The purpose of this study is to examine the research trends in job competencies within the domestic context by utilizing keyword network analysis and topic modeling. For this purpose, 131 papers related to job competencies, published in the Korea Rese...
The purpose of this study is to examine the research trends in job competencies within the domestic context by utilizing keyword network analysis and topic modeling. For this purpose, 131 papers related to job competencies, published in the Korea Research Foundation registered and candidate journals from 2013 to 2022, were analyzed in the Korea Citation Index(KCI).
The keyword network and topic modeling were conducted using the NetMiner 4.0 program. In the keyword network analysis, keyword frequency analysis, keyword co-occurrence analysis, and centrality analysis (degree centrality, closeness centrality, betweenness centrality) were performed. In the topic modeling analysis, latent topics and keywords within the papers were extracted using the Latent Dirichlet Allocation (LDA) technique.
As a result of the keyword network analysis, key terms such as education, subject, need, development, utilization, needs, importance, and impact emerged as major keywords. From the topic modeling analysis, four main topics were identified: Topic-1 (Schooling Needs), Topic-2 (Corporate Job Performance), Topic-3 (Education Program Needs), and Topic-4 (Factors Influencing Job Satisfaction).
Based on the analysis results, it was confirmed that the research has been centered on topics such as the educational needs related to job competencies and the causal relationships with various factors related to job competencies. It was suggested that future research should focus on continuous improvement and validation of the effects of educational needs in the context of individuals and organizations, as well as in a social context, regarding job competencies