Can algorithms that predict customer movie and shopping preferences also predict which employees are likely to leave and where they are likely to go, thus helping to retain talent? This study applies a type of machine learning (ML) technique, collaborative filtering (CF) recommender system algorithms, to investigate the comparison between satisfaction with the current job and potential satisfaction with job alternatives, which is inherent in theorizing about individual turnover decisions. The comparison of those anticipated ratings along with employee's current job satisfaction creates two operationalizations: the quantity of more desirable job alternatives and the quality (or extent of desirability) of job alternatives. To test the effectiveness of this novel approach, we applied recommender system algorithms to a longitudinal archival dataset of employees and had three main findings. First, the recommender system algorithms efficiently predicted job satisfaction based on just two sources of information (i.e., work history and job satisfaction in previous jobs), providing construct validity evidence for recommender systems. Second, both the quantity and the quality of more desirable job alternatives compared to the current job positively correlated with employees’ future turnover behavior. Finally, our CF recommender system algorithms predicted where employees moved to, and even more effectively if constraining the alternative jobs to the same occupation. We conclude with implications how recommender system algorithms can help scholars effectively test theoretical ideas and practitioners predict and reduce turnover.
All Science Journal Classification (ASJC) codes
- Applied Psychology
- Organizational Behavior and Human Resource Management