Exploiting linkedin to predict employee resignation likelihood
Ana Carolina Conceição de Jesus, Márcio Enio G. D. Júnior, Wladmir Cardoso Brandão · 2018
Turnover is the organizational movement of hiring and dismissing employees motivated by different reasons, such as termination, retirement, and resignations. High turnover rates can be harmful to organizational productivity, potentially disrupting investments in human resources, generating loss of tacit knowledge and non-scheduled costs with staff replacement. Usually, organizations are unprepared for premature employee resignation, and the problems arising from turnover are even more harmful in this case. Estimating the employees inclination to resignation is paramount to reduce turnover, thereby reducing its negative impact on organizational performance. In this article, we exploit LinkedIn to predict employee resignation likelihood. Particularly, we introduce the ERP approach, which collect professional profiles from LinkedIn and use them as a source of features about employees inclination to resignation. Additionally, we evaluate different algorithms used by ERP to classify employees, considering their resignation likelihood. Experimental results show that the decision tree is the most effective algorithm, classifying correctly more than 88% of the employees. Furthermore the kappa measure show a substantial agreement between the decision tree and an optimal classifier.