Assessing the impact of Artificial Intelligence enabling human resource management practices (Management) VILNIUS TECH

Overview

Research topic description:

The rapid integration of Artificial Intelligence (AI) into Human Resource Management (HRM) is transforming how organisations recruit, evaluate, and manage employees. AI-enabled HRM practices are increasingly used to support data-driven decision-making, improve efficiency, and standardise HR processes (Marler, 2024). However, recent research highlights that AI in HRM functions not only as a technological tool but also as a managerial and ethical system that reshapes organisational decision authority, power relations, and employee – organisation relationships (Naoum et al., 2026).

The rapid integration of Artificial Intelligence (AI) into Human Resource Management (HRM) has significantly transformed how organisations recruit, evaluate, develop, and manage employees. AI-enabled HRM practices are increasingly applied across recruitment and selection, performance management, career development, workforce analytics, and strategic decision support, with the aim of improving efficiency, consistency, and data-driven managerial decision-making (Marler, 2024). Recent research emphasises that AI in HRM should not be understood merely as a technological innovation but rather as a managerial and ethical system that reshapes organisational decision authority, power relations, and employee–organisation relationships (Naoum et al., 2026).

Contemporary studies highlight the dual nature of AI-enabled HRM practices. On the one hand, AI has the potential to enhance standardisation and objectivity in HR decisions, reduce certain forms of human bias, and expand access to diverse talent pools through consistent and scalable evaluation processes (Charlwood & Guenole, 2022; Köchling & Wehner, 2023). On the other hand, growing empirical and conceptual evidence indicates that AI-driven HR systems may reproduce historical inequalities, embed algorithmic bias, and reduce transparency and accountability when trained on biased data or implemented without sufficient human oversight (Naoum et al., 2026; Bankins et al., 2022). These challenges directly affect employee trust, as algorithmic HR decisions are often perceived as impersonal, opaque, and difficult to contest, even when they are presented as objective or neutral (Köchling & Wehner, 2023).

Recent literature also increasingly addresses the implications of AI-enabled HRM for employee wellbeing. While automation and predictive analytics may reduce administrative workload and support resource optimisation, AI-based monitoring, performance evaluation, and algorithmic control mechanisms have been associated with work intensification, stress, reduced autonomy, and perceptions of dehumanisation (Bankins et al., 2022; Marler, 2024). Research grounded in organisational justice theory demonstrates that employee wellbeing is strongly influenced by perceptions of procedural, distributive, and interactional justice in AI-supported HR decisions, particularly when employees lack explanations, voice, or opportunities for appeal (Naoum et al., 2026).

Despite the rapid growth of research on AI in HRM, important gaps remain. Existing studies frequently examine fairness, trust, wellbeing, or decision-making in isolation, with limited integrative analysis of how AI-enabled HRM practices simultaneously influence employee trust, employee wellbeing, and organisational decision-making processes. Moreover, much of the empirical literature focuses on recruitment and selection, while other HRM domains—such as employee relations, wellbeing management, and strategic HR decision-making—remain underexplored (Naoum et al., 2026). The dominance of conceptual and cross-sectional studies further limits understanding of the long-term organisational and human consequences of AI adoption in HRM.

Therefore, there is a clear need for systematic research that assesses the holistic impact of AI-enabled HRM practiceson employee trust, wellbeing, and organisational decision-making. Addressing this gap is essential for advancing ethically responsible, sustainable, and evidence-based HRM practices in organisations increasingly reliant on artificial intelligence.

The selected candidate will work on the PhD thesis under the supervision of Assoc. Prof. dr. Laima Jesevičiūtė-Ufartienė. The successful applicant will have to attend scientific conferences, meetings and internships at the other universities. 

Requirements:

• Required background: Master diploma in business management and administration or organisation management.

• Expected skills and knowledge:  English minimum B2 level in speaking and writing, methodological and analytical writing.

It is a prerequisite you can be present at and accessible to the institution daily.

Shortlisted candidates will be invited for an interview. The position may not be opened if no qualified candidate is found. Additional information regarding the position may be obtained from Assoc.Prof. dr. Laima Jesevičiūtė-Ufartienė, e-mail: laima.jeseviciute-ufartiene@vilniustech.lt