Causes and Consequences of Collective Turnover: A Meta-Analytic Review

Given growing interest in collective turnover (i.e., employee turnover at unit and organizational levels), the authors propose an organizing framework for its antecedents and consequences and test it using meta-analysis. Based on analysis of 694 effect sizes drawn from 82 studies, results generally support expected relationships across the 6 categories of collective turnover antecedents, with somewhat stronger and more consistent results for 2 categories: human resource management inducements/investments and job embeddedness signals. Turnover was negatively related to numerous performance outcomes, more strongly so for proximal rather than distal outcomes. Several theoretically grounded moderators help to explain average effect-size heterogeneity for both antecedents and consequences of turnover. Relationships generally did not vary according to turnover type (e.g., total or voluntary), although the relative absence of collective-level involuntary turnover studies is noted and remains an important avenue for future research

When Does Employee Turnover Matter? Dynamic Member Configurations, Productive Capacity, and Collective Performance

In theory, employee turnover has important consequences for groups, work units, and organizations. However, past research has not revealed consistent empirical support for a relationship between aggregate levels of turnover and performance outcomes. In this paper, we present a novel conceptualization of turnover to explain when, why, and how it affects important outcomes. We suggest that greater attention to five characteristics—leaver proficiencies, time dispersion, positional distribution, remaining member proficiencies, and newcomer proficiencies—will reveal dynamic member configurations that predictably influence productive capacity and collective performance. We describe and illustrate the five properties, explain how particular member configurations exacerbate or diminish turnover’s effects, and present a new measurement approach that captures these characteristics in a collective context and over time

The Data Are Coming! Reconceptualizing Big Data for the Organizational Sciences

The increasing prevalence and availability of big data represent a potentially revolutionary development for human resource management (HRM) scholars. Despite this, the current literature provides eclectic and often contradictory guidance for scholars attempting to conceptualize big data and subsequently incorporate it into relevant theoretical frameworks. The authors attempt to bridge this gap by discussing key considerations relevant to understanding and integrating big data into the existing theoretical landscape. Building on a novel, integrative definition of big data, the authors propose a parsimonious theoretical framework utilizing the established dimensions of complexity and dynamism as meta-attributes to bring order to the various attributes that have been proposed as central to defining big data (e.g., volume, variety, velocity, and variability). Throughout, the authors highlight numerous theoretical and empirical opportunities and considerations that this perspective holds for future HRM scholarship.This accepted book chapter is published as Howe, M., Summers, J.K. and Holwerda, J.A. (2022), "The Data Are Coming! Reconceptualizing Big Data for the Organizational Sciences", Buckley, M.R., Wheeler, A.R., Baur, J.E. and Halbesleben, J.R.B. (Ed.) Research in Personnel and Human Resources Management (Research in Personnel and Human Resources Management, Vol. 40), Emerald Publishing Limited, Bingley, pp. 133-156. https://doi.org/10.1108/S0742-730120220000040005. Posted with permission. Copyright © 2022 Michael Howe, James K. Summers and Jacob A. Holwerda<br/