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

doi:10.5465/AMBPP.2021.158

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

Bar-Ilan University - The Alexander Kofkin Faculty of Engineering

Afeka Tel Aviv Academic College of Engineering
Tel Aviv University

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

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 language	English
Journal	Academy of Management Annual Meeting Proceedings
DOIs	https://doi.org/10.5465/AMBPP.2021.158
State	Published - 2021
Event	81st Annual Meeting of the Academy of Management 2021: Bringing the Manager Back in Management, AoM 2021 - Virtual, Online Duration: 29 Jul 2021 → 4 Aug 2021

Bibliographical note

Publisher Copyright:
© AoM 2021.All right reserved.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

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title = "A HUMAN RESOURCES ANALYTICS EXAMINATION OF TURNOVER: IMPLICATION FOR THEORY AND PRACTICE",

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.",

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AU - Singer, Gonen

AU - Irad, Ben Gal

PY - 2021

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AB - 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.

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

Abstract

Bibliographical note

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