SEGMENTATION OF FRESH GRADUATES' JOB INTEREST AND MOTIVATION BASED ON FINITE MIXTURE PARTIAL LEAST SQUARES (FIMIX-PLS)

Abstract

Rapid technological advancements have significantly transformed the global labor market, impacting various industries and workers in Indonesia who may need to be adequately prepared to adapt. The current landscape demands individuals who can acquire knowledge quickly and adapt to modern technologies. The unemployment rate in Indonesia, especially among fresh graduates, is still a concern. Lack of motivation and interest in finding a job and high expectations of working conditions contribute to this problem. This research aims to address the gap in research by using the Finite Mixture Partial Least Squares (FIMIX-PLS) approach to examine the segmentation of fresh graduate characteristics about their interest and motivation in finding a job. Segmentation based on latent variable relationships in the structural model can be overcome with Finite Mixture Partial Least Square (FIMIX-PLS) to identify more homogeneous characteristics. This research analyzes explicitly the impact of compensation, work environment, and company reputation on the interest and motivation of fresh graduates in finding a job. This research resulted in the best segmentation of two segments: the 1st segment at 77.8% (265 samples) and the 2nd segment at 22.2% (75 samples).