This article explores the implications of directive change management, characterized by top-down leadership and minimal employee involvement, on organizational dynamics, employee morale, and job security. This approach’s psychological and operational impacts are underscored, emphasizing the imperative of addressing employee perceptions and fostering trust. Strategies for rebuilding trust and enhancing morale post-directive change management are presented, including transparent communication, participative decision-making, and recognition of employee contributions. The significance of enhancing job security through clear policies, open dialogue, and robust mental health and well-being support systems is highlighted. Practices that encourage job dedication are introduced, emphasizing goal alignment, meaningful work design, and a culture of innovation and continuous improvement. Long-term strategies for cultivating a healthy workplace, such as establishing feedback mechanisms, investing in leadership development, and maintaining organizational adaptability, are also discussed. This brief article is an introductory resource for business leaders, managers, and change practitioners seeking to be better equipped with the necessary tools and strategies to navigate the post-implementation effects of directive change management. It is anticipated that this information can assist leaders and organizations in navigating the challenges of directive change management, promoting resilience, employee well-being, and sustainable organizational success.

In a time of a growingly age-diverse workforce, modern organizations are facing the challenge of simultaneously maintaining job satisfaction for both younger and older workers. In that regard, this study aims to analyse and further explore the difference in job expectations of employees from the IT industry who belong to different age groups. Based on the extant literature, an appropriate research model was designed, which was subsequently tested using the data gathered through the surveys conducted over the past fourteen years. The research results show that the main difference between younger and older employees within the IT industry is related to professional and personal growth. Specifically, younger employees primarily look for personal development and rapid professional advancement, which are of minor importance to their older counterparts. Intriguingly, the obtained results showed no difference between the younger and older employees regarding the work environment, including its competitiveness.

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.

The study aimed to investigate the concept of workplace equality as experienced and perceived by female librarians of Punjab, Pakistan. Through this investigation, the study aimed to contribute to the broader discourse on creating equitable and inclusive workplaces for women in the field of library and information science. A qualitative research method based on semi-structured interviews was employed to meet the objectives of the study. The interview guide was used to collect data from female librarians working in the Higher Education Commission’s (HEC) recognized public and private sector universities of the Punjab, Pakistan. According to the results, female librarians shared that they have faced gender-based discrimination in job allocation as male librarians were favored for tasks with additional wages or representation at corporate events. Private sector candidates reported issues related to career development opportunities as managers often restrict participation in seminars, conferences, and higher education pursuits. The study also highlighted that inequalities or discriminations affect employees motivation and enthusiasm. This study highlights issues of inequality from a female perspective in the library and information science field, contributing to a deeper understanding of the key factors to ensure equitable workplaces. This study may be a useful contribution to the body of research literature, as well as the findings may help in sensitizing the management and authorities to control the work environment to facilitate females, and to make female-oriented policies.

Purpose: This study focuses on the effects of electronic-Human Resource Management (e-HRM) on organizational consequences. In this analysis, the effects of different configurations are assessed within the same socio-economic context. Design/Methodology: This study adopts a cross-sectional survey of e-HRM actors, such as human resource managers, IT professionals, and line managers. The data analysis was conducted using linear regression. A sample of 300 respondents was selected based on Gill et al.’s framework for obtaining a representative sample. Findings: ‘Integrated e-HRM configurations’ employed in multinational corporations (MNCs) generate positive and improved operational, relational, and transformational consequences or outcomes. In small-to-medium-sized organizations, the operational-user configuration exhibits positive but lower operational, relational, and transformational consequences. However, the socio-economic variables used to categorize e-HRM configurations do not apply in a developing economy context. Practical implications: The application of information technology in HRM is not the sole predictor of organizational consequences. The sophistication of the adopted e-HRM system deserves some consideration too. When managers adopt sophisticated e-HRM systems, they are likely to achieve positive and improved outcomes. More predictor variables need to be uncovered for an elaborate categorization of effective e-HRM configurations. Originality/value: The contextual factors that define effective e-HRM configurations are not consistent across different socio-economic contexts. Company-based categorization of effective configurations is advisable. This study establishes the limitations of current categorization variables in explaining effective e-HRM systems.

The artificial intelligence (AI)-based architect’s profile’s selection (simply iSelection) uses a polymathic mathematical model and AI-subdomains’ integration for enabling automated and optimized human resources (HR) processes and activities. HR-related processes and activities in the selection, support, problem-solving, and just-in-time evaluation of a transformation manager’s or key team members’ polymathic profile (TPProfile). Where a TPProfile can be a classical business manager, transformation manager, project manager, or an enterprise architect. iSelection-related selection processes use many types of artifacts, like critical success factors (CSF), AI-subdomain’ integration environments, and an enterprise-wide decision-making system (DMS). iSelection focuses on TPProfiles for various kinds of transformation projects, like the case of the transformation of enterprises’ HRs (EHR) processes, activities, and related fields, like enterprise resources planning (ERP) environments, financial systems, human factors (HF) evolution, and AI-subdomains. The iSelection tries to offer a well-defined (or specific) TPProfile, which includes HF’s original-authentic capabilities, education, affinities, and possible polymathical characteristics. Such a profile can also be influenced by educational or training curriculum (ETC), which also takes into account transformation projects’ acquired experiences. Knowing that selected TPProfiles are supported by an internal (or external) transformation framework (TF), which can support standard transformation activities, and solving various types of iSelection’s problems. Enterprise transformation projects (simply projects) face extremely high failure rates (XHFR) of about 95%, which makes EHR selection processes very complex.