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
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.

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The entrepreneurial performance of new ventures operating within the sustainable open innovation paradigm remains underexplored, particularly in terms of how specific sustainability-oriented practices translate into measurable performance outcomes. Prior research has largely examined sustainability, entrepreneurship, and open innovation in isolation, offering limited empirical evidence on their combined effects at the early venture stage. To address this gap, this study analyzes panel data from 407 new ventures incubated in science and technology parks, employing regression-based panel data analysis to examine the relationships between sustainable practices, open innovation engagement, and entrepreneurial performance. The findings suggest that new ventures widely adopt sustainable materials and energy as key strategies, which significantly influence entrepreneurial performance. In contrast, support from local communities does not have a statistically significant impact. Among the sociodemographic factors tested, only the number of years participating in open innovation networks shows a significant effect on entrepreneurial performance. Theoretically, this study advances sustainable open innovation literature by empirically integrating sustainability practices into entrepreneurship performance models. From a managerial perspective, the findings offer actionable insights for entrepreneurs and incubator managers, highlighting which sustainability strategies and network engagements are most likely to yield performance benefits in new ventures.

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This study aims to explore the integration challenges faced by immigrant entrepreneurs in Portugal. It employed a multiple case study approach, drawing on semi-structured interviews with nine immigrant entrepreneurs from three distinct communities in Portugal. The findings of this study highlight the role of social networks in enabling and shaping the entrepreneurial journeys of immigrants in Portugal. These networks act as a bridge to help immigrants overcome barriers such as unfamiliarity with local markets, restricted access to resources, and cultural differences. In this context, community knowledge and referrals play a particularly significant role. Furthermore, the findings also identify five types of challenges faced by these communities including the financial, regulatory, social, institutional, and psychological dimensions. This study is relevant due to the role of immigrants in fostering economic growth and social cohesion. Understanding and addressing the integration challenges is key to enabling their success, which in turn strengthens local economies and promotes inclusive growth. Additionally, exploring these issues helps policymakers and organizations develop targeted strategies to support immigrant entrepreneurs, ensuring they can fully realize their potential and contribute positively to the host society.