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2019 | OriginalPaper | Buchkapitel

Mirror, Mirror on the Wall, Who Is Leaving of Them All: Predictions for Employee Turnover with Gated Recurrent Neural Networks

verfasst von : Joao Marcos de Oliveira, Matthäus P. Zylka, Peter A. Gloor, Tushar Joshi

Erschienen in: Collaborative Innovation Networks

Verlag: Springer International Publishing

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Abstract

Employee turnover is a serious issue for organizations and disrupts the organizational behavior in several ways. Hence, predicting employee turnover might help organizations to react to these mostly negative events with, e.g., improved employee retention strategies. Current studies use a “standard analysis approach” (Steel, Academy of Management Review 27:346–360, 2002) to predict employee turnover; accuracy in predicting turnover by this approach is only low to moderate. To address this shortcoming, we conduct a deep learning experiment to predict employee turnover. Based on a unique dataset containing 12 months of time series of e-mail communication from 3952 managers, our model reached an accuracy of 80.0%, a precision of 74.5%, a recall of 84.4%, and a Matthews correlation coefficient value of 61.5%. This paper contributes to turnover literature by providing a novel analytical perspective on key elements of turnover models.

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Some employees had more than one e-mail account in this company. In these cases, we merged the multiple e-mail accounts of an employee into one.

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Titel: Mirror, Mirror on the Wall, Who Is Leaving of Them All: Predictions for Employee Turnover with Gated Recurrent Neural Networks
verfasst von: Joao Marcos de Oliveira
Matthäus P. Zylka
Peter A. Gloor
Tushar Joshi
Verlag: Springer International Publishing
Buch: Collaborative Innovation Networks
Print ISBN: 978-3-030-17237-4

Electronic ISBN: 978-3-030-17238-1

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-17238-1_2

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