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Cited by 850

The DOI matches its Crossref record; 173 of the 173 references it cites were found in a registry or an index.

Employee Retirement: A Review and Recommendations for Future Investigation

Wang, Mo, Shultz, Kenneth S.·2010·Journal of Management

Abstract

Despite the obvious importance of retirement to employees, their employing organizations, and the larger society, the last comprehensive review of employee retirement in the field of organizational science was published more than 20 years ago. As such, the first purpose of this review is to provide a summary of key theoretical and empirical developments in employee retirement research since Beehr in 1986. A second purpose of this review is to highlight inconsistent findings revealed by studies that were designed to answer the same research questions. By identifying and scrutinizing those inconsistent findings, this study expects to provide suggestions and recommendations to further the theoretical development in the field of retirement research to address these research gaps. As a result, this proposed review would be of interest to scholars in a wide variety of areas within the organizational sciences, including human resource management, organizational behavior, organizational theory, and research methods.

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Crossref · 10.1177/0149206309347957

Cites

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Metadata

Authors
Wang, M., & Shultz, K. S.
Venue
Journal of Management
Published
2009-10-06
Volume
36
Issue
1
Pages
172-206
ISSN
  • 0149-2063 (ISSN-L)
  • 1557-1211
OpenAlex
W2154803394
Cited by
850
References
173
FWCI
64.75
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Employee Retirement: A Review and Recommendations for Future Investigation. https://app.cochat.ai/papers/10.1177/0149206309347957
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Wang, M., & Shultz, K. S. (2010). Employee Retirement: A Review and Recommendations for Future Investigation. Journal of Management. https://doi.org/10.1177/0149206309347957

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.