抄録
We propose an HR-tech solution to promote behavioral change for self-career development. The purpose of this solution is to enable employees to consider and act toward their own career development by themselves. This solution visualizes skill information, which similar work contents groups each employee belongs to have. The work contents groups are created by machine learning methods: Word2Vec and k-means++, using 4,096 employees’ text information about work contents and work results. To evaluate whether this solution contributes to promote behavioral change for each employee, we showed the visualized words, which constructs the work contents group and skills, to 15 employees as experimental participants. The results show that this solution induces behavioral change to more than half participants. The participants, who felt strong relationship between their work contents and information of groups each participant belong to, significantly change their behavior by themselves. The ratio of the employees who change behavior is 78%. We believe that this is a valuable study case that shows the effects of grouping by machine learning and visualization for behavioral change in a real social situation.
