Intro: Voluntary turnover and its effects

Voluntary turnover, which represents the vast majority of all employee turnover, decreases organizational productivity and dampen employee morale while inflicting direct financial costs such as sourcing, recruiting and on-boarding costs.

However making frequent voluntary job changes, known as job-hopping, has become a trend in the recent past. The motivations for job-hopping, have been identified to be two-fold; advancement and escape. The advancement 1 motive represents the growth and career perspective while the escape motive represents a withdrawal or dislike of the work environment, especially among those who are described as impulsive and unpredictable. The latter is identified as a psychological property and commonly known as the ”hobo syndrome”. Further studies have shown the relationship between personality and voluntary turnover.

The ability to assess a candidate’s likelihood of job-hopping prior to selection can help both candidates and employers make better decisions and avoid future surprises and costs due to voluntary exits. The most frequently used approach to discover patterns of job-hopping is to explore the employment history listed in an applicant’s resume. Sifting through resumes can be both time consuming and unreliable, especially in situations of high volume recruitment. Resumes are also known to produce biased outcomes. Moreover, it is an ineffective method with novice job seekers such as new graduates who have insignificant job histories.

Given the demonstrated link between one’s personality and voluntary turnover, in this study, we evaluate whether the answers given by candidates to interview questions related to past behaviour and situational judgement can be used to infer their job-hopping likelihood. The basis for selecting interview answers as a possible predictor is two-fold. Firstly, one’s language use has been shown to be highly predictive of their personality. Personality traits have been successfully derived from informal (microblogs, social media posts), semiformal (blogs, interview questions) and formal (essays) contexts. Authors own prior work has shown that interview answers are a strong predictor of personality traits. Secondly, structured interviews where the same questions are asked from every candidate in a controlled conversation flow and evaluated using a well-defined rubric have shown to reduce bias and also increase the ability to predict future job performance. Computational inference of job-hopping likelihood from interview responses (Figure 1) further increases the utility of the structured interview and its applicability in high volume recruitment.

In this work, we make the following contributions to the crossroads of computational linguistics and organizational psychology domains.

1. We demonstrate that responses to typical interview questions related to past behaviour and situational judgement can be used to reliably infer one’s job-hopping likelihood.
2. We evaluate multiple methods of text representations and establish that the Glove based word-embedding method achieves the highest correlation of r=0.35 between text and job-hopping likelihood when used with a Random Forest regressor.
3. We validate the positive correlation between job-hopping motive and Openness to experience (one of the personality traits in the HEXACO personality model), both derived from text (r=0.25). This is in line with previous findings using standard personality tests.

The rest of the paper is organised as follows. Section 2 presents a detailed background into the research on employee turnover, the role of personality on turnover and the link between language and personality. In section 3, we describe the methodology including the data used and the five different text representation methods we evaluated, namely TF-IDF, LDA, Glove word embeddings, Doc2Vec document embeddings and LIWC. Results, in terms of the accuracies achieved by each text representation method, are presented in section 4 along with discussion and further analysis of salient correlations, demographics, and terms used. Section 5 concludes the paper with a summary and future research directions.

To view the rest of this whitepaper, please download it from the title page.