Abstract
ABSTRACT This study explores the impact of Green Knowledge Sharing (GKS) on Organizational Performance (OP), considering the mediating roles of Green Organizational Culture (GOC) and Technological Innovation (TI). Addressing current gaps in the literature, the research extends beyond sector-specific analyses and incorporates a cross-country perspective, examining 297 small and medium-sized enterprises (SMEs) in Portugal, Spain, and the United Kingdom. Additionally, this study acknowledges the influence of digital transformation in enhancing GKS, a factor often overlooked in previous research. By adopting a Structural Equation Modeling (SEM) approach, this article confirms a direct and positive effect on both OP and GOC, with GOC further influencing OP, establishing its mediating role in this relationship. However, the relationships between GKS and TI, as well as the indirect effect of GKS on OP through TI, are not supported. These findings offer theoretical advancements by broadening the conventional understanding of OP beyond financial metrics and present practical implications for SME managers, highlighting strategies to foster a green organizational culture and leverage technological innovation for sustainable performance.

Abstract
Understanding how structural change drives long-run growth requires jointly considering the dynamics of productive and scientific specialisations, and science-industry alignment. This paper develops and tests a unified framework that integrates evolutionary, structuralist, complexity, and innovation-systems perspectives to assess how productive and scientific specialisations, science-industry alignment, diversification, and global value chain integration shape economic performance. To operationalize this framework, we construct new indicators, including a Science-Industry Matching (SIM) index, measures of dynamic entry and relatedness density, and specialisation-based diversity indices, and apply them to a panel of up to 142 countries over 2000-2018/2023. Estimation relies on country fixed effects with Driscoll-Kraay standard errors to address heteroskedasticity, autocorrelation, and cross-sectional dependence. The results reveal that persistent specialisation in high- and medium-high-tech industries fosters growth, while low-tech dependence constrains it. Scientific specialisation in enabling fields such as mathematics, physics, chemistry, and energy/environmental sciences supports growth, but excessive concentration risks lock-in. Science-industry alignment enhances growth in advanced economies with strong absorptive capacity but penalises weaker systems. Industrial diversification often dilutes resources, whereas scientific diversification consistently promotes growth by broadening the knowledge base for recombination. Finally, integration into global value chains is growth-enhancing in developing economies, while advanced economies can sustain higher domestic value added without significant penalties.

Abstract
Social entrepreneurship is crucial for sustainable development as it blends innovative business models with a focus on economic, social and environmental impact. This synergy can potentially accelerate progress towards the sustainable development goals, creating a more equitable and sustainable future. This study aims to explore this phenomenon by carrying out a systematic review of literature. It is adopted the PRISMA framework to identify 54 relevant studies in this field. The findings characterize the evolution of articles in this field, the number of citations, the relationship between key terms, and the respective clusters. Moreover, seven contributions of social entrepreneurship for sustainable development are identified. Finally, the role of technology in promoting and supporting the interconnection between social entrepreneurship and sustainable development is explored. This study is relevant to enhance understanding of how technology supports social entrepreneurship and helps social entrepreneurs to achieve sustainable development goals.

Abstract
PurposeDetecting anomalous access to electronic health records (EHRs) is critical for safeguarding patient privacy and ensuring compliance with healthcare regulations. Traditional anomaly detection methods often struggle in this domain due to extreme class imbalance, limited labelled data and the subtlety of insider threats. This study proposes a lightweight, hybrid anomaly detection framework that integrates unsupervised, supervised and rule-based approaches using a meta-classifier architecture.Design/methodology/approachAn experimental and model-development approach is employed, combining machine learning techniques with domain-inspired rule modelling to construct a hybrid anomaly detection framework for healthcare access logs. Performance of the algorithm is measured using standard classification metrics such as precision, recall, F1-score and accuracy.FindingsEvaluated on a synthetic but realistic dataset of 50.000 normal and 500 labelled anomalous healthcare access events, the proposed framework achieved superior performance compared to standalone models as well as other hybrid models, with an F1-score of 0.8989 and recall of 0.8180. It also maintained low inference latency (0.028 ms) and energy consumption (4.03e-07 kg CO2), making it suitable for deployment in resource-constrained clinical environments.Originality/valueThis study highlights the potential of a hybrid meta-classifier to enhance anomaly detection in healthcare access logs, capturing both subtle and obvious anomalies while outperforming conventional models and remaining efficient, scalable and practical for real-time monitoring.

Abstract