Bibliometric Analysis of Research on Career Personalization

Abstract

Career personalization has emerged as an important research area in response to evolving workforce expectations, technological advancements, and the growing demand for individualized career development pathways. This study aims to examine the intellectual structure, research trends, and emerging themes within the field of career personalization through a bibliometric analysis of publications indexed in the Scopus database. Bibliographic data were analyzed using VOSviewer to perform co-occurrence, co-authorship, citation, and collaboration network analyses. The results indicate that research on career personalization has developed into an interdisciplinary field encompassing education, human resource management, employment, professional development, and artificial intelligence. Keyword mapping reveals that employment, personalization, learning systems, and professional aspects are the dominant themes, while recent studies increasingly focus on recommendation systems, predictive analytics, large language models, deep learning, and AI-enhanced career decision support. Collaboration analyses demonstrate growing international partnerships among researchers, institutions, and countries, highlighting the global relevance of the topic. Citation analysis further shows that foundational studies have primarily emphasized educational interest, personalized learning, career development, and work individualization, whereas contemporary research is increasingly driven by intelligent and data-driven approaches. The findings suggest a transition from traditional career management frameworks toward technology-enabled and highly personalized career ecosystems. This study contributes to the literature by providing a comprehensive overview of the evolution of career personalization research and identifying promising directions for future investigations.