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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]