Identifying Latent Profiles in Career Plateau of Employees in the Science and Technology Using Latent Profile Analysis

The present study aims to identify latent profiles based on career plateau among those working in the science and technology field using latent profile analysis. We also investigated the effect of predictors on the classification of the profiles and the differences between outcome variables among the profiles. We analyzed data from 2,059 employees from the 2022 Human Resources Development Survey in Science and Technology data collected by the Korea Institute of Human Resources Development in Science and Technology (KIRD). The result showed that five profiles were identified: average plateau, low plateau, promotion plateau, growth plateau, and career ceiling. As predictive variables, perceived career development support, informal learning, and social networking were significant, and there were significant differences in career satisfaction, organizational commitment, turnover intention, and work- life balance between the five profiles. Based on the findings, theoretical and practical implications were discussed.