Artículo

Human Resources Literature After Covid-19: A Human Versus AI Analysis

Resumen

In the post-Covid-19 era, identifying emerging trends in human resources management has become crucial for organizations seeking to adapt and thrive in the ever-changing landscape. Recognizing these trends enhances employee engagement, talent acquisition, and overall organizational effectiveness. One approach to unveil these trends is bibliometric analysis, which helps researchers identify influential topics and themes within the field by analyzing publication patterns, citation networks, and keyword co-occurrence. By evaluating bibliometric indicators such as citation counts and h-index, researchers can identify influential authors, institutions, and publications, fostering collaborations and knowledge-sharing opportunities. Additionally, this analysis reveals underexplored areas in literature, guiding the development of research agendas and addressing important unanswered questions in human resources. With the emergence of Artificial Intelligence (AI) tools such as Elicit, Iris, and Litmap, new possibilities arise to perform bibliometric analysis. Comparing the gains from AI-driven methods to traditional approaches, this research paper aims to understand their relative benefits in the context of human resource management after Covid-19. Although AI offers a wider range of data sources and more comprehensive insights, traditional methods still hold value, particularly when context-specific knowledge is vital. The choice between AI and traditional methods depends on research objectives, data availability, and the resources and expertise available to researchers. Ultimately, adopting emerging trends in human resources through effective bibliometric analysis can give organizations a competitive advantage, ensuring their ability to proactively adapt to the dynamic needs and expectations of their employees and achieve sustained success.
Hidayat, Erwin Yudi (57205439013); Hastuti, Khafiizh (56485990500); Muda, Azah Kamilah (23390362900)
Artificial intelligence in digital image processing: A bibliometric analysis
2025
10.1016/j.iswa.2024.200466
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212984349&doi=10.1016%2fj.iswa.2024.200466&partnerID=40&md5=14c2a82f6cb46e9efa80c050fc79c24f
Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, 50131, Indonesia; Dinus Research Group for AI in Medical Science (DREAMS), Universitas Dian Nuswantoro, Semarang, 50131, Indonesia; Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, 76100, Malaysia
All Open Access; Gold Open Access
Scopus
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