This review focuses on the emerging role of multi-energy microgrids (MEMG) in addressing energy poverty in remote regions, drawing on 220 highly cited publications from 2019 to 2026. PRISMA-guided research on ScienceDirect and IEEE Xplore was conducted to explore key trends, challenges, and opportunities in implementing MEMG. The findings reveal that growing research interest is focused on MEMG that integrates multiple types of renewable sources (e.g., solar photovoltaic (PV), wind, and biomass) with various energy storage technologies to overcome intermittency and improve reliability. The review identifies four major research efforts: problem formulation and simulation (52%), experimental (10%), systematic review (24%), and state-of-the-art assessments (14%). While most of the literature focuses on technical optimisation studies, several gaps in the experimental validation, socio-economic impact assessments, and community engagement strategies were identified. The review indicates that integrating advanced energy management systems, hybrid storage solutions, and decentralised control architectures is critical to efficiently deploying MEMG in remote areas. The present review provides perspectives to leverage the synergies of renewable energy via MEMG to address energy poverty in remote communities.
Global food insecurity and increasing freshwater scarcity continue to intensify as a result of population growth, urbanisation, climate variability, and the depletion of natural resources. Addressing these interconnected challenges requires a transition away from conventional, resource-intensive food systems toward sustainable protein alternatives that can deliver adequate nutrition with a reduced freshwater demand. Microalgae have emerged as a strong candidate in this context due to their rapid growth rates, broad environmental tolerance, ability to utilise carbon dioxide, and capacity to grow in brackish water, seawater, or nutrient-rich wastewater, thereby substantially lowering their blue-water footprint. While several microalgal species are already produced as single-cell protein (SCP), large-scale deployment across food, health, and industrial applications remains limited by economic, technical, and operational constraints. This review critically evaluates the potential and limitations of microalgae as a scalable solution for food and water security. Considering the increasing digitalisation of biomanufacturing, particular attention is given to the role of computational biology and artificial intelligence (AI)–enabled strategies in overcoming cultivation and process optimisation bottlenecks. Recent advances demonstrate that artificial intelligence (AI) approaches, particularly machine learning (ML), alongside Internet of Things (IoT)-based sensing, can significantly improve resource-use efficiency and nutrient recovery in microalgae production systems. In parallel, the growing application of multi-omics and systems biology tools is generating high-resolution datasets that are increasingly important for the development, validation, and deployment of robust ML models. This review distinguishes itself from previous studies by presenting an integrated perspective that links alternative protein production with environmental sustainability, particularly within the Water–Food–Energy nexus, while systematically examining ML applications across the microalgal bioprocess value chain. Key knowledge gaps, future research priorities, and the practical challenges associated with implementing AI-driven solutions in microalgae-based systems are also critically discussed. Microalgae are a sustainable alternative protein addressing food security and freshwater scarcity. Artificial intelligence, particularly machine learning-based microalgal cultivation optimization, resource efficiency, and nutrient recovery. Digital twins, IoT monitoring, and predictive control reduce economic and operational barriers to scale-up. AI-enabled bioprocessing links alternative proteins to sustainability within the Water–Food–Energy nexus.
The significance of fostering an internal corporate social responsibility (ICSR) plan to advance employee well-being is not fully understood. This article explores employee well-being from a positive balance perspective, combining the strategic approach to Internal Corporate Social Responsibility with the Theory of subjective well-being. Drawing on Sirgy's hierarchical model of well-being and the Job Demands-Resources. Framework, the study positions well-being as a multidimensional construct shaped by both organizational practices and individual experiences across various life domains. The main goal is to identify and model the factors that determine employee well-being within the organizational environment. The empirical analysis uses data from the European Working Conditions Telephone Survey 2021 (EWCTS 2021) (Eurofound 2022), covering a representative sample of 11,221 employed individuals across EU countries and the UK. Sets of variables structured in three blocks (traditional factors, organizational factors, and personal conditions) were tested. For estimation, the weighted logistic regression, multinomial regression, and Tobit models are used. For capturing cognitive, emotional, and eudaimonic aspects of well-being, subjective well-being is analyzed as a binary outcome (high vs. low well-being), as a trichotomous variable (low, medium, and high levels), and as a continuous index. The results show that employee well-being is associated with ICSR practices through a structural mechanism, which connects with the job demands-resources framework and the Positive Balance perspective, integrating different dimensions and levels of well-being. The modeling strategy identifies a nonlinear pattern in which the intermediate level of well-being emerges as a transitional zone between low and high well-being. At this level, ICSR-related factors exhibit weaker associations, and the overall configuration of well-being determinants becomes less uniform. More institutional ICSR practices, including organizational participation, are less popular, which may indicate that positive balance mechanisms are not completely engaged. The consistency of the results across alternative model specifications reinforces the view of internal corporate social responsibility not only as an ethical commitment but as a strategic enabler of organizational sustainability and resilience, through a differentiated, employee well-being-centered approach. These findings suggest that ICSR policies should be designed in a differentiated manner, combining managerial strategies aimed at activating employee well-being across different states with broader social objectives related to sustainable work, quality of working life, and social well-being.
PurposeThis study investigates the organizational mechanisms and management choices through which the strategic implementation of artificial intelligence (AI) reshapes decision-making and professional-technology interaction in healthcare organizations.Design/methodology/approachAdopting an inductive qualitative research methodology, the study analyses in-depth interviews with managers and professionals from healthcare organizations in Portugal. The analysis led to the development of a theoretically grounded model that articulates the mechanisms that facilitate and inhibit the strategic adoption of AI.FindingsThe results identify four key dimensions and their underlying mechanisms, demonstrating that enhancing efficiency is inherently connected to issues of trust, ethics and the reorganization of decision-making authority. The findings extend the theory by integrating perspectives from socio-technical systems and professional judgment.Practical implicationsA conceptual framework and propositions are proposed that articulate the link between AI strategy and professional practice. The managerial implications highlight the need for strategic intermediation strategies and organizational readiness for sustainable and viable implementation.Originality/valueThis research contributes theoretically by refining existing models based on empirical mechanisms and methodologically by justifying the inductive approach to the study of AI management processes, offering insights for the management of healthcare organizations.
PurposeThis study deals with the validation of a scale designed to measure creative personality traits and creative pedagogical practices in higher education.Design/methodology/approachTo fulfil these aims, a scale was validated using rigorous statistical methods, such as exploratory and confirmatory factor analysis. A snowball sampling was used, launching the questionnaire on social media. This questionnaire was administered to higher education lecturers at higher education institutions, obtaining 202 valid responses.FindingsThe scale was refined, resulting in a reliable instrument with four main dimensions: (1) creative personal characteristics, (2) stimulating the expression of ideas, (3) environment conducive to creativity and (4) student autonomy and learning.Practical implicationsThe dimensions reflect theoretical foundations of creativity in higher education and offer practical applications to promote innovation in educational environments. The results highlight the importance of lecturers adopting creative practices to encourage the development of creativity in students.Originality/valueIn this study, a new and innovative scale was validated. The Personality Traits and Creative Practices (PTCP) scale highlights the importance of lecturers adopting creative practices to encourage the development of creativity in students. The study contributes to theory by providing a robust tool for measuring creativity and to practice by directing teachers towards pedagogical strategies that foster creativity in educational contexts. Finally, conclusions, limitations and future avenues for research were drawn.
