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Cited by 415

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Sure Everyone Can Be Replaced … But At What Cost? Turnover As A Predictor Of Unit-Level Performance

Michele Kacmar, K., Andrews, Martha C., Van Rooy, David L., Chris Steilberg, R., Cerrone, Stephen·2006·Academy of Management Journal

Abstract

Most turnover research positions employee turnover as the dependent variable and focuses on identifying its antecedents. In this study, we viewed turnover as a key predictor in determining unit-level performance. Specifically, a structural model was developed and tested that links managerial and employee turnover with performance through efficiency. We tested the model using a sample of 262 BURGER KING® restaurants. Results demonstrate that efficiency, measured as customer “wait time,” mediates the relationships of both management and crew turnover to both sales and profit, and efficiency, measured as food waste, does not mediate the relationship of turnover to sales or profit.

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Registry

Crossref · 10.5465/amj.2006.20785670

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Metadata

Authors
Michele Kacmar, K., Andrews, M. C., Van Rooy, D. L., Chris Steilberg, R., & Cerrone, S.
Venue
Academy of Management Journal
Published
2006-02
Volume
49
Issue
1
Pages
133-144
ISSN
  • 0001-4273 (ISSN-L)
  • 1948-0989
OpenAlex
W2124739050
Cited by
415
References
7
FWCI
43.07
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Sure Everyone Can Be Replaced … But At What Cost? Turnover As A Predictor Of Unit-Level Performance. https://app.cochat.ai/papers/10.5465/amj.2006.20785670
The paper
Michele Kacmar, K., Andrews, M. C., Van Rooy, D. L., Chris Steilberg, R., & Cerrone, S. (2006). Sure Everyone Can Be Replaced … But At What Cost? Turnover As A Predictor Of Unit-Level Performance. Academy of Management Journal. https://doi.org/10.5465/amj.2006.20785670

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.