Search Results (108)

Healthcare Information Systems (HISs) are essential for improving care quality, managing chronic diseases, and supporting clinical decision-making. Despite significant investments, HIS implementations often fail due to the complexity of healthcare environments. Maturity Models (MMs) have emerged as tools to guide organizational improvement by assessing readiness, process efficiency, technology adoption, and interoperability. This study presents a comprehensive literature review identifying 45 Maturity Models used across various healthcare domains, including telemedicine, analytics, business intelligence, and electronic medical records. These models, often based on Capability Maturity Model Integration (CMMI), vary in structure, scope, and maturity stages. The findings demonstrate that structured maturity assessments help healthcare organizations plan, implement, and optimize HIS more effectively, leading to enhanced clinical and operational performance. This review contributes to an understanding of how different MMs can support healthcare digital transformation and provides a resource for selecting appropriate models based on specific organizational goals and technological contexts. Full article

The increasing integration of renewable energy sources (RES) in power systems presents challenges related to variability, stability, and efficiency, particularly in smart microgrids. This systematic review, following the PRISMA 2020 methodology, analyzed 66 studies focused on advanced energy storage systems, intelligent control strategies, and optimization techniques. Hybrid storage solutions combining battery systems, hydrogen technologies, and pumped hydro storage were identified as effective approaches to mitigate RES intermittency and balance short- and long-term energy demands. The transition from centralized to distributed control architectures, supported by predictive analytics, digital twins, and AI-based forecasting, has improved operational planning and system monitoring. However, challenges remain regarding interoperability, data privacy, cybersecurity, and the limited availability of high-quality data for AI model training. Economic analyses show that while initial investments are high, long-term operational savings and improved resilience justify the adoption of advanced microgrid solutions when supported by appropriate policies and financial mechanisms. Future research should address the standardization of communication protocols, development of explainable AI models, and creation of sustainable business models to enhance resilience, efficiency, and scalability. These efforts are necessary to accelerate the deployment of decentralized, low-carbon energy systems capable of meeting future energy demands under increasingly complex operational conditions. Full article

This study addresses the multifaceted challenges inherent in implementing effective smart-city waste-management systems. Recent global trends indicate increased adoption of Industry 4.0 technologies—such as the Internet of Things (IoT), artificial intelligence (AI), and data analytics—to optimize waste collection and processing. The central research question investigates the role of innovative business models and sustainable decision-making frameworks in advancing smart waste management within urban environments. This research integrates three interrelated domains: business-model innovation, smart-city paradigms, and sustainability in waste management. Its novelty lies in synthesizing these domains, conducting a comparative analysis of best practices from leading European smart cities, and proposing a conceptual framework to guide sustainable decision-making. Methodologically, the study employs a systematic literature review, case-study analyses, and the synthesis of theoretical and empirical data. Key findings demonstrate that innovative business models—such as product-as-a-service, circular-economy approaches, and waste-as-a-service—substantially enhance the sustainability and operational efficiency of urban waste systems. However, many cities lack comprehensive strategies for integrating these models, highlighting the necessity for deliberate planning and active stakeholder engagement. Based on these insights, the study offers actionable recommendations for policymakers and urban managers to embed sustainable business models into smart-city waste infrastructures. These contributions aim to promote the development of resilient, efficient, and environmentally responsible waste-management systems in smart cities. Full article

The electric vehicle (EV) business model integrates advanced battery technology, dynamic power train architectures, and intelligent energy management systems with ecosystem strategies and digital services. It incorporates environmental sustainability through lifecycle analysis and renewable energy integration. China, with 9.49 million EV sales in 2023 (33% market share), faces infrastructure gaps constraining further growth. China is strategically mitigating CO₂ emissions while fostering economic expansion, notwithstanding constraints such as suboptimal battery technology advancements, elevated production expenditure, and enduring ecological impacts. This Political, Economic, Social, Technological, Legal, Environmental (PESTLE) assessment, operationalized through a survey of 800 stakeholders and Statistical Package for the Social Sciences IBM SPSS SPSS (Version 28) quantitative analysis (factor loading = 0.73 for Technology; eigenvalue = 4.12), identifies infrastructure gaps as the dominant barrier (72% of stakeholders). Political factors (β = 0.82) emerged as the strongest adoption predictor, outweighing economic subsidies in significance. The adoption of EVs in China presents a significant prospect for reducing CO₂ emissions and advancing technology. However, economic barriers, market dynamics, inadequate infrastructure, regulatory uncertainty, and social acceptance issues are addressed in the assessment. The study recommends prioritizing infrastructure investment (e.g., 500 K fast-charging stations by 2027) and policy stability to overcome adoption barriers. This study provides three key advances: (1) quantification of PESTLE factor weights via factor analysis, revealing technological (infrastructure) and political factors as dominant; (2) identification of infrastructure gaps, not subsidies, as the primary adoption barrier; and (3) demonstration of infrastructure’s persistence post-subsidy cuts. These insights redefine EV adoption priorities in China. Full article

Background: The integration of artificial intelligence (AI) into healthcare research has the potential to enhance research capacity, streamline protocol development, and reduce barriers to engagement. Medway NHS Foundation Trust identified a plateau in homegrown research participation, particularly among clinicians with limited research experience. A generative AI-driven chatbot was introduced to assist researchers in protocol development by providing step-by-step guidance, prompting ethical and scientific considerations, and offering immediate feedback. Methods: The chatbot was developed using OpenAI’s GPT-3.5 architecture, customised with domain-specific training based on Trust guidelines, Health Research Authority (HRA) requirements, and Integrated Research Application System (IRAS) submission protocols. It was deployed to guide researchers through protocol planning, ensuring compliance with ethical and scientific standards. A mixed-methods evaluation was conducted using a qualitative-dominant sequential explanatory design. Seven early adopters completed a 10-item questionnaire (5-point Likert scales), followed by eight free-flowing interviews to achieve thematic saturation. Results: Since its launch, the chatbot has received an overall performance rating of 8.86/10 from the seven survey respondents. Users reported increased confidence in protocol development, reduced waiting times for expert review, and improved inclusivity in research participation across professional groups. However, limitations in usage due to free-tier platform constraints were identified as a key challenge. Conclusions: AI-driven chatbot tools show promise in supporting research engagement in busy clinical environments. Future improvements should focus on expanding access, optimising integration, and fostering collaboration among NHS institutions to enhance research efficiency and inclusivity. Full article

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in Building Energy Management Systems (BEMSs) offers transformative potential for improving energy efficiency, enhancing occupant comfort, and supporting grid stability. However, the adoption of these technologies in the European Union (EU) is significantly influenced by a complex regulatory landscape, including the EU AI Act, the General Data Protection Regulation (GDPR), the EU Cybersecurity Act, and the Energy Performance of Buildings Directive (EPBD). This review systematically examines the legal, technological, and economic implications of these regulations on AI- and IoT-driven BEMS. Following the PRISMA-ScR guidelines, 64 relevant sources were reviewed, comprising 34 peer-reviewed articles and 30 regulatory or policy documents. First, legal and regulatory barriers that may hinder innovation are identified, including data protection constraints, cybersecurity compliance, liability concerns, and interoperability requirements. Second, technological challenges in designing regulatory-compliant AI and IoT solutions are examined, with a focus on data privacy-preserving architectures (e.g., edge computing versus cloud processing), explainability requirements for AI decision-making, and cybersecurity resilience. Finally, the economic opportunities arising from regulatory alignment are highlighted, demonstrating how compliant AI and IoT-based BEMS can enable energy savings, operational efficiencies, and new business models in smart buildings. By synthesizing current research and policy developments, this review offers a comprehensive framework for understanding the intersection of regulatory requirements and technological innovation in AI-driven building management. Strategies are discussed for navigating regulatory constraints while leveraging AI and IoT for energy-efficient, intelligent building operations. The insights presented aim to support researchers, policymakers, and industry stakeholders in advancing regulatory-compliant BEMS that balance innovation, security, and sustainability. Full article

Background: The integration of Artificial Intelligence (AI) into Business Process Management Systems (BPMSs) has led to the emergence of AI-Augmented Business Process Management Systems (ABPMSs). These systems offer dynamic adaptation, real-time process optimization, and enhanced knowledge management capabilities. However, key challenges remain, particularly regarding explainability, user engagement, and behavioral integration. Methods: This study presents a novel framework that synergistically integrates the Socialization, Externalization, Combination, and Internalization knowledge model (SECI), Agile methods (specifically Scrum), and cutting-edge AI technologies, including explainable AI (XAI), process mining, and Robotic Process Automation (RPA). The framework enables the formalization, verification, and sharing of knowledge via a well-organized, user-friendly software platform and collaborative practices, especially Communities of Practice (CoPs). Results: The framework emphasizes situation-aware explainability, modular adoption, and continuous improvement to ensure effective human–AI collaboration. It provides theoretical and practical mechanisms for aligning AI capabilities with organizational knowledge management. Conclusions: The proposed framework facilitates the transition from traditional BPMSs to more sophisticated ABPMSs by leveraging structured methodologies and technologies. The approach enhances knowledge exchange and process evolution, supported by detailed modeling using BPMN 2.0. Full article

Digital transformation is increasingly relevant in food certification systems, improving processes, coordination, and data accessibility. In state-led certification systems, public entities hold a political mandate to promote digital transformation, yet little is known about digital maturity in these systems or how to assess it. This study assesses the digital maturity of a state-led food certification system in Baden-Württemberg, Germany, focusing on private sector stakeholders involved in its administration. Additionally, it examines potential measures that the governing public entity can take and evaluates the suitability of the methods used. A total of 25 out of 43 organisations were surveyed using the Digital Maturity Assessment (DMA) framework validated for the European Union (EU). Six dimensions were analysed: Digital Business Strategy, Digital Readiness, Human-Centric Digitalisation, Data Management, Automation and Artificial Intelligence, and Green Digitalisation. Data Management and Human-Centric Digitalisation were the most developed, highlighting strong data governance and workforce engagement. Automation and Artificial Intelligence were ranked lowest, reflecting minimal adoption but also indicating that not all dimensions might be of the same relevance for the variety of organisations. The variability in scores and organisation-specific relevance underscores the European DMA framework’s value, particularly due to its subsequent tailored consultation process and its integration into EU policy. Full article

In recent years, as an important alternative to traditional gasoline-powered vehicles, new electric vehicles (NEVs) have gained widespread attention and rapid development globally. In the traditional automotive industry chain, downstream vehicle manufacturers need to master core technologies, such as engines, chassis, and transmissions. In contrast to the traditional automotive industry chain, where downstream vehicle manufacturers must master core technologies, like engines, chassis, and transmissions, the electric vehicle industry chain has evolved in a way that the development of core components is gradually separated from the vehicle manufacturers. Downstream vehicle manufacturers can now outsource key components, such as batteries, electric controls, and motors. Additionally, in terms of sales models, the electric vehicle industry chain can adopt either the traditional 4S dealership model or a direct-sales model. As the research and development of core components are increasingly separated from vehicle manufacturers, the downstream vehicle manufacturers can source components, like batteries, electric controls, and motors, externally. At the same time, they can choose to use either the traditional 4S dealership model or the direct-sales model. The underlying mechanisms and channel selection in this context require further exploration. Based on this, a mathematical model is established by incorporating terminal marketing input, product competitiveness, and after-sales service levels from the literature to solve for the optimal pricing under centralized and decentralized pricing strategies. Using numerical examples, the pricing and profit performance under different market structures are analyzed to systematically examine the impact of the electric vehicle supply chain on business operations, as well as the changes in various elements across different channels. We will focus on how after-sales services (including the spare part supply) influence the pricing strategy and profit distribution in the supply chain, aiming to provide insights into advanced manufacturing system management for manufacturing enterprises and improve the efficiency of intelligent logistics management. The research indicates that (1) The direct-sales model helps to improve the terminal marketing input, after-sales service quality, and product competitiveness for supply chain stakeholders; (2) It is noteworthy that the manufacturer’s direct-sales model also significantly contributes to lowering prices, highlighting that the direct-sales model has substantial impacts on both supply chain stakeholders and, importantly, consumers. Full article

Efficient waste management remains critical to achieving sustainable urban development, addressing challenges related to resource conservation, environmental preservation, and carbon emissions reduction. This review synthesizes advancements in waste management technologies, focusing on three transformative areas: optimization techniques, the integration of electric vehicles (EVs), and the adoption of smart technologies. Optimization methodologies, such as vehicle routing problems (VRPs) and dynamic scheduling, have demonstrated significant improvements in operational efficiency and emissions reduction. The integration of EVs has emerged as a sustainable alternative to traditional diesel fleets, reducing greenhouse gas emissions while addressing infrastructure and economic challenges. Additionally, the application of smart technologies, including Internet of Things (IoT), artificial intelligence (AI), and the Geographic Information System (GIS), has revolutionized waste monitoring and decision-making, enhancing the alignment of waste systems with circular economy principles. Despite these advancements, barriers such as high costs, technological complexities, and geographic disparities persist, necessitating scalable, inclusive solutions. This review highlights the need for interdisciplinary research, policy standardization, and global collaboration to overcome these challenges. The findings provide actionable insights for policymakers, municipalities, and businesses, enabling data-driven decision-making, optimized waste collection, and enhanced sustainability strategies in modern waste management systems. Full article

This review synthesizes current knowledge on the transformative impacts of artificial intelligence (AI)—computational systems capable of performing tasks requiring human-like reasoning—on business innovation. It addresses the potential of AI to reshape strategies, operations, and value creation across various industries. Key themes include AI-driven business model innovation, human–AI collaboration, ethical governance, operational efficiency, customer experience personalization, organizational capability development, and adoption disparities. AI enables scalable product development, personalized service delivery, and data-driven strategic decisions. Successful implementations hinge on overcoming technical, cultural, and ethical barriers, with ethical AI adoption enhancing consumer trust and competitiveness, positioning responsible innovation as a strategic imperative. For practitioners, this review offers evidence-based frameworks for aligning AI with business objectives. For academics, it identifies research frontiers, including longitudinal impacts, context-specific roadmaps for small- and medium-sized enterprises, and sustainable innovation pathways. This review conceptualizes AI as a driver of systemic organizational transformation, requiring continuous learning, ethical foresight, and strategic ability for competitive advantage. Full article

In the intelligent transportation field, object recognition, detection, and location applications face significant real-time challenges. To address these issues, we propose an automatic sensor-based data loading and unloading optimization strategy for algorithm models. This strategy is designed for artificial intelligence (AI) application systems that leverage edge computing. It aims to solve resource allocation optimization and improve operational efficiency in edge computing environments. By doing so, it meets the real-time computing requirements of intelligent transportation business applications. By adopting node and sensor management mechanisms as well as efficient communication protocols, dynamic sensor-based data management of AI algorithm models was achieved, such as pedestrian object recognition, vehicle object detection, and ship object positioning. Experimental results show that while maintaining the same recall rate, the inference time is reduced to one tenth or even one twentieth of the original time. And this strategy can enhance privacy protection of sensor-based data. In the future research, we may consider integrating distributed computing under high load conditions to further optimize the response time of model loading and unloading for multi-service interaction, and enhance the balance and scalability of the system. Full article

Low Earth Orbit (LEO) constellations support the transmission of various communication services and have been widely applied in fields such as global Internet access, the Internet of Things, remote sensing monitoring, and emergency communication. With the surge in traffic volume, the quality of user services has faced unprecedented challenges. Achieving accurate low Earth orbit constellation network traffic prediction can optimize resource allocation, enhance the performance of LEO constellation networks, reduce unnecessary costs in operation management, and enable the system to adapt to the development of future services. Ground networks often adopt methods such as machine learning (support vector machine, SVM) or deep learning (convolutional neural network, CNN; generative adversarial network, GAN) to predict future short- and long-term traffic information, aiming to optimize network performance and ensure service quality. However, these methods lack an understanding of the high-dynamics of LEO satellites and are not applicable to LEO constellations. Therefore, designing an intelligent traffic prediction model that can accurately predict multi-service scenarios in LEO constellations remains an unsolved challenge. In this paper, in light of the characteristics of high-dynamics and the high-frequency data streams of LEO constellation traffic, the authors propose a DST-LEO satellite-traffic prediction model (a dynamic spatio-temporal low Earth orbit satellite traffic prediction model). This model captures the implicit features among satellite nodes through multiple attention mechanism modules and processes the traffic volume and traffic connection/disconnection data of inter-satellite links via a multi-source data separation and fusion strategy, respectively. After splicing and fusing at a specific scale, the model performs prediction through the attention mechanism. The model proposed by the authors achieved a short-term prediction RMSE of 0.0028 and an MAE of 0.0018 on the Abilene dataset. For long-term prediction on the Abilene dataset, the RMSE was 0.0054 and the MAE was 0.0039. The RMSE of the short-term prediction on the dataset simulated by the internal low Earth orbit constellation business simulation system was 0.0034, and the MAE was 0.0026. For the long-term prediction, the RMSE reached 0.0029 and the MAE reached 0.0022. Compared with other time series prediction models, it decreased by 22.3% in terms of the mean squared error and 18.0% in terms of the mean absolute error. The authors validated the functions of each module within the model through ablation experiments and further analyzed the effectiveness of this model in the task of LEO constellation network traffic prediction. Full article

With the rapid advancement of artificial intelligence (AI), chatbots represent a transformative tool in digital customer engagement, reshaping customer–brand relationships. This paper explores AI chatbots on customer–brand interactions by analyzing key features, such as interaction, perceived enjoyment, customization, and problem-solving. Based on the Technology Acceptance Model (TAM), the research investigates how these attributes influence perceived ease of use, perceived usefulness, customer attitudes, and ultimately, customer–brand relationships. Adopting a mixed-methods approach, this study begins with qualitative interviews to identify key engagement factors, which then inform the design of a structured quantitative survey. The findings reveal that AI chatbot features significantly enhance customer perceptions, with ease of use and usefulness in shaping positive attitudes and strengthening brand connections. The research further underscores the role of AI-driven personalization in delivering sustainable customer engagement by optimizing digital interactions, reducing resource-intensive human support, and promoting long-term brand loyalty. By integrating TAM with customer–brand relationship theories, this study contributes to AI and sustainability research by highlighting how intelligent chatbots can facilitate responsible business practices, enhance operational efficiency, and promote digital sustainability through automation and resource optimization. The findings provide strategic insights for businesses seeking to design AI-driven chatbot systems that improve customer experience and align with sustainable digital transformation efforts. Full article

The MAISTRO methodology introduces a comprehensive and integrative, agile framework for managing Artificial Intelligence (AI) system development projects, addressing familiar challenges such as technical complexity, multidisciplinary collaboration, and ethical considerations. Designed to align technological capabilities with business objectives, MAISTRO integrates iterative practices and governance frameworks to enhance efficiency, transparency, and adaptability throughout the AI lifecycle. This methodology encompasses seven key phases, from business needs understanding to operation, ensuring continuous improvement and alignment with strategic goals. A comparative analysis highlights MAISTRO’s advantages over traditional methodologies such as CRISP-DM and OSEMN, particularly in flexibility, governance, and ethical alignment. This study applies MAISTRO in a simulated case study of the PreçoBomAquiSim supermarket, demonstrating its effectiveness in developing an AI-powered recommendation system. Results include a 20% increase in product sales and a 15% rise in average customer ticket size, highlighting the methodology’s ability to deliver measurable business value. By emphasizing iterative development, data quality, ethical governance, change and risk management, MAISTRO provides a robust approach for AI projects and suggests directions for future research across diverse industries context for facilitating large-scale adoption. Full article