Semantic Scholar Open Access 2022 152 sitasi

Predicting and explaining employee turnover intention

M. Lazzari José Manuel Saiz-Alvarez Salvatore Ruggieri

Abstrak

Turnover intention is an employee’s reported willingness to leave her organization within a given period of time and is often used for studying actual employee turnover. Since employee turnover can have a detrimental impact on business and the labor market at large, it is important to understand the determinants of such a choice. We describe and analyze a unique European-wide survey on employee turnover intention. A few baselines and state-of-the-art classification models are compared as per predictive performances. Logistic regression and LightGBM rank as the top two performing models. We investigate on the importance of the predictive features for these two models, as a means to rank the determinants of turnover intention. Further, we overcome the traditional correlation-based analysis of turnover intention by a novel causality-based approach to support potential policy interventions.

Topik & Kata Kunci

Penulis (3)

M

M. Lazzari

J

José Manuel Saiz-Alvarez

S

Salvatore Ruggieri

Format Sitasi

Lazzari, M., Saiz-Alvarez, J.M., Ruggieri, S. (2022). Predicting and explaining employee turnover intention. https://doi.org/10.1007/s41060-022-00329-w

Akses Cepat

Lihat di Sumber doi.org/10.1007/s41060-022-00329-w
Informasi Jurnal
Tahun Terbit
2022
Bahasa
en
Total Sitasi
152×
Sumber Database
Semantic Scholar
DOI
10.1007/s41060-022-00329-w
Akses
Open Access ✓