Predicting job-hopping motive of candidates using answers to open-ended interview questions

Madhura Jayaratne () and Buddhi Jayatilleke ()
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Madhura Jayaratne: PredictiveHire Pty. Ltd.
Buddhi Jayatilleke: PredictiveHire Pty. Ltd.

Journal of Computational Social Science, 2022, vol. 5, issue 1, No 26, 628 pages

Abstract: Abstract A significant proportion of voluntary employee turnover includes people who frequently move from job to job, known as job-hopping. Our work shows that language used in responding to interview questions on past behaviour and situational judgement is predictive of job-hopping motive as measured by the Job-Hopping Motives (JHM) Scale. The study is based on responses from over 45,000 job applicants who completed an online chat interview and self-rated themselves on JHM Scale. Five different methods of text representation were evaluated, 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 correlation of r = 0.35 between sequences of words used and the JHM Scale. Further analysis also showed a correlation of r = 0.25 between language-based job-hopping motive and the personality trait Openness to experience and a correlation of r = − 0.09 with the trait Agreeableness.

Keywords: Job-hopping; Turnover; Structured interviews; Natural language processing; Computational linguistic analysis; Machine learning; HEXACO personality model (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s42001-021-00138-4

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