While there is burgeoning interest in the influence of human resource management (HRM) on sustainability organizational outcomes (SOO), the intricate interplay with Occupational Health and Safety (OHS) has not yet been explored, particularly in manufacturing firms’ contexts. Therefore, this study aims to probe the symbiotic relationship between HRM practices, OHS, and SOO, spotlighting manufacturing firms. Data collection was conducted by utilizing a cross-sectional survey, convenience sampling technique, and a web-based form among the 256 respondents from an industrial company (Balexco) in Bahrain. Structural Equation Modeling (SEM) by Smart-PLS was used to analyze the collected data. Our analysis highlighted a significant positive relationship between HRM, SOO, and OHS. Moreover, this study highlighted the dual impact of direct and indirect HRM on SOO, mediated by OHS. These multi-layered insights reinforce the assumption that a comprehensive approach to HRM, aligning performance aspirations with employee well-being, is instrumental in improving SOO. This study is a novel contribution to the literature because, by uncovering the intricate interplay of HRM, OHS, and SOO, practical implications and limitations were provided.

Driving heavy vehicles with dangerous cargo involves various work environments that can significantly impact road safety. This research aims to study the impact of oil and gas tanker drivers’ work environment on driving performance to identify and address any issues that may affect their ability to carry out their jobs effectively. To achieve this, a quantitative approach was employed using a questionnaire survey adapted from the literature review. The data collected from a sample of drivers of oil- and gas-heavy vehicles were analyzed using structural equation modelling. The study’s findings reveal a significant association between the drivers’ work environment and driving performance, represented by a path coefficient of β = 0.237. These results highlight the substantial contribution of the work environment to driving performance, with an effect of 63%. Consequently, the study emphasizes the importance of considering the work environment as a potential factor when assessing and enhancing tanker drivers’ driving abilities during oil and gas transportation.

This study examines the impact of transformational leadership (TL), psychological empowerment (PE), and work engagement (WE) on organizational citizenship behavior (OCB) among academic staff in Malaysian universities. Data was collected through a survey of 385 academics and analyzed using Structural Equation Modeling (SEM) via Smart-PLS. The results revealed that transformational leadership significantly enhances psychological empowerment and organizational citizenship behavior but does not directly affect work engagement. Psychological empowerment was found to fully mediate the relationship between transformational leadership and work engagement and partially mediate the relationship between transformational leadership and organizational citizenship behavior. Furthermore, psychological empowerment and work engagement directly influence organizational citizenship behavior. The findings suggest that enhancing psychological empowerment and work engagement is crucial for fostering citizenship behaviors among academic staff. Therefore, leadership strategies should focus on cultivating psychological empowerment as a key driver of engagement and citizenship behavior in higher education institutions.

This study seeks to understand and explore how women social entrepreneurs select their employees and how this can affect their social impact. This study has two objectives. The first is to explore the most attractive employees' employability characteristics chosen by women entrepreneurs. The second objective was to explore the relationship between employability characteristics and the social impact of the social enterprises. This study used an exploratory quantitative paradigm to reach the study's findings. Multiple correspondence analysis (MCA) explores employability characteristics the women social entrepreneurs select and the relationship between the selected employability characteristics and the social impact of the women's social enterprises. The data were collected using a cross-sectional questionnaire. Data were collected from 382 employees working in 174 female social enterprises. Women social entrepreneurs were found to prefer employees with high interpersonal skills, high ethics, high physical abilities, high abilities to learn, high implicit knowledge, high reliability, and low soft skills. Linking these employability skills to social impact revealed that implicit knowledge and interpersonal skills are the most influential. A paucity of research discusses how women social entrepreneurs recruit their employees. This problem is getting worse when it comes to small social enterprises. This study led to a discussion about employability characteristics in women's social enterprises and their impact on social impact.

This research paper is part of an extensive study conducted by the researcher on the UAE strategy for artificial intelligence, and its role in changing the mechanism of government services, leading to a complete data analysis by 2031, according to the latest techniques and tools of artificial intelligence and their application in various fields of work efficiently. The problems of smart devices lie in the bubble filter phenomenon, which causes a lack of elasticity, increased systemic fragility, which can have serious and unexpected systemic effects. The paper objectives were; to discuss the decision-making process and the employment of artificial intelligence in the United Arab Emirates. Through the Descriptive Analytical method, the topic was presented in two sections. The discussions showed that the UAE's strategy for artificial intelligence targets several vital sectors in the country, including the transportation sector, by reducing accidents and operational costs. The health sector by reducing the proportion of chronic and dangerous diseases. The space sector conducts accurate experiments and reduces costly errors. Energy sector through utility management and smart consumption. The water sector by conducting accurate analysis and studies to save resources. The technology sector by raising the rate of production and helping in public spending, the education sector by reducing costs and increasing the desire to learn. The environment sector by increasing the proportion of afforestation and planting suitable plants. Traffic Sector Develop preventive mechanisms such as predicting accidents and traffic congestion, and setting more effective traffic policies. With the presence of the infrastructure and knowledge in the successful recruitment of artificial intelligence in the Ministry of Interior in the United Arab Emirates.