Search Results (995)

This paper examines whether broad enterprise AI adoption in Europe is best understood as an isolated technology decision or as the outcome of a wider bundle of digital capabilities. Using harmonized Eurostat data for European enterprises, the analysis builds a repeated cross-section at the country–size-class–year level and models high AI adoption with a combination of random forest and elastic-net estimation. The dependent variable captures enterprises using at least one AI technology, while the explanatory set focuses on cloud adoption, cloud CRM, cloud ERP, cloud database hosting, cloud security, cloud software use, e-sales intensity, and enterprise size. The findings reveal a stable predictive structure and consistent classification performance across specifications. Across models, cloud CRM and e-sales emerge as the strongest predictors of high AI adoption, followed by general cloud use and selected data-related cloud capabilities. This ordering remains largely stable in threshold-sensitivity checks based on alternative definitions of high adoption. The pattern also remains visible when country controls are removed, which suggests that the result is not merely a reflection of national heterogeneity. The paper contributes by shifting attention from broad claims about “digital readiness” to a narrower and more operational notion of capability complementarity: AI uptake tends to cluster where firms already possess customer-facing, cloud-based, and commercially digital infrastructures. In that sense, the paper offers a transparent, reproducible, and policy-relevant account of the digital foundations of enterprise AI adoption in Europe. Full article

The integration of Building Information Modeling [BIM] and Digital Twins [DT] has emerged as a central driver of digital transformation in the architecture, engineering, and construction sector. Yet, its systemic impact remains constrained by conceptual fragmentation and uneven institutional adoption. This study synthesizes contemporary BIM–DT scalability and each to identify dominant technological and application dimensions, examine the governance conditions shaping scalability, and develop an analytical framework that advances understanding beyond technology-centered syntheses. A two-stage analytical design was employed, combining bibliometric keyword co-occurrence analysis of 1295 Scopus-indexed records with systematic qualitative synthesis of 56 peer-reviewed journal articles published between 2020 and 2025, following PRISMA guidelines. Six interrelated analytical dimensions characterize the current BIM–DT research landscape: BIM–DT integration advancements and applications; interoperability and visualization; safety enhancement; energy efficiency; data-driven decision making; and stakeholder collaboration. Across these dimensions, a persistent misalignment emerges between technological capability and organizational readiness, with deficiencies in standards, governance, and sociotechnical coordination constituting the principal barriers to large-scale deployment. The findings reframe BIM–DT convergence not as a discrete technological upgrade but as the emergence of a coordinated socio-technical information ecosystem spanning the full building lifecycle. By foregrounding governance conditions, data stewardship, and institutional coordination, this study extends understanding of how digital twins expand BIM from design coordination to operational governance and establishes a foundation for more systematic implementation of intelligent, resilient, and sustainable built-environment systems. Full article

Energy transition is most often examined through the lens of technological development and integration, including renewable energy sources, energy storage systems, and digital energy management solutions. In practice, however, the actual performance of energy systems—understood as both energy efficiency and environmental impact across the life cycle—depends not only on technical parameters but also on decision-making processes, operational practices, and management capabilities. This paper aims to conceptualize energy and environmental awareness as a determinant influencing energy system performance at organizational and system levels. The study is based on a structured review of the literature from energy engineering, life cycle assessment, and energy management, complemented by a comparative analysis of how similar energy technologies are utilized under different decision-making contexts. On this basis, an integrated analytical framework is proposed that combines conventional energy and environmental performance indicators with awareness-related dimensions, including energy knowledge, perception of environmental risk, and managerial competence. The analysis demonstrates that insufficient energy awareness leads to systematic gaps between the technological potential of energy systems and their actual performance, resulting in increased environmental burdens despite high nominal technical efficiency. The proposed framework helps to explain performance variability in energy systems operating under comparable technical conditions and highlights the importance of incorporating managerial and competency-related factors into life cycle assessments and energy transition policies. The paper contributes to the literature by extending energy system evaluation beyond purely technical criteria and offers practical implications for the design of energy systems, industrial energy management, and policy instruments supporting sustainable energy transitions. Full article

The deep integration of digital and intelligent technologies is reshaping urban disaster emergency management capabilities; however, improvements in their effectiveness are constrained by complex, multidimensional factors. Identifying the key driving factors and their mechanisms is of great significance for enhancing urban disaster emergency response capabilities. Based on literature analysis and expert consultation, this paper constructs a framework of factors influencing the digital and intelligent empowerment of urban emergency management capabilities. By employing the IT2FS-DEMATEL-AISM multi-criteria hybrid decision-making method, an analytical framework comprising factor identification, relationship decomposition, and hierarchical evolution is established. The study found that 15 key factors, including the soundness of emergency management systems and the level of smart platform development, exert a significant influence on urban emergency management capabilities through direct or indirect mechanisms. Meanwhile, the institutional framework for emergency management serves as a deep-seated driving force, systematically promoting the deep integration of emergency management operations with digital and intelligent technologies. This, in turn, enhances the operational effectiveness of urban disaster emergency response and comprehensively strengthens the city’s overall disaster emergency management capabilities. Full article

Artificial intelligence (AI) is increasingly embedded in digital marketing, enabling organisations to personalise communication, analyse consumer data, and optimise decision-making processes. Despite its widespread adoption, limited empirical research has examined whether AI-driven digital marketing contributes to responsible consumption and production, as articulated in Sustainable Development Goal 12 (SDG 12). Grounded in a capability-based and marketing intelligence framework, this study investigates the mechanisms through which AI-driven digital marketing influences responsible marketing outcomes. Using survey data from 120 professionals in multinational corporations (MNCs) operating in South Africa, the study examines how AI-driven digital marketing influences responsible marketing outcomes aligned with Sustainable Development Goal 12 (SDG 12), with particular emphasis on the mediating roles of predictive consumer analytics and sentiment-based consumer understanding as distinct dimensions of AI-enabled marketing intelligence. Instead, its influence operates indirectly through sentiment-based consumer understanding, while predictive consumer analytics show no significant effect. These results suggest that AI contributes to responsible consumption primarily when it enhances firms’ capacity to interpret consumer values, emotions, and ethical concerns. The study advances the digital marketing and sustainability literature by reframing AI as a relational and sense-making capability while offering practical guidance for aligning AI-driven marketing strategies with SDG 12 in emerging markets. Full article

In highly digitalized and networked modern trading systems, large volumes of heterogeneous data are continuously generated from multiple sources during market operations. However, due to the complexity of data structures, significant differences in temporal scales, and constraints imposed by data privacy protection, traditional single-source modeling approaches are unable to fully exploit multisource information. To address this issue, a federated multimodal prediction framework for complex market systems, termed Federated Market-Sensor Transformer (FMST), is proposed. In this framework, data originating from different information sources are uniformly modeled as multimodal time series. A multimodal market-sensor representation module is constructed to perform unified feature encoding, and a cross-modal Transformer fusion architecture is employed to characterize dynamic interaction relationships among different information sources. Meanwhile, a federated collaborative learning mechanism is introduced during the training phase, enabling multiple data nodes to perform collaborative model optimization without sharing raw data. In this manner, data privacy can be preserved while improving the cross-region generalization capability of the model. Systematic experimental evaluation is conducted on the constructed multimodal market-sensor dataset. The experimental results demonstrate that the proposed method consistently outperforms traditional statistical models and deep learning approaches across multiple evaluation metrics. In the main prediction experiment, FMST achieves a root mean square error (RMSE) of $0.1136$, a mean absolute error (MAE) of $0.0832$, and a coefficient of determination $R^2$ of $0.8517$, while the direction prediction accuracy reaches $74.56\%$, clearly outperforming baseline models including ARIMA, LSTM, Temporal CNN, Transformer, and FedAvg-LSTM. In the cross-region generalization experiment, FMST maintains strong performance, achieving an RMSE of $0.1242$, an MAE of $0.0908$, an $R^2$ value of $0.8261$, and a direction prediction accuracy of $72.48\%$. The ablation study further indicates that the three core components—multimodal market-sensor representation, cross-modal Transformer fusion, and federated collaborative learning—each make important contributions to the overall model performance. These experimental findings demonstrate that the proposed method can effectively integrate multisource market information and significantly enhance the prediction capability for complex market dynamics, providing a new technical pathway for the application of artificial intelligence-driven multimodal sensing systems in economic data analysis. Full article

Autonomous supply chains (ASC) are the next generation of digitally empowered logistics and operations systems that can make adaptive, data-driven, and intelligent decisions. Innovations in artificial intelligence (AI), digital twins (DT), and predictive analytics (PA) are transforming traditional supply chains into integrated and interactive networks to detect disruptions, simulate the future, and automatically modify operational decisions. This paper reviews the ASC mechanism and summarizes the increasing literature on the technologies and analytical capabilities available to support intelligent supply chain decision systems. A structured literature review was conducted using Scopus, Web of Science, and Google Scholar, resulting in 52 relevant studies after screening and eligibility assessment. The paper discusses the recent advances in AI-based forecasting, simulation environments using digital twins, data integration using the Internet of Things (IoT), and predictive analytics. These technologies can help an organization gain real-time visibility of the supply chain networks. They improve the precision of demand forecasting, optimize inventory and production planning, and dynamically coordinate logistics operations. Digital twins allow the development of virtual models of supply chain ecosystems, which could be used to test scenarios, analyze risks, and plan strategies. These capabilities combined can be used to create predictive and self-adaptive supply networks capable of being responsive to uncertainty and market volatility. Besides examining the technological foundations, the paper also tracks key challenges related to the move towards autonomous supply chains, such as data governance, system interoperability, cybersecurity risks, algorithm transparency, and the necessity of successful human-AI collaboration in decision-making. The synthesis leads to a multi-layered framework that integrates data acquisition, analytics, simulation, and execution for autonomous decision-making in supply chains. Future research directions in relation to resilient supply networks, intelligent automation, and adaptive supply chain ecosystems are also provided in the study. Through integrating existing information on the new forms of intelligent technology and how it can be incorporated into the supply chain systems, this review contributes to the literature on next-generation supply chains. It will also offer information to both researchers and practitioners aiming at designing autonomous as well as data-driven supply networks. Full article

The adoption of disruptive technologies has become increasingly critical for organizations, particularly following the global shifts prompted by the COVID-19 pandemic. Despite the potential benefits, many organizations, including those in the Fast-Moving Consumer Goods (FMCG) industry, face significant hurdles in this transition. Consequently, this study aims to understand the primary challenges and enabling factors influencing the adoption of disruptive technologies for sustainable digital transformation within the South African FMCG sector. A quantitative methodology was employed, utilizing a questionnaire for data collection. Data from 102 respondents were analyzed using SPSS version 28, involving descriptive statistics (mean item score) to rank factors and exploratory factor analysis (EFA) to identify underlying constructs, and a reliability test was carried out with a score of 0.7. Key challenges identified include high initial costs and poor collaboration. Prominent enabling factors include top management commitment and operational cost reduction. The EFA revealed significant underlying challenge dimensions such as “Infrastructural and Resources Constraints” and “Human Factors Constraints,” and enabling dimensions including “Organizational Commitment and Strategy” and “Leadership.” The study concludes with key implications for promoting successful adoption. The adoption of disruptive technologies has become a strategic imperative for sustainable digital transformation (SDT), particularly in emerging markets such as South Africa’s FMCG sector. This study investigates the key challenges and enabling factors shaping technology adoption within this context. A quantitative methodology was employed, using a structured questionnaire distributed to 102 professionals across FMCG organizations in Gauteng. Exploratory factor analysis (EFA) revealed latent dimensions within both challenges and enablers, which were then interpreted through the lens of Rogers’ Diffusion of Innovation (DOI) theory. To enhance analytical clarity, a matrix model was developed linking factor dimensions to DOI attributes such as relative advantage, complexity, compatibility, trialability, and observability. The study found that high initial costs, poor collaboration, and human capability gaps significantly impede adoption, while strong leadership, strategic alignment, and operational cost savings facilitate it. The findings underscore the need for systemic interventions that address not only technical readiness but also leadership, organizational culture, and structural alignment. Practical implications are outlined for both policy and management, particularly in leveraging DOI attributes to accelerate digital transformation, as well optimize innovation diffusion within resource-constrained environments. For the future, the study proposed a hybrid methodology incorporating qualitative interviews to enhance depth and suggests longitudinal tracking to capture temporal shifts in transformation maturity. Full article

Edge intelligence is often limited by the computation–energy trade-off in resource-constrained devices. Boltzmann machines (BMs) provide strong unsupervised learning capability, yet their reliance on Gibbs sampling makes digital implementations costly in both computation and energy. In this paper, we present a voltage-controlled magnetic anisotropy magnetic tunnel junction (VCMA-MTJ)-based MRAM system that performs in-memory stochastic sampling for state generation and updates in restricted/deep Boltzmann machines (RBMs/DBMs). By exploiting the intrinsic stochastic switching of VCMA-MTJs, the proposed system achieves probabilistic sampling with an energy as low as ∼10 fJ per sample. Implemented on a microcontroller-based edge platform, it enables real-time multi-sensor anomaly detection with an F1-score of 0.9854 and stable operation. The proposed hardware–algorithm co-design achieves in situ stochastic computing and storage within a single MRAM cell, providing an ultra-low-power substrate for probabilistic inference at the edge. Full article

This study comprehensively reviews the literature on knowledge management (KM) to explain its impact on organizational performance and business sustainability. It examines the dominant KM frameworks and theories; performance and sustainability outcomes; and key contextual enablers and constraints across sectors. Following the PRISMA 2020 guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analysis), a systematic review was used to find and collect relevant empirical and theoretical studies through Google Scholar, Scopus, and Web of Science. Thematic descriptive analysis of articles published between January 2020 and January 2026 revealed major themes, research trends, and conceptual gaps, which informed the key research agenda. A total of 70 studies were included after screening and eligibility assessment. The findings indicate that KM consistently enhances innovation capability and operational efficiency to boost competitive advantage and support social, economic, and environmental outcomes. These relationships are largely mediated by organizational learning and innovation, especially green innovation, and are moderated by leadership, organizational culture, and technological integration. Adoption patterns vary across industries and sectors based on differences in resources, digital maturity, and regulatory environments. Ongoing challenges include resistance to change, difficulties in managing tacit knowledge, measurement limitations, and limited longitudinal and cross-sectoral research. Overall, this systematic review highlights the need for integrated KM frameworks that align leadership, culture, and technology to strengthen performance and sustainability outcomes. It advances KM theory by clarifying the dominant models and mechanisms to offer actionable insights for managers and policymakers. Full article

Digital mining has become a tangible reality in recent years and the digital revolution enables and requires data exchange for autonomous machines and operational flow management. LoRa technology and its underground propagation behavior can make an important contribution to this digitalization. This paper presents a Data Mule approach that enabled progress in digitalization at refueling stations in active underground mining areas of a mine near Werra, Germany, operated by the K+S Group. This demonstration aimed to automate manual data collection at fuel gauges by using a dynamic LoRa network. We used specially developed LoRa Data Mule modules for operations over many square kilometers. LoRa was chosen for its industrial functionality and long-range capabilities, particularly in underground environments. The Data Mule modules used were in-house-designed units with underground mining-rated casing and connectors, as well as commercial LoRa boards and custom communication protocols. Connectivity between all systems was realized at travel speeds of 20 to 40 km/h, with connection data successfully relayed for 180 to 770 m, despite 90° turns and no line of sight. It was shown that the LoRa Data Mule approach can be used in a network of remote but active data generation points. Full article

Artificial intelligence (AI) is transforming logistics and supply chain management by enhancing operational efficiency, predictive analytics, and decision-making capabilities; however, the determinants of AI adoption in emerging logistics ecosystems remain insufficiently understood. This study develops and empirically examines an integrated framework combining technology-organization-environment (TOE), task-technology fit (TTF), and unified theory of acceptance and use of technology (UTAUT) to explain AI adoption in Thailand. Using survey data from 500 logistics and supply chain professionals, covariance-based structural equation modeling (SEM) was employed to validate the measurement model and test the proposed relationships. The results show that technological, organizational, and environmental factors significantly influence AI adoption at the organizational level, while task and technology characteristics enhance task-technology fit at the operational level. At the behavioral level, performance expectancy, effort expectancy, and social influence positively influence behavioral intention, which in turn drives AI adoption, with facilitating conditions also exerting a direct effect. These findings indicate that AI adoption is shaped by a cross-level mechanism involving structural conditions, operational alignment, and individual acceptance, offering theoretical and practical insights for advancing digital transformation in logistics contexts. Full article

This study explains how strategy–execution gaps become self-reinforcing during digital process transformation in layered manufacturing organizations. Drawing on an embedded qualitative process study of a large Chinese brewery’s transformation (2020–2024), we triangulate 10 semi-structured interviews across hierarchical levels with longitudinal public disclosures to reconstruct the initiative timeline and trace mechanisms across change phases. The analysis shows that platform-based process governance can scale faster than shared meaning and dialog, producing frontline sensemaking gaps and formalistic, top-down communication. These conditions thin employee voice and weaken feedback closure, which in turn erodes the legitimacy of organizational diagnosis and fragments implementation support. As interface problems are handled through local workarounds, management intensifies visibility-based monitoring, further suppressing voice and reinforcing the execution gap. We develop an organizational development process model that centers feedback closure and diagnosis legitimacy as bridging mechanisms linking soft change dynamics (meaning, trust, voice) with hard digital governance (process standards, data infrastructures, monitoring). The model offers actionable implications for leaders to build closure and legitimate diagnosis as operational capabilities throughout transformation. Full article

Digital Twin (DT) technology is increasingly recognised as a promising approach for predictive and optimised railway maintenance; however, its current applications remain fragmented and lack systematic evaluation across railway domains. This study aims to critically review DT-enabled monitoring, analysis, and maintenance decision-support systems in railway engineering, while identifying key research gaps and future directions. A DT is defined in this study as an integrated cyber–physical system comprising a physical asset, its virtual representation, and continuous bidirectional data exchange enabling real-time monitoring, prediction, and decision-making. A systematic and transparent review methodology was adopted to select 34 representative peer-reviewed studies published between 2020 and 2025, focusing explicitly on DT applications in railway infrastructure and operations. Among these, a subset of 10 key studies was further analysed in greater depth based on their level of technical implementation, data integration capability, and relevance to predictive maintenance applications, which cover multiple domains, including track systems, rolling stock, bridges, and communication networks. Results show that DT-based approaches can enhance fault detection, enable condition-based and predictive maintenance, and reduce reliance on manual inspections. However, significant limitations remain. Most studies are conceptual or pilot-scale, with limited validation under real operating conditions. Key challenges include a lack of standardisation and interoperability, constraints in real-time scalability, data governance and cybersecurity issues, and insufficient integration of multi-source sensing and advanced analytics. This review provides a structured synthesis of current DT implementations in railway systems and highlights critical gaps that must be addressed to enable scalable, reliable, and fully integrated DT-driven maintenance frameworks. Full article

The current literature on electrical system maintenance highlights three technology domains—Building Information Modeling (BIM), Digital Twin (DT), and extended reality (XR)—that have independently demonstrated strong potential for improving lifecycle information management, predictive analytics, and operational support. However, their convergence remains largely underexplored, particularly in electrical system maintenance. This paper provides a structured review of BIM–DT–XR convergence in electrical system lifecycle management, examining their roles across lifecycle phases and their integration through literature synthesis and cross-domain implementation evidence. BIM is analyzed as a basis for modeling and integrating facility management with electrical asset lifecycles; DT as a framework for dynamic system representation and applications in electrical and power systems; and XR as a means of visualizing and interacting with BIM-DT environments. Cross-domain implementation evidence from an industrial electrical facility and a tertiary smart-building pilot shows that BIM–DT–XR integration is technically feasible at pilot scale. However, the analysis identifies five structural integration gaps: semantic misalignment between building-oriented IFC and grid-oriented CIM ontologies; fragmented standard adoption; inconsistent data governance and naming practices; validation approaches focused on syntactic rather than dynamic model fidelity; and the separation of XR visualization from predictive DT capabilities. The implementation evidence further indicates that real-world deployment remains constrained by data quality limitations, integration complexity, cost factors, and interoperability with legacy systems. The review concludes that, despite the maturity of individual technologies, their effective application depends on advances in semantic alignment, lifecycle data governance, validation of dynamic models, and scalable integration frameworks, enabling the transition toward integrated, interoperable, and lifecycle-aware infrastructures for electrical system maintenance. Full article