Search Results (1,524)

In an increasingly centralized digital society, the widespread reliance on third-party cloud services has heightened user concerns about data privacy and control, driving a significant movement toward self-hosted solutions. This study investigates factors influencing continued use of self-hosting, proposing an extended research model that combines classic TAM constructs with domain-specific factors like perceived autonomy, privacy concerns, perceived trust, personal innovativeness and perceived enjoyment. A quantitative survey was conducted with $n=2158$ active self-hosting users, and the data was analyzed using PLS-SEM. The findings reveal that perceived enjoyment, perceived autonomy and perceived usefulness are the most significant positive drivers of continuance intention, confirming that intrinsic satisfaction and the desire for control are powerful motivators for sustained engagement. The study also found that the relationship between intention and usage is significantly moderated by perceived competence, highlighting that a user’s technical skill strengthens the link between their intent and actual use. The research offers key insights for developers and policymakers and contributes to academic discourse on sustained technology use by providing a validated measurement scale for self-hosted software usage and underscoring the importance of user empowerment and an enjoyable experience to foster the sustained engagement of decentralized digital solutions essential for the socio-technical sustainability of the digital society. Full article

Robotaxi services represent a major step in the commercialization of autonomous driving, offering efficiency, consistency, and safety benefits. However, despite technological advances, their large-scale adoption is far from guaranteed. Most urban users already rely on mature ride-hailing platforms such as Didi and Uber, making the real behavioral question not whether to adopt Robotaxi, but whether to migrate from existing services. Prior studies based on TAM, UTAUT, or trust models have primarily examined users’ initial adoption decisions, overlooking the substitution behavior that better captures how people shift between competing mobility services in real contexts. This study addresses this gap by applying the Push–Pull–Mooring (PPM) framework to examine users’ migration from ride-hailing to Robotaxi services, based on survey data collected from 1206 respondents across four Chinese cities (Beijing, Shanghai, Guangzhou, and Wuhan). The model was tested using structural equation modeling and multi-group analysis (SEM–MGA). Push factors reflect negative experiences with ride-hailing, including social anxiety and insecurity caused by drivers’ behaviors; pull factors emphasize Robotaxi’ autonomy and service reliability; while mooring factors capture habitual ride-hailing use and perceived Robotaxi risk. Findings indicate that push and pull factors significantly promote migration intentions, whereas mooring factors hinder them. Among all factors, perceived risk exerted the strongest negative effect (β = −0.36), underscoring its critical role as a barrier to Robotaxi migration. Gender differences are also evident, with women more sensitive to risks and men more influenced by reliability. By situating adoption within a migration context, this study enriches high-risk innovation theory and offers practical guidance for designing gender-sensitive and user-specific promotion strategies. Full article

In recent years, animal welfare has become a high priority in livestock production systems owing to the pressure to balance environmental sustainability, productivity, and ethics as demand continues to grow. This review presents the latest advances in small ruminant welfare, with emphasis on the effects of climate change, the main new innovative managerial and husbandry methods, and the use of precision livestock farming (PLF) technologies. In the first part, this review will examine how climate change is already re-shaping environmental and physiological conditions for farmed sheep and goats, with rising heat stress and negative impacts on both productive and reproductive performance. Secondly, more recent advances in small ruminant management will be presented, including improved housing systems, nutritional strategies, and behavioral monitoring, aimed at enhancing animal resilience and performance. Finally, particular focus will be given to the use of PLF tools for assessing milk quality and monitoring animal welfare. Evidence suggests that real-time monitoring technologies and sensor systems can accurately capture physiological and production parameters and provide an early sign of stress or health issues. Overall, the findings suggest that an integrated approach, combining climate adaptation strategies, welfare management, and the integration of precision technologies can serve as a key driver toward more ethical, sustainable, and resilient livestock production systems. Full article

Digital finance, representing the deep integration of finance and technology, has become a critical enabler of sustainable industrial transformation. Focusing on resource-based enterprises (RBEs)—key actors in transitioning towards sustainable practices—this study investigates how digital finance development fosters new quality productive forces (NQPFs), a core driver of high-quality, sustainable development. Utilizing panel data from Chinese A-share listed RBEs (2008–2022), we measure NQPF using the entropy method and gauge regional digital finance development with the Peking University Digital Financial Inclusion Index (DFII). Empirical analysis employing two-way fixed effects and panel threshold regression models provides robust evidence that digital finance significantly enhances NQPFs within RBEs. Crucially, mechanism analysis identifies three fundamental pathways underpinning sustainability: (1) mitigating financial constraints; (2) facilitating technological innovation and transformation; (3) strengthening green transition awareness. Furthermore, the impact of digital finance exhibits synergistic enhancement alongside increasing environmental regulation intensity and improved financial resource allocation efficiency. Heterogeneity analysis reveals that the effect is more pronounced in regions with lower marketization, within state-owned enterprises, and among RBEs in recession stages. Collectively, these findings offer significant implications for policymakers and industry practitioners aiming to strategically leverage digital finance to accelerate the sustainable transformation of resource-intensive industries, thereby contributing directly to environmentally sustainable and resilient economic development. Full article

Artificial Intelligence (AI) is increasingly central to sustainable development, yet its advancement varies across G7 economies. This study employs Method of Moments Quantile Regression (MMQR) to examine how Financial Technology (FinTech), Economic Growth (EG), Human Capital (HC), and Renewable Energy Consumption (RENC) influence AI development in G7 countries from 2000 to 2022. By analyzing heterogeneous effects across quantiles, the study captures stage-specific drivers often overlooked in average-based models. Results indicate that FinTech and human capital significantly promote AI adoption in lower and middle quantiles, enhancing digital inclusion and innovation capacity, while RENC becomes relevant primarily at advanced stages of AI adoption. Economic growth exhibits negative or inconsistent effects, suggesting that GDP expansion alone is insufficient for technological transformation without alignment to supportive policies and institutional contexts. The lack of long-run cointegration further highlights the dominance of short- and medium-term dynamics in shaping the AI–sustainability nexus. These findings provide actionable insights for policymakers, emphasizing targeted FinTech development, skill-building initiatives, and renewable-powered AI solutions to foster sustainable and inclusive AI adoption. Overall, the study demonstrates how financial, human, and environmental factors jointly drive AI development, offering a mechanism-based perspective on technology-driven sustainable development in advanced economies. Full article

Against the backdrop of global economic digital transformation and the rapid flow of creative factors, innovation spaces, as the key carriers of inventive activities, drive high-quality development in urban agglomerations. This study develops a three-dimensional framework of “Spatial Structure–Factor Synergy–Institutional Drivers” to uncover the evolution of innovation spaces and industrial shifts in the Beijing–Tianjin–Hebei urban agglomeration, China. Methodologically, spatial econometric techniques were applied to capture both the overall concentration and spatial disparities of innovation. Spatial Gini and variation coefficients measured innovation clustering, while standard deviation ellipses and location entropy identified spatial linkages among high-tech innovation clusters. Geographically weighted regression models explored spatial heterogeneity in influencing factors, and a policy intensity index was constructed to assess the effectiveness of differentiated policy interventions in optimizing innovation resources. Key findings include the following: (1) Innovation spaces are spatially polarized in a “core–periphery” pattern, yet require cross-regional collaboration. Concurrently, high-tech industries demonstrate a gradient structure: central cities leading in R&D, sub-central cities driving industrial applications, and node cities achieving specialized development through industrial transfer. (2) The driving mechanisms exhibit significant spatial heterogeneity: economic density shows diminishing returns in core areas, whereas R&D investment and ecological quality demonstrate increasingly positive effects, with foreign investment’s role evolving positively post-institutional reforms. (3) Regional innovation synergy has formed a preliminary framework, but strengthening sustainable policy mechanisms remains pivotal to advancing market-driven coordination and dismantling administrative barriers. These findings underscore the importance of integrated policy reforms for achieving balanced and high-quality innovation development in administratively coordinated urban agglomerations like BTH. Full article

The energy sector profoundly influences both economic growth and ecosystems, making it pivotal to human development. Substantial evidence confirms that scientific and technological innovations, particularly breakthrough achievements, are key drivers of sustainable development in this sector. Consequently, comprehending the evolutionary trajectory of such breakthroughs is crucial for policymakers and practitioners to refine their strategic approaches. To analyze the evolution of energy-related breakthrough innovations, this study leverages a dataset of 552 projects awarded China’s State Science and Technology Advancement Prize. By employing large language models and word cloud analysis, we trace the shifting research priorities of the 552 projects to delineate the pathway of scientific and technological development in the energy sector. Furthermore, we utilize social network analysis to reveal the evolving collaboration patterns underlying these innovations. Our findings indicate that, consistent with China’s current energy consumption structure, most innovations remain concentrated in fossil fuels. However, a clear trend emerges: research is focusing less on fossil fuels and more on clean energy and high efficiency equipment. Regarding collaborative innovation, the pattern in China differs from that of countries like the United States; in China, universities, rather than enterprises, occupy the central position in innovation networks. The insights from this study can assist government officials in designing effective policies to support breakthrough innovations across different types of research entities. Moreover, research entities can adjust their collaboration strategies based on our findings to enhance innovation performance. Full article

This study investigates the impact of digitalization on sustainability, resilience, risk management, integration, and performance within supply chains. It addresses gaps in the existing literature concerning the role of digitalization in fostering sustainable supply chains. Drawing upon Innovation Diffusion Theory, the Triple Bottom Line (TBL), Risk Theory (RT), Organizational Information Processing Theory (OIPT), and the Resource-Based View (RBV), a comprehensive structural model was developed to examine the interrelationships among these constructs. A quantitative research design was employed, with data collected through an online survey of 420 supply chain practitioners. Data analysis, conducted using SPSS 28 and Smart PLS 4, tested the relationships between digitalization and supply chain performance. The findings demonstrate that digitalization exerts a significant positive influence on sustainability, resilience, risk management, and integration, thereby enhancing overall supply chain performance. Results further underscore the importance of aligning supply chain strategies with the unique characteristics of different organizational contexts in order to maximize efficiency and adaptability. In today’s dynamic business environment, digitalization emerges as a critical driver of competitiveness, enabling supply chains to achieve greater responsiveness and long-term sustainability. This study contributes to theory and practice by integrating multiple perspectives to provide nuanced insights into digital transformation, while also offering practical guidance for enterprises seeking to optimize supply chain strategies. Full article

Background: Rare diseases represent a significant research challenge due to the limited availability of data, small patient cohorts, and heterogeneous phenotypes. Data augmentation and synthetic data generation are increasingly adopted to mitigate these limitations. Methods: This scoping review maps the application of data augmentation and synthetic data generation methods as strategies to address these limitations. A total of 118 studies published between 2018 and 2025 were identified through PubMed, Scopus, and Electronics Engineers (IEEE) Xplore. Results: Imaging data headed the field, followed by clinical and omics datasets. Classical augmentation, mainly geometric and photometric transformations, emerged as the most frequent approach, while deep generative models have rapidly expanded since 2021. Rule- and model-based methods were less common but demonstrated high interpretability in small datasets. Conclusions: Overall, these techniques enabled dataset expansion and improved model robustness. However, both approaches require rigorous validation to confirm biological plausibility. Together, these methods can transform data scarcity from a barrier into a driver of methodological innovation, enabling more inclusive rare disease research. Full article

This review provides a comprehensive analysis of the technological, environmental, economic, regulatory, and social dimensions shaping the development and operation of agricultural biogas plants. The paper adopts a primarily European perspective, reflecting the comparatively high share of agricultural inputs in anaerobic digestion (AD) across EU Member States, while drawing selective comparisons with global contexts to indicate where socio-geographical conditions may lead to different outcomes. It outlines core principles of the AD process and recent innovations—such as enzyme supplementation, microbial carriers, and multistage digestion systems—that enhance process efficiency and cost-effectiveness. The study emphasises substrate optimisation involving both crop- and livestock-derived materials, together with the critical management of water resources and digestate within a circular-economy framework to promote sustainability and minimise environmental risks. Economic viability, regulatory frameworks, and social dynamics are examined as key factors underpinning successful biogas implementation. The paper synthesises evidence on cost–benefit performance, investment drivers, regulatory challenges, and support mechanisms, alongside the importance of community engagement and participatory governance to mitigate land-use conflicts and ensure equitable rural development. Finally, it addresses persistent technical, institutional, environmental, and social barriers that constrain biogas deployment, underscoring the need for integrated solutions that combine technological advances with policy support and stakeholder cooperation. This analysis offers practical insights for advancing sustainable biogas use in agriculture, balancing energy production with environmental stewardship, food security, and rural equity. The review is based on literature identified in Scopus and Web of Science for 2007 to 2025 using predefined keyword sets and supplemented by EU policy and guidance documents and backward- and forward-citation searches. Full article

Cultural heritage conservation faces escalating challenges from environmental threats and resource constraints, necessitating innovative preservation strategies that balance predictive accuracy with interpretability. This study presents a hybrid neuro-fuzzy framework addressing critical gaps in heritage conservation practice through sequential integration of feedforward neural networks (FF-NNs) and Mamdani-type fuzzy inference systems (MFISs). The system processes multi-sensor data (temperature, vibration, pressure) through a two-stage architecture: an FF-NN for pattern recognition and an MFIS for interpretable decision-making. Evaluation on 1000 synthetic heritage building monitoring samples (70% training, 30% testing) demonstrates mean accuracy of 94.3% (±0.62%), precision of 92.3% (±0.78%), and recall of 90.3% (±0.70%) across five independent runs. Feature importance analysis reveals temperature as the dominant fault detection driver (60.6% variance contribution), followed by pressure (36.7%), while vibration contributes negatively (−2.8%). The hybrid architecture overcomes the accuracy–interpretability trade-off inherent in standalone approaches: while the FF-NN achieves superior fault detection, the MFIS provides transparent maintenance recommendations essential for conservation professional validation. However, comparative analysis reveals that rigid fuzzy rule structures constrain detection capabilities for borderline cases, reducing recall from 96% (standalone FF-NN) to 47% (hybrid system) in fault-dominant scenarios. This limitation highlights the need for adaptive fuzzy integration mechanisms in safety-critical heritage applications. Full article

Sustainable practices are increasingly recognized as critical drivers of business performance in contemporary markets. This study examines the influence of Circular Economy (CE), Eco-Innovation (EI), and Sustainable Marketing (SM) on Business Performance (BP) among micro, small, and medium-sized enterprises (MSMEs) located in Aguascalientes, Mexico, a representative emerging-economy context within Latin America. The relationships among these constructs were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4.0. Results show that CE significantly drives both EI and SM, and that EI positively influences BP. However, the direct effect of SM on BP was marginal, indicating that its contribution to business performance is weaker than that of circular and eco-innovative practices in the analyzed sample. Among CE dimensions, enablers were the strongest driver of circular adoption, while in EI, adoption incentives exerted the greatest influence. This research contributes to the sustainability and management literature by rigorously analyzing and comparing the relative impact of different sustainability-oriented strategies—circular, innovative, and marketing-based—on business performance. By examining how these strategies interact and differ in their effects, the study offers evidence-based guidance for MSMEs with limited resources to prioritize initiatives with greater potential to enhance competitiveness. The proposed integrated CE–EI–SM–BP framework, validated through a predictive PLS-SEM approach (PLSpredict), provides robust empirical evidence from an underexplored emerging-economy context. The results also open avenues for future research to examine contextual or temporal factors influencing the effectiveness of sustainability-based strategies. Full article

Astrotourism is gaining international recognition as a practice that integrates science, culture and sustainability, addressing global challenges such as light pollution and fostering inclusive local development. Although its environmental and economic impacts are widely acknowledged, its potential as a driver of social innovation remains underexplored. This study addresses this gap by examining how astrotourism activates social innovation across multiple governance scales. The objective is to identify the mechanisms, enabling conditions, and territorial arrangements through which astrotourism operates as social innovation in peripheral contexts. The research adopts a qualitative and exploratory approach, based on documentary and bibliographic analysis of four international cases: Alfa Aldea in Chile, Dark Sky Alqueva in Portugal, the Jasper Dark Sky Festival in Canada, and the National Astrotourism Strategy in South Africa. A comparative framework was applied to identify three recurrent dimensions of social innovation—social capital, redistribution of power, and collaborative responses to crises—drawing on both classical and contemporary literature. Findings show that, despite institutional and territorial differences, all four cases demonstrate the capacity of astrotourism to build trust networks, strengthen community protagonism, and generate adaptive responses to socioeconomic vulnerabilities. The study proposes an interpretive matrix that outlines pathways of social innovation, offering policymakers tools to design multi-scalar strategies that connect community initiatives with national frameworks to achieve the Sustainable Development Goals. Beyond astrotourism, the framework also provides insights for other sustainable tourism modalities based on natural and cultural heritage. Full article

The construction industry is transforming toward innovative and sustainable building methods, with modular building emerging as a viable alternative to conventional techniques. Modular building practices (MBPs) are regarded as an efficient and sustainable alternative driven by the need to reduce costs, minimise material waste, improve project timelines, and meet technological and environmental demands in the construction industry. This study, therefore, explores the key factors promoting the adoption of MBPs within the construction space. Using a quantitative research design, data were collected through structured questionnaires administered to registered and practising construction professionals, including architects, engineers, project managers, and quantity surveyors across various construction firms in South Africa. The instrument’s reliability was confirmed with a high Cronbach’s alpha coefficient of 0.974, indicating excellent internal consistency. Findings reveal that education and training, increased funding for research and development, tax imposition on traditional building components, introducing relevant support policies and legislations, and awareness creation among the stakeholders are key drivers of MBPs. The findings underscore the importance of aligning industry practices with policy incentives, investing in workforce upskilling, and enhancing stakeholder engagement to accelerate the transition toward modular construction. The study contributes to both the academic literature and industry knowledge by providing empirical evidence on the multidimensional factors promoting modular practices. Implementation of supportive regulations and incentives that promote sustainability, streamlined approval processes, and innovation is highly recommended. Full article

The urgent pursuit of net-zero emissions presents a critical challenge for modern societies, necessitating a speedup of transformative shifts across sectors to mitigate climate change. Predicting trends and drivers in the integration of energy technologies is essential to addressing this challenge, as it informs policy decisions, strategic investments, and the deployment of innovative solutions crucial for transitioning to a sustainable energy future. Despite the importance of accurate forecasting, current methods remain limited, especially in leveraging the vast, unlabelled energy literature available. However, with the advent of large language models (LLMs), the ability to interpret and extract insights from extensive textual data has significantly advanced. Sentiment analysis, in particular, has just emerged as a vital tool for detecting scientific opinions from the energy literature, which can be harnessed to forecast energy trends. This study introduces a novel multi-agent framework, EnergyEval, to evaluate the sentiment and factuality of the energy literature. The core novelty of the multi-agent framework is found to be the use of heterogeneous energy-specialised roles with different LLMs. This investigation, using both multiple persona agents and different LLMs, provides a bespoke collaboration mechanism for multi-agent debate (MAD). In addition, we believe our approach can extend across the energy industry, where deep application of MAD is yet to be exploited. We apply EnergyEval to the case of UK offshore wind literature, assessing its predictive performance. Our findings indicate that the sentiment predicted by the EnergyEval effectively aligns with observed trends in increasing the installed capacity and reductions in Levelised Cost of Energy (LCOE). It also helps us to identify key drivers in offshore wind development. The advantage of employing a multi-agent LLM debate team allows us to achieve competitive accuracy compared to single-LLM-based methods, while significantly reducing computational costs. Overall, the results highlight the potential of EnergyEval as a robust tool for forecasting technology developments in the pursuit of net-zero emissions. Full article