Mapping the Evolution and Paradoxes of Artificial Intelligence in Human Resource Management: A Bibliometric Synthesis (2002–2026)
Kata Kunci:
Artificial Intelligence, Human Resource Management, Bibliometric Analysis, Systematic Literature Review, Paradoxical Tensions, Digital Transformation, Algorithmic Decision-MakingAbstrak
Introduction: The rapid advancement of Artificial Intelligence (AI) is fundamentally transforming Human Resource Management (HRM) practices, shifting the paradigm from administrative automation to strategic, data-driven decision-making. However, the integration of intelligent systems into organizational workflows introduces complex ethical challenges and paradoxical tensions that remain conceptually fragmented in the existing literature. This study aims to systematically map the intellectual structure, evolutionary trends, and research hotspots of the AI-HRM domain over more than two decades. Method: Employing a hybrid approach, this research combines a Systematic Literature Review (SLR) following the PRISMA protocol with a comprehensive bibliometric analysis. A total of relevant peer-reviewed articles published between 2002 and 2026 were retrieved from the Scopus database. Data processing and visualization were performed using R-Biblioshiny and VOSviewer to conduct keyword co-occurrence, co-authorship, and thematic evolution analyses. Results: The findings reveal an exponential growth in scholarly output since 2016, with a significant surge following the COVID-19 pandemic. Four major thematic clusters were identified: (1) AI applications in recruitment and operational HR functions; (2) infrastructure and advanced technologies such as Machine Learning and Big Data; (3) ethical, legal, and privacy considerations; and (4) strategic HRM focusing on decision-making, leadership, and diversity. Furthermore, the analysis highlights a shift in research focus from purely technical applications toward a "human-centric" perspective and the management of paradoxical tensions, such as autonomy versus algorithmic control. Discussion: The study underscores the necessity of a balanced approach that integrates technological innovation with rigorous ethical governance to mitigate algorithmic bias and technostress. While AI offers transformative potential for organizational performance, the gap between technical promises and socio-cultural implementation remains a critical challenge. Future research should prioritize longitudinal studies on employee well-being, the impact of Generative AI, and context-sensitive investigations in emerging economies.
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