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

Joao Marcos Oliveira (), Matthäus P. Zylka (), Peter A. Gloor () and Tushar Joshi
Additional contact information
Joao Marcos Oliveira: galaxyadvisors AG
Matthäus P. Zylka: University of Bamberg
Peter A. Gloor: MIT Center for Collective Intelligence
Tushar Joshi: Genpact

A chapter in Collaborative Innovation Networks, 2019, pp 43-59 from Springer

Abstract: 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.

Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:seschp:978-3-030-17238-1_2

Ordering information: This item can be ordered from
http://www.springer.com/9783030172381

DOI: 10.1007/978-3-030-17238-1_2

Access Statistics for this chapter

More chapters in Studies on Entrepreneurship, Structural Change and Industrial Dynamics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().