Predicting job-hopping likelihood using answers to open-ended interview questions

Voluntary employee turnover incurs significant direct and indirect financial costs to organizations of all sizes. A large proportion of voluntary turnover includes people who frequently move from job to job, known as job-hopping. The ability to discover an applicant's likelihood towards job-hopping can help organizations make informed hiring decisions benefiting both parties. In this work, we show that the language one uses when responding to interview questions related to situational judgment and past behaviour is predictive of their likelihood to job hop. We used responses from over 45,000 job applicants who completed an online chat interview and also self-rated themselves on a job-hopping motive scale to analyse the correlation between the two. We evaluated five different methods of text representation, namely four open-vocabulary approaches (TF-IDF, LDA, Glove word embeddings and Doc2Vec document embeddings) and one closed-vocabulary approach (LIWC). The Glove embeddings provided the best results with a positive correlation of r=0.35 between sequences of words used and the job-hopping likelihood. With further analysis, we also found that there is a positive correlation of r=0.25 between job-hopping likelihood and the HEXACO personality trait Openness to experience. In other words, the more open a candidate is to new experiences, the more likely they are to job hop. The ability to objectively infer a candidate's likelihood towards job hopping presents significant opportunities, especially when assessing candidates with no prior work history. On the other hand, experienced candidates who come across as job hoppers, based purely on their resume, get an opportunity to indicate otherwise.