Search Results (160)

This study examines how Industry 4.0 (I4.0) technologies enhance supply chain resilience (SCR) in manufacturing firms by testing the mediating roles of supply chain agility (SCAG), supply chain adaptability (SCAD) and the moderating effect of customer integration (CI). Grounded in the Resource-Based View (RBV) and Dynamic Capabilities View (DCV), the research conceptualizes digital technologies—such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI)—as both strategic resources and enablers of dynamic capabilities in turbulent environments. Survey data were collected from 273 manufacturing firms in Turkey, a context shaped by geopolitical and economic disruptions, and analyzed using structural equation modeling (SEM). The results indicate that I4.0 technologies positively affect SCR directly and indirectly through SCAG and SCAD. However, while agility consistently strengthens resilience, adaptability shows a negative mediating effect, suggesting context-specific constraints. CI significantly amplifies the positive impact of I4.0 on SCR, underscoring the importance of external relational capabilities. Theoretically, this research advances supply chain literature by integrating RBV and DCV to explain how digital transformation drives resilience through distinct dynamic capabilities. Practically, it offers guidance for managers to combine digital infrastructure with collaborative customer relationships to mitigate disruptions and secure long-term performance. Overall, the study provides an integrated framework for building resilient supply chains in the digital era. Full article

In the era of digital transformation and data-driven decision-making, big data analytics capability (BDAC) is crucial for firms to enhance innovation and sustainable competitive advantage in highly dynamic markets. Grounded in dynamic capability theory, this study used a moderated mediation model to explore the impact of BDAC on innovativeness. Empirical analysis was conducted by using survey data from 270 enterprises to test the hypotheses. The results reveal that BDAC significantly and positively influences innovativeness, and sustained innovation mediates this relationship. Moreover, organizational slack positively moderates the effect of BDAC on innovativeness, both the direct effect and indirect effect. These findings provide theoretical support and practical implications for understanding how BDAC enhances firm innovativeness. Full article

This study investigates the role of Big Data Analytics Capabilities (BDACs) in ambidexterity explorative innovation (EXPLRI) and exploitative (EXPLOI) innovation for achieving a sustainable performance (SP) in the manufacturing sector of a resource-constrained developing economy. While a BDAC has been widely linked to innovation in developed economies, its effectiveness in developing contexts shaped by indigenous innovation practices like Jugaad remains underexplored. Anchored in the Resource-Based View (RBV) and Dynamic Capabilities (DC) theory, we propose a model where the BDAC enhances both EXPLRI and EXPLOI, which subsequently leads to an improved sustainable performance. We further examine the Jugaad capability as a cultural moderator. Using survey data from 418 manufacturing firms and analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM), results confirm that BDA capabilities significantly boost both types of innovations, which positively impact sustainable performance dimensions. Notably, Jugaad positively moderates the relationship between EXPLOI and financial, innovation, and operational performance but negatively moderates the link between EXPLRI and innovation performance. These findings highlight the nuanced influence of culturally embedded innovation practices in BDAC-driven ecosystems. This study contributes by extending the RBV–DC framework to include cultural innovation capabilities and empirically validating the contingent role of Jugaad in enhancing or constraining innovation outcomes. This study also validated the Jugaad capability measurement instrument for the first time in the context of Pakistan. For practitioners, aligning data analytics strategies with local innovative cultures is vital for sustainable growth in emerging markets. Full article

The exponential increase in textual data generated across sectors such as healthcare, finance, and smart manufacturing has intensified the need for effective Big Data analytics. Large language models (LLMs) have become critical tools because of their advanced language processing capabilities. However, their static nature limits their ability to incorporate real-time and domain-specific knowledge. Retrieval-augmented generation (RAG) addresses these limitations by enriching LLM outputs through external content retrieval. Nevertheless, traditional RAG systems remain inefficient, often exhibiting high retrieval latency, redundancy, and diminished response quality when scaled to large datasets. This paper proposes an innovative structured RAG framework specifically designed for large-scale Big Data analytics. The framework transforms unstructured partial prompts into structured semantically coherent partial prompts, leveraging element-specific embedding models and dimensionality reduction techniques, such as principal component analysis. To further improve the retrieval accuracy and computational efficiency, we introduce a multi-level filtering approach integrating semantic constraints and redundancy elimination. In the experiments, the proposed method was compared with structured-format RAG. After generating prompts utilizing two methods, silhouette scores were computed to assess the quality of embedding clusters. The proposed method outperformed the baseline by improving the clustering quality by 32.3%. These results demonstrate the effectiveness of the framework in enhancing LLMs for accurate, diverse, and efficient decision-making in complex Big Data environments. Full article

Rapid urbanization and escalating disaster risks necessitate resilient urban governance systems. Smart city initiatives that leverage digital technologies—such as the internet of things (IoT), big data analytics, and artificial intelligence (AI)—demonstrate transformative potential in enhancing emergency management capabilities. However, empirical evidence regarding their causal impact and underlying mechanisms remains limited, particularly in developing economies. Drawing on panel data from 275 Chinese prefecture-level cities over the period 2006–2021 and using China’s smart city pilot policy as a quasi-natural experiment, this study applies a multi-period difference-in-differences (DID) approach to rigorously assess the effects of smart city construction on emergency management capabilities. Results reveal that smart city construction produced a statistically significant improvement in emergency management capabilities, which remained robust after conducting multiple sensitivity checks and controlling for potential confounding policies. The benefits exhibit notable heterogeneity: emergency management capability improvements are most pronounced in central China and in cities at the extremes of population size—megacities (>10 million residents) and small cities (<1 million residents)—while effects remain marginal in medium-sized and eastern cities. Crucially, mechanism analysis reveals that digital technology application fully mediates 86.7% of the total effect, whereas factor allocation efficiency exerts only a direct, non-mediating influence. These findings suggest that smart cities primarily enhance emergency management capabilities through digital enablers, with effectiveness contingent upon regional infrastructure development and urban scale. Policy priorities should therefore emphasize investments in digital infrastructure, interagency data integration, and targeted capacity-building strategies tailored to central and western regions as well as smaller cities. Full article

Facing growing sustainability challenges and the critical priority of digital transformation, this study explores, through the lens of the dynamic capability view, the links between big data, sustainable performance, and green supply chain in a circular economy logic, filling a notable gap in emerging markets, particularly the pharmaceutical sector. Our study proposes an original conceptual model linking big data analytics to the circular economy, tested with 275 employees from the Saudi pharmaceutical sector. The results, obtained through state-of-the-art PLS-SEM modeling, indicate a significant positive impact of big data analytics on sustainable performance and green supply chain management within the circular economy framework. The study also reveals the crucial mediating role of sustainable performance and green supply chain management in the relationship between big data analytics and the circular economy. Our study proposes an integrated framework for understanding how digital technologies support the circular economy in emerging markets, with practical implications for pharmaceutical sector actors and policymakers, in line with Saudi Arabia’s Vision 2030. Full article

Yeast genetic improvement is entering a transformative phase, driven by the integration of artificial intelligence (AI), big data analytics, and synthetic microbial communities with conventional methods such as sexual breeding and random mutagenesis. These advancements have substantially expanded the potential for innovative re-engineering of yeast, ranging from single-strain cultures to complex polymicrobial consortia. This review compares traditional genetic manipulation techniques with cutting-edge approaches, highlighting recent breakthroughs in their application to beer and wine fermentation. Among the innovative strategies, adaptive laboratory evolution (ALE) stands out as a non-GMO method capable of rewiring complex fitness-related phenotypes through iterative selection. In contrast, GMO-based synthetic biology approaches, including the most recent developments in CRISPR/Cas9 technologies, enable efficient and scalable genome editing, including multiplexed modifications. These innovations are expected to accelerate product development, reduce costs, and enhance the environmental sustainability of brewing and winemaking. However, despite their technological potential, GMO-based strategies continue to face significant regulatory and market challenges, which limit their widespread adoption in the fermentation industry. Full article

The integration of distributed big data analytics into modern industrial environments has become increasingly critical, particularly with the rise of data-intensive applications and the need for real-time processing at the edge. While High-Performance Computing (HPC) systems offer robust petabyte-scale capabilities for efficient big data analytics, the performance of big data frameworks, especially on ARM-based HPC systems, remains underexplored. This paper presents an extensive experimental study on deploying Apache Spark 3.0.2, the de facto standard in-memory processing system, on an ARM-based HPC system. This study conducts a comprehensive performance evaluation of Apache Spark through representative big data workloads, including K-means clustering, to assess the effects of latency variations, such as those induced by network delays, memory bottlenecks, or computational overheads, on application performance in industrial IoT and edge computing environments. Our findings contribute to an understanding of how big data frameworks like Apache Spark can be effectively deployed and optimized on ARM-based HPC systems, particularly when leveraging vectorized instruction sets such as SVE, contributing to the broader goal of enhancing the integration of cloud–edge computing paradigms in modern industrial environments. We also discuss potential improvements and strategies for leveraging ARM-based architectures to support scalable, efficient, and real-time data processing in Industry 4.0 and beyond. Full article

Structural health monitoring (SHM) plays a vital role in ensuring the safety, durability, and performance of civil infrastructure. This review delves into the significant advancements in optical fiber sensor (OFS) technologies such as Fiber Bragg Gratings, Distributed Temperature Sensing, and Brillouin-based systems, which have emerged as powerful tools for enhancing SHM capabilities. Offering high sensitivity, resistance to electromagnetic interference, and real-time distributed monitoring, these sensors present a superior alternative to conventional methods. This paper also explores the integration of OFSs with Artificial Intelligence (AI), which enables automated damage detection, intelligent data analysis, and predictive maintenance. Through case studies across key infrastructure domains, including bridges, tunnels, high-rise buildings, pipelines, and offshore structures, the review demonstrates the adaptability and scalability of these sensor systems. Moreover, the role of SHM is examined within the broader context of civil and urban infrastructure, where IoT connectivity, AI-driven analytics, and big data platforms converge to create intelligent and responsive infrastructure. While challenges remain, such as installation complexity, calibration issues, and cost, ongoing innovation in hybrid sensor networks, low-power systems, and edge computing points to a promising future. This paper offers a comprehensive amalgamation of current progress and future directions, outlining a strategic path for next-generation SHM in resilient urban environments. Full article

This systematic review examines the critical intersection of digital transformation, supply chain resilience, and sustainability within manufacturing contexts, with specific implications for Saudi Arabian industries. Through a comprehensive analysis of 124 peer-reviewed articles published between 2018 and 2024, we identify how emerging technologies—including Internet of Things (IoT), artificial intelligence, blockchain, and big data analytics—transform traditional supply chains into dynamic ecosystems capable of withstanding disruptions while advancing sustainability goals. Our findings reveal that digital transformation positively influences both resilience and sustainability outcomes. Still, these relationships are significantly moderated by three key factors: supply chain dynamism, regulatory uncertainty, and integration of innovative technologies. The study demonstrates that while high supply chain dynamism amplifies the positive effects of digital technologies on resilience capabilities, regulatory uncertainty creates implementation barriers that potentially diminish these benefits. Moreover, successfully integrating innovative technologies is a critical mediating mechanism translating digital initiatives into tangible sustainability improvements. The review synthesises these findings into an integrated conceptual framework that captures the complex interrelationships between these domains and provides specific strategic recommendations for Saudi Arabian manufacturing organisations. By addressing the identified research gaps—particularly the lack of industry-specific investigations in emerging economies—this review offers valuable insights for researchers and practitioners seeking to leverage digital transformation for simultaneously efficient, resilient, and sustainable supply chain operations in rapidly evolving business environments. Full article

In the digital economy era, firms pursue innovation while also considering their environmental impact to ensure alignment with sustainability. However, existing research offers limited insights into how corporate environmental ethics influence the relationship between big data analytics capabilities (BDACs) and green radical innovation (GRI). This study investigates the impact of BDACs, environmental ethics, and GRI, using a sample of 291 firms and integrating resource-based theory with an environmental ethics perspective. Empirical results indicate that environmental ethics positively moderate the positive relationships between the three dimensions of BDAC—managerial, technical, and talent capability—and GRI. Moreover, there are differences in the moderating effects on this relationship. This study enriches boundary condition research on how BDACs impact GRI. Additionally, it contributes to understanding the mechanisms through which environmental ethics affect GRI, highlighting the combined effect of environmental ethics and BDAC. Furthermore, this study advances research on the heterogeneous role of environmental ethics, emphasizing the importance of enhancing corporate environmental ethics in transforming BDA technical capability into GRI. This contribution offers a new perspective on how firms can more effectively leverage their BDAC toward sustainable development. Full article

With the increasing adoption of digital governance and big data analytics, the quality of government hotline data significantly affects urban governance and public service efficiency. However, existing methods for assessing data record standards focus predominantly on structured data, exhibiting notable inadequacies in handling the complexities inherent in unstructured or semi-structured textual hotline records. To address these shortcomings, this study develops a comprehensive scoring method tailored for evaluating multi-dimensional data record standards in government hotline data. By integrating advanced deep learning models, we systematically analyze six evaluation indicators: classification predictability, dispatch accuracy, record correctness, address accuracy, adjacent sentence similarity, and full-text similarity. Empirical analysis reveals a significant positive correlation between improved data record standards and higher work order completion rates, particularly highlighting the crucial role of semantic-related indicators (classification predictability and adjacent sentence similarity). Furthermore, the results indicate that the work order field strengthens the positive impact of data standards on completion rates, whereas variations in departmental data-handling capabilities weaken this relationship. This study addresses existing inadequacies by proposing a novel scoring method emphasizing semantic measures and provides practical recommendations—including standardized language usage, intelligent analytic support, and targeted staff training—to effectively enhance urban governance. Full article

School dropout in higher education remains a significant global challenge with profound socioeconomic consequences. To address this complex issue, educational institutions increasingly rely on business intelligence (BI) and related predictive analytics, such as machine learning and data mining techniques. This systematic review critically examines the application of BI and predictive analytics for analyzing and preventing student dropout, synthesizing evidence from 230 studies published globally between 1996 and 2025. We collected literature from the Google Scholar and Scopus databases using a comprehensive search strategy, incorporating keywords such as “business intelligence”, “machine learning”, and “big data”. The results highlight a wide range of predictive tools and methodologies, notably data visualization platforms (e.g., Power BI) and algorithms like decision trees, Random Forest, and logistic regression, demonstrating effectiveness in identifying dropout patterns and at-risk students. Common predictive variables included personal, socioeconomic, academic, institutional, and engagement-related factors, reflecting dropout’s multifaceted nature. Critical challenges identified include data privacy regulations (e.g., GDPR and FERPA), limited data integration capabilities, interpretability of advanced models, ethical considerations, and educators’ capacity to leverage BI effectively. Despite these challenges, BI applications significantly enhance institutions’ ability to predict dropout accurately and implement timely, targeted interventions. This review emphasizes the need for ongoing research on integrating ethical AI-driven analytics and scaling BI solutions across diverse educational contexts to reduce dropout rates effectively and sustainably. Full article

A fundamental debate among sustainable development researchers and practitioners is whether, and through what mechanisms, sustainability contributes to gaining a competitive advantage. To address this consideration, this study draws on the general systems theory (GST), organisational information processing theory (OIPT), resource-based theory and recent studies on big data analytics capability (BDAC), digitalisation, sustainability, digital sustainability reporting (DSR), and business performance. A comprehensive research model was developed to assess the interrelationships and mediating effects of the mentioned constructs; the analyses utilised a sample of 75 large Latvian organisations preparing for the first reporting under Corporate Sustainability Reporting Directive. The results obtained using partial least squares structural equation modelling contribute to the theory by revealing that digital sustainability reporting fully mediates the relationship between sustainability and business performance. Moreover, BDAC emerges as a fundamental enabler, forming the foundation of a “house” where digitainability and DSR serve as its walls and enhanced business performance as its roof. This study also contributes to the sustainability and sustainability accounting literature by presenting a sustainable business development framework that offers practical insights for organisations navigating the integration of sustainability reporting in highly uncertain environments. Full article

Optimization has become an indispensable tool in the food industry, addressing critical challenges related to efficiency, sustainability, and product quality. Traditional approaches, such as one-factor-at-a-time analysis, have been supplanted by more advanced methodologies like response surface methodology (RSM), which models interactions between variables, identifies optimal operating conditions, and significantly reduces experimental requirements. However, the increasing complexity of modern food production systems has necessitated the adoption of multi-objective optimization techniques capable of balancing competing goals, such as minimizing production costs while maximizing energy efficiency and product quality. Advanced methods, including evolutionary algorithms and comprehensive modeling frameworks, enable the simultaneous optimization of multiple variables, offering robust solutions to complex challenges. In addition, artificial neural networks (ANNs) have transformed optimization practices by effectively modeling non-linear relationships within complex datasets and enhancing prediction accuracy and system adaptability. The integration of ANNs with Industry 4.0 technologies—such as the Internet of Things (IoT), big data analytics, and digital twins—has enabled real-time monitoring and optimization, further aligning production processes with sustainability and innovation goals. This paper provides a comprehensive review of the evolution of optimization methodologies in the food industry, tracing the transition from traditional univariate approaches to advanced, multi-objective techniques integrated with emerging technologies, and examining current challenges and future perspectives. Full article