This study explored the competencies required for informal community leaders to effectively promote health within Thai communities, employing an exploratory sequential mixed-methods design. The qualitative phase, comprising in-depth interviews with thirteen community leaders, identified four critical domains of competency: basic health knowledge, communication skills, network building, and cultural awareness. These domains were subsequently validated through second-order confirmatory factor analysis, which confirmed their reliability and construct validity. The findings highlighted the pivotal role of these competencies in enabling community-led health promotion initiatives. This research provides a robust, evidence-based framework to inform the development of training programs, policy strategies, and targeted interventions aimed at enhancing health outcomes within Thai communities.

The main objective of the study was to examine factors that influence employee performance in general and, more specifically, in public enterprises. The research approach was qualitative, with an in-depth literature review and content analysis. The findings of the study reflect that some factors have a positive and some have a negative influence on employee performance. The study also shows a significant relationship between factors and employee performance, which in turn has a multiplier effect on employee development. Recommendations include the need to provide resources for employee training and development, and the strategic aims and objectives of public enterprises should be aligned with the training and development programs.

Purpose: This article explores the adoption of Artificial Intelligence (AI) in Human Resource Management (HRM) in the UAE, focusing on the critical challenges of fairness, bias, and privacy in recruitment processes. The study aims to understand how AI is transforming HR practices in the UAE, highlighting the issues of bias and privacy while examining real-world applications of AI in recruitment, employee engagement, talent management, and learning and development. Methodology: Through case study methodology, detailed insights are gathered from these companies to understand real-world applications of AI in HRM. A comparative analysis is conducted, comparing AI-driven HRM practices in UAE-based organizations with international examples to highlight global trends and best practices. Findings: The research reveals that while AI holds significant potential to streamline HR functions such as recruitment, onboarding, performance monitoring, and talent management, it also discusses challenges and strategies companies face and develop in integrating AI into their HRM processes, reflecting the broader context of AI adoption in the UAE’s HR landscape. Originality: This paper contributes to the growing body of literature on AI in HRM by focusing on the unique context of the UAE, a rapidly developing market with a highly diverse workforce. It highlights the specific challenges and opportunities faced by organizations in the UAE when implementing AI in HRM, particularly regarding fairness, bias, and data privacy.