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

Tel Aviv University, Bar-Ilan University

Research output: Contribution to conference › Paper › peer-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 language	English
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

Conference

Conference	81st Annual Meeting of the Academy of Management 2021: Bringing the Manager Back in Management, AoM 2021
City	Virtual, Online
Period	29/07/21 → 4/08/21

UN SDGs

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

SDG 9 Industry, Innovation, and Infrastructure

All Science Journal Classification (ASJC) codes

Industrial relations
Management Information Systems
Management of Technology and Innovation

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10.5465/AMBPP.2021.158

Cite this

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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.",

author = "Ben-Gal, \{Hila Chalutz\} and Dan Avrahami and Dana Pessach and Gonen Singer and Irad, \{Ben Gal\}",

note = "Publisher Copyright: {\textcopyright} AoM 2021.All right reserved.; 81st Annual Meeting of the Academy of Management 2021: Bringing the Manager Back in Management, AoM 2021 ; Conference date: 29-07-2021 Through 04-08-2021",

year = "2021",

doi = "10.5465/AMBPP.2021.158",

language = "الإنجليزيّة",

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TY - CONF

T1 - A HUMAN RESOURCES ANALYTICS EXAMINATION OF TURNOVER

T2 - 81st Annual Meeting of the Academy of Management 2021: Bringing the Manager Back in Management, AoM 2021

AU - Ben-Gal, Hila Chalutz

AU - Avrahami, Dan

AU - Pessach, Dana

AU - Singer, Gonen

AU - Irad, Ben Gal

PY - 2021

Y1 - 2021

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=85123464382&partnerID=8YFLogxK

U2 - 10.5465/AMBPP.2021.158

DO - 10.5465/AMBPP.2021.158

M3 - محاضرة

Y2 - 29 July 2021 through 4 August 2021

ER -

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

Abstract

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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