Search Results (650)

Urban sustainability challenges demand integrated frameworks capable of addressing the dynamic, non-linear nature of cities. This study explores how the principles of the circular economy and complexity theory intersect to support systemic transformation in sustainable urban planning. Through a systematic literature review of 71 peer-reviewed articles published between 2015 and 2025, we analyze conceptual, methodological, and practical articulations across multiple thematic axes, including circular governance, urban metabolism, regenerative design, adaptive planning, digital integration, and environmental justice. Bibliometric and content analyses were conducted using Scopus metadata, VOSviewer for thematic clustering, and the StArt software (Version 3.4) to structure article selection. The findings reveal that circular economy provides practical tools for resource efficiency and regeneration, while complexity theory offers an adaptive framework to navigate uncertainty, emergent behaviors, and feedback dynamics. The synthesis suggests that their integration enables a more holistic and resilient approach to urban transformation. However, gaps remain in social inclusivity, long-term assessment, and the operationalization of complexity-informed planning. This study contributes to advancing a transdisciplinary agenda for circular and adaptive urban futures, offering insights for scholars, planners, and policymakers aiming to reconfigure cities within planetary boundaries. Full article

This study offers the first systematic exploration of the Greek e-Government team, a public sector innovation unit that operated within the Office of the Prime Minister of Greece from 2009 to 2012—the sole example of such a unit in the country. It illustrates how strategically positioned innovation units can function as change agents within government bureaucracies. The purpose of this work was to analyze how this distinctive unit functioned by bridging policy formulation, legislative drafting, and technological implementation at the highest government levels. The research involved thematic analysis of original interviews conducted with most core members of the team. The findings highlight successes, notably the Diavgeia transparency platform, which markedly improved administrative transparency, accountability, and citizen access to government decisions. Important challenges were also identified, particularly regarding the sustainability of the unit, issues of institutionalization, and meaningful citizen engagement. The experience of the Greek e-Government team suggests that public sector innovation (PSI) units are most effective when they combine high-level political access with multidisciplinary expertise and operational flexibility. The analysis also reveals inherent tensions between the need for centralized coordination and the benefits of decentralized implementation, as well as challenges in maintaining citizen participation throughout the policy development process. Full article

In the context of increasing Artificial Intelligence integration in higher education, understanding the factors influencing university teachers’ adoption of AI tools is critical for effective implementation. This study adopts a perception–intention–behavior framework to explores the roles of perceived usefulness, perceived ease of use, perceived trust, perceived substitution crisis, and perceived risk in shaping teachers’ behavioral intention and actual usage of AI tools. It also investigates the moderating effects of peer influence and organizational support on these relationships. Using a comprehensive survey instrument, data was collected from 487 university teachers across four major regions in China. The results reveal that perceived usefulness and perceived ease of use are strong predictors of behavioral intention, with perceived ease of use also significantly influencing perceived usefulness. Perceived trust serves as a key mediator, enhancing the relationship between perceived usefulness, perceived ease of use, and behavioral intention. While perceived substitution crisis negatively influenced perceived trust, it showed no significant direct effect on behavioral intention, suggesting a complex relationship between job displacement concerns and AI adoption. In contrast, perceived risk was found to negatively impact behavioral intention, though it was mitigated by perceived ease of use. Peer influence significantly moderated the relationship between perceived trust and behavioral intention, highlighting the importance of peer influence in AI adoption, while organizational support amplified the effect of perceived ease of use on behavioral intention. These findings inform practical strategies such as co-developing user-centered AI tools, enhancing institutional trust through transparent governance, leveraging peer support, providing structured training and technical assistance, and advancing policy-level initiatives to guide digital transformation in universities. Full article

As organizations continue to embrace digital transformation, the need for robust cybersecurity strategies has never been more critical. This paper explores the Zero Trust Architecture (ZTA) as a contemporary cybersecurity framework that addresses the challenges posed by increasingly interconnected systems. Zero Trust (ZT) operates under the principle of “never trust, always verify,” ensuring that every access request is thoroughly authenticated, regardless of the requester’s location within or outside the network. However, implementing ZT is a challenging task, requiring an adequate roadmap to prioritize the different initiatives in agreement with company culture, exposure and cyber posture. We apply multi-criteria decision analysis (MCDA) to evaluate the relative importance of various components within a ZT framework, using the Incomplete Analytic Hierarchy Process (IAHP). Expert opinions from professionals in cybersecurity and IT governance were gathered through structured questionnaires, leading to a prioritized ranking of the eight key ZT pillars, as defined by the Cybersecurity and Infrastructure Security Agency (CISA), Washington, DC, USA, along with a prioritization of the sub-elements within each pillar. The study provides actionable insights into the implementation of ZTA, helping organizations prioritize security efforts to mitigate risks effectively and build a resilient digital infrastructure. The evaluation results were used to create a prioritized framework, integrated into the ZEUS platform, developed with Teleconsys S.p.A., to enable detailed assessments of a firm’s cyber partner regarding ZT and identify improvement areas. The paper concludes by offering recommendations for future research and practical guidance for organizations transitioning to a ZT model. Full article

Fuel price increases have long been a contentious issue in Indonesia, sparking intense public and political debates. This study examines how digital media, particularly Kompas.com and Tempo.co, shape public discourse on fuel price hikes through mediatization. Using discourse network analysis, this study compares the political narratives surrounding fuel price increases during the administrations of Susilo Bambang Yudhoyono (2013) and Joko Widodo (2022). The findings reveal a shift in dominant discourse—opposition to price hikes was prominent in both periods, with government authority and economic justification emphasized in 2013, whereas concerns over rising living costs and social unrest dominated in 2022. This study highlights how mediatization has transformed policymaking from deliberative discussions into fragmented media battles, where digital platforms amplify competing narratives rather than facilitating consensus. Kompas.com predominantly featured counter-discourses, while Tempo.co exhibited stronger pro-government narratives in 2013. This study suggests that while digital media plays a crucial role in shaping policy perceptions, it does not necessarily translate into policy influence. It contributes to the broader understanding of the media’s role in policy debates. It underscores the need for more strategic government communication to manage public expectations and mitigate political unrest surrounding fuel price adjustments. Full article

Sustainability has emerged as a critical issue in development. Digital transformation functions as both an enabler and an effective tool for promoting sustainability. Strategy plays a pivotal role in the process of digital transformation. However, there is a paucity of existing research focused on strategic renewal in digital transformation within the context of China. This study employs organizational learning theory to examine the antecedents and consequences of strategic renewal in digital transformation. Data were collected from 389 government employees through a questionnaire survey and a quantitative analysis was performed to evaluate four hypotheses using structural equation modeling (SEM). The results indicate that knowledge acquisition and organizational memory significantly influence strategic renewal, which in turn affects government performance. The findings of this study could serve as a guide and provide concrete practical approaches for successful digital transformation among governments, thereby laying a foundation for sustainable development. Full article

Artificial intelligence has taken a seat at the executive table and is threatening the fact that human beings are the only ones who should be in a position of power. This article gives conjectures on the future of leadership in which managers will collaborate with learning algorithms in the Neural Adaptive Artificial Intelligence Leadership Model, which is informed by the transformational literature on leadership and socio-technical systems, as well as the literature on algorithmic governance. We assessed the model with thirty in-depth interviews, system-level traces of behavior, and a verified survey, and we explored six hypotheses that relate to algorithmic delegation and ethical oversight, as well as human judgment versus machine insight in terms of agility and performance. We discovered that decisions are made quicker, change is more effective, and interaction is more vivid where agile practices and good digital understanding exist, and statistical tests propose that human flexibility and definite governance augment those benefits as well. It is single-industry research that contains self-reported measures, which causes research to be limited to other industries that contain more objective measures. Practitioners are provided with a practical playbook on how to make algorithmic jobs meaningful, introduce moral fail-safes, and build learning feedback to ensure people and machines are kept in line. Socially, the practice is capable of minimizing bias and establishing inclusion by visualizing accountability in the code and practice. Filling the gap between the theory of leadership and the reality of algorithms, the study provides a model of intelligent systems leading in organizations that can be reproduced. Full article

The integration of artificial intelligence (AI), big data analytics, and blockchain technologies within the digital economy presents transformative opportunities for promoting low-carbon urban development. However, a systematic understanding of how these digital innovations influence urban carbon mitigation remains limited. This study addresses this gap by proposing two research questions (RQs): (1) What are the key success factors for artificial intelligence, big data, and blockchain in urban carbon emission reduction? (2) How do these technologies interact and support the transition to low-carbon cities? To answer these questions, the study employs a hybrid methodological framework combining the decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) techniques. The data were collected through structured expert questionnaires, enabling the identification and hierarchical analysis of twelve critical success factors (CSFs). Grounded in sustainability transitions theory and institutional theory, the CSFs are categorized into three dimensions: (1) digital infrastructure and technological applications; (2) digital transformation of industry and economy; (3) sustainable urban governance. The results reveal that e-commerce and sustainable logistics, the adoption of the circular economy, and cross-sector collaboration are the most influential drivers of digital-enabled decarbonization, while foundational elements such as smart energy systems and digital infrastructure act as key enablers. The DEMATEL-ISM approach facilitates a system-level understanding of the causal relationships and strategic priorities among the CSFs, offering actionable insights for urban planners, policymakers, and stakeholders committed to sustainable digital transformation and carbon neutrality. Full article

Amid intensifying global ESG regulations and the expanding influence of green finance, China’s digital economy policies have emerged as key institutional instruments for promoting corporate sustainability. Leveraging the implementation of the National Big Data Comprehensive Pilot Zone as a quasi-natural experiment, this study utilizes panel data of Chinese listed firms from 2009 to 2023 and applies multi-period Difference-in-Differences (DID) and Spatial DID models to rigorously identify the policy’s effects on corporate ESG performance. Empirical results indicate that the impact of digital economy policy is not exerted through a direct linear pathway but operates via three institutional mechanisms, enhanced information transparency, eased financing constraints, and expanded fiscal support, collectively constructing a logic of “institutional embedding–governance restructuring.” Moreover, disruptive technological innovation significantly amplifies the effects of the transparency and fiscal mechanisms, but exhibits no statistically significant moderating effect on the financing constraint pathway, suggesting a misalignment between innovation heterogeneity and financial responsiveness. Further heterogeneity analysis confirms that the policy effect is concentrated among firms characterized by robust governance structures, high levels of property rights marketization, and greater digital maturity. This study contributes to the literature by developing an integrated moderated mediation framework rooted in institutional theory, agency theory, and dynamic capabilities theory. The findings advance the theoretical understanding of ESG policy transmission by unpacking the micro-foundations of institutional response under digital policy regimes, while offering actionable insights into the strategic alignment of digital transformation and sustainability-oriented governance. Full article

This study evaluates the impact of digital systems and IT audit frameworks on tax performance and integrity within tax administrations. Using international data from organizations like the World Bank, OECD (Organisation for Economic Co-operation and Development), and IMF (International Monetary Fund), the research examines the relationship between tax revenue as a percentage of GDP, digital infrastructure, corruption perception, e-government development, and cybersecurity readiness. Quantitative analysis, including correlation, regression, and clustering methods, reveals a strong positive relationship between digital maturity, e-governance, and tax performance. Countries with advanced digital governance systems and robust IT audit frameworks, such as COBIT, tend to show higher tax revenues and lower corruption levels. The study finds that e-government development and anti-corruption measures explain over 40% of the variance in tax performance. Cluster analysis distinguishes between digitally advanced, high-compliance countries and those lagging in IT adoption. The findings suggest that digital transformation strengthens fiscal integrity by automating compliance and reducing human contact, which in turn mitigates bribery risks and enhances fraud detection. The study highlights the need for adopting international best practices to guide the digitalization of tax administrations, improving efficiency, transparency, and trust in public finance. Full article

The rapid evolution of Industry 4.0 technologies has resulted in a scenario in which collaborative networks are essential to overcome the challenges related to their implementation. However, the frameworks to guide such collaborations remain underexplored. This study addresses this gap by proposing Business Rules and Goals Models to operationalize Industry 4.0 solutions through enterprise collaboration. Using the For Enterprise Modeling (4EM) method, the research integrates qualitative insights from expert opinions, including interviews with 12 professionals (academics, industry professionals, and consultants) from Brazilian manufacturing sectors. The Goals Model identifies five main objectives—competitiveness, efficiency, flexibility, interoperability, and real-time collaboration—while the Business Rules Model outlines 18 actionable recommendations, such as investing in digital infrastructure, upskilling employees, and standardizing information technology systems. The results reveal that cultural resistance, limited resources, and knowledge gaps are critical barriers, while interoperability and stakeholder integration emerge as enablers of digital transformation. The study concludes that successfully adopting Industry 4.0 requires technological investments, organizational alignment, structured governance, and collaborative ecosystems. These models provide a practical roadmap for companies navigating the complexities of Industry 4.0, emphasizing adaptability and cross-functional synergy. The research contributes to the literature on collaborative networks by connecting theoretical frameworks with actionable enterprise-level strategies. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

The age of entrepreneurship calls for the evolving of learning and development (L&D) models to meet the dynamic demands of innovation, sustainability, and technology innovation. This study examines the trends and issues of L&D models for entrepreneurs, more so focusing on how these models influence business success in a rapidly changing global landscape. The research employs bibliometric analysis, VOSviewer cluster analysis, and co-citation analysis to explore the literature from 1994 to 2024. Data collected from the Web of Science Core Collection database reflect significant trends in entrepreneurial L&D, with particular emphasis on the use of digital tools, sustainability processes, and governance systems. Findings emphasize the imperative role of L&D in fostering entrepreneurship, more so in areas such as digital transformation and the adoption of new technologies. The study also identifies central regions propelling this field, such as UK and USA. Future studies will be centered on the role of digital technologies, innovation, and green business models within entrepreneurial L&D frameworks. This study provides useful insight into the future of L&D within the entrepreneurial domain, guiding academia and companies alike in the planning of effective learning strategies to foster innovation and sustainable business growth. Full article

Amid ongoing societal development, tourists’ travel behavior patterns have been undergoing substantial transformations, and understanding their evolution has emerged as a key area of scholarly interest. Taking Beijing as a case study, this research aims to uncover the spatiotemporal evolution patterns of tourist flows and their underlying driving mechanisms. Based on digital footprint relational data, a dual-perspective analytical framework—“tourist perception–tourist flow network”—is constructed. By integrating the center-of-gravity model, social network analysis, and regression models, the study systematically examines the dynamic spatial structure of tourist flows in Beijing from 2012 to 2024. The findings reveal that in the post-pandemic period, Beijing tourists place greater emphasis on the cultural connotation and experiential aspects of destinations. The gravitational center of tourist flows remains relatively stable, with core historical and cultural blocks retaining strong appeal, though a slight shift has occurred due to policy influences and emerging attractions. The evolution of the spatial network structure reveals that tourism flows have become more dispersed, while the influence of core scenic spots continues to intensify. Government policy orientation, tourism information retrieval, and the agglomeration of tourism resources significantly promote the structure of tourist flows, whereas the general level of tourism resources exerts no notable influence. These findings offer theoretical insights and practical guidance for the sustainable development and regional coordination of tourism in Beijing, and provide a valuable reference for the spatial restructuring of urban tourism in the post-COVID-19 era. Full article

The main objective of this study is to offer fresh empirical insight into the evolving relationship between digitalisation and agroecology by examining Mulini Di Segalari, a biodynamic vineyard in Italy. While much of the existing literature positions digital agriculture as potentially misaligned with agroecological principles, this case study unveils how digital tools can actively reinforce agroecological practices when embedded within supportive socio-technical networks. Novel findings of this study highlight how the use of digital technologies supported agroecological practices and led to the reconfiguration of social relations, knowledge systems, and governance structures within the farm. Employing a technographic approach revealed that the farm’s transformation was driven not just by technology but through collaborative arrangements involving different stakeholders. These interactions created new routines, roles, and information flows, supporting a more distributed and participatory model of innovation. By demonstrating how digital tools can catalyse agroecological transitions in a context-sensitive and socially embedded manner, this study challenges the binary framings of technology versus ecology and calls for a more nuanced understanding of digitalisation as a socio-technical process. Full article