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.
Tao, Sha-Sha (55923358700); Tang, Jian (59164595100); Yang, Xiao-Ke (55428143500); Fang, Xi (57651754300); Luo, Qing-Qing (59164728100); Xu, Yi-Qing (58506838600); Ge, Man (58507954100); Ye, Fan (59165379100); Wang, Peng (56982050800); Pan, Hai-Feng (58516287500)
Two Decades of Publications in Journals Dedicated to Autoimmunity: A Bibliometric Analysis of the Autoimmunity Field from 2004 to 2023
2024
10.1007/s10238-024-01369-1
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195479682&doi=10.1007%2fs10238-024-01369-1&partnerID=40&md5=d24655fd578888e63375207f1e94e3e9
Department of Epidemiology and Biostatistics, School of Public Health, Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Medical University, Anhui, Hefei, 230032, China; Preventive Medicine Experimental Teaching Center, School of Public Health, Anhui Medical University, Anhui, Hefei, 230032, China; Department of Rheumatology and Immunology, The First Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230032, China; Department of Environmental Health, School of Public Health, Shanxi Medical University, Shanxi, TaiYuan, 030001, China; Teaching Center for Preventive Medicine,School of Public Health, Anhui Medical University, Anhui, Hefei, 230032, China
All Open Access; Hybrid Gold Open Access
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