Improving educational outcomes in subjects such as English and mathematics remains a significant challenge for educators and policymakers. Strategic Human Resource Management (SHRM), which aligns human resource practices with organizational goals, has proven effective in business sectors but is less explored in educational contexts, especially from students’ perspectives. Existing studies often focus on teacher development, overlooking direct impacts on student performance. This research addresses the gap by examining how SHRM influences students’ performance in English and mathematics, incorporating student feedback to assess SHRM’s effectiveness. In the quantitative study, 200 students were analyzed to explore the relationship between SHRM practices and academic outcomes. The findings indicate that SHRM significantly affects student performance, with high predictive relevance and explanatory power in both subjects. The results suggest that strategic HR practices, such as professional development, performance management, and resource allocation, are critical to academic success. These insights provide valuable implications for educators and policymakers, highlighting the importance of integrating strategic HR management into educational frameworks to enhance curriculum design and resource distribution. The study demonstrates the broad applicability of SHRM across different academic disciplines, suggesting a need for comprehensive HR strategies that focus on both teacher and student performance. Future research should explore how SHRM influences educational outcomes and identify contextual factors that moderate its impact, enhancing effective HR practices in diverse academic settings.

Leadership is one of the important factors that ensured organizational achievement. Servant leadership offers a unique point of view on leadership which developed around the idea of service to subordinates. The implementation of servant leadership can lead to various positive outcomes, including increased engagement, organizational citizenship behavior, and improved performance. However, engagement and organizational citizenship behavior can serve as mediators to enhance organizational performance even further. The present study aimed to explore a prediction model of servant leadership using mediating variables such as employee engagement and organizational citizenship behavior, with employee performance as the outcome. The sampling method used was purposive sampling. This study used a structural equation model analysis approach to determine the predicted model of servant leadership. The research showed that the role of mediating variables indicated that employee engagement and organizational citizenship behavior had a positive effect in mediating the relationship between servant leadership and employee performance. The study indicated that applying servant leadership, with employee engagement, and organizational citizenship behavior as mediating variables would have an impact on better results of employee performance.

Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.

The aim of this paper is to introduce a research project dedicated to identifying gaps in green skills by using the labor market intelligence. Labor Market Intelligence (LMI). The method is primarily descriptive and conceptual, as the authors of this paper intend to develop a theoretical background and justify the planned research using Natural Language Processing (NLP) techniques. This research highlights the role of LMI as a tool for analysis of the green skills gaps and related imbalances. Due to the growing demand for eco-friendly solutions, there arises a need for the identification of green skills. As societies shift towards eco-friendly economic models, changes lead to emerging skill gaps. This study provides an alternative approach for identification of these gaps based on analysis of online job vacancies and online profiles of job seekers. These gaps are contextualized within roles that businesses find difficult to fill due to a lack of requisite green skills. The idea of skill intelligence is to blend various sources of information in order to overcome the information gap related to the identification of supply side factors, demand side factors and their interactions. The outcomes emphasize the urgency of policy interventions, especially in anticipating roles emerging from the green transition, necessitating educational reforms. As the green movement redefines the economy, proactive strategies to bridge green skill gaps are essential. This research offers a blueprint for policymakers and educators to bolster the workforce in readiness for a sustainable future. This article proposes a solution to the quantitative and qualitative mismatches in the green labor market.

Transitioning to a green economy is a global concern, considered a pathway to sustainable development. This paper aims to investigate the effect of the transition into a green economy on Vietnam’s sustainable development and its two economic and environmental dimensions, with consideration of several essential issues including renewable energy, technological innovation, natural resource rents (oils, forest, and minerals), foreign direct investment, and trade. This paper utilizes data from 1996 to 2020 and then applies the autoregressive distributed lag (ARDL) method for analysis. The results conclude that renewable energy is a driving key to reducing environmental degradation, but it hampers economic growth, while the contrast occurs with technology. Our results emphasize the dependence on non-renewable energy, whereas the innovation of technology does not show a green orientation in Vietnam. Furthermore, there is a lack of sustainability in the effect of natural resource rents, foreign direct investment, and trade. Overall, the transition into a green economy in Vietnam does not illustrate the sustainable orientation. The findings of this research provide empirical evidence to clarify the relationship between this transition and its driving factor, with sustainable development and the two economic environment dimensions. In addition, this study will bring worthwhile implications for the policymakers and scholars on whether the transition to a green economy fulfills the orientation towards sustainability, then enhancing the economy's efficiency to achieve green growth, following the pathway to sustainable development.

The Malaysian government’s heightened focus on Technical and Vocational Education and Training (TVET) reflects a strategic move towards economic and social development, particularly in addressing youth unemployment. Recognizing the potential of TVET to contribute to these goals, there is a specific emphasis on enhancing the marketability of women in the workforce from the current 62 percent to an ambitious 95 percent. However, a notable gender gap persists in entrepreneurial pursuits within the TVET sector in Malaysia, with female representation lagging. To bridge this gap, this study aims to construct a comprehensive framework that nurtures future-ready female TVETpreneur talent. This initiative aligns with the Malaysian Higher Education Blueprint, 2021–2025, i.e., fostering a diverse and innovative workforce. An extensive literature survey was conducted to identify the factors influencing female TVET students’ entrepreneurial intention. The literature revealed that social psychological and organizational approaches are commonly used to explore and analyze the relationship between the influence of female TVET students’ talents and behavior, their exposure to entrepreneurship, mentorship and support programs, role models in TVET, curriculum design, and access to resources. A comprehensive theoretical framework was developed based on these findings, which offers significant insights related to enhancing TVET opportunities for women and advancing Malaysia’s economic and social development goals in a sustainable way.