Search Results (82)

This systematic review examines how digital technologies enable circular economy (CE) transitions across sectors and value chains. Analysing 266 peer-reviewed publications (2016–2025), we develop a comprehensive taxonomy of digital enablers—including IoT, AI, blockchain, cloud computing, additive manufacturing, and digital platforms—and map their applications to circular strategies such as reuse, remanufacturing, and recycling. Our findings reveal that data-driven technologies dominate CE implementation, with 89% of studies involving data collection, storage, analysis, or sharing functions. IoT emerges as the foundational technology for real-time tracking and monitoring, while AI and big data analytics optimise circular processes and predict maintenance needs. Blockchain ensures traceability and trust in circular supply chains, and cloud computing provides scalable infrastructure for collaboration. Manufacturing (41%) and construction (15.5%) are the most studied sectors, with strong European research leadership reflecting policy drivers such as Digital Product Passports. We identify three impact types: enabling (process optimisation), disruptive (business model innovation), and facilitating (ecosystem collaboration). Key barriers include technical complexity, organisational resistance, high implementation costs, and regulatory gaps. The review concludes with recommendations for integrated, multi-stakeholder approaches to realise a digitally enabled circular economy. Full article

Olive oil is a high-value product that is highly exposed to fraud, making robust traceability systems essential to protect authenticity, consumer trust, and competitiveness. This study examines how digital traceability technologies influence fraud mitigation and the sustainable performance of olive oil mills in southern Brazil. A systematic literature review, conducted according to the PRISMA 2020 protocol in Scopus and Web of Science, identified state-of-the-art supply chain and authentication technologies, including blockchain, IoT, RFID, QR codes, cloud computing, Big Data, artificial intelligence, and physicochemical methods. Two structured questionnaires were then applied to managers from nine mills in the main Brazilian olive oil cluster, and the data were analyzed using descriptive statistics, Chi-Square tests, and correlation measures within a framework grounded in Resource-Based View and Institutional Isomorphism theories. The results show that adoption of digital traceability is still incipient, while internal factors such as organizational commitment and marketing strategies play a more decisive role than external pressures in explaining adoption. Although managers do not yet perceive a direct impact on fraud mitigation, adoption is positively associated with economic, environmental, and social sustainability outcomes. Given the exploratory design and the small, non-probabilistic sample (n = 9), the findings should be interpreted as indicative rather than definitive. The proposed framework is intended as a transferable analytical lens that can be adapted and further validated in other agri-food and industrial contexts using larger samples and objective fraud-related indicators. Full article

The proliferation of big data applications across various industries has led to a paradigm shift in data architecture, with traditional approaches giving way to more agile and scalable frameworks. The evolution of big data architecture began with the emergence of the Hadoop-based data stack, leveraging technologies like Hadoop Distributed File System (HDFS) and Apache Spark for efficient data processing. However, recent years have seen a shift towards modern data stacks, offering flexibility and diverse toolsets tailored to specific use cases. Concurrently, cloud computing has revolutionized big data management, providing unparalleled scalability and integration capabilities. Despite their benefits, navigating these data stack paradigms can be challenging. While existing literature offers valuable insights into individual data stack paradigms, there remains a dearth of studies that offer practical, in-depth comparisons of these paradigms across the entire big data value chain. To address this gap in the field, this paper examines three main big data stack paradigms: the Hadoop data stack, modern data stack, and cloud-based data stack. Indeed, we conduct in this study an exhaustive architectural comparison of these stacks covering the entire big data value chain from data acquisition to exposition. Moreover, this study extends beyond architectural considerations to include end-to-end use case implementations for a comprehensive evaluation of each stack. Using a large dataset of Amazon reviews, different data stack scenarios are implemented and compared. Furthermore, the paper explores critical factors such as data integration, implementation costs, and ease of deployment to provide researchers and practitioners with a relevant and up-to-date reference for navigating the complex landscape of big data technologies and making informed decisions about data strategies. Full article

This study examines the impact of sustainability “big data” analytics on “product innovation” in Jordanian pharmaceutical companies, focusing on the mediating roles of “knowledge management capabilities” and “agile supply chain” management. Using structural equation modelling with data from 381 pharmaceutical managers, we tested eight hypotheses relating to direct and indirect relationships between these constructs. The findings revealed that big data has a significant positive direct effect on “sustainability product innovation” (β = 0.28, p < 0.001), accounting for 46.3% of the total effect. “Knowledge management” and “agile supply” chain were found to mediate this relationship, contributing (31.3% and 22.4%) of the total effect, respectively. Our supplementary analysis demonstrated that big data has a notably stronger impact on radical innovation compared to incremental innovation (36.3% stronger effect). The model demonstrated robust explanatory power, accounting for (43.5%) of the variance in product innovation, (37.2%) in knowledge management, and (45.1%) in agile supply chain. All measurement scales showed strong psychometric properties with factor loadings exceeding 0.75 and composite reliability values ranging from 0.889 to 0.932. These findings expand our understanding of how “sustainability big data” fosters pharmaceutical innovation and offer pragmatic insights for managers seeking to leverage data capabilities for a competitive advantage in the pharmaceutical industry of Jordan. Full article

As an important component of the global fishery economy, the crab breeding and processing industry faces the dual challenges of sustainable development and technological upgrading. This paper first systematically analyzes the regional distribution and core biological characteristics of major global economic crab species, laying a foundation for the targeted design of processing technologies and equipment. Secondly, based on advances in crab processing technology, the industry is categorized into two systems: live crab processing and dead crab processing. Live crab processing has formed a full-chain technological system of “fishing–temporary rearing–depuration–grading–packaging”. Dead crab processing focuses on high-value utilization: high-pressure processing enhances the quality of crab meat; liquid nitrogen quick-freezing combined with modified atmosphere packaging extends shelf life; and biological fermentation and enzymatic hydrolysis facilitate the green extraction of chitin from crab shells. In terms of intelligent equipment application, sensor technology enables full coverage of aquaculture water quality monitoring, precise classification during processing, and vitality monitoring during transportation. Automation technology reduces labor costs, while fuzzy logic algorithms ensure the process stability of crab meat products. The integration of the Internet of Things (IoT) and big data analytics, combined with blockchain technology, enables full-link traceability of the “breeding–processing–transportation” chain. In the future, cross-domain technological integration and multi-equipment collaboration will be the key to promoting the sustainable development of the industry. Additionally, with the support of big data and artificial intelligence, precision management of breeding, processing, logistics, and other links will realize a more efficient and environmentally friendly crab industry model. Full article

Background: Digital supply chain transformation research exhibits a critical gap, examining technologies in isolation rather than as integrated ecosystems. Methods: This study addresses this limitation by developing a comprehensive orchestration frame-work through PRISMA-guided systematic review of 96 publications (2012–2024) using bibliometric analysis, structural topic modeling, and thematic synthesis across Scopus and Web of Science databases. Results: Analysis revealed three distinct research clusters: Supply Chain Management (centrality: 14.95), Digital Transformation (centrality: 9.50, density: 101.05), and Big Data Analytics (density: 113.22), with substantial negative correlations (−0.48 to −0.54) indicating organizational evolution from fragmented adoption toward integration. Conclusions: Publications increased 78% year-over-year during 2021–2022, while Supply Chain Management dominated topic prevalence (41%) and Big Data Analytics declined from 0.9 to 0.15 as practices normalized. The Digital Supply Chain Orchestration Framework conceptualizes transformation as multi-layered with hierarchical relationships between foundational domains, technological enablers, integration mechanisms, and value creation dimensions. This framework provides structured approaches for organizations to assess digital maturity, identify technological gaps, and develop strategic roadmaps aligned with Sustainable Development Goals, bridging theory and practice for integrated, value-driven digital transformation. Full article

This study advances the hypothesis that supply chain power structure is a critical contingency factor for realizing investment value from integrating blockchain and big data. We develop a game-theoretic model of a two-tier supply chain to analyze investment decisions. The model examines cost–benefit dynamics under supplier-led, manufacturer-led, and balanced power structures and proposes a coordination mechanism to align incentives. Results demonstrate that power structure determines pricing and profit distribution, allowing the dominant party to capture a larger benefit share. Furthermore, power structure systematically interacts with technological performance: profitability increases with customer heterogeneity satisfaction and demand enhancement but can be eroded by a high technology cost coefficient that triggers disproportionate investment. We identify a critical investment cost threshold for achieving Pareto improvement. Finally, the demand premium from enhanced transparency ensures economic viability even when adoption increases prices. These insights offer strategic frameworks for blockchain investment tailored to specific power distributions. Full article

Consumers and businesses are often connected online in today’s digitally connected world. Fast and barrier-free communication, easier and faster operation, and automation and networking of robots and production offer many competitive advantages. Recognizing the limiting factors of new technology, such as the significant dependency on technology and the vulnerability of IT devices, is crucial. As digitalization might increase the competitiveness of companies and have an impact on both the supply and value chains, we need to consider and assess their vulnerability from an information security perspective. Consequently, competitive advantage is not only about creating value more cost-efficiently and with higher quality but also about extracting the correct information from big data, interpreting and integrating it into business operations, and protecting it. This study proposes a fishbone model to help identify and overcome these challenges. It allows companies to identify the root cause of each information security incident. Full article

Integrated permitting systems are being progressively implemented by governments worldwide owing to their beneficial effects on site-level environmental management. However, different countries have implemented different systems that reflect their own national conditions, resulting in varying efficacy. Based on experience from Korea, here, we propose an e-permit system that implements (i) package management of industries related to or within the value chain of permitted industries, (ii) site-specific best available techniques (BATs), (iii) automatic calculations of BAT-associated emission levels (BAT-AELs), and (iv) code-based management of environmental factors using a ReGreen-BAT (RG-BAT) diagram. The e-permit system improves permitting process efficiency and reduces the review period from 35 days to 2–23 days depending on the industry sector. Additionally, by leveraging big data collected through the Integrated Environmental Permitting System for post-permit management, administrative efficiency could be further improved. Integrating industries related to or within the value chain of the target industries in the Integrated Environmental Management System (IEMS) can reduce pollution, improve resource circulation, and promote energy efficiency and cost savings. When selecting BATs, the balloon effect and internal site-specific factors should be evaluated to realize more tailored and acceptable BAT selection. Automation of BAT-AEL calculations using Python considerably reduces the processing time to 2–3 weeks. The RG-BAT diagram helps visualize BAT effectiveness and allows businesses to easily identify suitable BATs. The insights gained herein indicate that countries implementing IEMS can benefit from sharing implementation experiences and should collaborate on the development of advanced systems. Full article

Small and medium-sized enterprises (SMEs) constitute the backbone of the EU economy, yet their uneven digital transformation raises challenges for competitiveness and territorial cohesion. This article examines the organizational and spatial aspects of SME digitalization across the European Union using Flash Eurobarometer 486 data and latent class analysis (LCA) combined with Bayesian multilevel multinomial regression. The results reveal four SME digitalization profiles—Digitally Conservative Backbone; Partially Digital and Upgrading; Digitally Advanced and Diversified; and Focused Digital Integrators—reflecting diverse adoption patterns of key technologies such as AI, big data and cloud computing. Digitalization is shaped by organizational factors (firm size, value chain integration, digital barriers) and territorial factors (urbanity, border proximity, national digital infrastructure as measured by the Digital Economy and Society Index, DESI). Contrary to linear modernization assumptions, digital adoption follows geographically embedded trajectories, with sectoral uptake occurring even in low-DESI or non-urban regions. These results challenge core–periphery models and highlight the significance of place-based innovation networks. The study contributes to modernization theory and regional innovation systems by showing that digital inequalities exist not only between countries but also within regions and among adoption profiles, emphasizing the need for nuanced, multi-level digital policy approaches across Europe. Full article

The retail sector is the most demand-sensitive echelon in the supply chain, where non-moving items accumulate and become dead stock. Existing inventory management studies focus on fast-moving products and income generation. This paper focuses on dead stock management and proposes a mixed strategy solution using a pure integer non-linear programming model that minimizes the dead stock management cost of a retail chain operator. The number of products and volume of product-related data in a retail chain system require big data analysis to ensure sustainable inventory practices that reduce waste generated from dead stock inventory. Through hypothetical data sets, the 3-store, 10-product run showed that discount percentage, expected sales success probability of a product in a store location, and disposition of unsold products were the main drivers of the decisions made by the model. The most significant cost contributors arising from these decisions were the unrecovered product cost (UPC), disposed product cost (PC), and salvage value from the successful sale of dead stock. Inventory managers must balance the effect on these cost components when they choose the strategies to use in managing dead stock. Full article

Energy-intensive industries (EIIs) face mounting pressure to reduce greenhouse gas emissions while maintaining international competitiveness—a balance that is central to achieving the EU’s 2030 and 2050 climate objectives. In this context, energy management (EM) emerges as a strategic instrument to decouple industrial growth from fossil energy consumption. This study proposes a redefinition of EM to support carbon-neutral supply chains within the European Union’s EIIs, addressing critical limitations of conventional EM frameworks under increasingly stringent carbon regulations. Using a modified systematic literature review based on PRISMA methodology, complemented by expert insights from EU Member States, this research identifies structural gaps in current EM practices and highlights opportunities for integrating sustainable innovations across the whole industrial value chain. The proposed EM concept is validated through an analysis of 24 EM definitions, over 170 scientific publications, and over 80 EU legal and strategic documents. The framework incorporates advanced digital technologies—including artificial intelligence (AI), the Internet of Things (IoT), and big data analytics—to enable real-time optimisation, predictive control, and greater system adaptability. Going beyond traditional energy efficiency, the redefined EM encompasses the entire energy lifecycle, including use, transformation, storage, and generation. It also incorporates social dimensions, such as corporate social responsibility (CSR) and stakeholder engagement, to cultivate a culture of environmental stewardship within EIIs. This holistic approach provides a strategic management tool for optimising energy use, reducing emissions, and strengthening resilience to regulatory, environmental, and market pressures, thereby promoting more sustainable, inclusive, and transparent supply chain operations. Full article

This study analyses the state-of-the-art application of Life Cycle Assessment (LCA) in the production and recycling of rare earth elements (REEs), highlighting its strategic role in promoting sustainability across resource-intensive sectors. A systematic literature review (SLR) was conducted in accordance with PRISMA guidelines using the Scopus database. A total of 78 peer-reviewed studies were included, with no time restrictions applied. The review focused on studies applying LCA to REE production from both primary and secondary sources, particularly those integrating emerging digital technologies such as artificial intelligence, big data, and process simulations. Studies lacking LCA methodology or not specifically addressing REEs were excluded. The findings show that LCA, when enhanced by digital tools, serves as a key enabler for making industrial processes more sustainable by improving traceability, reducing environmental impacts, and supporting responsible decision making along the value chain. Recycling from secondary sources such as electronic waste emerges as a practical solution to reduce dependency on primary resources and to promote circular models. In particular, recycling has been shown to reduce environmental impacts by 64–96%, underscoring its effectiveness in mitigating the ecological footprint of REE production. The innovative contribution of this study lies in demonstrating how the integration of LCA and digital technologies can accelerate the transition toward more sustainable, resilient, and transparent rare earth value chains. Full article

The integration of Industry 4.0 technologies, Circular Economy (CE) principles, and Green Human Resource Management (GHRM) offers transformative potential to address global sustainability challenges. Industry 4.0, characterized by advanced digital technologies like IoT, Additive Manufacturing (AM), and Big Data Analytics (BDAA), enhances operational efficiency, resource optimization, and waste minimization. Concurrently, CE redefines economic models through resource conservation, lifecycle extension, and reduced environmental impact, supported by frameworks like ReSOLVE. GHRM aligns human resource practices with sustainability objectives, fostering Green behaviors and embedding environmental considerations into organizational culture. Despite the individual benefits of these frameworks, their combined application remains underexplored, with limited research on their systemic integration. This study addresses this gap by examining the synergies between Industry 4.0 technologies, CE principles, and GHRM strategies, identifying opportunities and challenges in their implementation. A theoretical model is proposed, emphasizing systemic innovation, resource efficiency, and collaborative value chains as key enablers of sustainable development. The model highlights the necessity of aligning technological advancements with human-centric approaches to overcome behavioral, organizational, and infrastructural barriers in transitioning toward sustainability. The findings offer practical insights for policymakers and industry leaders, outlining strategies for integrating Industry 4.0 with CE and GHRM to drive sustainability transitions. By synthesizing technological, environmental, and human resource dimensions, this research contributes both theoretically and practically, positioning organizations to enhance sustainability while maintaining competitiveness in evolving economic landscapes. Full article

In the big data era, global business competition focuses on industrial chain coordination. The whole rural e-commerce industry chain, as an advanced system characterized by digital transformation, is experiencing rapid growth. This paper aims to explore the evolutionary mechanism of collaborative behavior in the digital transformation of platform enterprises and participating enterprises across the whole rural e-commerce industry chain. To achieve this, this paper combines prospect theory and evolutionary game theory, introduces the value function and decision weight of prospect theory, and constructs a two-party game model between platform enterprises and participating enterprises. Based on the demonstration of the impact of individual changes in major objective factors, such as the cooperative innovation benefit coefficient, as well as major behavioral characteristic factors, such as decision-makers’ risk attitude coefficients, on enterprises’ strategic choices, we further reveal the influence of the interaction of key factors on the evolutionary results through case simulations. The findings indicate that when the behavior characteristics of the players are introduced, the threshold interval of the cost–benefit ratio of the two sides to reach the optimal state of decision-making is obviously reduced. Under moderate risk attitudes and degrees of loss sensitivity, enhancing the resource absorption capacity of enterprises in the chain and reducing the potential risk loss of platform enterprises to alleviate the influence of subjective behavior characteristics on cooperation willingness are effective measures. Improving innovation ability is the key factor in alleviating the negative impact of uncertainty on the decision-making of both parties. This paper is one of the few studies to integrate prospect theory with evolutionary game analysis in examining the collaborative behaviors between platform enterprises and participating enterprises. Effective strategies are proposed to promote enterprises achieving synergy. Full article