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- W3185955146 abstract "Purpose. What do antecedents of turnover tell us when examined using human resources (HR) analytics and machine-learning tools, and what are the respective theoretical and practical implications? Although the turnover literature is expansive, empirical evidence on turnover antecedents studied using data science tools remains limited. Methodology. To help reinvigorate research in this field, 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. Findings. Using archival data from 700,000 employees of a large organization collected over a period of ten years, we find that turnover is generally associated with varying levels of these antecedents. However, in more fine-grained analysis, their effect on turnover is contingent upon role, person and cultural background. Value. We discuss the implications on turnover and HR research and the potential of machine-learning methods in the design and implementation of managerial and HR planning initiatives." @default.
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- W3185955146 date "2021-08-01" @default.
- W3185955146 modified "2023-10-02" @default.
- W3185955146 title "A Human Resources Analytics Examination of Turnover: Implications for Theory and Practice" @default.
- W3185955146 doi "https://doi.org/10.5465/ambpp.2021.158" @default.
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