjo et al., 2023) [24]							●	●	●		●								●	●			●	●
17	(Szlávik and Szép, 2023) [78]					●			●	●			●	●	●						●		●
18	(Kumar et al., 2023) [79]							●	●	●	●	●	●	●		●								●		●		●
19	(Contini and Peruzzini, 2022) [80]				●					●		●		●				●			●			●		●
20	(Javaid et al., 2022) [81]								●	●				●					●				●			●	●
21	(Aheleroff et al., 2022) [56]					●			●	●				●		●						●		●			●	●
22	(Abdul-Hamid et al., 2022) [82]								●			●		●												●		●
23	(Torres Da Rocha et al., 2022) [83]	●		●					●		●	●		●		●	●	●		●	●			●		●	●
24	(Maddikunta et al., 2022) [84]					●			●	●		●												●			●
25	(Nahavandi, 2019) [11]	●				●				●		●		●				●		●						●	●
26	(Karmaker et al., 2023) [25]		●				●		●	●	●	●		●			●				●			●
27	(Ghobakhloo et al., 2023) [54]	●		●			●			●		●	●	●	●	●	●	●	●	●				●		●
28	(Demir et al., 2019) [85]											●		●										●			●
29	(Longo et al., 2020) [86]								●			●															●
30	(Iqbal et al., 2022) [61]	●				●			●					●			●							●	●		●
31	(Fraga-Lamas et al., 2021) [87]						●		●	●				●												●	●
32	(Kasinathan et al., 2022) [55]	●				●			●	●			●	●			●			●	●					●
33	(Akundi et al., 2022) [14]								●	●		●	●	●										●		●	●
34	(Lu et al., 2022) [88]					●												●			●	●		●	●	●	●
35	(Ghobakhloo et al., 2022) [18]	●			●			●		●			●	●						●	●	●		●	●	●	●	●
36	(Saniuk et al., 2022) [89]				●	●	●	●		●				●			●				●			●		●	●	●
37	(Sharma et al., 2024) [90]	●				●	●	●				●		●		●		●		●						●	●	●
38	(Sindhwani et.al., 2022) [91]				●	●								●												●	●	●
39	(Mukherjee et al., 2023) [92]								●			●					●							●	●		●
40	(Dwivedi et al., 2023) [39]	●								●		●					●	●		●
41	(Javaid et al., 2020) [93]	●			●	●			●	●												●		●			●
42	(Javaid and Haleem, 2020) [94]					●			●					●													●	●
43	(Zizic et al., 2022) [95]					●			●	●	●		●	●					●					●	●	●	●	●
44	(Minculete, 2021) [96]	●				●	●		●	●				●				●			●			●	●	●	●
45	(Majerník et al., 2022) [97]	●				●						●	●	●												●	●
46	(Battini et al., 2022) [98]	●				●	●		●	●				●												●	●	●
47	(Leng et al., 2022) [19]	●					●										●									●	●
48	(Adel, 2022) [4]					●			●			●		●			●					●				●	●	●

. Based on these enablers, an FDM questionnaire was distributed to 12 experts with more than 5 years of research experience in the field. The completed questionnaires were collected, and the expert scores were calculated. It was found that all 27 items fell within the twofold standard deviation range and thus did not need to be removed as outliers. In this study, the thresholds for sustainability, people-centeredness, and resilience were set at 7.44, 7.36, and 7.3, respectively. Factors with $G^i$ values below these thresholds were excluded to identify the key I5.0 enablers for the three major areas. The resulting key enablers are summarized in

Table 4. Categorization of key enablers.

Primary Indicator	Secondary Indicators	${\mathit{G}}^{\mathit{i}}$
Sustainability	Green manufacturing	7.96
	Support from government, regulators, and financial resources	7.90
	Sustainable corporate governance model	7.81
	Resource availability and functionality	7.64
	Creation of business models and new value networks promoting inclusivity in the value chain	7.44
People-Centred	Improvement of working conditions and employee satisfaction	7.90
	Persistence in product and service quality to enhance customer satisfaction	7.61
	Human resource development, including training and development plans for employees	7.53
	Enhancement of digital knowledge, skills, and capabilities of employees	7.51
	Introduction of more flexible, safe, cost-effective, feasible, and efficient robotic systems for human–robot interaction and collaboration	7.36
Resilience	Support, active participation, and effective governance by top management	8.43
	Enhancement of industrial resilience and security assurance along the SC	7.71
	Establishment of infrastructure and efficient information management systems	7.60
	Flexible, efficient, and intelligent manufacturing systems	7.46
	Digitalization level, transparency, integration, and flexibility of the SC, and cooperation among relevant participants	7.30

, and their rankings are presented in

Table 5. Key enablers for I5.0.

ID	Key Enablers for I5.0
A1	Support, active participation, and effective governance by top management
A2	Green manufacturing
A3	Support from government, regulators, and financial resources
A4	Improvement of working conditions and employee satisfaction
A5	Sustainable corporate governance model
A6	Enhancement of industrial resilience and security assurance along the SC
A7	Resource availability and functionality
A8	Persistence in product and service quality to enhance customer satisfaction
A9	Establishment of infrastructure and efficient information management systems
A10	Human resource development, including training and development plans for employees
A11	Enhancement of digital knowledge and skill levels of employees
A12	Implementation of flexible, efficient, and intelligent manufacturing systems
A13	Creation of business models and new value networks promoting inclusivity in the value chain
A14	Introduction of more flexible, safe, cost-effective, feasible, and efficient robotic systems for human–robot interaction and collaboration
A15	Digitalization level, transparency, integration, and flexibility of the SC, and cooperation among relevant participants

.

3.2. Grey-DEMATEL

To ensure the depth and breadth of the research, we ultimately invited 32 experts from SMEs (4 production managers, 3 operations managers, 8 SC managers, 5 enterprise management consultants, 4 workshop supervisors, 2 logistics managers, 2 advanced manufacturing professors, and 4 industrial engineering professors). These experts either work in manufacturing enterprises or provide consulting services to such enterprises. They possess extensive knowledge and practical experience in I5.0 and its applications in SC management. Each expert has at least three years of management experience in manufacturing and market strategy.

We conducted a structured questionnaire survey aimed at collecting detailed opinions from the experts on the importance and causal relationships of 15 key enablers. The questionnaire was distributed electronically to ensure a high response rate and convenient data collection. The experts were given two weeks to complete and return the questionnaire.

After the questionnaires were collected, we used Excel and Python for data analysis and visualization. The data analysis process is as follows:

Step 1: Organize Questionnaire Data and Construct Greyscale Matrices

Organize the recovered questionnaire data to construct the direct relationship matrix A. Convert the influence degree values obtained from the experts’ questionnaires into greyscale values using Table 2 to construct the greyscale matrix $\otimes A_{\mathrm{xy}}^l$. According to Equation (7), construct the average greyscale matrix $\otimes {\overline{A}}_{\mathrm{xy}}$, as shown in

Table 6. Lower bound average greyscale matrix.

ID	A1	A2	A3	A4	A5	A6	A7	A8	A9	A10	A11	A12	A13	A14	A15
A1	0	0.65	0.32	0.71	0.60	0.49	0.53	0.58	0.57	0.59	0.56	0.51	0.41	0.52	0.41
A2	0.41	0	0.45	0.37	0.45	0.33	0.32	0.42	0.29	0.34	0.30	0.32	0.27	0.23	0.21
A3	0.58	0.56	0	0.75	0.54	0.50	0.52	0.48	0.55	0.53	0.55	0.45	0.44	0.45	0.38
A4	0.27	0.67	0.03	0	0.64	0.42	0.31	0.45	0.34	0.44	0.44	0.38	0.35	0.55	0.36
A5	0.43	0.45	0.32	0.28	0	0.44	0.35	0.34	0.41	0.38	0.29	0.39	0.31	0.33	0.40
A6	0.47	0.38	0.36	0.09	0.44	0	0.34	0.39	0.33	0.24	0.23	0.38	0.34	0.30	0.34
A7	0.50	0.60	0.48	0.72	0.56	0.56	0	0.48	0.51	0.59	0.59	0.54	0.45	0.47	0.50
A8	0.42	0.37	0.35	0.23	0.42	0.29	0.23	0	0.28	0.36	0.35	0.34	0.23	0.32	0.27
A9	0.38	0.45	0.37	0.27	0.47	0.44	0.37	0.33	0	0.37	0.37	0.38	0.27	0.38	0.52
A10	0.34	0.45	0.36	0.45	0.46	0.46	0.36	0.51	0.37	0	0.59	0.45	0.36	0.45	0.40
A11	0.34	0.45	0.33	0.26	0.43	0.41	0.29	0.47	0.38	0.59	0	0.48	0.36	0.41	0.34
A12	0.41	0.45	0.37	0.20	0.41	0.33	0.31	0.41	0.32	0.32	0.31	0	0.35	0.34	0.33
A13	0.42	0.32	0.36	0.14	0.41	0.31	0.31	0.25	0.20	0.20	0.27	0.32	0	0.27	0.36
A14	0.25	0.54	0.27	0.75	0.53	0.51	0.28	0.43	0.43	0.55	0.45	0.53	0.37	0	0.39
A15	0.48	0.34	0.31	0.16	0.38	0.40	0.35	0.38	0.22	0.20	0.22	0.28	0.27	0.30	0

and

Table 7. Upper limit mean greyscale matrix.

	A1	A2	A3	A4	A5	A6	A7	A8	A9	A10	A11	A12	A13	A14	A15
A1	0	0.90	0.56	0.96	0.85	0.74	0.78	0.83	0.82	0.84	0.81	0.75	0.66	0.77	0.66
A2	0.66	0	0.68	0.62	0.70	0.56	0.52	0.65	0.53	0.58	0.54	0.56	0.52	0.48	0.45
A3	0.83	0.81	0	1.00	0.79	0.75	0.76	0.72	0.80	0.77	0.79	0.69	0.69	0.70	0.61
A4	0.50	0.91	0.23	0	0.89	0.67	0.51	0.70	0.57	0.66	0.65	0.61	0.59	0.79	0.60
A5	0.67	0.70	0.57	0.52	0	0.69	0.56	0.58	0.65	0.63	0.54	0.64	0.56	0.58	0.64
A6	0.72	0.63	0.60	0.29	0.69	0	0.54	0.60	0.54	0.48	0.47	0.63	0.59	0.54	0.59
A7	0.75	0.85	0.73	0.97	0.81	0.81	0	0.73	0.76	0.84	0.84	0.79	0.70	0.71	0.74
A8	0.67	0.62	0.60	0.48	0.66	0.48	0.44	0	0.47	0.59	0.59	0.53	0.42	0.57	0.47
A9	0.63	0.70	0.61	0.52	0.71	0.66	0.58	0.52	0	0.61	0.62	0.58	0.47	0.63	0.75
A10	0.59	0.70	0.59	0.70	0.71	0.70	0.61	0.75	0.62	0	0.84	0.69	0.61	0.69	0.64
A11	0.59	0.70	0.58	0.51	0.68	0.66	0.50	0.72	0.63	0.84	0	0.73	0.61	0.66	0.58
A12	0.66	0.70	0.61	0.44	0.66	0.58	0.51	0.62	0.52	0.56	0.56	0	0.60	0.59	0.58
A13	0.66	0.57	0.60	0.31	0.66	0.52	0.52	0.46	0.39	0.43	0.50	0.53	0	0.51	0.57
A14	0.50	0.79	0.52	1.00	0.78	0.76	0.52	0.68	0.68	0.80	0.70	0.78	0.62	0	0.62
A15	0.73	0.59	0.54	0.35	0.62	0.61	0.56	0.59	0.41	0.42	0.42	0.49	0.48	0.54	0

.

Step 2: Construct the normalized relationship matrix Bxy from Equations (8)–(11) as shown in

Table 8. Standardized relationship matrix B_xy.

	A1	A2	A3	A4	A5	A6	A7	A8	A9	A10	A11	A12	A13	A14	A15
A1	0	0.93	0.63	0.90	0.89	0.82	0.92	0.93	0.93	0.94	0.89	0.87	0.82	0.89	0.76
A2	0.67	0	0.83	0.49	0.68	0.56	0.55	0.68	0.50	0.56	0.49	0.57	0.57	0.43	0.42
A3	0.93	0.80	0	0.95	0.80	0.83	0.89	0.77	0.89	0.84	0.86	0.77	0.88	0.78	0.69
A4	0.45	0.94	0.11	0	0.94	0.71	0.52	0.74	0.56	0.69	0.67	0.65	0.71	0.93	0.68
A5	0.70	0.67	0.63	0.38	0	0.74	0.60	0.56	0.68	0.62	0.49	0.69	0.64	0.59	0.73
A6	0.77	0.56	0.69	0.14	0.66	0	0.57	0.62	0.53	0.42	0.39	0.67	0.70	0.54	0.64
A7	0.81	0.87	0.91	0.91	0.83	0.93	0	0.78	0.84	0.93	0.93	0.92	0.91	0.81	0.91
A8	0.70	0.55	0.69	0.33	0.63	0.47	0.42	0	0.45	0.59	0.57	0.56	0.45	0.58	0.49
A9	0.63	0.66	0.71	0.37	0.70	0.71	0.63	0.52	0	0.60	0.60	0.63	0.53	0.67	0.93
A10	0.57	0.67	0.68	0.59	0.69	0.77	0.65	0.82	0.62	0	0.93	0.77	0.74	0.77	0.74
A11	0.57	0.67	0.65	0.36	0.65	0.70	0.51	0.77	0.64	0.93	0	0.84	0.74	0.72	0.64
A12	0.67	0.67	0.71	0.29	0.63	0.57	0.52	0.64	0.52	0.53	0.51	0	0.72	0.62	0.63
A13	0.69	0.49	0.69	0.19	0.62	0.52	0.53	0.42	0.34	0.35	0.45	0.55	0	0.49	0.65
A14	0.44	0.78	0.54	0.95	0.79	0.84	0.52	0.71	0.72	0.87	0.73	0.91	0.75	0	0.71
A15	0.78	0.52	0.59	0.21	0.57	0.65	0.60	0.61	0.36	0.35	0.36	0.49	0.53	0.54	0

:

Step 3: The integrated impact matrix T is then calculated from Equations (12)–(14), as shown in

Table 9. Integrated impact matrix T.

	A1	A2	A3	A4	A5	A6	A7	A8	A9	A10	A11	A12	A13	A14	A15
A1	0.20	0.28	0.24	0.22	0.28	0.27	0.25	0.27	0.25	0.27	0.26	0.28	0.27	0.26	0.26
A2	0.19	0.14	0.20	0.14	0.20	0.19	0.17	0.19	0.16	0.18	0.17	0.19	0.18	0.17	0.17
A3	0.27	0.26	0.19	0.22	0.27	0.27	0.24	0.26	0.25	0.25	0.25	0.26	0.27	0.25	0.25
A4	0.19	0.23	0.16	0.11	0.23	0.21	0.18	0.21	0.18	0.20	0.19	0.21	0.21	0.22	0.21
A5	0.20	0.20	0.19	0.14	0.16	0.21	0.18	0.19	0.19	0.19	0.18	0.21	0.20	0.19	0.21
A6	0.19	0.18	0.19	0.11	0.19	0.14	0.17	0.18	0.16	0.16	0.16	0.19	0.19	0.17	0.19
A7	0.27	0.27	0.27	0.22	0.28	0.28	0.18	0.27	0.25	0.27	0.26	0.28	0.28	0.26	0.28
A8	0.18	0.17	0.18	0.12	0.19	0.17	0.15	0.13	0.15	0.17	0.16	0.18	0.17	0.17	0.17
A9	0.20	0.20	0.20	0.14	0.21	0.21	0.18	0.19	0.14	0.19	0.19	0.20	0.20	0.20	0.22
A10	0.21	0.22	0.21	0.17	0.23	0.23	0.20	0.23	0.20	0.16	0.23	0.23	0.23	0.22	0.23
A11	0.20	0.21	0.20	0.15	0.21	0.22	0.18	0.22	0.19	0.22	0.15	0.23	0.22	0.21	0.21
A12	0.19	0.19	0.19	0.13	0.20	0.19	0.17	0.19	0.17	0.18	0.17	0.15	0.20	0.18	0.19
A13	0.17	0.16	0.17	0.11	0.18	0.16	0.15	0.15	0.14	0.15	0.15	0.17	0.12	0.16	0.17
A14	0.20	0.23	0.21	0.20	0.24	0.24	0.19	0.22	0.21	0.23	0.21	0.25	0.23	0.17	0.23
A15	0.18	0.17	0.17	0.11	0.17	0.18	0.16	0.17	0.14	0.15	0.14	0.17	0.17	0.16	0.12

Step 4: Calculate the degree of influence Dx, the degree of influenced Cx, the degree of centrality Jx, and the degree of cause Kx according to Equations (15)–(18), respectively, and categorize the factors into cause factors (Kx > 0) versus effect factors (Kx < 0), as shown in

Table 10. Calculation results.

Factor	Influence Degree Dx	Affected Degree Cx	Centrality Jx	Causality Kx	Factor Category
A1	3.85	3.05	6.89	0.80	Cause factor
A2	2.63	3.13	5.75	−0.50	Effect factor
A3	3.75	2.95	6.70	0.79	Cause factor
A4	2.94	2.29	5.23	0.65	Cause factor
A5	2.82	3.22	6.04	−0.40	Effect factor
A6	2.58	3.15	5.73	−0.57	Effect factor
A7	3.90	2.74	6.64	1.16	Cause factor
A8	2.46	3.07	5.53	−0.62	Effect factor
A9	2.87	2.77	5.64	0.10	Cause factor
A10	3.20	2.96	6.16	0.23	Cause factor
A11	3.00	2.85	5.85	0.15	Cause factor
A12	2.67	3.18	5.85	−0.51	Effect factor
A13	2.30	3.12	5.42	−0.82	Effect factor
A14	3.25	2.99	6.24	0.26	Cause factor
A15	2.36	3.09	5.45	−0.73	Effect factor

:

Step 5: From the calculated centrality Jx and causality Kx, the relative position of each enabler can also be labelled in the coordinate system to obtain a cause and effect diagram, as in Figure 2.

The factors with positive causality Kx are A1, A3, A4, A7, A9, A10, A11, and A14; the factors with negative causality Kx are A2, A5, A6, A8, A12, A13, and A15; the centrality degrees Jx in order of magnitude were A1, A3, A7, A14, A10, A5, A11, A12, A2, A6, A9, A8, A15, A13, and A4.

4. Discussion of Findings

In the first stage of the analysis, the three core elements of I5.0—“sustainability”, “human-centeredness”, and “resilience”—were divided into three enabler clusters. The top five enablers within each cluster were identified based on their importance, resulting in 15 key enablers of I5.0, as summarized in Table 4 and Table 5. In the second phase of the study, the Grey-DEMATEL method was utilized to determine the importance and causality of these 15 factors. These results were then summarized into the 15 key enablers of I5.0. As illustrated in Table 10, this study derived the degree of influence, the degree of being influenced, the centrality, and the causality of the 15 factors. In the following section, we further explore the mechanisms by which these enablers influence the triple bottom line of SC sustainability. By analyzing and discussing these factors, we aim to provide more precise strategic guidance for the sustainable development of small and medium-sized manufacturing SCs in emerging economies.

As can be seen from Figure 2, eight of the enablers are identified as causal factors. These include:

“Support, active participation, and effective governance by top management” (A1);

“Support from government, regulators, and financial resources” (A3);

“Improvement of working conditions and employee satisfaction” (A4);

“Resource availability and functionality” (A7);

“Establishment of infrastructure and efficient information management systems” (A9);

“Human resource development, including training and development plans for employees” (A10);

“Enhancement of digital knowledge and skill levels of employees” (A11);

“Introduction of more flexible, safe, cost-effective, feasible, and efficient robotic systems for human–robot interaction and collaboration” (A14).

In the Grey-DEMATEL method, centrality and causality jointly describe the importance and influence of system factors in the network structure. For SMEs with limited resources, priority should be given to enablers with higher centrality and causality. Causal factors are root cause-type factors that significantly influence other factors in the system and are considered the root cause or driving force of system problems, often serving as keys to improvement or problem-solving [53]. The following is a ranking and discussion of the magnitude of the causality of each causal factor:

From Figure 3, it can be clearly seen that: “Resource availability and functionality” (A7) is the primary causal factor and has the greatest influence on the other factors; it belongs to the “Sustainability” enabler group. This factor encompasses access to technical support, R&D, and training in relevant I5.0 technologies, access to financial capital for digital expertise, and the ability to align available resources with the operational requirements of the digital transformation under I5.0. Factor A7 impacts all other factors, making access to digital technology resources and financial capital foundational and critical for achieving SC sustainability in SMEs during the fifth industrial revolution. Firms need to possess the capability to utilize these resources effectively for the I5.0 transformation, thereby enhancing market resilience and, consequently, SC sustainability. The study by Ghobakhloo et al. (2023) [54] also indicates that prioritizing the availability and functionality of resources is beneficial for managing the I5.0 transformation towards sustainable development. In contrast, research focused on developed economies indicates that the development of disruptive technologies and the training of employees in new technologies are given higher priority [55,56].

In second place is “Support, active participation, and effective governance by top management” (A1) for the “Resilience” enabler cluster. Supportive management governance at the top provides a strong backbone for the firm’s operations and is particularly crucial for the selection and management of projects. Such governance must offer leadership, increase employee motivation, and foster technological linkages between projects to ensure both immediate and long-term benefits, optimizing the outcomes for both the projects and the firm’s competitiveness. For example, Chatterjee and Chaudhuri (2024) [57] argue that the successful adoption and utilization of I5.0 to maintain SC flows in the post-COVID-19 era is not possible without management support and effective governance.

The third most important enabler is “Support from government, regulators, and financial resources” (A3), which belongs to the same “Sustainability” enabler group as A7. For SMEs, if the government formulates relevant policies and clarifies the direction and objectives of the transformation to I5.0, it will provide policy guidelines for the sustainable development of these enterprises. Enhanced supervision by regulatory authorities regarding environmental protection and social responsibility will enable industrial development to shift from a profit-oriented approach to a concept of sustainable development. Government and financial support for SMEs, along with joint efforts by research institutions to promote cooperation with financial institutions, can provide financing and consulting services to these industries. This can help enterprises better undertake I5.0 transformations and increase their enthusiasm for such changes. The EU proposed in 2021 that an overhaul of the structure and support mechanisms of public funding is necessary to create the conditions for financing a portfolio of early- and mid-term actions that can more effectively facilitate unexpected cross-sectoral combinations and transformative choices for large-scale structural change [58]. It is worth noting that, on this point, Ghobakhloo et al. [18], after discussions with EU experts, also emphasize the significant role of government in promoting I5.0 and sustainability in developed economies such as the EU. This is one of the few studies that explore the contributions of I5.0 to sustainability in developed economies.

In the fourth place is “Improvement of working conditions and employee satisfaction” (A4), which belongs to the “People-centred” enabler group. According to [26], well-being is increasingly becoming a key measure of social prosperity, and manufacturing needs to be people-centred, placing the well-being of industry workers at the centre of the production process. In the context of I5.0, the importance of people in the production chain is emphasized; employees are the lifeblood of production and treating them better will not only help improve efficiency and potentially stimulate innovation but also assist SMEs in balancing the economic, social, and environmental needs. This, in turn, helps in building a more equitable and sustainable SC system. It is worth noting that while A4 has the lowest centrality compared to other causal factors, its causality ranks fourth, indicating that although it is not the most direct or significant factor in the system, its strong influence means policymakers should give it special attention. Neglecting these factors could pose a risk to SMEs when implementing I5.0 to promote SC sustainability, potentially triggering a series of negative impacts. Nazarejova et al. (2024) [59] found in their study that from the employees’ perspective, a better working environment is crucial for reducing physical injuries, which is essential for companies to realize the people-centred ethos of I5.0.

Ranked fifth through eighth, these factors show small differences in their causal degrees among themselves and are all influenced to some extent by the first four factors. “Introduction of more flexible, safe, cost-effective, feasible, and efficient robotic systems for human–robot interaction and collaboration” (A14) was ranked fifth. In the era of I5.0, when monotonous processes no longer burden workers and instead collaborate seamlessly with them, this enhances the ability to meet large-scale personalized market demands, thereby improving enterprise competitiveness. Aheleroff et al. (2022) [60] found that to cope with the waves of globalization and digital transformation, the world recognizes the need for a shift towards better interaction among people, machines, and advanced technologies, necessitating the introduction of systems capable of such interactions. “Human resource development, including training and development plans for employees” (A10) is in the sixth place. As society evolves, deficiencies in human capital and technological backwardness can threaten the emerging socio-economic norms. Therefore, a robust framework for human capital and continuous learning is required to achieve a balanced blend of cognitive skills, social behavioural competencies, and labour skills. The study by Iqbal et al. (2022) [61] underscores the importance of employees capable of collaborating with robots in a safe and healthy environment. Firms with a higher percentage of educated and skilled workforces tend to perform better and deliver higher returns to the business. “Enhancement of digital knowledge and skill levels of employees” (A11) and “Establishment of infrastructure and efficient information management systems” (A9) rank seventh and eighth, respectively. A11 facilitates quicker adaptation of employees to the I5.0 era and enables them to better meet individual customer needs, which is mutually reinforced with A10. Meanwhile, A9 enhances digitization and provides a foundation for meeting higher standards of demand, significantly impacting the support for developing I5.0 SC sustainability. Yu (2024) [62] also highlights the role of digitization and I5.0 in planning for smart and sustainable reverse logistics, noting that building an efficient digitization system can contribute to sustainable development. To construct digital systems, it is necessary to enhance the digital skills of employees and leverage support from other sectors such as government and finance. This indirectly demonstrates that enablers with lower causal degrees are susceptible to the influence of those with higher causal degrees.

In the above discussion of the results, the four factors—A7, A1, A3, and A4—which exhibit higher causality than the others and also align with the triple bottom line of sustainability, can be considered fundamental enablers of diffusion SC sustainability in SMEs. This does not imply that the other causal factors are unimportant; they also play a critical role in the success of enterprises leveraging I5.0 to promote diffusion SC sustainability. However, a strategic approach to resource allocation and capacity considerations for SMEs involves prioritizing limited resources and efforts on the more pivotal components first. Developing the most influential components can catalyze the development of other factors with relatively lower causality, allowing the subsequent use of surplus resources to enhance other higher causality factors, such as A14 and A10.

Additionally, the remaining seven enablers were categorized as outcome factors: “Sustainable corporate governance model ” (A5), “Green manufacturing” (A2), “Implementation of flexible, efficient, and intelligent manufacturing systems” (A12), “Enhancement of industrial resilience and security assurance along the SC” (A6), “Persistence in product and service quality to enhance customer satisfaction” (A8), “Digitalization level, transparency, integration, and flexibility of the SC, and cooperation among relevant participants ” (A15), and “Creation of business models and new value networks promoting inclusivity in the value chain” (A13). Outcome factors are highly interrelated, and improvements in this category do not directly affect the success of strategic practises [63]. They play a crucial role in helping industrial managers and practitioners understand how one enabler influences another, which can further assist organizational management in developing business strategies. Outcome factors can be considered the desired goals of I5.0 in driving sustainability within the SC. Therefore, controlling the causal factors is necessary to ensure that the outcome factor enablers achieve a high level of performance.

5. Concluding Remarks

The manufacturing industry stands poised for a more advantageous sustainability transformation as a result of the insights derived from this study concerning the realization of sustainability in SMEs. These insights facilitate productivity gains through technological advancements while concurrently balancing social and environmental responsibilities. This study aims to identify the key enablers of I5.0 in promoting sustainability within the SCs of SMEs in emerging economies. To ensure the applicability of the findings, the study uses China—the world’s largest emerging economy—as a backdrop and collects data from industry experts and scholars. Initially, this study identified 27 enablers through a review of the literature and the collection of expert opinions. Using the FDM, it then screened down to 15 key enablers of I5.0 to promote SC sustainability. The causal relationships among these 15 key enablers were analyzed and explained using Grey-DEMATEL, which also provided an importance ranking. The results indicate that there are interrelationships among the 15 key enablers identified.

By analyzing the opinions of experts from the manufacturing industry and research institutions, eight enablers were determined to be causal enablers: “Resource availability and functionality” (A7); “Support, active participation, and effective governance by top management” (A1); “Support from government, regulators, and financial resources” (A3); “Improvement of working conditions and employee satisfaction” (A4); “Introduction of more flexible, safe, cost-effective, feasible, and efficient robotic systems for human–robot interaction and collaboration” (A14); “Human resource development, including training and development plans for employees” (A10); “Enhancement of digital knowledge and skill levels of employees” (A11); “Establishment of infrastructure and efficient information management systems ” (A9).

The other seven factors are outcome enablers: “Sustainable corporate governance model” (A5); “Green manufacturing” (A2); “ Implementation of flexible, efficient, and intelligent manufacturing systems ” (A12); “Enhancement of industrial resilience and security assurance along the SC” (A6); “Persistence in product and service quality to enhance customer satisfaction” (A8); “Digitalization level, transparency, integration, and flexibility of the SC, and cooperation among relevant participants” (A15); “Creation of business models and new value networks promoting inclusivity in the value chain” (A13).

5.1. Theoretical and Research Implications

An investigation into the principal motivating factors will assist SMEs in allocating their constrained financial and material resources to the most critical areas, thereby enhancing the likelihood of successfully implementing I5.0 and proliferating sustainable SCs. Currently, there is a notable lack of research on the integration of I5.0 with SC sustainability, especially within SMEs in emerging economies. This study aims to identify the key enablers influencing SC sustainability by synthesizing insights from the literature analysis and employing a hybrid MCDM approach as its theoretical foundation. The use of FDM and Grey-DEMATEL further aids in pinpointing these critical factors. By doing so, this work not only addresses a significant gap in current scholarship but also offers a fresh perspective for future researchers on the interplay between technological advancement and sustainable development. Moreover, this study clarifies that I5.0 emphasizes sustainability, human-centricity, and resilience, which complements and extends the principles of Industry 4.0 rather than merely continuing or supplanting it. This insight aids both academics and practitioners in gaining a clearer understanding of the core concepts and developmental direction of I5.0.

5.2. Managerial and Practical Implications

From both managerial and practical perspectives, the research findings hold significant implications for management and operations within SMEs and governmental bodies in emerging economies. To enhance SC sustainability, it is imperative that SMEs prioritize both internal enterprise factors and external environmental factors when formulating development plans and implementing I5.0 measures. Regarding the enterprise itself, the top manager of SMEs should assume a leadership role. This requires active and effective governance, necessitating that enterprise managers possess a high level of managerial skill and vision, as well as a comprehensive understanding of resource availability and functionality. This will enable them to best support diffuse sustainability in the supply chain. Furthermore, it is essential that enterprises prioritize the well-being of employees and proactively integrate novel technologies that facilitate human–robot collaboration. By doing so, enterprises can enhance their resilience and ensure the long-term sustainability of their operations. In terms of external environmental factors, the industry is a significant driving force for economic development. SMEs require greater support from the government and the financial sector compared to large enterprises. The government and financial institutions should provide loans or subsidies to support the sustainable development of SMEs. This will contribute to the nation’s economic, social, and environmental well-being.

5.3. Research Limitations

Although this study explores how small and medium-sized manufacturers can achieve SC sustainability in the context of I5.0, it is not without limitations. Our research content may have some biases in perspective, although our sample consists of 32 experts from small and medium-sized manufacturing industries, which may not fully represent the entire industry. To alleviate this issue, we ensure the diversity of expert roles when selecting experts (such as production managers, operations managers, consultants, professors, etc.). In addition, the research was conducted within a specific timeframe and, given the rapid and dynamic evolution of I5.0 concepts, future studies may identify new enablers or reassess the significance of existing ones. Finally, differences across countries and regions could affect the generalizability of the findings. Future research could validate these results through additional case studies or field investigations and further explore effective strategies under varying conditions.

5.4. Future Research Directions

Considering the key enablers identified in this study and their potential impact on corporate sustainability, future research could be expanded in several promising directions. Firstly, as new technologies continue to emerge, it is essential for future studies to explore the application potential of emerging technologies, such as artificial intelligence and blockchain, in SC management. These investigations should also examine how these technologies can be integrated with I5.0 principles to further enhance the sustainability and resilience of SCs.

Secondly, comparative studies across different industries and regions would help identify common patterns and best practises under varying contexts. This approach would provide more targeted strategic recommendations for enterprises in various sectors, thereby increasing the generalizability and practical applicability of the research findings.

Lastly, it is crucial to investigate the long-term mechanisms through which changes in government policies influence the adoption of I5.0 technologies by small and medium-sized manufacturers and their efforts to improve SC sustainability. Understanding these dynamics will provide valuable insights into creating a supportive policy environment that fosters innovation and sustainable development.