A HUMAN RESOURCES ANALYTICS EXAMINATION OF TURNOVER: IMPLICATION FOR THEORY AND PRACTICE

Hila Chalutz Ben-Gal, Dan Avrahami, Dana Pessach, Gonen Singer, Ben Gal Irad

Research output: Contribution to conferencePaperpeer-review

Abstract

The turnover literature is expansive, however empirical evidence on turnover using data science tools remains limited. We propose a novel examination of turnover antecedents-competencies, commitment, trust and values-using big data tools to develop a granular, case-dependent measure of turnover. Using archival data from 700,000 employees of a large organization collected over a decade, we find that turnover changes according to its antecedents' levels. However, in more fine-grained analysis, their effect on turnover is contingent upon role, person and cultural background. We discuss turnover implications and the potential of data science methods in the implementation of managerial and HR initiatives.

Original languageEnglish
DOIs
StatePublished - 2021
Event81st Annual Meeting of the Academy of Management 2021: Bringing the Manager Back in Management, AoM 2021 - Virtual, Online
Duration: 29 Jul 20214 Aug 2021

Conference

Conference81st Annual Meeting of the Academy of Management 2021: Bringing the Manager Back in Management, AoM 2021
CityVirtual, Online
Period29/07/214/08/21

Fingerprint

Dive into the research topics of 'A HUMAN RESOURCES ANALYTICS EXAMINATION OF TURNOVER: IMPLICATION FOR THEORY AND PRACTICE'. Together they form a unique fingerprint.

Cite this