TY - JOUR
T1 - A MACHINE LEARNING EXAMINATION OF TURNOVER
T2 - HIDDEN PATTERNS AND NEW INSIGHTS.
AU - BEN-GAL, HILA CHALUTZ
AU - AVRAHAMI, DAN
AU - PESSACH, DANA
AU - SINGER, GONEN
PY - 2021/1/1
Y1 - 2021/1/1
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 finegrained 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 finegrained 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.
U2 - 10.5465/AMBPP.2021.158
DO - 10.5465/AMBPP.2021.158
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SN - 0065-0668
VL - 2021
SP - 1
EP - 6
JO - Academy of Management Annual Meeting Proceedings
JF - Academy of Management Annual Meeting Proceedings
IS - 1
ER -