This study aims to explore the evolution of the human resources field in Western academia during the 1970s and 1980s, focusing on the trends in research topics across different decades. The analysis utilizes citation co-citation analysis, multivariate statistical analysis, and social network analysis. The research data were drawn from the Web of Science (WoS) database, comprising 1278 documents. By distinguishing between different time periods, the study identifies shifts in the field across two distinct time frames, visualized through multidimensional scaling maps. The results indicate that the 1970s were dominated by seven major research streams, while the 1980s introduced eight research streams, with “human resources” emerging for the first time as a prominent research frontier. The volume of literature, co-citation frequency, and citation counts all increased over time, reflecting the growing vibrancy and expanding scope of research in the field. Although citation co-citation analysis provides objective quantitative insights, issues such as the purpose of citations, the extent to which cited documents influence citing documents, and the varying layers of citation impact may introduce potential errors in the co-citation analysis results.

The demography of Saudi Arabia has been discussed many times but its conflict with the theories of transition and associated structural changes is unexplained. This research explains the demographic differentials stated as lag - real from theoretical – separately for the native and total population. This research developed demographic indicators revealing trends and patterns by adopting a secondary data analysis method, utilizing the General Authority for Statistics census data and other online data. The demographic transition of Saudi Arabia is in line with the theoretical contentions of pretransition and transition (early, mid, and late) stages but at definite time intervals. The absolute size, percentage change, and annual growth rate are explanatory for natives and are considered separately. Moreover, the structural population changes reveal transition stages from expansive to near expansive and constricting and stabilizing. Furthermore, broad age groups indicate rapid declines in the percentage of children, rapid increases in young adults, slow increases in older adults, and no changes in older persons. Even the sex ratio of natives is at par with other populations in transition (slightly above 100). Thus, it could be concluded that a demographic transition with structural changes as per theories: flawless growth rates with an expanding demographic dividend. At this juncture, the integration of migrants into society by endorsing family life and enabling social and demographic balance appears as imperative to improving the labor sector, productivity, and the image of the country in the international spheres for comparisons and benchmarking.

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 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.

HRIS is a crucial tool for HR departments as it provides a digital platform for managing and automating various HR functions. HRIS is a comprehensive solution that integrates HRM functions with IT, enhancing the daily operations of HR professionals. In today’s knowledge-based economy, business success relies heavily on the performance of its human resources, which are essential in a rapidly changing global environment. Businesses continually strive to stay ahead of the curve in the ever-evolving technology landscape to thrive in the market. Some scholars have highlighted the negative impact of Human Resource Information Systems, primarily focusing on the invasion of privacy as the main disadvantage. The study indicates that implementing a Human Resource Information System (HRIS) enhances business performance in the tourism and hospitality industry of the Maldives. It highlights that user satisfaction and ease of use are positively influenced by these systems. The research surveyed 211 professionals and managers from the Maldives tourism and hospitality sector using a Likert Scale questionnaire to assess the impact of the HRIS on business performance. The study used SPSS 22.0 to analyze the impact of the Human Resource Information System (HRIS) on the dependent variable. The findings indicate that managerial personnel and human resource specialists in organisations find a user-friendly and satisfying HRIS motivating and beneficial for enhancing their performance. Organisations implement the HRIS to achieve their goals, identify system shortcomings, and develop strategies to improve business performance in the Maldives’ tourism and hospitality sector.

This study compares Human Resource Development (HRD) in Vietnam and Malaysia, looking at their methods, problems, and institutional frameworks in the context of ASEAN economic integration and Industry 4.0. Based on Cho and McLean’s (2004) integrated HRD model, this paper looks at recent research (from 2018 to 2023) to look at important topics such globalization, demographic changes, vocational training alignment, and technology disruption. Vietnam has a vast workforce, but it still has problems with low productivity, skill mismatches, and not being ready for the global market. On the other hand, Malaysia’s institutional HRD structures are making more progress, even though its workforce is getting older and not everyone is adapting to digital transformation at the same rate. The study shows that we need HRD policies that are tailored to each industry, training that is delivered in a decentralized way, and stronger relationships between the public and commercial sectors. It also stresses how important it is for national HRD policies to include global competences and initiatives that help everyone learn new skills. The study adds a unique framework for comparing HRD and gives policymakers, educators, and practitioners useful information, even though it is constrained by its use of secondary data. Future study should use mixed-methods to confirm results and look into interventions that work in specific situations. The study shows that Vietnam and Malaysia need personalized, inclusive, and forward thinking HRD systems to produce strong and competitive workforces in the post-pandemic, digital driven global economy.