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The Sustainable Development Goals and circular economy are two critical aspects of the 2030 Agenda for Sustainable Development. They both seek to reduce the waste of natural resources and enhance society’s social, economic, and environmental goals. This study aims to identify, develop, test, and verify the significant antecedents that affect the ad...
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Industry 5.0 (I5.0) is increasingly emerging as a prominent paradigm across the global business landscape, with three distinctive core values: human-centricity, sustainability, and resilience. This shift introduces notable changes in multiple aspects and requires academics and practitioners to reexamine the concept of supply chain management. Whils...
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... (Otwong et al. 2021) Flexibility structures (R13) Include clauses that allow for flexible regulation in response to evolving conditions and advances in technology. (Shafique et al. 2023) Stakeholder engagement (R14) ...
The escalating challenges of climate change, land degradation, biodiversity loss, and resource depletion highlight the urgent need for sustainable environmental strategies. The circular economy presents a vital approach to enhancing climate resilience and sustainable land management by minimizing waste, optimizing resource efficiency, and promoting ecosystem restoration. Effective regulatory innovations play a crucial role in facilitating this transition, ensuring policies support circularity, ecological sustainability, and the rehabilitation of degraded ecosystems. This study adopts a comprehensive framework incorporating the fuzzy Analytical Hierarchy Process (AHP) method to assess and rank factors and sub‐factors essential for regulatory enhancement. Through fuzzy AHP analysis, six key factors and 24 sub‐factors influencing the circular economy were assessed. The findings reveal that economic incentives (R2), the legislative framework (R1), and innovation support (R4) are the most critical factors driving circular economy progress. Additionally, taxation strategies (R21), policy orientation (R11), and legal transparency (R12) emerged as the top‐ranked sub‐factors. By examining climate change as a key driver of land degradation, this study offers valuable insights into regulatory strategies that bolster climate resilience, support sustainable land management, and facilitate the transition toward a circular economy. The findings provide a policy‐oriented framework for enhancing regulatory structures aimed at mitigating environmental degradation and fostering long‐term sustainability.
... The second stage employed artificial neural networks to capture nonlinear relationships (Shafique et al. 2023), offering advantages in nonlinear representation and predictor importance ranking. Using multilayer perceptron architecture with backpropagation, the network included input layers from PLS-SEM predictors, a hidden layer calculated by Nh = (Ni + No)/2, and an output layer representing circular economy practices. ...
... This finding extends Shafique et al.'s (2023) work by specifying how knowledge processes transform technological inputs into circular practices. However, while Shafique et al. emphasised formal knowledge management systems in large enterprises, this study highlights the importance of both formal and informal knowledge processes in a Ghanaian manufacturing context. ...
This study extends the natural resource‐based view (NRBV) theory by examining how Industry 4.0 readiness influences circular economy practices in resource‐constrained manufacturing contexts. The research challenges established theoretical assumptions about sustainability culture's role and circular economy implementation hierarchies while investigating the mediating mechanisms of knowledge management capabilities. Data from 704 manufacturing SMEs in Ghana was analyzed using an innovative two‐stage PLS‐SEM and artificial neural network approach that captured both linear and nonlinear relationships between constructs. This methodical triangulation revealed complex interaction patterns that traditional modeling might miss. The study makes three significant theoretical contributions: (1) Industry 4.0 readiness enhances circular economy practices even at basic levels of technological sophistication, challenging technology‐deterministic theoretical perspectives; (2) knowledge management capabilities transform technological inputs into circular outcomes through mechanisms that differ from those in resource‐rich contexts, emphasizing informal knowledge sharing rather than formal codification; and (3) sustainability‐orientated culture functions as the strongest direct predictor of circular economy practices rather than as a moderator, contradicting Western‐centric theoretical frameworks. The study reconceptualizes how NRBV theory applies in resource‐constrained environments by demonstrating that circular economy implementation hierarchies are context‐dependent rather than universal; value creation from technological resources depends more on effective orchestration than sophistication; and sustainability culture functions differently in collectivist contexts than is theorized in individualistic societies. These theoretical extensions transcend the specific Ghanaian context to offer generalizable insights into how resource endowments and cultural contexts shape sustainability capability development.
... Key technologies for logistics 4.0 and digital supply chain include the Internet of Things, Blockchain, digitalization, artificial intelligence, machine learning, cyberphysical, augmented reality, virtual reality, robotics, additive manufacturing, and cloud computing [8][9][10][11][12]. However, studies on logistics 4.0 and digital supply chains have often focused on individual technologies such as artificial intelligence [13][14][15], blockchain [16], Internet of Things [17][18][19], big data [15,[20][21][22], supply chain analytics [23], and wearable technologies [24]. The literature found inconsistencies in the use of information technology in digital supply chain and logistics 4.0. ...
... RMG's sustainable practices, such as using eco-friendly materials, minimizing waste through efficient production, and recycling fabrics, align well with CE's goals. For instance, some garment manufacturers are establishing closed-loop systems that collect, disassemble, and reuse old garments to create new ones (Shafique et al. 2023;Ozili and Opene 2022;Rahimi et al. 2023). This aligns with the CE principle of a product life cycle that does not end with disposal but instead transitions back into production. ...
... The elements of GI were sourced from S. Ali et al. (2022) and Rahimi et al. (2023). Items for SRMG and CEA were adopted from (Shafique et al. 2023;Ozili and Opene 2022;Rahimi et al. 2023;Marrucci et al. 2021;Wu et al. 2021;Sarkar et al. 2020aSarkar et al. , 2020b. The study scrutinized data adequacy for factor analysis through correlation matrix measurement, revealing correlations and determining the number of retained factors via eigenvalue parameters (Cattell 1966). ...
This study explores the role of sustainability practices in facilitating the adoption of a circular business model within the readymade garment (RMG) sector in Bangladesh. Specifically, the study wants to investigate how green supply chain practices (GSCPs), green investment (GI), and green human capital (GHC) contribute to ensuring sustainability practices in RMG (SRMG) and transitioning businesses towards a circular economy (CE). A survey of 589 Bangladeshi RMG managers using a 7-point Likert scale informed this quantitative study. PLS-SEM revealed significant predictor-outcome correlations, while necessity condition analysis (NCA) identified essential conditions for desired outcomes. This study found that GSCM, GHC, and SRMG practices significantly and positively influence CE adoption. Additionally, it revealed that sustainable practices (SPs) mediate the relationship between GSCM, GSC, GI, and CE adoption. This study enriches existing knowledge by exploring key relationships in the proposed model, highlighting significant direct and indirect effects.
... The adoption of supply chain analytics influences circular economy performance. The study highlights critical technological, organizational, and environmental factors, showing that effective analytics can improve supply chain integration, flexibility, and sustainability, resulting in social, economic, and ecological advantages (Shafique [46]). By leveraging decentralized and immutable record systems, blockchain offers a novel approach to ensuring transparency and reliability in supply chain operations (Abdelgani et al. [2]; Bharthvajan [9]; Gómez et al. [22]). ...
China has prioritized improving its higher education system and growing its green economy in recent years. This study examines how important a university English education is to China’s green economy’s development, particularly in the Internet era. The transition of global economies to more sustainable models highlights the critical role that university English education plays in fostering green economic growth, particularly in its alignment with digital supply networks. This research investigates the economic implications of integrating China’s university English education proficiency (UEEP) with the advancements in digital supply chains (DSC) and their collective influence on the development of the green economy (DGE). Utilizing comprehensive datasets from the China National Bureau of Statistics, Ministry of Education, and industry reports for 2000–2021, we observed a significant positive correlation between UEEP and the digitalization of green supply chains (DGSC). Results concluded that the regional differences (RD) and cultural attitudes towards sustainability (CATS) moderate these relationships, suggesting the necessity of localized strategies. Economic factors, primarily driven by government policies and regulations (GPR), were control variables that significantly influenced the green economic trajectory. The study underscores that, beyond technological advancements, the efficacy of English education at the tertiary level acts as a catalyst in optimizing green supply chains and fostering economic sustainability. Policymakers and educators are urged to recognize and harness this synergy to ensure China remains at the forefront of global green economic conversion.
... Conventional linear regression techniques and singular transformations fall short in capturing the interactive dynamics among these three components. As computer technologies advance, machine learning models have increasingly been used for forecasting economic growth, tracking environmental pollution trends, and evaluating economic-environmental risks [34,35]. These challenges often involve complex variable interactions, significant randomness, and nonlinear behavior. ...
Amid the rapidly changing digital environment and the growing flow of information, the digital economy has become a significant driving force behind economic progress. At the same time, governments worldwide are increasingly prioritizing environmental protection and green development, making the challenge of harmonizing economic growth with environmental protection to achieve high-quality development, a critical issue. This study, using data from 285 prefecture-level cities in China, places the digital economy, environmental regulations, and high-quality economic development in the same framework to explore their interconnections. Furthermore, machine learning and the SHAP (Shapley Additive Explanations) model were employed to analyze the complex nonlinear effects and interactions between these factors, clarifying the significance of various parameters. The findings reveal significant regional differences in high-quality economic growth, digital industrialization, industrial digitization, digital governance, data valorization, and environmental regulations across China, with generally low overall levels. Digital governance, industrial digitization, and digital industrialization all contribute to high-quality economic development, with digital industrialization having the most significant impact. However, challenges such as data privacy concerns and inadequate governance can hinder the positive effects of data valorization on high-quality economic progress.
... CEP activities involve reusing, recycling, and repairing the products as long as possible. Additionally, CEPs have focused on reducing the utilization of resources and increasing the output, which was found in the supply chain literature [45]. Moreover, recycling and remanufacturing, circular design, and circular manufacturing constitute three principles of CEPs [26]. ...
The industrial shift from Industry 4.0 to Industry 5.0 has transformed organizational thinking, moving the focus from purely technological implementation to a more human-centered approach. The current study has focused on the Industry 5.0 technological capabilities to bring into circular economy practices aligned with sustainable development goals, aiming to enhance sustainable performance. Moreover, the resource-based theory has grounded the development of the comprehensive framework on Industry 5.0 technological capabilities (artificial intelligence capabilities, big data analytical capabilities, Internet of Things capabilities, machine learning capabilities, and blockchain technology capabilities) and circular economy practices (eco-design, management system, and investment recovery) to achieve sustainable performance (environmental performance, social performance, and economic performance). Data have been collected from 179 respondents from the Chinese manufacturing industry. Additionally, data have been analyzed using the structural equation modeling technique. The results showed that Industry 5.0 technological capabilities directly affect sustainable performance. Moreover, circular economy practices played a dual, moderating, and mediating role between Industry 5.0 technological capabilities and sustainable performance. The current study has contributed to filling a gap in the literature on Industry 5.0 capabilities, especially in the circular economy and sustainable performance perspective. The practical contribution recommended is that if organizations focused on their Industry 5.0 technological capabilities, it would boost circular economy practices and sustainable performance to achieve sustainable development goals.
... With the development of computer technology, neural network models have been widely used to predict economic development, changes in environmental pollution, and risk assessment of economic environments [33][34][35][36]. These problems share a common characteristic: the relationships between variables are complex, highly stochastic, and exhibit nonlinear responses. ...
Based on panel data from 285 prefecture-level cities in China from 2019 to 2023, the synergistic effects of the digital economy, green technology innovation, and ecological environment quality were analyzed. First, using the entropy method, the measurement dimensions of the indicators of the digital economy, green technology innovation, and ecological environment quality were obtained. Second, employing a neural network model with these measurements as input variables, the interactive relationship among the digital economy, green technology innovation, and ecological environment quality was explored. Finally, based on the calculation results of the neural network model, the importance and impact of each input parameter on ecological environment quality were determined using weight analysis methods. The research findings indicate: (1) Utilizing the entropy method, the measurement dimensions of the indicators of the digital economy, green technology innovation, and ecological environment quality were obtained. Analysis of each indicator measurement reveals that environmental pressure has a significant impact on ecological environment quality, with significant differences in environmental pressure among different regions. Industrial digitization emerges as the core factor influencing the digital economy, being the most significant driving effect, followed by digital industrialization. Green technology innovation is crucial for promoting environmental protection and achieving high-quality green economic development. (2) Based on the neural network model, the interactive relationship among the digital economy, green technology innovation, and ecological environment quality was revealed. The results indicate that the digital economy has a direct impact on improving ecological environment quality. The relationship between the digital economy and the ecological environment exhibits nonlinear effects, with the rate of change in environmental pressure and environmental status measurements initially increasing significantly and then gradually slowing down as the measurement levels of digital industrialization and industrial digitization increase. Improvement in digital governance and data value measurement levels will contribute to enhancing environmental status and environmental governance levels. (3) Through weight analysis, it was found that in terms of direct effects, industrial digitization, and digital industrialization have the most significant impact on environmental pressure, with importance coefficients of 0.45 and 0.3, respectively, while data valorization has the least impact. Regarding intermediary effects, industrial digitization and green technology innovation have the most significant impact on environmental pressure, while digital governance and green technology innovation have a relatively clear impact on environmental status and environmental governance. These results lay the foundation for promoting the coordinated cooperation between the digital economy and green technology innovation and for advancing the establishment of a win–win situation between economic development and environmental protection.
... Big data analytics in supply chain has got the attention of recent scholars (Fernando et al., 2020;Papadopoulos et al., 2017;Shafique et al., 2023). But, organisations are still facing the data synchronization a big challenge, especially in their supply chain operations (Fernando et al., 2020). ...
With the global digitalisation, big data has received growing attention from academicians and practitioners. However, only a few empirical studies examined the benefits of big data predictive analytics (BDPA) and its influence on supply chain collaboration (SCC) and supply chain performance (SCP). Addressing the identified gaps of the implementation of organisational information processing theory (OIPT), the current study provided the foundation to develop a conceptual framework. All relevant data were collected from 197 employees in the Chinese logistics industry. Partial least squares-structural equation modelling technique was performed. The obtained empirical results supported top management support and compatibility as critical factors for the adoption of BDPA. Moreover, BDPA exhibited positive influence on SCC and SCP. Additionally, SCC mediated the relationship between BDPA and SCP. This study presented significant theoretical contributions and provided guidelines that can benefit policymakers and organisations in the efforts of implementing BDPA for enhanced SCP. After all, improving SCP would benefit customers and the society in the case of reduction and wastage of resources.
... This background lays the foundation for understanding the role of supply chain efficiency as a mediator between digitalization and supply chain performance. The existing literature has established a positive relationship between digitalization and supply chain efficiency [88,100,[105][106][107]. Efficiency, in turn, has been identified as a critical determinant of supply chain performance [29,56,83,96,98,[108][109][110][111][112] with streamlined processes and reduced resource wastage contributing to enhanced overall supply chain outcomes. ...
In the context of the rapidly evolving landscape shaped by the increased prevalence of digital technologies and the transformative dynamics of supply chains in the post-COVID-19 era, this research seeks to address a crucial gap by examining the mediating role played by supply chain integration and efficiency. The primary aim is to provide a more comprehensive and nuanced understanding of how digitalization influences networks of supply chain performance. Moreover, we delve into the moderating impact of supply chain dynamism on shaping this association. Through a simple random sampling technique, survey data were collected from 293 Turkish manufacturing firms via an online survey and analyzed using structural equation modeling. The findings underscore that digitalization significantly enhances supply chain integration and efficiency, thereby contributing to improved supply chain performance. Notably, supply chain integration and efficiency were identified as key mediators in the relationship between digitalization and supply chain performance. Furthermore, we investigate the moderating effect of supply chain dynamism, revealing its positive influence on the association between digitalization and supply chain integration. Rooted in key theories such as the resource-based view and dynamic capabilities, this study provides valuable insights by unraveling the intricate processes through which digitalization’s impact is channeled in the post-COVID-19 era. The research extends the current literature by considering the contextual role of supply chain dynamism, shedding light on the complex dynamics between digitalization and supply chain outcomes.
