Search Results (322)

The strain on the global food sector to reconcile environmental sustainability with operational efficiency has been intensifying. In a growing economy, this study investigates the revolutionary potential of integrated digital ecosystems that include blockchain, big data analytics, and IoT-enabled real-time monitoring on the performance of Green Supply Chain Management (GSCM). The research, that relies on the Technology–Organization–Environment (TOE) framework, utilizes a rigorous mixed-methods approach which utilizes Fuzzy-Set Qualitative Comparative Analysis (fsQCA) and Structural Equation Modeling (SEM) on data from food-processing firms in Pakistan. Green innovation is an important moderating catalyst, and SEM results confirm that digital integration significantly enhances waste reduction and energy efficiency, explaining 62% of performance variance. A further configurational analysis indicates causal equifinality and reveals 3 distinct paths to superior sustainability, from “Innovation-Driven Institutionalization” to “Government-Supported Scaling.” It demonstrates that various combinations of external support and internal readiness may ultimately contribute to success. The findings are supported by post-implementation evaluations, which show a 29% decrease in energy consumption and a 55% reduction in cold-chain losses. These findings offer novel insights for practitioners and policymakers, validating that environmental stewardship and commercial profitability are mutually reinforcing objectives in the digital age. Full article

This study investigates the evolving relationship between supply chain integration (SCI) and firm performance through a comprehensive bibliometric analysis of 148 publications retrieved from the Scopus database. Using VOSviewer 1.6.20 software, the research maps the intellectual structure of the field, highlighting influential authors, journals, and thematic developments. Findings reveal that SCI conceptualized across internal, supplier, and customer integration has consistently been linked to improved operational efficiency, responsiveness, and competitive advantage. However, empirical evidence also indicates mixed outcomes, particularly under conditions of environmental uncertainty and excessive dependence on partners. Recent scholarship demonstrates a notable shift toward sustainability-oriented integration and the adoption of digital technologies such as blockchain, big data analytics, and artificial intelligence, which collectively enhance resilience and adaptability. The analysis underscores gaps in research across developing economies and service industries, suggesting opportunities for future inquiry. Overall, the study deepens understanding of SCI’s role in shaping resilient, sustainable, and technologically enabled supply chains. Full article

Snowpack dynamics in continental climates are important for water-resource monitoring and snow water equivalent (SWE) estimation, yet the response of the snow depth–snow water equivalent (SD-SWE) relationship to changing thermodynamic and accumulation forcing remains insufficiently understood. This study develops a process-based framework to evaluate how moderate perturbations in air temperature and precipitation influence snowpack evolution and depth–mass coupling in representative snow regimes of northeastern Kazakhstan. SNTHERM (the Snow Thermal Model) simulations were combined with regression analysis, ANCOVA diagnostics, and bulk-density evaluation under controlled delta-change perturbations of air temperature (±1–2 °C) and precipitation (±5–10%). The results show that the SD-SWE relationship remains approximately linear within the tested perturbation range (R² ≈ 0.78–0.84), although its parameters are partially sensitive to precipitation-driven accumulation. Temperature perturbations mainly affect melt timing, seasonal persistence, and snow-density redistribution, whereas precipitation modifies snowpack mass and overburden, enhancing mechanical compaction and increasing the regression slope. These findings indicate that snow density is a key integrative state variable linking energy balance, phase change, and compaction processes. Under the tested conditions, snow depth remains a physically consistent proxy for SWE, although the conclusions are limited by the one-dimensional model structure, reanalysis-based forcing, and restricted observational coverage. Full article

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

Purpose: This study systematically reviews research on six emerging technologies in external auditing, Big Data, Blockchain, Machine Learning, Deep Learning, Artificial Intelligence (AI), and Robotic Process Automation (RPA), to clarify what is currently known and to identify where the main gaps remain. Rather than treating each technology in isolation, this study brings them together under a single integrative review to provide a consolidated reference point for scholars assessing their impact on external audit practices. Design/Methodology/Approach: Following a structured systematic review protocol, searches were conducted in Scopus, ScienceDirect and SpringerLink (2000–2024) using technology-related keywords combined with “audit”, “auditor” and “auditing”. After applying explicit inclusion and exclusion criteria, 471 records were reduced to 32 ABS-listed journal articles, which were analysed thematically. Findings: The review shows that research on emerging technologies in external auditing is still fragmented, with substantial variation in the depth and maturity of evidence across the six technologies. The strongest empirical base is concentrated in Big Data analytics and ML-based predictive models (including more advanced Deep Learning variants), whereas Blockchain and RPA work remains predominantly conceptual or confined to small-scale design-science implementations. Across technologies, most studies are single-country and either rely on auditors’ self-reported perceptions of adoption and impact or evaluate model performance without tracing effects on audit strategies and engagement outcomes, which limits external validity and construct measurement. Very few articles explicitly integrate the Audit Risk Model or other formal theories, and almost no work examines multi-technology “audit stacks” or generative AI, leaving substantial gaps in understanding how these tools jointly reshape inherent, control and detection risk across the audit cycle. Originality/Value: By integrating six technologies within a single external audit framework, the review offers a technology-specific evidence map and a targeted future research agenda that can guide scholars, audit firms and regulators in designing studies and policies aligned with actual gaps in the current literature. Full article

The transition to electric mobility requires the coordinated evolution of vehicles, charging infrastructure, power systems, and intelligent digital platforms. This study examines the role of Industry 4.0 technologies in enabling large-scale electric vehicle (EV) adoption and effective EV grid integration and synthesizes the existing evidence into a coherent analytical framework to support planning and policy decision-making. A systematic review of 27 peer-reviewed studies published between 2018 and 2025 was conducted in accordance with PRISMA 2020 guidelines, capturing the acceleration of electromobility following the consolidation of Industry 4.0 technologies and the emergence of large-scale policy commitments worldwide. The analysis covers six technology families, including the Internet of Things, big data and analytics, artificial intelligence and machine learning, blockchain, digital twins, and extended reality, and examines their applications in smart charging, grid vehicle coordination, fleet optimization, and vehicle-to-grid services. The findings show that analytics and artificial intelligence consistently enhance operational reliability and efficiency, while digital twins are increasingly applied to infrastructure siting, grid impact assessment, and scenario analysis. Building on these results, the study proposes a three-layer analytical framework composed of physical, digital, and decision layers, together with a functional EV grid generation integration model that links technology readiness to system-level deployment. In addition, a transition timeline for the 2025–2040 period and a concise set of key performance indicators are introduced to support evaluation and comparison. Policy implications for Ecuador and Latin America emphasize interoperability, data governance, realistic cost assessment, and a phased approach to vehicle-to-grid deployment. Full article

This study presents a systematic review of the role of key technologies in advancing sustainable logistics and supply chain management. Specifically, it explores the integration of Industry 4.0 (I4.0), logistics 4.0, and digital supply chains, focusing on technologies such as artificial intelligence (AI), augmented reality (AR), big data analytics (BDA), blockchain, cloud computing (CC), industrial internet of things (IIoT), machine learning (ML), robotics, virtual reality (VR), and internet of things (IoT). The aim is to examine how these technologies contribute to green logistics (GL), green supply chain management, sustainability, and the circular economy (CE). Data were collected from the Scopus database, covering studies published between 2019 and 2024. A total of 1471 publications were initially identified, and 39 studies met the selection criteria. The PRISMA approach was employed for the systematic review, revealing that leading research on I4.0 is concentrated in top-tier journals, with a significant number of publications from Italy focusing on digitalization in the agriculture and food sectors. Systematic literature reviews and resource-based theory are predominant, yet there is a notable gap in aligning research with the United Nations Agenda 2030 Sustainable Development Goals (SDGs). This paper provides insights into technological adoption trends and offers recommendations for industry leaders seeking to enhance sustainability, eco-friendliness, and alignment with the SDGs within their supply chains. Full article

The manufacturing sector drives industrialisation and contributes substantially to economic growth and employment creation. Despite this, it faces the challenges of diminishing size and lack of competitiveness, mainly due to operational uncertainty. The study developed an approach to managing operational uncertainty using Industry 4.0 and 5.0 technologies. It employed a multimethod quantitative design based on the post-positivist paradigm, with data collected from 22 experts and 262 responses from a manufacturing firms’ survey. The study employed an integrated fuzzy decision-making trial and evaluation laboratory (DEMATEL) with partial least squares structural equation modelling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). The fuzzy DEMATEL results reveal that growing geopolitical tension, cost-of-living-driven consumer behavioural change, pandemic turbulence, lack of energy stability and security, and the entrenched power of large firms are causal dimensions of operational uncertainty. Industry 4.0 and 5.0 technologies, with capabilities for scenario planning and supply chain integration, flexible production and mass customisation, real-time system and process monitoring and response, root cause analysis, and sustainable solutions, can manage operational uncertainty. These technologies include artificial intelligence (AI), the Internet of Things (IoT), big data analytics, and, to a lesser extent, advanced robotics, blockchain, and augmented and virtual reality (AR/VR). This study advanced configuration theory and a new integrated methodology (fuzzy-DEMATEL-PLS-SEM-fsQCA) to develop solutions for sustained performance during operational uncertainty in manufacturing. This research offers valuable information to advance the subject, make meaningful changes in day-to-day manufacturing operations, and promote practical real-world problem solving. Full article

In this work, we present the Snow Depth and Snow Water Equivalent Dataset for specific areas located in the East Kazakhstan Region that can be exploited to monitor and understand water resource dynamics in mountain regions. The present dataset represents a georeferenced collection of snow depth, snow density, and derived snow water equivalent (SWE) measurements obtained through manual snow surveys. Snow survey observations were conducted during field campaigns in the East Kazakhstan Region during the period of maximum snow accumulation from 27 February to 6 March 2025. Snow survey sites were selected to maximize coverage of diverse landscape settings and snow accumulation conditions. In total, 111 snow survey sites were established across the East Kazakhstan Region, and 2331 snow depth measurements and 555 snow density measurements were collected. In post-field (laboratory) processing, snow water equivalent (SWE) was calculated for all snow survey sites based on measured snow depth and snow density values. Full article

Disputes become an accepted reality of construction projects, often resulting in serious consequences, including time and cost overruns, and broader macroeconomic impacts on the national economy. Disputes need to be managed effectively to reduce their negative impacts. Recently, an increasing trend has emerged toward integrating dispute management practices with innovative technologies of the digital era. Therefore, this research aims to investigate the applications of emerging digital technologies and Artificial Intelligence (AI) to manage disputes proactively. This research begins with a scientometric analysis, followed by a systematic review of dispute management using digital technologies with a special focus on AI. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, the systematic review identified 66 previous studies that combine dispute management and digital technologies. The analysis revealed that technologies such as Artificial Intelligence (AI), Building Information Modelling (BIM), blockchain, smart contracts, Document Management System (DMS), big data, cloud computing, and Unmanned Aerial Vehicle (UAV) are utilized, while AI and its technologies significantly contribute to managing disputes. AI technologies, especially natural language processing, show a growing trend in applications for predicting disputes using project documents. In addition, the study develops a conceptual framework to predict disputes using AI technologies. The study identified potential research areas involving the integration of digital technologies for dispute management in the construction industry, offering valuable direction for future research. The research suggests that the use of AI and emerging digital technologies potentially predicts and mitigates disputes, thereby paving the way for proactive dispute management. Full article

The rapid expansion of health big data, encompassing genomic profiles and wearable device telemetry, has significantly escalated personal privacy risks. This systematic literature review (SLR) synthesizes 86 peer-reviewed studies (2014–2025) through the dual lens of the NIST Cybersecurity and Privacy Frameworks to evaluate emerging risks, mitigation technologies, and regulatory landscapes. Our analysis identifies unauthorized access as the predominant threat, while blockchain-based solutions comprise 22.1% of proposed interventions. However, a comparative evaluation reveals critical performance trade-offs: differential privacy mechanisms incur a 15–35% utility loss, whereas blockchain implementations impose a 40–50% computational overhead. Furthermore, an assessment of major regulatory frameworks (GDPR, HIPAA, PIPL, and emerging regional laws in Sub-Saharan Africa) elucidates significant cross-jurisdictional conflicts. To address these challenges, we propose the Bio-inspired Adaptive Healthcare Privacy (BAHP) framework, validated through retrospective case study analysis, offering a dynamic approach to securing sensitive health ecosystems. 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

(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies—artificial intelligence (AI), blockchain, edge computing, and digital twins—transform accounting practices through intelligent automation, continuous compliance, and predictive decision support. (2) Methods: The study synthesizes 176 peer-reviewed sources (2015–2025) selected using explicit inclusion criteria emphasizing empirical evidence. Thematic analysis across seven domains—conceptual foundations, system evolution, financial reporting, fraud detection, audit transformation, implementation challenges, and emerging technologies—employs systematic bias-reduction mechanisms to develop evidence-based theoretical propositions. (3) Results: Key findings document fraud detection accuracy improvements from 65–75% (rule-based) to 85–92% (machine learning), audit cycle reductions of 40–60% with coverage expansion from 5–10% sampling to 100% population analysis, and reconciliation effort decreases of 70–80% through triple-entry blockchain systems. Edge computing reduces processing latency by 40–75%, enabling compliance response within hours versus 24–72 h. Four propositions are established with empirical support: IoT-enabled reporting superiority (15–25% error reduction), AI-blockchain fraud detection advantage (60–70% loss reduction), edge computing compliance responsiveness (55–75% improvement), and GDPR-blockchain adoption barriers (67% of European institutions affected). Persistent challenges include cybersecurity threats (300% incident increase, $5.9 million average breach cost), workforce deficits (70–80% insufficient training), and implementation costs ($100,000–$1,000,000). (4) Conclusions: The research contributes a four-layer technology architecture and challenge-mitigation framework bridging technical capabilities with regulatory requirements. Future research must address quantum computing applications (5–10 years), decentralized finance accounting standards (2–5 years), digital twins with 30–40% forecast improvement potential (3–7 years), and ESG analytics frameworks (1–3 years). The findings demonstrate accounting’s fundamental transformation from historical record-keeping to predictive decision support. Full article

Background: Digital technologies are increasingly integrated into circular supply chains (CSCs) to enhance resource efficiency and extend product lifecycles. However, the practical adoption of intelligent circular supply chains (iCSCs) remains underexplored. Methods: This study provides a comprehensive review of how digital technologies enable circular practices across industries. It systematically reviews 95 peer-reviewed articles from WoS and Scopus, identifying 107 real-world iCSC cases. The cases are categorized by (1) digital enablers including AI, Big Data, Blockchain, IoT, Digital Twin, Additive Manufacturing, Cloud Platforms, and Cyber-Physical Systems; (2) alignment with Circular Economy (CE); (3) sector-specific circular practices; and (4) mapping implementations to the EU Circular Economy Action Plan (CEAP). This study develops a conceptual model illustrating how digital technologies support data-driven decision-making, automation, and circular transitions. Results: The analysis shows IoT, Blockchain, and AI as the most frequently applied technologies, facilitating collaboration, traceability, sustainability, and cost efficiency. “Reduce” and “Recycle” dominate among CE strategies, while circular transition pathways such as sustainable design, waste prevention, and digital platforms link policy to practice. Conclusions: By integrating systematic evidence with a holistic framework, this work provides actionable insights, identifies key implementation gaps, and lays a foundation for advancing iCSCs in research and practice. Full article

The convergence of artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics is transforming the governance, sustainability, and resilience of modern banking ecosystems. This study provides a multivariate bibliometric analysis using Principal Component Analysis (PCA) of research indexed in Scopus and Web of Science to explore how decentralized digital infrastructures and AI-driven analytical capabilities contribute to sustainable financial development, transparent governance, and climate-resilient digital societies. Findings indicate a rapid increase in interdisciplinary work integrating Distributed Ledger Technology (DLT) with large-scale data processing, federated learning, privacy-preserving computation, and intelligent automation—tools that can enhance financial inclusion, regulatory integrity, and environmental risk management. Keyword network analyses reveal blockchain’s growing role in improving data provenance, security, and trust—key governance dimensions for sustainable and resilient financial systems—while AI/ML and big data analytics dominate research on predictive intelligence, ESG-related risk modeling, customer well-being analytics, and real-time decision support for sustainable finance. Comparative analyses show distinct emphases: Web of Science highlights decentralized architectures, consensus mechanisms, and smart contracts relevant to transparent financial governance, whereas Scopus emphasizes customer-centered analytics, natural language processing, and high-throughput data environments supporting inclusive and equitable financial services. Patterns of global collaboration demonstrate strong internationalization, with Europe, China, and the United States emerging as key hubs in shaping sustainable and digitally resilient banking infrastructures. By mapping intellectual, technological, and collaborative structures, this study clarifies how decentralized intelligence—enabled by the fusion of AI/ML, blockchain, and big data—supports secure, scalable, and sustainability-driven financial ecosystems. The results identify critical research pathways for strengthening financial governance, enhancing climate and social resilience, and advancing digital transformation, which contributes to more inclusive, equitable, and sustainable societies. Full article