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Employee attrition remains a persistent challenge for ICT organisations in Gauteng, South Africa, impacting performance, continuity, and workforce stability. High turnover disrupts project timelines, erodes institutional knowledge, and inflates recruitment and training costs. This study investigates attrition predictors by applying logistic regression to a synthetically generated structured dataset of 1,470 employee records in Gauteng. It examines whether workplace, demographic, and relational factors significantly influence turnover outcomes. Guided by three work-based practice frameworks: Social Exchange Theory, Herzberg’s Two-Factor Theory, and Job Embeddedness Theory, the research aims to identify meaningful retention drivers beyond surface-level attributes. A stratified random sampling method was used to construct a representative subset of 50 records, balancing class proportions within practical constraints. Pre-processing, encoding, and scaling were performed using Python libraries to ensure data integrity, followed by hypothesis testing and regression analysis to evaluate predictor significance. Findings show that relationship satisfaction and organisational commitment negatively correlate with attrition, reinforcing the importance of intrinsic motivators in retention. In contrast, gender, age, tenure, and income-related variables were statistically insignificant, challenging conventional HR assumptions. These results contribute to organisational behaviour literature by highlighting relational and psychological factors over demographic predictors. They suggest that fostering a supportive work environment and cultivating employee engagement may be more effective than relying solely on compensation or tenure-based incentives. The study recommends future research that incorporates larger samples from sectors beyond ICT, employs hybrid methodologies and longitudinal designs, and integrates qualitative insights from interviews or surveys to deepen understanding and inform proactive retention policies.

Modern businesses require flexible, agile systems that can track progress in real time, provide continuous feedback and adapt to dynamic workplace environment. As the workplace is evolving rapidly, so are the expectations around employee performance. Using qualitative approach, this paper unpacks the opportunities and challenges of EPMS as well as provide recommendations on how organizations in Lesotho can make use of it. Despite the vast literature on digitalization, there has been a lot of hesitation on the total adoption of EPMS in most organization mostly in developing countries like Lesotho.  The methodology involved a comprehensive review of previous studies, primarily relying on secondary data to explore the concept of digital HRM and its various dimensions. Thematic analysis was conducted to identify patterns and insights related to EPMS. The findings revealed that EPMS can provide opportunities for orgnaisations in Lesotho which include real-time feedback, objective continuous performance monitoring, data-driven insights, reduction in operational costs and improved communication. However, despite EPMS’s benefits it comes with challenges, such as data overload, costs, algorithmic bias, data privacy concerns and loss of personal interaction. Based on the findings, organisations must invest in robust data security measures, such as encryption and secure storage protocols, to mitigate these risks and having clear policies that relates to EPMS.

The fast-changing technological environment compels organisations to make swift and adaptive strategic choices, necessitating the use of tools and systems that can effectively manage complexities, foresee challenges, and capitalise on opportunities; as a result, there is a growing trend towards the adoption of human resource analytics to improve decision-making processes in the ever-evolving field of human resources. Despite the increasing use of HR metrics and analytics in human resource management, HR professionals have been relatively slow to embrace a data-driven approach, and existing research on data-driven decision-making in HRM often lacks the comprehensive frameworks necessary to provide practical guidance for the effective integration of human resource analytics. Hence, this study aims to explore the utilisation of human resource analytics to improve decision-making processes within organisations, highlighting both the potential benefits and the challenges that may arise. A systematic literature review revealed that the adoption of Human Resource Analytics offers organisations significant benefits, such as enhanced decision-making, better alignment with strategic objectives, improved employee experiences, a stronger competitive advantage, and reduced time expenditures. However, challenges persist, including concerns related to data privacy and security, the quality, integrity, and accuracy of data, a lack of data literacy and skills among employees, and inadequate technological infrastructure. To address data security concerns, HR technology providers must adopt robust security measures, including encryption, firewalls, and intrusion detection systems. Additionally, they should develop user-friendly data management systems and analytical tools that enable HR professionals to derive valuable insights from the data effectively.

Human resource management (HRM) is a crucial component of an organization’s management and aims to enhance employee efficiency and an organization’s competitiveness. As organizations advance and grow, recruitment and selection decisions become more critical. The thinking of human resources practitioners and experts must be transformed to accommodate the current workplace. Practitioners and experts must ensure that qualified candidates are attracted to the organization at the right time to fill open positions. Traditional HRM methods are insufficient for the progressively more complicated HRM challenges. Recruitment and selection, as one HRM component, is a process with numerous challenges. This study examines how the use of AI technologies can assist in mitigating recruitment and selection challenges. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and soliciting data from databases such as Web of Science, Google Scholar, and Science Direct, a systematic literature review analysis was conducted to investigate the human resources challenges in recruitment and selection and explore how AI can moderate these challenges. The analysis indicated several challenges, including high costs, bribery and corruption, political interference, inadequate job descriptions, nepotism and favoritism, and lengthy recruitment and selection processes. Recommendations of the study suggest that the accuracy and efficiency of recruitment and selection can be enhanced by involving AI technologies, which can assist in lowering the risks and expenses associated with recruitment and selection.