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Wisdom from the crowd : can recommender systems predict employee turnover and its destinations?
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Min, H., Yang, B., Allen, David G., Grandey, A. A. and Liu, M. (2024) Wisdom from the crowd : can recommender systems predict employee turnover and its destinations? Personnel Psychology . doi:10.1111/peps.12551 ISSN 0031-5826 . (In Press)
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Official URL: https://doi.org/10.1111/peps.12551
Abstract
Can algorithms that predict customer movie and shopping preferences also predict which employees are likely to leave and where they are likely to go, thus helping to retain talent? The current study applies a type of machine learning technique, collaborative filtering (CF) recommender system algorithms, to investigate the comparison between satisfaction with the current job and potential satisfaction with job alternatives, which is inherent in theorizing about individual turnover decisions. The comparison of those anticipated ratings along with employee’s current job satisfaction create two operationalizations: the quantity of more desirable job alternatives and the quality (or extent of desirability) of job alternatives. To test the effectiveness of this novel approach, we applied recommender system algorithms to a longitudinal archival dataset of employees and had three main findings. First, the recommender system algorithms efficiently predicted job satisfaction based on just two sources of information (i.e., work history and job satisfaction in previous jobs), providing construct validity evidence for recommender systems. Second, both the quantity and the quality of more desirable job alternatives compared to the current job positively correlated with employees’ future turnover behavior. Finally, our CF recommender system algorithms predicted where employees moved to, and even more effectively if constraining the alternative jobs to the same occupation. We conclude with implications how recommender system algorithms can help scholars effectively test theoretical ideas and practitioners predict and reduce turnover.
Item Type: | Journal Article | ||||||||
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Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management H Social Sciences > HF Commerce |
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Divisions: | Faculty of Social Sciences > Warwick Business School > Entrepreneurship, Innovation & Management Faculty of Social Sciences > Warwick Business School |
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Library of Congress Subject Headings (LCSH): | Employees -- Resignation, Labor turnover, Labor mobility, Recommender systems (Information filtering), Organizational behavior, Psychology, Industrial | ||||||||
Journal or Publication Title: | Personnel Psychology | ||||||||
Publisher: | Wiley-Blackwell Publishing, Inc. | ||||||||
ISSN: | 0031-5826 | ||||||||
Official Date: | 2024 | ||||||||
Dates: |
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DOI: | 10.1111/peps.12551 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | In Press | ||||||||
Re-use Statement: | This is the peer reviewed version of the following article: Min, H., Yang, B., Allen, D. G., Grandey, A. A., & Liu, M. (2022). Wisdom from the crowd: can recommender systems predict employee turnover and its destinations? Personnel Psychology, 00, 1– 22. Advance online publication., which has been published in final form at https://doi.org/10.1111/peps.12551. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Copyright Holders: | © 2022 Wiley Periodicals, Inc. | ||||||||
Date of first compliant deposit: | 31 October 2022 | ||||||||
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